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Introduction

OpenCrabs adalah agen orkestrasi AI yang di-hosting sendiri dan tidak terikat provider, berjalan sebagai satu biner Rust. Ia mengotomatiskan terminal, browser, channels (Telegram/Discord/Slack/WhatsApp/Trello), dan codebase Anda, sambil menghormati privasi Anda dan menjaga kendali tetap di tangan Anda.

11,091 test mencakup provider, tools, channels, TUI, self-healing, dan otomatisasi browser.

Apa yang Membedakan OpenCrabs

Nol Telemetri, Bahkan Tanpa Opt-In

  • OpenCrabs tidak pernah menelepon pulang. Sesekali pun tidak. Tanpa analitik, tanpa pelacakan, tanpa statistik penggunaan, tanpa logging jarak jauh, tanpa laporan crash
  • Percakapan, tools, memori, konfigurasi, dan kunci API Anda tidak pernah meninggalkan mesin Anda
  • Satu-satunya trafik keluar adalah yang Anda mulai sendiri secara eksplisit: panggilan LLM API, pencarian web, perintah GitHub, otomasi browser
  • Ini bukan sekadar kotak centang kebijakan privasi: tidak ada kode telemetri untuk dinonaktifkan, tidak ada flag opt-out, tidak ada layanan analitik untuk diblokir. Memang tidak ada apa pun untuk dikirim

Bebas Provider Sejak Desain

  • 16 provider bawaan + Custom OpenAI Compatible: Anthropic Claude, OpenAI, Gemini, Xiaomi MiMo, OpenRouter, Qwen (DashScope), MiniMax, Ollama, z.ai GLM, GitHub Copilot, Codex, Codex CLI, OpenCode, OpenCode CLI, Moonshot AI (Kimi), Command Code CLI
  • Integrasi CLI natif: gunakan Claude Code CLI, OpenCode CLI, Codex CLI, dan Command Code CLI sebagai provider tanpa kunci API
  • Rantai fallback lengket: failover otomatis saat rate limit atau error, persistensi sadar-kesehatan bertahan melewati restart
  • Prompt caching di Anthropic, OpenRouter, Gemini, Qwen DashScope memangkas biaya hingga 95%
  • Override jendela konteks: batasi atau perluas konteks model apa pun lewat konfigurasi (provider OpenCrabs bawaan adalah pengecualian: jendelanya tetap 200k)
  • Xiaomi MiMo: 30+ model termasuk seri reasoning MiMo, provider berkunci
  • Moonshot AI (Kimi): endpoint API plan (api.moonshot.ai) dan Coding plan (api.kimi.com), konfigurasi reasoning-effort, jendela konteks sesuai tier paket

Multi-Agent Orchestration

  • Sessions adalah agen yang terisolasi penuh, masing-masing dengan brain, provider, model, direktori kerja, dan riwayatnya sendiri
  • Sub-agent bertipe: general, explore, plan, code, research dengan akses tools yang disesuaikan
  • Orkestrasi tim: team_create, team_broadcast, team_delete untuk alur kerja terkoordinasi
  • Override provider/model per panggilan: campur model antar tim (plan dengan GLM, code dengan Deepseek, review dengan Kimi)
  • Protokol A2A: gateway JSON-RPC 2.0 untuk komunikasi antar agen

Komunikasi Natif di Channels

  • Telegram, Discord, Slack, WhatsApp, Trello, integrasi bot penuh dengan DM, grup, dan thread
  • Rich message Telegram: tabel, heading, daftar, dan matematika natif lewat konfigurasi rich_messages
  • Streaming draf pesan: pembaruan “sedang mengetik…” secara langsung saat token dihasilkan di DM
  • Blok yang bisa dilipat: bagian <details>/<summary> untuk keluaran panjang
  • Isolasi session per topik forum: setiap topik di supergrup Telegram mendapat session-nya sendiri
  • Reaksi Telegram: bot membaca reaksi emoji masuk dan bisa membalas dengan reaksi alih-alih pesan bila cocok
  • Reaksi bingkai (v0.3.61): reaksi masuk dibaca berdasarkan sentimen, agen menyapa pengguna dengan nama depan
  • Reaksi di tengah giliran (v0.3.61): reaksi saat giliran sedang berjalan disuntikkan ke loop saat ini alih-alih memicu giliran kedua
  • Saran tindak lanjut (v0.3.69): tindak lanjut opsional sekali-ketuk di Telegram, Discord, Slack, WhatsApp; isi-tanpa-kirim di TUI
  • Penempelan ulang kartu rencana Telegram (v0.3.71): kartu rencana ditempel ulang dan prosa dilipat ke dalamnya saat terkubur pesan
  • Penangkapan riwayat grup penuh (v0.3.73): setiap pesan grup disimpan ke riwayat, bahkan dari pengirim dan bot di luar allowlist
  • Pembicara grup yang benar (v0.3.73): berhenti menyapa pengirim riwayat grup seolah-olah dia pembicara saat ini
  • Pewarisan session: /new mewarisi direktori kerja dari session terbaru Anda
  • /goal di semua channels: tetapkan tujuan otonom dari Telegram, Discord, Slack, WhatsApp
  • Dukungan suara: Whisper STT + Piper TTS lokal, sepenuhnya offline
  • Pemulihan crash lintas channel: permintaan tertunda dialihkan kembali ke channel asal saat restart
  • /cowork: buat workspace bersama dari channels dan TUI
  • /rename, /profiles, /cd, kelola sessions, profil, dan direktori dari channel mana pun

Self-Healing & Peningkatan Diri

  • Recursive Self-Improvement (RSI): agen menganalisis kinerja, mengenali pola, dan menulis ulang brain files

  • Deteksi tool call fantom: menangkap saat model menceritakan perubahan tanpa benar-benar mengeksekusi tools

  • Self-heal deteksi fantom (v0.3.72): menangkap giliran tanpa tool yang mengklaim efek samping berisiko tinggi (ship/push/tag/release)

  • Edit konfigurasi hanya via config_manager (v0.3.74), agen tidak pernah mengedit config.toml/keys.toml secara mentah; setiap penulisan divalidasi sebelum diterapkan

  • Dedup brain (v0.3.75): perintah /dedup plus pemindaian pasca-tulis dan mingguan lintas file menjaga brain files agar tidak mengganda, dengan persetujuan inline-keyboard Telegram

  • Petunjuk brain-file saat tools meleset (v0.3.75): catatan brain yang relevan muncul otomatis saat tool call meleset atau error

  • Kemunculan memori proaktif (v0.3.76): memori relevan muncul tanpa diminta saat konteks percakapan cocok dengan pengetahuan tersimpan

  • Penamaan model natural (v0.3.76): /models opus berhasil; nama percakapan diresolusi menjadi ID model yang tepat

  • Routing provider plan/eksekusi (v0.3.76): arahkan perencanaan dan eksekusi ke provider/model terpisah

  • Timeout loop berpikir (v0.3.78): menangkap loop penalaran tak berujung, memicu penegakan fantom dengan sticky fallback

  • Mesin epistemik (v0.3.78): pelacakan keyakinan dengan tingkat kepercayaan, mesh kognitif OODA/BDI, gerbang Orient sebelum eksekusi rencana

  • Gerbang verifikasi Ralph (v0.3.78): verifikasi tugas sadar-kriteria dengan batas iterasi; tugas harus memenuhi kriteria penerimaan yang dinyatakan

  • Gerbang keamanan TOML (v0.3.78): blocklist bash yang bisa dikonfigurasi saat runtime dengan hot-reload, redaksi rahasia bertanda kutip, verifikasi brain file

  • Analitik Mission Control (v0.3.78): filter D/W/M, grid kartu tiga kolom, tab phantom/model/streaming, DB event khusus

  • Onboarding OpenAI TTS (v0.3.78): pemilih suara dan kolom kunci API di wizard onboarding

  • Instalasi Homebrew (v0.3.79): brew install opencrabs dari homebrew-core: biner siap pakai, kedua arsitektur, tanpa toolchain Rust

  • /restart dan /exit (v0.3.79): restart atau matikan dari channel atau TUI mana pun; hanya owner di channels, mengumumkan sebelum bertindak

  • Pemutus arus kuota (v0.3.79): provider dengan kuota habis mendapat breaker TTL dan dilewati dalam perjalanan fallback; kehabisan rantai dilaporkan eksplisit

  • Status rencana lintas session (v0.3.79): rencana bertahan melewati batas session; eksekusi tugas rencana terisolasi aktif secara default, Ralph memverifikasi di direktori session itu sendiri

  • Sistem tema (v0.5.0): preset terverifikasi, pemilih interaktif, dan tema Anda sendiri dari ~/.opencrabs/themes/*.toml; truecolor dideteksi dengan tingkat cadangan ANSI-256, dan preset yang kontrasnya membuat teks tak terbaca akan ditolak

  • Memori struktur kode (v0.5.0): tree-sitter membangun indeks simbol dan graf pemanggilan di samping indeks teks, sehingga “siapa yang memanggil X” menelusuri sisi pemanggilan yang sebenarnya sementara pertanyaan konseptual tetap lewat jalur teks (#1324)

  • Transfer berkas lewat SSH (v0.5.0): berkas yang dijatuhkan ke TUI di host jarak jauh ditarik kembali melalui koneksi yang sudah terbuka, dengan opencrabs drop-agent di sisi klien (#1289)

  • Putusan pengiriman notify (v0.5.0): session_notify mengembalikan hasil kirim yang terbaca mesin, tanda terima injeksi kedalaman 3 membuat notify_id dapat diperiksa, dan pengiriman dialihkan ke session yang memiliki channel saat itu

  • Pemadatan di latar belakang (v0.5.0): peringkas berjalan di luar giliran alih-alih menahan session selama bekerja

  • Penyuntingan token saat penulisan (v0.5.0): galat provider yang membawa URL tidak lagi bisa membocorkan token bot ke log, dari mana pun dicatat (#1322)

  • Userbot Telegram hanya-terima (v0.5.0, eksperimental): penangkapan MTProto di balik feature, dengan login lokal via QR/kode/2FA; menyimpan teks yang diizinkan untuk diambil dan tidak pernah bertindak sebagai pengguna

  • Penjangkaran waktu (v0.5.1): agen selalu tahu jam berapa sekarang: setiap giliran membawa penanda waktu dalam zona waktu pengguna, dan prompt sistem tetap stabil byte demi byte untuk caching penyedia (#1516)

  • Sesi cron per job (v0.5.1): setiap job terjadwal mendapat sesinya sendiri, sehingga dua job yang berjalan bersamaan tidak pernah berbagi konteks atau penyedia (#1511)

  • Gerbang glob skill (v0.5.1): daftar globs: sebuah skill dapat menolak panggilan tool ke berkas yang cocok sampai agen benar-benar membaca body skill tersebut (#1515)

  • Tulisan config divalidasi terhadap struct (v0.5.1), penjaga tulis dan saran bagian config_manager kini diturunkan dari tipe Config itu sendiri, agar tidak pernah menyimpang dari kode yang dikirim (#1508)

  • Desain plan ditolak saat ditulis (v0.5.1): desain plan yang rusak langsung dipantul dengan menyebutkan bagian yang hilang, dan tulisan yang ditolak mengembalikan scaffold alih-alih menghapus draf (#1507)

  • Tekanan balik anggaran penyedia (v0.5.1): penyedia yang melaporkan anggaran token output memicu satu komaksi berbatas lalu percobaan ulang, alih-alih kaskade 400 (#1518)

  • Token A2A terikat dan config khusus owner (v0.5.1), token A2A terikat ke gateway yang menerbitkannya, dan config.toml/keys.toml berpindah ke mode khusus owner, dengan rahasia disingkirkan dari log

  • Mode server ACP (v0.5.2): opencrabs acp berbicara Agent Client Protocol lewat stdio JSON-RPC, sehingga Zed, MonoCode, dan klien ACP mana pun menjalankan brain yang sama dengan channels Anda, lengkap dengan streaming progres dan persetujuan yang kembali ke editor (#1540)

  • Penjelajahan sadar shadow-DOM (v0.5.2): resolusi selector menembus shadow root, sehingga otomasi menjangkau web component yang sebelumnya tidak terlihat oleh tools

  • Permukaan WhatsApp penuh (v0.5.2): pesan suara native, video/stiker/lokasi/kontak masuk, suara polling, pemulihan presence dan media, tanda terima reaksi, streaming dengan edit di tempat, pesan sementara, pemblokiran kontak, pin/teruskan/profil, pembaruan status, penemuan newsletter, tombol native flow, riwayat per chat

  • Tracing langsung dan tabel native di Discord (v0.5.2), giliran panjang mengalir sebagai balasan langsung ber-tracing, jawaban otomatis masuk thread, tabel markdown dirender secara native, dan giliran dengan tools tidak lagi mengirim jawaban akhir dua kali (#1603, #1608)

  • Topik forum Telegram dan /clear (v0.5.2): buat/ganti nama topik forum dengan pengikatan session, dan /clear memulai session baru tanpa panggilan summariser (#1585)

  • Anggaran payload menu perintah (v0.5.2): setMyCommands Telegram diam-diam menolak body permintaan di atas ~7,8KB sambil melaporkan BOT_COMMANDS_TOO_MUCH; deskripsi kini dipendekkan bertingkat agar seluruh menu muat (#1613)

  • Cron dengan trigger (v0.5.3): job menjalankan pre-flight check dan hanya mengeksekusi saat kondisi Anda terpenuhi (output tidak kosong, exit code, regex), run yang dilewati biayanya 0 token, dan trigger yang terpicu bisa mengirim goal ke sebuah session (#233)

  • Windows kelas satu (v0.5.3): penanganan shell per-platform, pembacaan file BOM/UTF-16, fallback tilde dan cwd, dengan CI membangun Windows dan meng-gate PR (#627)

  • Memori epistemik (v0.5.3): fakta di MEMORY.md membawa confidence dan decay (#1643), section yang dingin mengarsipkan diri sendiri, dan opencrabs memory prune menampilkan apa yang sudah dingin sebelum menyentuhnya (#1657)

  • Mesin tema selesai (v0.5.3): setiap widget TUI menyelesaikan warnanya melalui tema, dengan suite guard terhadap situs ANSI mentah baru, dan tema bisa mendeklarasikan canvas background (#1634)

  • Poll saran WhatsApp (v0.5.3): kumpulan follow-up yang melebihi batas tombol native dirender sebagai poll, dan satu suara memilih opsinya (#1616)

  • Keluarga session ACP (v0.5.4): session kini first-class di permukaan ACP: context meter dipulihkan saat load dan mengikuti usage updates, session/load memutar ulang transcript dan memulihkan model per-session, set_model bertahan antar proses, session/set_mode native menerapkan approval policy di sisi server, session/compact melakukan push, dan session/new menawarkan katalog model secara live

  • Serah terima terminal interaktif (v0.5.4): !vi/!nano mengambil alih terminal secara interaktif dan mengembalikannya dengan session utuh; hanya terminal Unix, di Windows tetap memakai capture lewat pipe (#1744, #1759)

  • Paste & drop universal (v0.5.4): satu router attachment bersama menerima semua jenis file saat paste atau drop dan mengklasifikasikan kode, docs, arsip, dan data agar masing-masing masuk ke pipeline yang tepat (#1740, #1743)

  • Mesin pencari Serper (v0.5.4): serper_search menambahkan hasil Google SERP, dan fan-out web_search mendedupe berdasarkan URL antar engine (#1731)

  • Decision cache (v0.5.4): decide_cached menjawab keputusan berulang dari ring reuse L1 dengan tier shadow/live dan config [decisions] (#1648)

  • Biaya yang dilaporkan provider dan satu rantai timeout (v0.5.4), ledger usage memilih biaya yang benar-benar dilaporkan provider daripada estimasi tabel (#1707), dan flag timeout diselesaikan [providers.<name>] -> [agent] -> default untuk semua provider (#1688)

  • Perintah terkompilasi /architecture dan /attach, audit trail opsional (v0.5.4), keduanya dikirim sebagai arms terkompilasi, dan audit trail opsional mencatat apa yang berjalan dengan viewer /audit (#933, #1705)

  • Permukaan native Discord (v0.5.5): polls native via send_poll, jawaban TTS dikirim sebagai pesan suara, dan commands.toml diproyeksikan ke slash commands native sehingga command Anda sendiri muncul di picker Discord (#1848, #1849, #1850)

  • Flow groups dan write governors (v0.5.5): Discord dan Slack membuka flow group di awal turn dengan clock live yang menutupi masa berpikir dan teks aktivitas memimpin baris, dan satu governor write bersama mengatur budget setiap edit serta melakukan park saat 429 (#1841, #1843, #1844, #1845, #1807, #1808, #1809, #1910, #1912, #2012)

  • /respond_to dan /cowork (v0.5.5): mode respond_to dan open per kanal di konfigurasi, plus dua command untuk mengubahnya langsung dari kanal itu sendiri (#2014, #2015)

  • Disiplin rate limit Telegram (v0.5.5): polls getUpdates dan handoff handler diinstrumenkan untuk triase stall, floor jeda sub-sekon lintas permukaan dengan telemetri admission, jeda 429 per chat di balik satu accessor Cooldown, dan link markdown ke file lokal dikirim sebagai dokumen (#1927, #1916, #1968)

  • Session lifecycle ACP v1 (v0.5.5): lima metode v1 session lifecycle telah diimplementasikan dan plan diterbitkan sebagai pembaruan plan v1 native (#1815)

  • Footer dialog bersama, URL yang bisa diklik, timeline onboarding (v0.5.5): setiap dialog berbagi satu command footer dengan verb persetujuan a/r langsung, URL dan path file bisa diklik, dan onboarding dibangun ulang sebagai timeline langkah di sisi kiri dengan command setup langsung (#1775, #1772, #1979, #1980, #1981, #1982)

  • Retirement cron one-shot (v0.5.5): job one-shot pensi saat terkirim alih-alih menyala lagi setahun kemudian, setiap run yang terkirim membawa run id-nya, dan laporan cron Discord tiba sebagai satu post forum (#544, #1703, #1851)

  • Percobaan pertama tanpa penyiapan (v0.5.11): instalasi baru langsung masuk ke chat dengan provider bawaan sudah disiapkan, config.toml dan keys.toml sudah dibuat, dan agen menyapa Anda. Wizard bersifat opsional lewat /onboard atau --onboard (#2079)

  • Turn pertama yang tidak bisa menggagalkan proses (v0.5.11): thread worker agen berjalan di stack 16 MiB, jadi turn pertama tidak lagi meluap di stack bawaan (#2080)

  • Tag rilis hanya dengan CI hijau (v0.5.11): workflow rilis membaca kesimpulan run CI milik commit itu sendiri sebelum menerbitkan. Run merah menggagalkan rilis, run yang masih berjalan akan ditunggu, dan commit tanpa run juga gagal (#2078)

  • Evolve me-restart unit yang benar (v0.5.11): restart terjadwal menyasar nama unit yang sudah diresolusi alih-alih glob penemuan opencrabs*.service, yang juga cocok dengan unit sementara yang menjalankan restart itu sendiri dan membuat systemd menghentikan eksekutornya sendiri (#2081)

  • Guest queries Telegram mendapat permukaan penuh (v0.5.12): tag di chat tempat bot tidak pernah diundang menjalankan permukaan tools, blok status langsung, dan rendering rich yang sama seperti turn biasa, dengan satu turn yang bisa dibatalkan per guest session dan rate limiting (#2087, #2109, #2133, #2135, #2138, #2143)

  • Plan diwajibkan setelah ambang tool calls (v0.5.12): begitu turn melewati ambang tool calls, agent harus berhenti dan menulis plan sebelum melanjutkan (#2128)

  • Transcript bertahan saat berganti session (v0.5.12): peta lipatan diindeks dengan anchor role dan timestamp yang stabil, bukan UUID yang dibuat setiap render, jadi berganti session lalu kembali tidak lagi melipat semua turn yang sudah Anda buka (#2155)

  • Instalasi headless menyiapkan provider bawaan (v0.5.12): opencrabs run, daemon, agent, acp, dan cron membuat token bawaan di home baru seperti yang sudah dilakukan TUI, sementara status dan doctor tetap hanya baca (#2151)

  • Mode perbaikan doctor –fix (v0.3.83): baris cron macet, marker basi, dan izin rusak diperbaiki di tempat alih-alih sekadar dilaporkan

  • Kunci tulis per path (v0.3.83): penulisan bersamaan ke file yang sama diserialisasi; penulis terakhir tidak lagi menang diam-diam

  • Isolasi worktree sub-agent (v0.3.83): setiap agen anak mendapat worktree dan branch sendiri, sehingga fan-out tidak bisa merusak pohon bersama

  • Kriteria penerimaan sadar-tipe (v0.3.83): kriteria rencana ditegakkan terhadap toolchain yang benar-benar dipakai proyek (#1133)

  • Verifikasi rencana sadar-proyek (v0.3.83): rencana diverifikasi dengan perintah proyeknya sendiri, bukan selalu cargo, dan menemukan proyek dari folder session

  • /stop membatalkan seketika (v0.3.83): menyela bahkan saat handshake provider dan jeda retry (#1148)

  • Resume session A2A (v0.3.83): session diresume berdasarkan context id, gateway dialamatkan per profil (#1159, #1161)

  • Kenop thinking DeepSeek (v0.3.83): DeepSeek mendapat parameter thinking yang benar-benar dibacanya, plus penanganan giliran khusus tool-call

  • Korelasi log session_id (v0.3.82): setiap giliran membuka span tracing berisi session id-nya, tugas cron dan mesin RSI punya miliknya sendiri, jadi satu grep menyusun ulang seluruh giliran dari log harian bersama

  • Keandalan logger (v0.3.82): logger berhenti membuang event dan berhenti menulis ke terminal TUI; penulisan yang macet tidak lagi membungkam logging thread lain

  • Brain files milik pengguna dilindungi (v0.3.82): SOUL.md, USER.md, dan MEMORY.md tidak pernah diisi dari upstream, dan direktif dialihkan ke AGENTS.md, yang selalu dimuat

  • Penyatuan seksi konfigurasi (v0.3.82): ejaan legacy [gateway] dimigrasikan di disk menjadi [a2a], jadi satu nama bertahan melewati putaran reload

  • Cache chunk-hash (v0.3.82): memori melewatkan embedding ulang chunk yang isinya tidak berubah

  • Biner pra-rilis (v0.3.82): lima target platform plus SHA256SUMS, dipublikasikan sebagai pre-release bergulir agar kontributor bisa menguji kode yang belum dirilis tanpa menunggu tag. Dijalankan sesuai permintaan, bukan setiap push

  • Pelepasan rate-limit (v0.3.82): jendela rate-limit Telegram yang panjang langsung kembali alih-alih memarkir pengiriman di antrean

  • Penyimpanan memori in-tree (v0.3.81): dependensi qmd dihapus; pencarian SQLite FTS5 + vektor dikelola in-tree

  • Path indeks eksternal (v0.3.81): pencarian memori membaca indeks di luar direktori profil, default-deny di session bersama

  • Mermaid sebagai gambar di Telegram (v0.3.81): diagram dirender sebagai gambar sementara tabel tetap formatting natif

  • Penalaran Qwen lintas giliran (v0.3.81): preserve_thinking, kenop thinking per keluarga, tier effort rekomendasi secara default, Qwen hosted terjangkau di mana saja

  • Perbaikan kehilangan kunci keys.toml (v0.3.81), kunci API provider tidak lagi hilang saat runtime; kunci yang teracuni menyembuhkan diri

  • Deteksi loop dini (v0.3.81): putaran tool berulang tertangkap sebelum provider menolak giliran

  • Gerbang mesin RSI (v0.3.81): RSI otonom mati secara default untuk daemon headless, aktifkan dengan rsi_enabled

  • Cakupan memory_search (v0.3.80): cari log harian (memory), brain files (brain), atau keduanya (all); brain files diindeks saat ditulis

  • Pengambilan memori ter-chunk (v0.3.80): dokumen di-chunk sebelum embedding sehingga chunk belakang tetap bisa dicari; recall diperingkat dengan BM25

  • Rendering natif Slack (v0.3.80): tabel dan heading datang dalam format Slack sendiri alih-alih markdown mentah

  • Perintah channel terkunci owner (v0.3.80): perintah dan skills kustom hanya untuk owner pada lengan catch-all channel di grup

  • Gerbang konvergensi RSI (v0.3.80): self-improvement berhenti saat konvergen, mundur saat beruntun tanpa perbaikan, meng-hash temuan berdasarkan identitas stabil

  • Penjaga loop (v0.3.80): penjaga pengumuman berulang dan pengulangan hampir-identik di semua tools; tembakan detektor loop sampai ke rantai fallback

  • Mode inventaris browser (v0.3.80): browser_find polos menghitung setiap elemen yang bisa diklik di halaman

  • Pembaruan templat brain-file (v0.3.80): templat AGENTS.md, BOOT.md, dan SOUL.md berubah; seeding tidak pernah menimpa file yang ada, jadi diff terhadap src/docs/reference/templates/ dan merge manual jika Anda ingin default baru

  • Rebuild brain sistem: brain files dibangun ulang dari disk saat berubah, tanpa perlu restart

  • Penemuan tools proaktif: mencari tools yang tersedia sebelum mengaku tidak bisa

  • Analyzer prompt multi-bahasa (v0.3.67): PromptAnalyzer bersama dengan dorongan lembut di TUI dan Telegram, 6 paket bahasa (EN/ES/FR/ID/PT/RU)

  • Mode rencana (v0.3.67-v0.3.68): trek design/checklist, kartu rencana persisten, perintah /plan <query>, persetujuan-diri agen saat pengguna memberi otonomi

  • Klik-untuk-membuka (v0.3.69): klik kiri pada blok tool-call atau penalaran untuk membuka blok itu saja, melengkapi Ctrl+O

  • review_gate skills (v0.3.70): deklarasi frontmatter untuk skills berisiko tinggi yang memerlukan persetujuan pengguna sebelum efek samping

  • Aktivasi tools JIT: tools extended diaktifkan sesuai permintaan, tanpa pra-registrasi

  • Perbaikan konfigurasi otomatis: config.toml rusak diperbaiki otomatis, konfigurasi terakhir-yang-baik tidak pernah dirusak

  • Manajemen anggaran konteks: ambang compaction 65% lunak / 90% keras dengan fallback LLM

  • Deteksi stream macet: jendela bergulir 2048 byte menangkap pola berulang

  • Pertahanan gaslighting: membuang pembuka penolakan-tool di tengah giliran

  • Pengiriman hasil build (v0.3.61): hasil rebuild/evolve sampai ke siapa pun yang meminta, di semua channels dan TUI

UI Terminal

  • Rendering markdown natif: penekanan, daftar, tautan, dan item tugas dirender langsung di terminal
  • Pengukur tok/s waktu-nyata: token per detik secara langsung saat streaming
  • Tampilan versi (v0.3.73): versi yang berjalan ditampilkan di header TUI dan di /help + /usage channel
  • Pembukaan penalaran 3-keadaan (v0.3.74): klik atau Ctrl+O memutar blok penalaran tertutup → dibatasi → penuh, jadi tidak pernah membanjiri tampilan
  • Header per giliran (v0.3.75): setiap giliran mendapat header ringkasan satu baris, proses berpikirnya dilipat ke dalamnya, dan giliran terlipat secara default sehingga tampilan tetap bersih
  • Pengelompokan tool call (v0.3.61): tool call beruntun dilipat menjadi satu blok yang bisa dibuka, menjaga TUI tetap bersih selama operasi banyak langkah
  • Lipatan teks menengah (v0.3.61): teks pemrosesan menengah dilipat ke log di tempat yang sama dengan tool call, jadi hanya jawaban akhir yang tetap terlihat
  • Pencarian session: filter pencarian + gulir viewport di semua sessions
  • Panel terpisah: sessions paralel gaya tmux dengan persistensi layout
  • Tempel gambar dari clipboard: tempel gambar dari browser atau aplikasi apa pun langsung ke TUI
  • Penyematan rencana: rencana aktif disemat di akhir prompt setiap giliran
  • Onboarding digerakkan agen: penyiapan pertama yang dipersonalisasi dengan alur terpandu
  • Loop otonom /goal: tetapkan tujuan dan agen berputar sampai juri LLM bilang selesai, dengan kontrol jeda/lanjut/status
  • Penetapan tujuan sendiri (v0.3.61): agen bisa menetapkan dan menggerakkan tujuan multi-gilirannya sendiri lewat tool goal_manage tanpa pengguna memanggil /goal

Pengalaman Developer

  • 50+ tools bawaan: operasi file, bash, pencarian web, eksekusi kode, otomasi browser, generasi gambar, suara, rendering PDF
  • Pencarian web paralel terpadu (v0.3.73): web_search, exa_search, dan brave_search menyebar paralel di bawah satu tool web_search
  • web_scrape: scraping URL-ke-markdown natif dengan proteksi SSRF, perayapan sitemap, deteksi JS-shell, dan ekspor sadar-profil
  • Deteksi captcha DDG (v0.3.73): captcha DuckDuckGo terdeteksi lewat HTTP 202 + pemeriksaan form struktural
  • Skill /github bawaan: kendali penuh gh CLI untuk issues, PR, review kode, dan manajemen repo
  • Penemuan tools proaktif: agen menemukan tools sebelum bilang “saya tidak bisa”
  • Sistem skills: templat alur kerja dengan pencarian fuzzy, otomatis terdaftar sebagai slash command
  • Tools dinamis: didefinisikan saat runtime lewat TOML (eksekutor HTTP + shell)
  • Sistem projects: sessions khusus dengan overlay brain per proyek, pengarsipan file, dan lencana warna
  • Editing hashline: editing file berjangkar hash dengan dukungan batch dan deteksi tabrakan
  • Mission Control: dasbor layar penuh dengan kotak masuk RSI, log aktivitas, dan jadwal cron
  • Unduh-otomatis RTK: proxy 4MB bawaan untuk penghematan token 53,5% pada 100+ perintah
  • Proteksi file rahasia: kunci SSH, .env, kredensial terlindungi secara default
  • AGENTS selalu dimuat: aturan keras dan tata kelola ditegakkan setiap giliran
  • debug_logs dari konfigurasi (v0.3.71): aktif/nonaktifkan log debug dengan hot-reload, tanpa restart
  • Cakupan redaksi rahasia (v0.3.71): batasi redaksi ke global/grup/DM dengan perintah /redact
  • Cakupan redaksi rahasia (v0.3.71): batasi redaksi ke global/grup/DM dengan perintah /redact
  • debug_logs dari konfigurasi (v0.3.71): aktif/nonaktifkan log debug dengan hot-reload, tanpa restart
  • Anjing penjaga Tmux (v0.3.71): hanya membunuh pane, peringatan 30 detik, sambung-ulang otomatis
  • Gerbang rencana tiga-keadaan (v0.3.71): GateDecision dengan bash menuju persetujuan di Editing pasca-init
  • ToolHints gaya MCP (v0.3.71): model risiko untuk klasifikasi tools, menggerakkan gerbang rencana

Browser Automation

  • Dukungan CDP penuh: navigasi, klik, ketik, tangkapan layar, JS eval, cari elemen
  • Mode headless atau headed dengan tangkapan layar per elemen
  • Persistensi cookie/session lintas sesi browser
  • Isolasi tab per session: tidak ada penginjakan DOM lintas session

Penemuan Direktif Proyek (v0.3.59)

OpenCrabs otomatis menemukan file aturan yang ditinggalkan tools coding AI lain di sebuah repo. Arahkan agen ke repositori mana pun (lewat /cd, workspace channel, atau menjalankannya di dalamnya) dan ia memindai konvensi dari Claude Code, Cursor, Windsurf, Cline, Gemini CLI, GitHub Copilot, OpenCode, dan standar lintas-tool AGENTS.md. Tanpa konfigurasi, tanpa langkah impor.

SumberFile
Standar lintas-toolAGENTS.md
Claude CodeCLAUDE.md, CLAUDE.local.md, .claude/CLAUDE.md, .claude/rules/**/*.md
Cursor.cursorrules, .cursor/rules/**/*.mdc
Windsurf.windsurfrules
Cline.clinerules (file or .clinerules/**/*.{md,txt})
Gemini CLIGEMINI.md
GitHub Copilot.github/copilot-instructions.md
OpenCode.opencode/AGENTS.md

Indeks dibangun ulang saat Anda /cd ke direktori baru, jadi file direktif selalu mutakhir.

Mulai Cepat

# Install (Linux/macOS)
ARCH=$(uname -m | sed 's/x86_64/amd64/;s/aarch64/arm64/')
OS=$(uname -s | tr A-Z a-z)
TAG=$(command -v jq >/dev/null 2>&1 && curl -s https://api.github.com/repos/opencrabs/opencrabs/releases/latest | jq -r .tag_name || curl -s https://api.github.com/repos/opencrabs/opencrabs/releases/latest | grep -o '"tag_name":"[^"]*"' | cut -d'"' -f4)
curl -fsSL "https://github.com/opencrabs/opencrabs/releases/download/${TAG}/opencrabs-${TAG}-${OS}-${ARCH}.tar.gz" | tar xz
./opencrabs

# Or via Cargo (requires Rust 1.94+)
cargo install opencrabs --locked

Pembaruan otomatis aktif secara default. Nonaktifkan dengan [agent] auto_update = false di ~/.opencrabs/config.toml.

Architecture

┌─────────────────────────────────────────┐
│           OpenCrabs Binary              │
│  (Single 34-36 MB Rust executable)      │
├─────────────────────────────────────────┤
│  ┌─────────────┐  ┌─────────────────┐  │
│  │   TUI       │  │   CLI Daemon    │  │
│  │  (crossterm)│  │  (systemd/launchd)││
│  └─────────────┘  └─────────────────┘  │
│                                         │
│  ┌─────────────────────────────────┐   │
│  │        Provider Registry         │   │
│  │  15 built-in + Custom OpenAI    │   │
│  │  Sticky fallback chain          │   │
│  └─────────────────────────────────┘   │
│                                         │
│  ┌─────────────────────────────────┐   │
│  │        Tool Layer                │   │
│  │  50+ built-in tools             │   │
│  │  Dynamic tools via TOML         │   │
│  │  JIT activation                 │   │
│  └─────────────────────────────────┘   │
│                                         │
│  ┌─────────────────────────────────┐   │
│  │        Channel Adapters          │   │
│  │  Telegram / Discord / Slack /    │   │
│  │  WhatsApp / Trello / Voice      │   │
│  └────────────────���────────────────┘   │
│                                         │
│  ┌─────────────────────────────────┐   │
│  │        Self-Healing Layer       │   │
│  │  Context budget / Stuck stream  │   │
│  │  Phantom detection / RSI        │   │
│  └─────────────────────────────────┘   │
│                                         │
│  ┌─────────────────────────────────┐   │
│  │        Persistence              │   │
│  │  SQLite + Brain files           │   │
│  │  FTS5 + vector search           │   │
│  └─────────────────────────────────┘   │
└─────────────────────────────────────────┘

Langkah Berikutnya

Komunitas & Dukungan

Menemukan bug atau ingin fitur? Buka issue di GitHub.

Installation

Five ways to get OpenCrabs running.

Quick install (Linux and macOS)

The fastest path. One command resolves the latest release, checks your platform, verifies the download against the published SHA256SUMS, installs opencrabs and the rtk helper, and installs the system libraries the Linux build needs.

curl -fsSL https://opencrabs.com/install.sh | bash

On Windows, use PowerShell:

iwr -useb https://opencrabs.com/install.ps1 | iex

It installs to /usr/local/bin when that directory is writable, otherwise to ~/.local/bin, and prints the export PATH line when the target is not on your PATH yet. It refuses to install when glibc is older than 2.38 (Ubuntu 24.04+, Debian 13+, Fedora 39+) or when the system uses musl, and it names the package to install instead of failing silently.

Flags go after bash -s -- when you pipe the script:

FlagEffect
--dry-runPrint the plan, download nothing
--version <tag>Install a specific release instead of the latest
--dir <path>Install into a different directory

Both scripts are plain shell and PowerShell, readable before you run them: install.sh and install.ps1.

Cara 1: Homebrew (disarankan di macOS dan Linux)

brew install opencrabs

OpenCrabs is in homebrew-core. Homebrew ships prebuilt bottles for Apple Silicon Macs (macOS 15 and newer) and for Linux amd64 and arm64, so no Rust toolchain is needed there. On an Intel Mac, Homebrew builds it from source instead; use the macos-amd64 release binary if you’d rather skip the build.

Jalankan opencrabs setelah instalasi. Langsung masuk ke chat dengan provider bawaannya sudah aktif, jadi crab menyapa Anda dan Anda bisa mulai bicara. Jalankan opencrabs --onboard kalau Anda mau wizard penyiapan.

Cara 2: Unduh Biner (semua platform)

Ambil biner siap pakai dari GitHub Releases.

Linux (amd64)

sudo apt update && sudo apt install -y curl jq libgomp1
TAG=$(curl -sL https://api.github.com/repos/opencrabs/opencrabs/releases/latest | jq -r .tag_name)
curl -fsSL "https://github.com/opencrabs/opencrabs/releases/download/${TAG}/opencrabs-${TAG}-linux-amd64.tar.gz" | tar xz
./opencrabs

Linux (arm64)

sudo apt update && sudo apt install -y curl jq libgomp1
TAG=$(curl -sL https://api.github.com/repos/opencrabs/opencrabs/releases/latest | jq -r .tag_name)
curl -fsSL "https://github.com/opencrabs/opencrabs/releases/download/${TAG}/opencrabs-${TAG}-linux-arm64.tar.gz" | tar xz
./opencrabs

macOS (arm64 / Apple Silicon)

TAG=$(curl -sL https://api.github.com/repos/opencrabs/opencrabs/releases/latest | jq -r .tag_name)
curl -fsSL "https://github.com/opencrabs/opencrabs/releases/download/${TAG}/opencrabs-${TAG}-macos-arm64.tar.gz" | tar xz
./opencrabs

Windows

$tag = (Invoke-RestMethod https://api.github.com/repos/opencrabs/opencrabs/releases/latest).tag_name
$ProgressPreference = 'SilentlyContinue'
Invoke-WebRequest "https://github.com/opencrabs/opencrabs/releases/download/$tag/opencrabs-$tag-windows-amd64.zip" -OutFile opencrabs.zip -UseBasicParsing
Expand-Archive opencrabs.zip -DestinationPath . -Force
.\opencrabs.exe

Ia langsung masuk ke chat dengan provider bawaannya sudah aktif. Jalankan opencrabs --onboard kalau Anda mau wizard penyiapan.

Windows adalah platform kelas satu (v0.5.3): penanganan shell yang sadar platform, pembacaan BOM/UTF-16 dan fallback tilde/cwd bawaan, dan CI membangun Windows di setiap PR (#627).

Verifikasi unduhan Anda (opsional): setiap rilis menerbitkan file SHA256SUMS bersama tarball-nya.

Perlu izin terminal. OpenCrabs membaca/menulis brain files, konfigurasi, dan file proyek. Aplikasi terminal Anda butuh akses sistem file atau OS akan memblokir operasi.

OSYang perlu dilakukan
macOSPengaturan Sistem → Privasi & Keamanan → Akses Disk Penuh → nyalakan aplikasi terminal Anda (Alacritty, iTerm2, Terminal, dll.). Jika tidak ada di daftar, klik “+” dan tambahkan dari /Applications/. Tanpa ini, macOS terus-menerus memunculkan “ingin mengakses data dari aplikasi lain”.
WindowsJalankan terminal (Windows Terminal, PowerShell, cmd) sebagai Administrator saat pertama kali, atau beri terminal akses tulis ke %USERPROFILE%\.opencrabs\ dan direktori proyek Anda. Windows Defender juga mungkin bertanya, klik “Izinkan”.
LinuxPastikan user Anda memiliki ~/.opencrabs/ dan direktori proyek. Di sistem SELinux/AppArmor, proses terminal butuh akses baca/tulis ke path tersebut. Terminal Flatpak/Snap mungkin perlu --filesystem=home atau izin setara.

/rebuild bekerja bahkan dengan biner siap pakai: ia meng-klon sumber ke ~/.opencrabs/source/ saat pertama dipakai, lalu membangun dan hot-restart.

Cara 3: Bangun dari Sumber

Diperlukan untuk /rebuild, menambah tools kustom, atau memodifikasi agen.

Penyiapan cepat (disarankan)

Skrip penyiapan otomatis mendeteksi platform Anda (macOS, Debian/Ubuntu, Fedora/RHEL, Arch) dan memasang semua dependensi build + Rust:

# Install all dependencies
curl -fsSL https://raw.githubusercontent.com/opencrabs/opencrabs/main/src/scripts/setup.sh | bash

# Clone and build
git clone https://github.com/opencrabs/opencrabs.git
cd opencrabs
cargo build --release
./target/release/opencrabs

Penyiapan manual

Jika Anda lebih suka memasang dependensi sendiri:

  • Rust stable: pasang Rust. Toolchain stable bekerja sejak v0.2.85
  • Kunci API dari minimal satu provider yang didukung
  • SQLite (di-bundle lewat rusqlite)
  • macOS: brew install cmake pkg-config
  • Debian/Ubuntu: sudo apt install build-essential pkg-config libssl-dev cmake
  • Fedora/RHEL: sudo dnf install gcc gcc-c++ make pkg-config openssl-devel cmake
  • Arch: sudo pacman -S base-devel pkg-config openssl cmake
git clone https://github.com/opencrabs/opencrabs.git
cd opencrabs
cargo build --release
./target/release/opencrabs

OpenCrabs memakai keys.toml alih-alih .env untuk kunci API. Wizard onboarding akan membantu menyiapkannya, atau edit ~/.opencrabs/keys.toml langsung.

Cara 4: Docker

Jalankan OpenCrabs dalam kontainer terisolasi. Build memakan ~15 menit (Rust release + LTO).

git clone https://github.com/opencrabs/opencrabs.git
cd opencrabs
docker compose -f src/docker/compose.yml up --build

Konfigurasi, workspace, dan DB memori bertahan di volume Docker lintas restart. Kunci API di keys.toml dipasang ke kontainer saat runtime, tidak pernah dibakar ke dalam image.

Mulai Otomatis saat Boot

Jaga OpenCrabs berjalan sebagai daemon latar belakang yang menyala bersama sistem Anda.

Linux (systemd)

The binary writes the unit itself, so the file cannot drift from the version you are running:

opencrabs service install

That writes ~/.config/systemd/user/opencrabs.service with Type=simple and OOMPolicy=continue, then reloads systemd. Start it with opencrabs service start: a user unit does not come back after a reboot unless lingering is enabled (see below), while a system-wide unit is enabled so it does. OOMPolicy=continue matters because the daemon is the parent of every tool call and cron child: without it, one child killed by the OOM killer stops the whole unit and takes every agent lane with it. The unit name carries the profile when you are not on default, as opencrabs-<profile>.service.

On a headless SSH session the per-user systemd bus is often missing, and the command fails with Failed to connect to bus. Enable lingering so the user service runs without a login, or install a system-wide unit instead:

sudo loginctl enable-linger $(whoami)
# or
sudo opencrabs service install

Manage the daemon with opencrabs service start|stop|restart|status, or with systemctl directly.

Cek status: systemctl --user status opencrabs | Log: journalctl --user -u opencrabs -f

macOS (launchd)

cat > ~/Library/LaunchAgents/com.opencrabs.agent.plist << 'EOF'
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN"
  "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.opencrabs.agent</string>
    <key>ProgramArguments</key>
    <array>
        <string>/usr/local/bin/opencrabs</string>
        <string>daemon</string>
    </array>
    <key>RunAtLoad</key>
    <true/>
    <key>KeepAlive</key>
    <true/>
    <key>StandardOutPath</key>
    <string>/tmp/opencrabs.log</string>
    <key>StandardErrorPath</key>
    <string>/tmp/opencrabs.err</string>
</dict>
</plist>
EOF

launchctl load ~/Library/LaunchAgents/com.opencrabs.agent.plist

Perbarui path di ProgramArguments agar cocok dengan lokasi instal Anda.

Windows (Task Scheduler)

  1. Win + R → taskschd.msc
  2. Create Basic Task → Nama: OpenCrabs
  3. Pemicu: When I log on
  4. Aksi: Start a program → C:\Users\<you>\.cargo\bin\opencrabs.exe, Argumen: daemon
  5. Di Properties > Settings, centang If the task fails, restart every 1 minute

Atau lewat PowerShell:

$action = New-ScheduledTaskAction -Execute "$env:USERPROFILE\.cargo\bin\opencrabs.exe" -Argument "daemon"
$trigger = New-ScheduledTaskTrigger -AtLogon
$settings = New-ScheduledTaskSettingsSet -RestartCount 3 -RestartInterval (New-TimeSpan -Minutes 1)
Register-ScheduledTask -TaskName "OpenCrabs" -Action $action -Trigger $trigger -Settings $settings

Pembaruan

  • Pengguna Homebrew: brew upgrade opencrabs (jika Anda ketik /evolve pada instalasi Homebrew, ia mendelegasikan ke Homebrew, biner tetap dikelola brew, dan tap lama dipensiunkan demi autobump homebrew-core)
  • Pengguna biner: ketik /evolve di TUI untuk mengunduh rilis terbaru
  • Pengguna sumber: git pull && cargo build --release, atau ketik /rebuild di TUI
  • Pengguna Docker: docker compose pull && docker compose up -d

Onboarding

A first run does not open the wizard. OpenCrabs goes straight to the chat, with its built-in provider already provisioned and the agent greeting you. The onboarding wizard is opt-in: run opencrabs --onboard, or /onboard from inside a session.

Panduan video ada di halaman Video Onboarding.

Mulai Cepat (3 menit)

Jalur cepat ini membuat Anda bisa mengobrol dengan agent dalam waktu kurang dari 3 menit.

LangkahAksi
1. ModeTekan Enter pada QuickStart
2. WorkspaceTekan Enter untuk menerima jalur default
3. ProviderGunakan panah untuk memilih provider Anda (misalnya z.ai), lalu tekan Enter. Gunakan panah bawah untuk memilih paket (misalnya Coding), lalu tekan Enter
4. API KeyTempel kunci Anda (Cmd+V / Ctrl+V / Cmd+Shift+V / Ctrl+Shift+V), lalu tekan Enter. Daftar model dimuat langsung
5. ModelGunakan panah untuk memilih model Anda (misalnya gemini-2.5-pro), lalu tekan Enter
6. DaemonGunakan panah untuk memilih apakah dijalankan sebagai daemon latar belakang, lalu tekan Enter
7. Vibe CheckSemua pemeriksaan harus menampilkan ✅. Tekan Enter
8. Tentang AndaTulis sesuatu tentang diri Anda. Semakin banyak yang diketahui agent, semakin baik. Tekan Enter
9. Tentang AgentTulis sesuatu tentang kepribadian agent. Tekan Enter
10. ChatAnda sudah masuk. Mulai berbicara dengan agent Anda

Pengaturan Lanjutan (7 menit)

Pengaturan lengkap dengan Telegram, suara lokal, vision, dan pembuatan gambar.

LangkahAksi
1. ModeTekan Enter pada QuickStart
2. WorkspaceTekan Enter untuk menerima jalur default
3. ProviderGunakan panah untuk memilih provider Anda (misalnya z.ai), lalu tekan Enter. Gunakan panah bawah untuk memilih paket, lalu tekan Enter
4. API KeyTempel kunci Anda, lalu tekan Enter. Daftar model dimuat langsung
5. ModelGunakan panah untuk memilih model Anda, lalu tekan Enter
6. ChannelsGunakan panah ke Other, tekan Space untuk memilih Telegram, lalu tekan Enter. Tempel bot token Anda, ikuti petunjuk untuk mendapatkan chat ID, lalu tekan Enter. Pilih mode mention, lalu tekan Enter. Setelah muncul tulisan Connected, tekan Enter lagi. Gunakan panah bawah ke Continue, lalu tekan Enter
7. STTPilih Local, lalu tekan Enter. Pilih ukuran model (misalnya tiny untuk kecepatan), lalu tekan Enter
8. TTSPilih Local lagi, lalu tekan Enter. Pilih suara (misalnya Ryan), lalu tekan Enter. Tunggu model selesai diunduh, gunakan panah bawah ke Continue, lalu tekan Enter
9. ImageTekan Space untuk memilih Vision dan Image Generation, lalu tekan Enter. Tempel API key Gemini Anda, lalu tekan Enter
10. DaemonGunakan panah untuk memilih apakah dijalankan sebagai daemon latar belakang, lalu tekan Enter
11. Vibe CheckSemua pemeriksaan harus menampilkan ✅. Tekan Enter
12. Tentang AndaTulis sesuatu tentang diri Anda. Semakin banyak yang diketahui agent, semakin baik. Tekan Enter
13. Tentang AgentTulis sesuatu tentang kepribadian agent. Tekan Enter
14. ChatAnda sudah masuk. Mulai berbicara dengan agent Anda

Alur Onboarding

Wizard ini adalah TUI yang dikendalikan keyboard dengan 8 langkah. Navigasi dengan tombol panah, Tab untuk maju, Esc untuk kembali.

LangkahLayarYang Anda lakukan
1Pilih ModePilih QuickStart (lewati channel) atau Advanced
2WorkspacePilih direktori kerja untuk operasi file
3Provider & AutentikasiPilih provider → tempel API key → pilih model (diambil langsung)
4ChannelsSpace untuk mengaktifkan atau menonaktifkan channel → Enter pada tiap channel untuk mengatur
5SuaraProvider STT (Groq, Whisper lokal, atau nonaktif) + suara TTS
6ImagePengaktifan Vision + model pembuatan + API key
7DaemonPasang daemon latar belakang (opsional)
8Pengaturan BrainBuat SOUL.md secara otomatis dari profil Anda

Pengaturan Channel (Langkah 4)

Layar channel menampilkan 5 integrasi: Telegram, Discord, WhatsApp, Slack, Trello.

  • Space mengaktifkan atau menonaktifkan channel
  • Enter pada channel yang aktif membuka layar pengaturannya (token, ID, allowlist)
  • Enter pada Continue atau Tab melewati ke langkah berikutnya
  • Setiap layar pengaturan channel memiliki tombol Test Connection

Lihat Ringkasan Channels untuk panduan navigasi lengkap.

Menjalankan Ulang Pengaturan

Anda dapat langsung ke langkah mana pun tanpa menjalankan ulang seluruh wizard:

PerintahLangkah
/onboardWizard lengkap
/onboard:providerPemilihan provider & model
/onboard:channelsPemilih channel
/onboard:voicePengaturan suara
/onboard:imagePengaturan image
/onboard:brainPembuatan file brain

Setelah onboarding, agent Anda aktif dan memperkenalkan diri. Ia membaca file brain-nya (SOUL.md, AGENTS.md, TOOLS.md) dan memulai percakapan.

Bootstrap

Pada saat pertama kali dijalankan, agent melewati fase bootstrap:

  • Mengenal Anda (nama, preferensi, gaya kerja)
  • Menentukan identitasnya (nama, kepribadian, emoji)
  • Membuka SOUL.md bersama Anda untuk membahas nilai-nilai
  • Menyiapkan USER.md dengan profil Anda

File bootstrap (BOOTSTRAP.md) akan menghapus dirinya sendiri setelah selesai.

Migrasi dari Tool Lain

Sudah memakai ClaudeCode, OpenClaw, Hermes, atau harness agent AI lainnya? Agent Anda dapat memindahkan data yang sudah ada (memori, skill, perintah kustom, preferensi) ke file brain miliknya menggunakan bahasa alami. Tidak perlu memindahkan file secara manual.

Ini berfungsi dengan semua agent atau harness coding yang menyimpan konfigurasi secara lokal. Migrasi mencari direktori konfigurasi tool lain di filesystem Anda, membaca isinya, dan memetakannya ke format file brain OpenCrabs (SOUL.md, USER.md, TOOLS.md, MEMORY.md, AGENTS.md, CODE.md).

Migrasi Terpandu

Jika Anda ingin meninjau apa saja yang akan dimigrasi sebelum prosesnya berjalan:

Cari data ClaudeCode/OpenClaw/Hermes saya secara lokal (atau harness agent lain yang pernah saya pakai) dan audit migrasi ke file brain kita, laporkan setelah selesai, jalankan setelah saya konfirmasi dan setujui.

Agent akan:

  1. Mencari direktori konfigurasi tool lain di filesystem Anda
  2. Membaca dan mengurai semua file terkait (memori, perintah, skill, preferensi)
  3. Membuat audit yang menunjukkan secara tepat apa dipetakan ke mana
  4. Menunggu konfirmasi Anda
  5. Menjalankan migrasi dan melaporkan rincian lengkap

Migrasi Otomatis

Jika Anda ingin langsung selesai:

Cari data agent/harness saya yang sudah ada secara lokal dan migrasikan semuanya ke file brain kita, tanpa perlu persetujuan saya. Langsung rencanakan dan jalankan, lalu laporkan setelah selesai dengan rincian lengkap.

Agent melewati langkah audit, merencanakan migrasi secara internal, menjalankannya, dan memberi Anda ringkasan semua yang dipindahkan.

Apa yang Dipetakan

SumberTujuanContoh
Memori / CLAUDE.md / file konteksMEMORY.mdKeputusan proyek, konteks sebelumnya
Perintah kustomcommands.tomlPerintah slash dan definisinya
Skill / instruksi agentAGENTS.md, TOOLS.mdAturan alur kerja, konfigurasi tool
Preferensi / profil penggunaUSER.mdNama, zona waktu, gaya coding
Standar coding / aturan lintingCODE.mdPanduan gaya, konvensi

Tips: Jalankan migrasi tepat setelah onboarding selagi konteksnya masih segar. Agent sudah memiliki USER.md dan SOUL.md Anda dari wizard, sehingga ia dapat menggabungkan data secara cerdas alih-alih menimpanya.

Perintah Utama

PerintahDeskripsi
/helpTampilkan semua perintah yang tersedia
/modelsGanti provider atau model
/newBuat sesi baru
/sessionsPindah antar sesi
/cdUbah direktori kerja
/compactRingkas konteks secara manual
/evolveUnduh versi terbaru
/rebuildBuild dari source
/approveAtur kebijakan persetujuan

Mode Persetujuan

Atur seberapa besar otonomi yang dimiliki agent:

ModePerilaku
/approveTanya sebelum setiap penggunaan tool (default)
/approve autoSetujui otomatis untuk sesi ini
/approve yoloSelalu setujui otomatis (tersimpan)

Direktori Kerja

Agent bekerja di dalam direktori kerja untuk operasi file. Ubah dengan:

  • Perintah /cd di chat
  • Pemilih direktori di TUI (Tab untuk memilih)
  • Tool config_manager set_working_directory

Direktori kerja disimpan per sesi. Saat berpindah sesi, direktori dipulihkan secara otomatis.

Petunjuk Filter Model (v0.3.78)

Pemilih model onboarding sekarang selalu menampilkan petunjuk filter di atas daftar model (“Type to filter…”). Sebelumnya, petunjuk ini hanya muncul setelah Anda mulai mengetik, sehingga pengguna tidak tahu bahwa filter tersedia. Selain itu, ID model yang diketik persis sekarang mengalahkan pencocokan substring: jika Anda mengetik claude-opus-4-8 secara persis, model tersebut dipilih meskipun model lain memiliki substring yang sama.

Video Onboarding

Video panduan instalasi dan onboarding OpenCrabs tersedia di kanal YouTube OpenCrabs. Setiap OS memiliki video mulai cepat dan video pengaturan lanjutan. Klik miniatur untuk memutar videonya di sini.

macOS

Install OpenCrabs on macOS in under 2 minutes (Homebrew) Install in under 2 minutes (Homebrew)
OpenCrabs advanced setup on macOS Advanced setup on macOS (Telegram, voice, images)

Ubuntu/Linux

Install OpenCrabs on Ubuntu/Linux in 3 minutes Install in 3 minutes (one paste)
OpenCrabs advanced setup on Linux Advanced setup on Linux (Telegram, voice, images)

Windows

Install OpenCrabs on Windows in 2 minutes Install in 2 minutes (PowerShell)
OpenCrabs advanced setup on Windows Advanced setup on Windows (Telegram, voice, images)

Semua video: youtube.com/@OpenCrabs

Halaman Onboarding berisi langkah demi langkah jalur Mulai Cepat dan Lanjutan dalam bentuk teks.

Configuration

OpenCrabs uses two config files stored at ~/.opencrabs/:

FilePurpose
config.tomlProvider settings, features, channel connections
keys.tomlAPI keys and secrets (never committed to git)

Workspace Layout

~/.opencrabs/
├── config.toml          # Main configuration
├── keys.toml            # API keys (gitignored)
├── commands.toml        # Custom slash commands
├── opencrabs.db         # SQLite database
├── SOUL.md              # Agent personality
├── USER.md              # Your profile
├── MEMORY.md            # Long-term memory
├── AGENTS.md            # Agent behavior docs
├── TOOLS.md             # Tool reference
├── SECURITY.md          # Security policies
├── HEARTBEAT.md         # Periodic check tasks
├── memory/              # Daily memory notes
│   └── YYYY-MM-DD.md
├── images/              # Generated images
├── logs/                # Application logs
└── skills/              # Custom skills/plugins

Provider Configuration

See Provider Setup for detailed provider configuration.

Quick example: add Anthropic:

# config.toml
[providers.anthropic]
enabled = true
default_model = "claude-sonnet-4-20250514"
# keys.toml
[providers.anthropic]
api_key = "sk-ant-..."

Provider Priority

When multiple providers are enabled, the first one found in this order is used for new sessions:

MiniMax > OpenRouter > Anthropic > OpenAI > Gemini > Custom

Each session remembers which provider and model it was using. Switch providers per-session via /models.

Config Safety (v0.3.74)

A malformed line in config.toml or keys.toml corrupts the whole file and takes the agent down (no provider keys, no bot token, no way back in). v0.3.74 makes that class of failure nearly impossible:

  • Config edits via config_manager only: the agent edits config.toml / keys.toml only through the config_manager tool, never with raw file edits. The write path validates the result and refuses anything that would break parsing (#715).
  • Guard config-breaking writes: edit_file / write_file writes that would break config.toml / keys.toml are denied outright (#713).
  • Re-validate before write: config is re-validated immediately before fs::write in the config_manager write paths (#714).
  • Validate keys before snapshot: keys.toml is validated before it is saved as the last-good snapshot, so a bad snapshot can’t poison recovery (#712).

v0.3.79 hardened this further:

  • Every config write is atomic (#911): a crash or a concurrent reader mid-write can never observe a half-written config.toml or keys.toml.
  • Config loads no longer race on shared state, concurrent reloads can’t interleave and leave a torn config in memory.
  • Reload failures are specific: a failed reload says what failed, and a write race is named as the transient condition it is instead of a scary generic error.
  • Never select a provider you didn’t configure, fallback and model routing can no longer land on a provider section that doesn’t exist in your config.

Agent Behavior

[agent]
working_directory = "/path/to/default/dir"
thinking = "on"                     # "on", "off", or "budget_XXk"
approval_policy = "auto-always"     # "ask", "auto-session", "auto-always"
max_concurrent = 4                  # max parallel tool calls
context_limit = 200000              # context window cap (tokens)
max_tokens = 65536                  # max output tokens per API call
auto_update = true                  # auto-install releases on startup
silent_compaction = false           # suppress post-compaction personality narration
compaction_notice = false            # show the compaction notice pair; off by default (v0.5.4)
lazy_tools = true                   # JIT tool-schema loading (ships core + tool_search only)
redact_sensitive_data = true        # redact API keys, tokens, passwords, IPs from output
debug_logs = false                  # enable debug file logging (hot-reloads, no restart)
default_provider = "minimax"        # fallback provider when no provider is active (v0.3.62)
default_model = "MiniMax-M2.7"      # fallback model when no model is active (v0.3.62)
FieldDefaultDeskripsi
working_directoryhome dirDefault working directory for the agent
thinking"on"Extended thinking mode: "on", "off", or "budget_XXk"
approval_policy"auto-always""ask" = confirm every tool call, "auto-session" = auto-approve for session, "auto-always" = never ask
max_concurrent4Max tool calls running in parallel
context_limit200000Context window limit in tokens. When exceeded, oldest messages are dropped
max_tokens65536Max output tokens per single API call
auto_updatetrueAutomatically install new releases on startup (binary mode only)
silent_compactionfalseWhen true, suppresses the agent’s playful post-compaction narration. Useful for corporate/formal deployments
compaction_noticefalseShow the compaction notice pair in the TUI. Off by default since v0.5.4 (#1686)
lazy_toolstrueShips only core tool schemas (~4k tokens) plus tool_search per request. The agent discovers and activates extended tools on demand via tool_search. Set false to load all ~95 schemas every request
redact_sensitive_datatrueRedacts API keys, tokens, passwords, and IPs from tool outputs and display. Set false during sysadmin/devops work where seeing IPs/tokens/passwords is necessary
debug_logsfalseEnable debug file logging to ~/.opencrabs/logs/. Hot-reloads on change (no restart). The --debug CLI flag always wins when set (OR logic)
default_providerNone (uses active provider)Fallback provider when no provider is active in the current session. Also used for cron jobs without an explicit provider (v0.3.62)
default_modelNone (uses active model)Fallback model when no model is active in the current session. Also used for cron jobs without an explicit model (v0.3.62)

Timeout flags resolve per provider first: [providers.<name>] wins over [agent], which wins over the built-in default, and every provider family honours the chain, anthropic and gemini included (v0.5.4, #1688).

Sub-agent and RSI Overrides

Route spawned sub-agents and RSI (self-improvement) cycles to separate providers so they never compete with your main chat for quota:

[agent]
subagent_provider = "minimax"           # provider for spawned sub-agents
subagent_model = "MiniMax-M2.7"         # model for spawned sub-agents
self_improvement_provider = "minimax"   # provider for RSI self-improvement cycles
self_improvement_model = "MiniMax-M2.7" # model for RSI cycles
FieldDefaultDeskripsi
subagent_providerNone (uses session provider)Provider for spawned sub-agents. Keeps sub-agents off your main provider
subagent_modelNone (uses session model)Model for spawned sub-agents
self_improvement_providerNone (uses session provider)Provider for RSI self-improvement cycles. RSI runs on its own provider chain
self_improvement_modelNone (uses session model)Model for RSI cycles. Prefer cheap, fast models since results are deterministic

Channel Configuration

Telegram

[channels.telegram]
enabled = true
token = "123456:ABC-DEF1234ghIkl-zyx57W2v1u123ew11"
allowed_users = ["123456789"]       # numeric Telegram user IDs
allowed_channels = ["-100123456"]   # restrict to specific group/channel IDs (empty = all)
respond_to = "mention"              # "all", "dm_only", "mention" (default)
session_idle_hours = 24.0           # idle timeout for non-owner sessions
rich_messages = true                # native Telegram rich messages (Bot API 10.1, default since v0.3.64)
silence_group_start = true          # silently ignore /start from non-allowed users in groups
bot_owner = ["123456789"]           # owner IDs (gated commands, /cd hidden dirs, /profiles)
FieldDefaultDeskripsi
enabledfalseEnable the Telegram bot channel
tokenNoneTelegram Bot API token from @BotFather
allowed_users[] (accept all)Numeric Telegram user IDs. Accepts int or string arrays. Empty = open mode
allowed_channels[] (all channels)Restrict bot to specific channel/group IDs. DMs always pass
respond_to"mention"When to respond in groups: "all" = every message, "dm_only" = ignore groups, "mention" = only when @mentioned or replied-to
session_idle_hoursNone (no timeout)Idle timeout in hours for non-owner sessions. Owner sessions never expire
rich_messagesfalseSend structured replies as native Telegram rich messages (tables, headings, lists, math). Only works on current mobile/desktop Telegram clients. Telegram Web and older clients show a “not supported” placeholder. Enable only when your audience is on modern clients
silence_group_starttrueSilently ignore /start from non-allowed users in group chats. Users who need their ID can DM the bot
bot_owner[] (first allowed_user)Bot owner user IDs. Owners can access gated commands (/profiles, hidden files in /cd), manage profiles. When unset, defaults to first entry in allowed_users

Discord

[channels.discord]
enabled = true
token = "your-discord-bot-token"
allowed_users = ["123456789012345678"]
allowed_channels = ["123456789012345678"]
respond_to = "mention"
session_idle_hours = 24.0
FieldDefaultDeskripsi
enabledfalseEnable the Discord bot channel
tokenNoneDiscord bot token from the Developer Portal
allowed_users[] (accept all)Discord user IDs. Accepts int or string arrays
allowed_channels[] (all channels)Restrict bot to specific channel IDs
respond_to"mention"When to respond: "all", "dm_only", "mention"
session_idle_hoursNoneIdle timeout for non-owner sessions

Slack

[channels.slack]
enabled = true
token = "xoxb-your-bot-token"
app_token = "xapp-your-app-token"      # Socket Mode token
allowed_users = ["U12345678"]
allowed_channels = ["C12345678"]
respond_to = "mention"
session_idle_hours = 24.0
FieldDefaultDeskripsi
enabledfalseEnable the Slack bot channel
tokenNoneBot token (xoxb-...)
app_tokenNoneApp-level token for Socket Mode (xapp-...)
allowed_users[] (accept all)Slack user IDs (U12345678)
allowed_channels[] (all channels)Restrict bot to specific channel IDs
respond_to"mention"When to respond: "all", "dm_only", "mention"
session_idle_hoursNoneIdle timeout for non-owner sessions

WhatsApp

[channels.whatsapp]
enabled = true
allowed_phones = ["+15551234567"]      # E.164 format
session_idle_hours = 24.0
FieldDefaultDeskripsi
enabledfalseEnable the WhatsApp channel
allowed_phones[] (accept all)E.164 phone numbers. Empty = accept everyone (not recommended for business numbers)
session_idle_hoursNoneIdle timeout for non-owner sessions

Trello

[channels.trello]
enabled = true
token = "your-trello-api-token"
app_token = "your-trello-api-key"
allowed_users = ["memberId1"]
board_ids = ["boardId1", "boardId2"]
poll_interval_secs = 60
session_idle_hours = 24.0
FieldDefaultDeskripsi
enabledfalseEnable the Trello channel
tokenNoneTrello API token
app_tokenNoneTrello API key (stored as app_token for keys.toml symmetry)
allowed_users[] (accept all)Trello member IDs
board_ids[] (all boards)Board IDs to monitor for @mentions. Also accepts allowed_channels as an alias
poll_interval_secsNone (tool-only)Polling interval in seconds. Absent or 0 = no polling (tool-only mode)
session_idle_hoursNoneIdle timeout for non-owner sessions

Other Channels (Preview)

Signal, Google Chat, and iMessage are available as preview placeholders:

[channels.signal]
enabled = true
allowed_phones = ["+15551234567"]

[channels.google_chat]
enabled = true
token = "your-google-chat-token"
allowed_users = ["user@example.com"]

[channels.imessage]
enabled = true
allowed_phones = ["+15551234567"]

Cron Defaults

Route cron jobs to cheaper providers so they never compete with your interactive session:

[cron]
default_provider = "minimax"
default_model = "MiniMax-M2.7"
FieldDefaultDeskripsi
default_providerNone (uses active provider)Default provider for cron jobs without an explicit provider
default_modelNone (uses active model)Default model for cron jobs without an explicit model

Memory and Embeddings

[memory]
vector_enabled = true

[memory.embedding]
url = "https://api.openai.com/v1"
model = "text-embedding-3-small"
# api_key loaded from keys.toml: [providers.memory_embedding] api_key = "sk-..."
# dimensions = 1536  # auto-detected from first API response if unset
FieldDefaultDeskripsi
vector_enabledtrue (desktop), false (VPS)Enable vector embeddings for semantic memory search. When disabled, only FTS5 keyword search is used. Auto-disabled on systems with < 2GB RAM or detected cloud instances
embedding.urlNoneOpenAI-compatible API base URL. The /embeddings path is appended automatically
embedding.modelNoneEmbedding model name (e.g. text-embedding-3-small, nomic-embed-text)
embedding.api_keyNoneAPI key for the embedding endpoint. Also loaded from keys.toml under [providers.memory_embedding]
embedding.dimensionsNone (auto-detected)Embedding vector dimensions. Auto-detected from the first API response if unset. Local GGUF model always produces 768-dim vectors
extra_paths[]External paths indexed into the external collection. Bare path strings or { path, pattern } tables; relative paths resolve against the OpenCrabs home
excludeVCS/build/secret globsGlob excludes for external indexing, global across all entries
external_allowed_in_sharedfalseAllow scope="external" results in shared/group sessions. Default-deny: external content stays owner-session-only unless opted in
sweep_interval_secs300Seconds between external-path freshness sweeps; modified files are also caught lazily at search time

When [memory.embedding] is not set, embeddings are generated locally via the embeddinggemma-300M GGUF model (~300MB download, ~2.9GB RAM). Setting [memory.embedding] with an API endpoint eliminates the local model overhead.

To index your own docs (or an entire scraped docs site) into memory search, see Memory System → External Index Paths for the full knowledge-base workflow.

Brain Files

[brain]
strip_empty_sections = true
default_cap = 500

[brain.caps]
SOUL.md = 300
AGENTS.md = 800
FieldDefaultDeskripsi
strip_empty_sectionstrueStrip empty header stubs (## Header with no body) from brain-file reads. Writes are never affected, only the loaded view is filtered
default_cap500Per-file line cap for sync_templates. When a merged file exceeds its cap, the sync bails instead of writing
caps{} (empty)Per-file line caps overrides. Keys are filenames (case-sensitive: TOOLS.md and tools.md are distinct)

Browser

[browser]
cdp_endpoint = "http://localhost:9222"
FieldDefaultDeskripsi
cdp_endpointNone (spawn new browser)CDP endpoint for an existing Chromium instance. When set, connects via Chrome DevTools Protocol instead of spawning a new browser. Useful for sharing a single browser across multiple profiles to save memory (~250-300MB per instance)

To start a standalone Chromium with CDP enabled:

chromium --remote-debugging-port=9222 --headless --no-sandbox

A2A (Agent-to-Agent) Gateway

[a2a]
enabled = false
bind = "127.0.0.1"
port = 18790
allowed_origins = ["https://your-app.com"]
# api_key = "your-secret-key"  # Bearer token for incoming requests
FieldDefaultDeskripsi
enabledfalseEnable the A2A JSON-RPC 2.0 gateway
bind"127.0.0.1"Bind address
port18790Gateway port
allowed_origins[]CORS origins. Must be set explicitly, no cross-origin requests allowed by default
api_keyNoneBearer token for authenticating incoming A2A requests. If unset, no authentication required

Daemon Mode

[daemon]
health_port = 8080
FieldDefaultDeskripsi
health_portNone (no health server)HTTP port for GET /health endpoint. Useful for systemd watchdog, uptime monitors, and external health probes

OpenCrabs runs in two modes: TUI (interactive terminal UI with chat) and Daemon (headless background service for channels + cron). For any one profile, run only one at a time. The TUI always wins: opening it while a daemon runs shuts the daemon down and takes over the channels.

For full service lifecycle management (TUI vs Daemon comparison, opencrabs service install/start/stop, profile-aware services, OPENCRABS_PROFILE env var, troubleshooting), see the CLI Commands reference.

Image Generation and Vision

[image.generation]
enabled = true
model = "gemini-3.1-flash-image-preview"

[image.vision]
enabled = true
model = "gemini-3.1-flash-image-preview"
provider = "openrouter"           # bypasses enabled gate for vision-only providers (v0.3.63)
SectionFieldDefaultDeskripsi
image.generationenabledfalseEnable image generation via the generate_image tool
image.generationmodel"gemini-3.1-flash-image-preview"Model for image generation
image.visionenabledfalseEnable vision analysis via the analyze_image tool. Since v0.3.64: setting vision_model alone is sufficient to enable vision. enabled is no longer required when vision_model is set
image.visionmodel"gemini-3.1-flash-image-preview"Model for image/vision analysis
image.visionproviderNone (auto-detect)Dedicated provider for vision. Bypasses the enabled gate so you can use a vision-only provider without enabling it for general chat (v0.3.63)

Vision analysis automatically scans all enabled providers (Google, OpenRouter, OpenAI-compatible, Anthropic) before returning an error. No configuration needed.

Voice Provider Fallback

STT and TTS providers support automatic failover via fallback_chain. When the primary returns a 5xx, fails a liveness probe (Voicebox), or is otherwise unreachable, the dispatcher walks the chain in order and tries each entry that has the credentials/config it needs.

[providers.stt]
fallback_chain = ["groq", "openai_compatible", "local"]

[providers.tts]
fallback_chain = ["openai_compatible", "openai", "local"]
ChainValid labels
STTvoicebox, openai_compatible, groq, local (aliases: whisper, local_whisper)
TTSvoicebox, openai_compatible, openai, local (aliases: piper, local_piper). groq is STT-only, the TTS chain rejects it

Empty or omitted chain means “use the default priority order with the primary removed.”

TOML Bash Blocklist (v0.3.78)

A new TOML-driven bash blocklist provides runtime-configurable safety gates for shell commands. The blocklist lives in a TOML file and defines patterns that the agent’s bash tool will refuse to execute:

# safety.toml (example)
[bash_blocklist]
patterns = ["rm -rf /", "dd if=", "mkfs", ":(){ :|:& };:"]
  • Patterns are matched against the command string before execution
  • The blocklist reloads on file change (no restart needed)
  • Configurable per-profile, so different environments can have different safety gates
  • Blocked commands return a clear error explaining which pattern matched

This replaces hardcoded safety checks with a user-extensible system. You can add project-specific dangerous commands without code changes.

Safety TOML Reload (v0.3.78)

All safety-related TOML files (bash blocklist, brain verification rules) now hot-reload on change. Edit the file and the new rules take effect immediately on the next tool call. No restart, no /reload, no config manager dance.

CLI Commands

OpenCrabs punya CLI lengkap dengan 20+ subperintah untuk mengelola setiap aspek agen.

Penggunaan

opencrabs [COMMAND] [OPTIONS]

Perintah

PerintahDeskripsi
chat (default)Menjalankan antarmuka chat TUI
daemonBerjalan di latar belakang (hanya channels, tanpa TUI)
agentChat multi-giliran interaktif atau mode pesan-tunggal
runMenjalankan satu prompt secara non-interaktif; --quiet menyembunyikan chrome UI untuk stdout murni mesin, --format memilih text/json/markdown (v0.5.4)
cronKelola tugas terjadwal (add/list/remove/enable/disable/test)
channelManajemen channel (list, doctor)
memoryManajemen memori (list, get, stats, prune)
sessionManajemen session (list, get)
dbManajemen database (init, stats, clear)
logsManajemen log (status, view, clean, open)
serviceManajemen layanan sistem (install/start/stop/restart/status/uninstall)
statusTampilkan status agen
doctorJalankan pemeriksaan kesehatan koneksi; --fix memperbaiki baris cron macet, marker basi, dan izin (v0.3.83)
onboardJalankan wizard penyiapan
completionsBuat completion shell (bash/zsh/fish/powershell)
versionTampilkan info versi
!commandOperator bang: jalankan perintah shell apa pun seketika tanpa bolak-balik LLM. Keluaran ditampilkan sebagai pesan sistem. Mis. !git status, !ls -la
/evolvePembaruan otomatis: mengunduh rilis terbaru dan hot-restart. Berjalan otomatis saat startup bila [agent] auto_update = true
/restartRestart (v0.3.79): menjalankan ulang biner yang sama dengan argumen yang sama, jadi cara ia dulu dijalankan tidak perlu diketahui atau diulang. Giliran yang belum selesai dilanjutkan otomatis saat startup. Di TUI ia melanjutkan session saat ini. Hanya owner di channels; mengumumkan sebelum bertindak.
/exitShutdown (v0.3.79): mematikan OpenCrabs. Menyalakannya lagi butuh akses ke mesin tempat ia berjalan, jadi ini satu-satunya perintah tanpa jalan kembali dari chat. Hanya owner di channels; mengumumkan sebelum bertindak.
/clearSession baru (v0.5.2): Menutup session saat ini dan memulai session kosong di tempatnya, tanpa panggilan summariser, jadi instan. Hanya owner di channels.
/btwAgen paralel: memunculkan sub-agent terisolasi untuk tugas sampingan sementara percakapan utama berlanjut. Mis. /btw research the latest Rust async patterns
/mission-controlMission Control: dasbor layar penuh berisi kotak masuk RSI (proposal tertunda), log aktivitas (perbaikan yang diterapkan), dan jadwal cron. Navigasi dengan tombol vim, terima/tolak proposal dengan a/r.
/skillsPemilih skills: jelajahi dan jalankan templat alur kerja dengan pencarian fuzzy. Setiap skill yang dimuat otomatis terdaftar sebagai slash command.
/security-auditAudit keamanan: audit keamanan & CVE menyeluruh tanpa terikat bahasa. Mendeteksi tipe proyek, menjalankan scanner yang tepat, meninjau diff terbaru untuk pola injeksi/auth/kripto, memberi skor 0-100.
/cost-estimateEstimasi biaya: estimasi biaya pembangunan codebase, rincian ROI berbantu AI, dan valuasi harga wajar. Menanyakan konteks bisnis sebelum menghasilkan rentang valuasi.
/repo-auditAudit repo: pemeriksaan kesehatan repositori tanpa terikat bahasa. Pipa 5 fase: deteksi bahasa → eksekusi tools natif → metrik git → analisis AST → skor + rekomendasi. Mencakup Rust, JS/TS, Python, Go.
/goalLoop tujuan otonom: tetapkan tujuan dengan /goal <teks> dan agen berputar otonom: mengeksekusi, menilai dirinya sendiri dengan juri LLM, dan berlanjut sampai tujuan tercapai atau anggaran giliran (default 20) habis. Mendukung /goal pause, /goal resume, /goal status, /goal clear.
/models <provider/model>Pergantian model langsung (v0.3.66): pindah langsung ke model tertentu di setiap channel. Gunakan /models anthropic/claude-sonnet-4-20250514 untuk langsung berpindah. Pemilih cakupan-terapan memungkinkan memilih hanya-session atau global.
opencrabs session set-modelPergantian model headless (v0.3.66): ganti model dari CLI tanpa TUI. Berguna untuk skrip dan otomasi.

Flag Konfigurasi

FlagDefaultDeskripsi
[agent] auto_updatetruePasang otomatis rilis baru saat startup dan hot-restart. Set false untuk mempertahankan dialog prompt manual.
[agent] force_defaultfalse(v0.3.66) Bila true, mendorong pasangan provider/model default ke semua sessions saat konfigurasi di-reload. Berguna untuk menegakkan pergantian model global.
[agent] config_drift_warningstrue(v0.3.66) Tampilkan peringatan startup saat nilai konfigurasi diam-diam bergeser dari default yang diharapkan. Membantu menangkap perubahan konfigurasi yang tak disengaja.

Pintasan Keyboard (TUI)

ShortcutAksi
F12Nyalakan/matikan penangkapan mouse untuk seleksi teks terminal natif

Prompt Pembaruan Saat Startup

Saat versi baru tersedia, dialog di tengah muncul di layar splash menawarkan menerima (Enter) atau lewati (Esc). Menerima otomatis memicu /evolve. Setelah pembaruan, biner restart dan splash menampilkan versi baru.

Perintah Channel

/doctor, /help, /usage, /evolve, /restart, /exit, dan perintah sistem bekerja langsung di Telegram, Discord, Slack, dan WhatsApp tanpa melalui LLM. Dieksekusi seketika dan mengembalikan hasil di channel.

/restart dan /exit mengumumkan diri sebelum bertindak, karena begitu prosesnya hilang tidak ada yang tersisa untuk melaporkan kegagalan. Keduanya hanya untuk owner, seperti semua perintah lain yang mengubah proses yang berjalan. /exit adalah satu-satunya perintah tanpa jalan kembali dari chat: pemulihan butuh akses ke mesin tempat OpenCrabs berjalan.

Sejak v0.3.80, stop dengan bahasa natural juga dihormati, menyuruh bot berhenti dengan bahasa sehari-hari bekerja di channels dan TUI, bukan hanya kata polos “stop”.

Seluruh logika perintah channel dipusatkan di src/channels/commands.rs (847 baris) – handler bersama yang menghapus duplikasi logika perintah di 5 implementasi channel. Setiap channel mendelegasikan ke try_execute_text_command() untuk perilaku yang konsisten.

/evolve di channels kini berjalan langsung (mengunduh + memasang biner) tanpa bolak-balik LLM. Sebelumnya diarahkan melalui agen.

Mode Chat

# Default: launch TUI
opencrabs

# Same as above
opencrabs chat

Mode Agen

Mode non-interaktif untuk skrip dan otomasi:

# Interactive multi-turn chat
opencrabs agent

# Single-message mode
opencrabs agent -m "File apa yang berubah hari ini?"

Mode Daemon vs TUI

OpenCrabs berjalan dalam salah satu dari dua mode. Pilih yang cocok untuk mesinnya. Untuk satu profil, jalankan hanya satu pada satu waktu.

ModeCara memulaiApa ituDipakai di
TUI (interaktif)opencrabsUI terminal lengkap: panel chat, sessions, pengaturan. Channels Anda juga berjalan dan berbagi session Anda.Mesin yang Anda duduki (laptop/desktop)
Daemon (headless)opencrabs daemon, or install as a service: opencrabs service install && opencrabs service startTanpa UI. Hanya channels (Telegram, Discord, Slack, WhatsApp) + cron. Bertahan dari reboot, koneksi SSH terputus, dan crash.Mesin selalu-aktif atau VPS

Kenapa tidak keduanya sekaligus?

Satu kredensial bot (mis. token Telegram) hanya bisa memegang satu poll getUpdates yang hidup. Jika daemon dan TUI sama-sama memegang token profil yang sama, mereka berkelahi (HTTP 409) dan channel jatuh.

TUI selalu menang

Saat Anda membuka TUI ketika daemon untuk profil yang sama sedang berjalan, TUI mematikan daemon itu lebih dulu, mengambil alih channels, dan menampilkan banner tentang hal itu. Channels Anda sudah terpasang, jadi mereka cukup lanjut tanpa menyambung ulang. Daemon tetap mati sampai Anda menyalakannya lagi (opencrabs service start, atau jalankan ulang opencrabs daemon).

Di mesin tempat daemon biasanya berjalan, alur sehari-harinya: buka opencrabs saat ingin duduk bersamanya; tutup TUI dan jalankan opencrabs service start saat ingin headless lagi.

Mulai otomatis saat boot

  • Daemon: gunakan installer layanan (opencrabs service install); ia memasang systemd (Linux) / launchd (macOS) untuk mulai saat boot dan restart saat crash. Ini setup selalu-aktif yang disarankan.
  • TUI: agar UI terminal terbuka otomatis saat login, gunakan autostart terminal/desktop, bukan installer layanan:
    • Desktop Linux: taruh file .desktop di ~/.config/autostart/ dengan Exec=x-terminal-emulator -e opencrabs.
    • macOS: Pengaturan Sistem > Umum > Item Login > tambahkan skrip .command yang menjalankan opencrabs.
    • VPS lewat SSH: jalankan di dalam tmux/screen dan sambungkan ulang. Di VPS headless Anda biasanya ingin daemon, bukan TUI.

Sangat disarankan untuk pengguna harian. Jika Anda berencana memakai OpenCrabs setiap hari, mintalah ia menyiapkan dirinya sebagai layanan sistem. Cukup katakan semacam “siapkan dirimu untuk menyala bersama komputerku” atau “hapus layanan mulai-otomatisnya”. Agen menangani pemasangan dan penghapusan launchd (macOS) atau systemd (Linux) secara otomatis untuk Anda.

Menjaga TUI tetap berjalan di VPS (tmux)

Jika Anda ingin TUI interaktif lengkap selalu tersedia di server jauh (bukan daemon headless), tmux adalah jalannya. Session Anda bertahan dari koneksi SSH terputus, laptop tidur, dan koneksi tidak stabil.

1. Pasang tmux

# Debian / Ubuntu
sudo apt install tmux

# RHEL / CentOS / Fedora
sudo yum install tmux

2. Cari di mana opencrabs terpasang

which opencrabs

# If that returns nothing, search the whole filesystem
find / -name "opencrabs" -type f 2>/dev/null

3. Mulai sesi tmux bernama

tmux new -s opencrabs

4. Jalankan opencrabs di dalamnya

opencrabs

5. Lepaskan (opencrabs tetap berjalan)

Tekan Ctrl+B, lalu D. Anda kini bisa menutup SSH dan opencrabs tetap berjalan.

6. Sambungkan ulang nanti

tmux attach -t opencrabs

Sudah selesai. Kembali dari mesin mana pun, sambungkan ulang, dan lanjutkan dari tempat Anda berhenti. Jika Anda hanya butuh channels (Telegram, Discord, Slack, WhatsApp) berjalan headless tanpa TUI, daemon lebih cocok: opencrabs service install && opencrabs service start.

Endpoint Kesehatan

Tambahkan ke config.toml untuk membuka pemeriksaan kesehatan:

[daemon]
health_port = 8080

Lalu GET http://localhost:8080/health mengembalikan 200 OK dengan status JSON. Berguna untuk watchdog systemd, monitor uptime, atau load balancer.

Manajemen Layanan

Pasang OpenCrabs sebagai layanan sistem (launchd di macOS, systemd di Linux):

opencrabs service install
opencrabs service start
opencrabs service stop
opencrabs service restart
opencrabs service status
opencrabs service uninstall

Layanan sadar-profil

Setiap profil mendapat layanan mandirinya sendiri:

# Install a specific profile as a service
opencrabs -p hermes service install
opencrabs -p hermes service start

# Each profile gets its own service name
# macOS: com.opencrabs.daemon.hermes
# Linux: opencrabs-hermes.service

# Manage independently
opencrabs -p hermes service status
opencrabs -p hermes service stop
opencrabs -p hermes service uninstall

Beberapa profil bisa berjalan sebagai layanan daemon simultan dengan isolasi penuh.

Variabel lingkungan OPENCRABS_PROFILE

Set OPENCRABS_PROFILE=hermes untuk memilih profil tanpa flag -p. Berguna untuk layanan systemd, tugas cron, dan mode daemon.

Penanganan masalah: daemon tetap mati

Jika opencrabs service status bilang stopped:

  1. Apakah Anda membuka TUI? Membuka opencrabs sengaja mematikan daemon agar session interaktif bisa memiliki channels. Daemon tetap mati sampai Anda menjalankan opencrabs service start lagi.
  2. Build lama mungkin masih punya Restart=on-failure alih-alih Restart=always. Buat ulang unit dengan opencrabs service install (lalu service start) untuk mendapatkan kebijakan selalu-restart.
  3. Konfigurasi, kunci, perintah, dan tools hot-reload saat runtime. Mengedit config.toml atau keys.toml tidak pernah butuh restart daemon. Jika perubahan tidak berlaku, periksa log untuk baris ConfigWatcher: reloaded alih-alih me-restart.

Manajemen Cron

# List all cron jobs
opencrabs cron list

# Add a new cron job
opencrabs cron add \
  --name "Daily Report" \
  --cron "0 9 * * *" \
  --tz "America/New_York" \
  --prompt "Check emails and summarize" \
  --provider anthropic \
  --model claude-sonnet-4-20250514 \
  --thinking off \
  --deliver-to telegram:123456

# Remove a cron job (accepts name or ID)
opencrabs cron remove "Daily Report"

# Enable/disable (accepts name or ID)
opencrabs cron enable "Daily Report"
opencrabs cron disable "Daily Report"

Pintasan Keyboard TUI

TombolAksi
EnterKirim pesan
EscBatal / tutup
Ctrl+NSession baru
Ctrl+LLayar sessions
Ctrl+KBersihkan session saat ini
Ctrl+OBuka/tutup lipatan grup tools
|Pisahkan panel horizontal
_Pisahkan panel vertikal
Ctrl+XTutup panel yang difokuskan
TabPutar fokus panel / Terima autocomplete
Up/DownNavigasi saran / sessions
/Mulai slash command (mis. /help, /models)
:Buka pemilih emoji

Troubleshooting

Common issues and how to fix them.

Windows Defender Blocking OpenCrabs

Windows Defender may flag opencrabs.exe as suspicious because it’s an unsigned binary that executes shell commands and makes network requests. This is a false positive.

Add an exclusion:

  1. Open Windows Security → Virus & threat protection
  2. Virus & threat protection settings → Manage settings
  3. Exclusions → Add or remove exclusions
  4. Add an exclusion → File → select opencrabs.exe

Or via PowerShell (admin):

Add-MpPreference -ExclusionPath "C:\path\to\opencrabs.exe"

If SmartScreen blocks the first run, click More info → Run anyway.


Binary Won’t Start or Crashes

Startup Errors

Run with debug logging to see what’s failing:

opencrabs -d chat

Logs are written to ~/.opencrabs/logs/.

Download a Previous Version

If the latest release crashes on your machine, download a previous working version from GitHub Releases:

# List all releases
gh release list -R opencrabs/opencrabs

# Download a specific version
gh release download v0.2.66 -R opencrabs/opencrabs -p "opencrabs-*$(uname -m)*$(uname -s | tr A-Z a-z)*"

/evolve: Update & Rollback

/evolve downloads the latest release from GitHub and hot-swaps the binary. It has built-in safety checks:

  1. Download: Fetches the platform-specific binary from GitHub Releases
  2. Pre-swap health check: Runs opencrabs health-check on the new binary (10s timeout). If it fails, the new binary is deleted and your current version stays untouched.
  3. Backup: Creates a backup at <binary-path>.evolve_backup
  4. Atomic swap: Replaces the current binary
  5. Post-swap health check: Verifies the swapped binary works. If it fails, auto-rolls back to the backup.
  6. Restart: exec()-restarts into the new version
  7. Brain update prompt: After restart, your crab announces the new version, diffs brain templates against your local files, and offers to update them

If /evolve Fails

The most common reason is the health check caught an issue, your current version stays safe. If something went wrong after the swap:

# Restore the backup manually
cp /path/to/opencrabs.evolve_backup /path/to/opencrabs
chmod +x /path/to/opencrabs

Cargo Install Fallback

When /evolve uses cargo install (building from source), it tries the stable toolchain first. If that fails, it automatically falls back to cargo +nightly. The progress message shows which toolchain succeeded.

Check-Only Mode

The agent can check for updates without installing:

/evolve check_only=true

Bash Tool Safety

The bash tool includes a hard command blocklist that prevents catastrophic commands even if accidentally approved:

  • rm -rf /, sudo rm -rf .
  • mkfs, dd to /dev/
  • Fork bombs
  • /etc overwrites, /proc writes
  • Sensitive file exfiltration
  • Crypto mining commands

These are blocked at the tool level: no configuration needed.


Older CPUs (Pre-2011 / No AVX)

Some features require AVX/AVX2 instructions. Since v0.2.67, OpenCrabs detects CPU capabilities at runtime and automatically hides unavailable options in the onboarding wizard.

What’s Affected

FeatureCPU RequirementFallback
Local embeddings (memory search)AVX (Sandy Bridge 2011+)FTS-only keyword search (still works)
Local STT (rwhisper/candle)AVX2 (Haswell 2013+)API mode (Groq Whisper) or disabled
Local TTS (Piper)None: tested on 2007 iMacWorks on any x86/ARM CPU

Symptoms

  • Local STT option doesn’t appear in /onboard:voice: your CPU lacks AVX2
  • Local TTS (Piper) should always be available: no CPU restrictions, works on machines as old as 2007
  • Memory search falls back to text-only FTS silently
  • Crash with “illegal instruction” on very old CPUs

Fix: Build from Source with CPU Targeting

# For your specific CPU (best performance)
RUSTFLAGS="-C target-cpu=native" cargo build --release

# For Sandy Bridge (AVX but no AVX2)
RUSTFLAGS="-C target-cpu=sandybridge" cargo build --release

macOS with Apple Silicon

Local STT uses Metal GPU acceleration on macOS, no CPU flags needed. Works out of the box on M1/M2/M3/M4.


Config Issues

Config Won’t Load

If config.toml has a syntax error, OpenCrabs will fail to start. Restore from backup:

# Check if a backup exists
ls ~/.opencrabs/config.toml.backup

# Restore it
cp ~/.opencrabs/config.toml.backup ~/.opencrabs/config.toml

Or reinitialize with defaults:

opencrabs init --force

Warning: --force overwrites your config. Back up keys.toml first, it contains your API keys.

Manual Backup

Always keep a backup of your critical files:

cp ~/.opencrabs/config.toml ~/.opencrabs/config.toml.backup
cp ~/.opencrabs/keys.toml ~/.opencrabs/keys.toml.backup
cp ~/.opencrabs/commands.toml ~/.opencrabs/commands.toml.backup

RSI Has Never Run

Mission Control shows ⚠ RSI has never run: N tool event(s) recorded and unprocessed even though rsi_enabled = true is set.

Most common cause: self_improvement_provider is misspelled. Its value is the bare provider section name, no custom: prefix, no model appended:

[providers.custom.glm-53-max]              # the name is "glm-53-max"

[agent]
self_improvement_provider = "glm-53-max"   # ✅ not "custom:glm-53-max"
self_improvement_model = "..."             # the model goes in its own key

Checklist:

  1. The provider value matches the config section name exactly ([providers.custom.moonshotai] → "moonshotai").
  2. No custom: / custom. / custom/ prefix anywhere in a provider reference.
  3. The model is in its own key (self_improvement_model), never appended to the provider.
  4. Restart after fixing: Mission Control confirms cycles on the next run.

Fixed at source in opencrabs#1314: current main normalises misspelled values with a warning. Released versions fail silently, which is exactly why the spelling matters.


Channel Issues

Telegram

Bot not responding:

  1. Verify token from @BotFather is in keys.toml
  2. Check your numeric user ID is in allowed_users
  3. If respond_to = "mention", you must @mention the bot in groups

Regenerate bot token:

  1. Open @BotFather on Telegram
  2. /mybots → select bot → API Token → Revoke
  3. Copy new token to keys.toml under [channels.telegram]
  4. Restart OpenCrabs

Re-setup from scratch: Run /onboard:channels in the TUI.

Telegram Won’t Connect / Reconnect

If the Telegram bot stops responding or you need to re-link it, re-run the channels setup and re-confirm the token + your numeric user ID.

Fix:

  1. Run /onboard:channels (TUI: opens the wizard; on a channel: the agent walks you through it).
  2. Paste your bot token again if it’s missing (get it from @BotFather).
  3. Paste your numeric user ID and hit Enter to confirm.
  4. If the bot sends you a message on Telegram, it worked.

On a channel you can do it in one line: /onboard:channels telegram <BOT_TOKEN> <YOUR_NUMERIC_ID>.

Why you have to provide your numeric ID: Telegram’s Bot API exposes only the bot’s identity from a token (via getMe), it has no way to reveal who created the bot in BotFather. A bot only learns a human’s ID when that human messages it. The onboarding wizard auto-detects your ID via getUpdates when you leave the field blank, but that only works if (a) you’ve already messaged the bot and (b) the bot isn’t already running and consuming those updates, which is exactly the case during a reconnect. So on reconnect, message the bot first, or just paste the ID (get it from @userinfobot).

WhatsApp

QR code / session expired:

WhatsApp sessions are stored at ~/.opencrabs/whatsapp/session.db. To reconnect:

# Delete the session file
rm ~/.opencrabs/whatsapp/session.db

# Re-pair via onboarding
opencrabs chat --onboard

Or press R on the WhatsApp onboarding screen to reset and get a fresh QR code.

Messages not received:

  • Verify phone number is in allowed_phones using E.164 format: "+15551234567"
  • Empty allowed_phones = [] means accept from everyone

WhatsApp Won’t Connect / Wrong Number Replying

Each OpenCrabs instance supports one WhatsApp account (one companion device). If you connected multiple numbers or the wrong number is replying, you need a full reset. The bot always uses the last number you connected.

Critical: Always reset the connection before connecting a new number. OpenCrabs only keeps the last paired number.

Full reset steps:

  1. Remove ALL linked devices from WhatsApp. Open WhatsApp on your phone, go to Settings > Linked Devices, and remove every device in the list. Don’t bother hunting for the opencrabs one. A stale device left over from an earlier pairing is the usual reason the old number keeps replying, so the reliable fix is to clear them all and start fresh.
  2. Reset the connection in OpenCrabs: in the TUI or from a channel, go to /onboard:channels and press R to reset the WhatsApp connection. Wait for confirmation that the reset is complete.
  3. Re-pair from scratch: after the reset is confirmed, go to WhatsApp > Settings > Linked Devices > Link a Device and scan the new QR code shown by OpenCrabs.

If the bot still shows the old number after resetting, make sure you completed step 1 (removing the device from WhatsApp) before step 2.

Common issues:

SymptomCauseFix
Old number still replyingA stale linked device is still pairedRemove ALL linked devices from WhatsApp > Settings > Linked Devices, then press R in /onboard:channels and re-pair
QR code doesn’t appearAgent is still connected (no restart triggered)Press R in /onboard:channels to force a restart, then wait for the new QR
Bot doesn’t reply to anyoneresponse_policy is too restrictiveSet response_policy = "allowlist" and add phone numbers to allowed_phones in config.toml
Bot replies to everyoneresponse_policy is openSet response_policy = "allowlist" or "owner_only" in config.toml
Bot doesn’t reply to self-chatallowed_phones doesn’t include the paired numberThe paired number’s self-chat is always allowed, regardless of allowed_phones. Check that response_policy isn’t too restrictive

Discord

Bot not receiving messages:

  1. Ensure Message Content Intent is enabled in Discord Developer Portal → Bot settings
  2. Required intents: gateway, guild_messages, direct_messages, message_content
  3. Use the bot token (starts with MTk...), not the application ID

Regenerate token: Discord Developer Portal → Bot → Regenerate Token

Slack

Both tokens required:

  • Bot token (xoxb-...): For sending messages
  • App token (xapp-...): For Socket Mode (receiving events)

Without the app token, the bot can send but not receive messages.

Socket Mode: Must be enabled in app settings → Features → Socket Mode → ON

Trello

Setup:

  1. Get API key: trello.com/app-key
  2. Generate token from the same page
  3. Add board_ids to config: the bot only monitors listed boards
  4. Set poll_interval_secs > 0 to enable polling (default 0 = disabled)

General: Re-run Channel Setup

For any channel issues, re-run the onboarding wizard:

opencrabs chat --onboard

Or type /onboard:channels in the TUI.


Agent Hallucinating Tool Calls

If the agent starts sending tool call approvals that don’t render in the UI (meaning it believes it executed actions that never actually ran) the session context has become corrupted.

Fix: Start a new session.

  1. Press / and type sessions (or navigate to the Sessions panel)
  2. Press N to create a new session
  3. Continue your work in the fresh session

This reliably resolves the issue. A fix is coming in a future release.


Claude Code CLI Refuses to Run as Root (VPS, Docker)

Symptom: the claude_cli provider is enabled and claude is authenticated, but every request fails as soon as it spawns. In the log the CLI exits immediately with a message about --dangerously-skip-permissions not being allowed for root, or the first spawn hangs waiting on a prompt nobody can answer.

Cause: OpenCrabs runs the CLI headless with --dangerously-skip-permissions (it owns tool execution itself, so the CLI never asks). Claude Code refuses that flag when the process runs as root unless it believes it is inside a sandbox, and on a fresh install it also wants a one-time interactive acknowledgement of that mode. A VPS or container where OpenCrabs runs as root hits both.

Fix: put the acknowledgement and the sandbox marker in the CLI’s own settings file, ~/.claude/settings.json for the user OpenCrabs runs as (/root/.claude/settings.json on a root box):

{
  "permissions": { "defaultMode": "bypassPermissions" },
  "skipDangerousModePermissionPrompt": true,
  "env": { "IS_SANDBOX": "1" }
}

Merge these keys into the file if it already exists (keep model, theme, and the rest). The CLI reads settings.json on every invocation, so no OpenCrabs restart is needed; the next spawn goes through. Verify as the same user:

echo "say OK" | claude -p --dangerously-skip-permissions

IS_SANDBOX=1 only tells Claude Code that the root check may be skipped. It does not sandbox anything, so keep the box firewalled the way you would for any root agent. On a desktop where OpenCrabs runs as your own user none of this is required.

Daemon Stays Down

Jika opencrabs service status bilang stopped:

  1. Apakah Anda membuka TUI? Membuka opencrabs sengaja mematikan daemon agar session interaktif bisa memiliki channels. Daemon tetap mati sampai Anda menjalankan opencrabs service start lagi.
  2. Build lama mungkin masih punya Restart=on-failure alih-alih Restart=always. Buat ulang unit dengan opencrabs service install (lalu service start) untuk mendapatkan kebijakan selalu-restart.
  3. Konfigurasi, kunci, perintah, dan tools hot-reload saat runtime. Mengedit config.toml atau keys.toml tidak pernah butuh restart daemon. Jika perubahan tidak berlaku, periksa log untuk baris ConfigWatcher: reloaded alih-alih me-restart.

For full daemon/service documentation, see CLI Commands: Daemon Mode vs TUI.


Local STT (Speech-to-Text)

Since v0.2.67, local STT uses rwhisper (candle, pure Rust) instead of whisper-rs/ggml. On macOS, it uses Metal GPU acceleration automatically.

Models

ModelSizeQuality
quantized-tiny~42 MBGood for short messages
base-en~142 MBBetter accuracy (English)
small-en~466 MBHigh accuracy
medium-en~1.5 GBBest accuracy

Models download automatically from HuggingFace on first use.

Common Issues

Local STT option not showing in wizard: Your CPU lacks AVX2. Use API mode (Groq Whisper) instead, or build from source with RUSTFLAGS="-C target-cpu=native".

“No audio samples decoded”: Audio file is corrupt or unsupported format. Supported: OGG/Opus, WAV.

Transcription hangs: Times out after 300 seconds. Try a smaller model (quantized-tiny).

Model download fails: Check network connection. Models are fetched from HuggingFace.

Audio too short: Messages under 1 second are automatically padded to prevent tensor errors.

Disabling

[voice]
stt_enabled = false

Local TTS (Text-to-Speech)

Requirements

  • Python 3 must be installed and in PATH
  • Piper installs automatically in a venv at ~/.opencrabs/models/piper/venv/

Voices

VoiceDeskripsiSize
ryanUS Male (default)~200-400 MB
amyUS Female~200-400 MB
lessacUS Female~200-400 MB
kristinUS Female~200-400 MB
joeUS Male~200-400 MB
coriUK Female~200-400 MB

Common Issues

“python3 -m venv failed”: Install Python 3. On Ubuntu: sudo apt install python3 python3-venv. On macOS: brew install python3.

“pip install piper-tts failed”: Network issue or pip corrupted. Fix pip first:

python3 -m pip install --upgrade pip

Telegram voice messages show no waveform: This was fixed in v0.2.64, audio is now properly encoded as OGG/Opus (RFC 7845). Update to latest version.

Voice preview not playing: Preview uses afplay (macOS), aplay (Linux), or powershell (Windows). Ensure audio output is available.

Re-setup

Run /onboard:voice in the TUI to reconfigure STT/TTS mode and re-download models.

Disabling

[voice]
tts_mode = "off"

The memory search engine uses a ~300 MB embedding model (llama.cpp) for semantic search. It requires AVX on x86 CPUs.

Fallback

If embeddings can’t initialize (no AVX, download failed, disk full), memory search falls back to FTS-only (keyword matching). It still works, just less semantic.

Fix for Older CPUs

Build from source with CPU targeting (see Older CPUs above).

Model Location

Models are stored in ~/.local/share/opencrabs/models/ (platform-specific data directory).


Database Issues

Location

  • Main database: ~/.opencrabs/opencrabs.db (SQLite + WAL)
  • WhatsApp session: ~/.opencrabs/whatsapp/session.db

Database Corruption

SQLite with WAL mode is very resilient, but if corruption occurs:

# Back up the corrupted file first
cp ~/.opencrabs/opencrabs.db ~/.opencrabs/opencrabs.db.corrupt

# Reinitialize (WARNING: loses all history)
opencrabs db init

Migration Errors

The database automatically migrates on startup (11 migrations). If migrating from an older version with sqlx, the transition is handled automatically, no manual steps needed.


Building from Source

Quick Setup

curl -fsSL https://raw.githubusercontent.com/opencrabs/opencrabs/main/scripts/setup.sh | bash
git clone https://github.com/opencrabs/opencrabs.git && cd opencrabs
cargo build --release

Build Without Voice (Smaller Binary)

cargo build --release --no-default-features --features telegram,whatsapp,discord,slack,trello

Feature Flags

FlagDefaultDeskripsi
local-sttOnwhisper.cpp for local speech-to-text
local-ttsOnPiper for local text-to-speech
telegramOnTelegram channel
whatsappOnWhatsApp channel
discordOnDiscord channel
slackOnSlack channel
trelloOnTrello channel

Debug Mode

Run with -d for verbose logging:

opencrabs -d chat

Logs go to ~/.opencrabs/logs/ with 7-day retention.

Supported AI Providers

OpenCrabs supports 15 built-in providers + Custom OpenAI Compatible. Switch between them at any time via /models in the TUI or any channel.

ProviderAuthModelsStreamingToolsNotes
Anthropic ClaudeAPI keyClaude Opus 4.6, Sonnet 4.5, Haiku 4.5YesYesExtended thinking, 200K context
OpenAIAPI keyGPT-5 Turbo, GPT-5, o3/o4-miniYesYesModels fetched live
GitHub CopilotOAuthGPT-4o, Claude Sonnet 4+YesYesUses your Copilot subscription: no API charges
OpenRouterAPI key400+ modelsYesYesFree models available. Reasoning output support (Qwen 3.6 Plus, etc.)
Google GeminiAPI keyGemini 2.5 Flash, 2.0, 1.5 ProYesYes1M+ context, vision, image generation
MiniMaxAPI keyM2.7, M2.5, M2.1, Text-01YesYesCompetitive pricing, auto-configured vision
Xiaomi MiMoAPI key (auto)MiMo V2.5 Pro, MiMo V2 Pro, MiMo V2.5, MiMo V2 Omni, MiMo V2 FlashYesYesDefault provider for new users. Keyless mode with automatic token provisioning during collab windows. 30 models across 5 tiers.
z.ai GLMAPI keyGLM-4.5 through GLM-5 TurboYesYesGeneral API + Coding API endpoints
Claude CLICLI authVia claude binaryYesYesUses your Claude Code subscription
CodexOAuth (PKCE)GPT-5.5, GPT-5.4, GPT-5.3-CodexYesYesNative Codex subscription auth via device-code PKCE, no CLI, no API key
Codex CLICLI authVia @openai/codex binaryYesYesUses your Codex subscription: free tier available
Qwen/DashScopeAPI keyqwen3.6-plus (default)YesYesDashScope API-key provider (replaced OAuth rotation). Local model tool-call extraction from text (bare JSON, Claude-style XML, Qwen formats). Prompt caching via cache_control, rate limit retry with exponential backoff
OllamaOptionalAny Ollama modelYesYesNative local provider: run any model via Ollama API
OpenCodeNoneAny OpenAI-compatible modelYesYesNon-CLI OpenAI-compatible provider
OpenCode CLINoneFree models (Mimo, etc.)YesYesFree: no API key or subscription needed
CustomOptionalAnyYesYesLM Studio, Groq, NVIDIA, any OpenAI-compatible API

How It Works

  • One provider active at a time per session: switch with /models
  • Per-session isolation: each session remembers its own provider and model. Changing provider in the TUI does not affect other active sessions (Telegram, Discord, Slack)
  • Fallback chain: configure automatic failover when the primary provider goes down
  • Models fetched live: no binary update needed when providers add new models
  • Function calling detection: OpenCrabs detects when a model doesn’t support tool use and warns you with a model switch suggestion, rather than silently failing
  • tool_choice: "auto": sent automatically for OpenAI-compatible providers when tools are active, enabling function calling on models that require explicit opt-in
  • vision_model works on ANY provider: add vision_model = "..." to any built-in or custom provider block and the agent routes incoming images through that model on the same endpoint. No second API key, no Gemini dependency. See Image Generation & Vision for the full two-path setup

Custom Provider Onboarding (v0.3.24)

Adding a custom OpenAI-compatible provider is now smoother:

  • Paste-by-default: Ctrl+V / Cmd+V on the API key field pastes immediately, no need to tab into the field first
  • Enter-to-load: type a model name not in the fetched list and press Enter, it’s added to the list and selected
  • Field refresh: saved values (base URL, API key, model list) appear instantly without restarting the dialog

Provider Registry (v0.3.34)

All provider resolution now routes through a single registry source of truth: no more hardcoded if-else ladders scattered across the codebase. The registry correctly enforces api_key requirements for API providers (Anthropic, OpenAI, GitHub Copilot, Gemini, OpenRouter, MiniMax), so resolution skips them cleanly when keys are missing instead of silently falling back. Adding a new provider is now a one-file change.

Retry & Fallback Overhaul (v0.3.36)

The provider retry and fallback system was rebuilt for reliability:

  • Patient backoff: defaults changed from 100ms hammering to 1s / 2s / 4s / 8s, giving rate-limited endpoints time to recover
  • In-place rate limit retry: hits 3 retries on the same provider before falling through the fallback chain, preventing unnecessary provider switches
  • Full fallback chain on any HTTP error: previously only 5xx/429 triggered fallback; now any HTTP error walks the chain
  • Fail fast on hard-down endpoints: DNS failures and connection refused bail immediately instead of wasting retries
  • Retry events surfaced to user: each retry shows as RetryAttempt 1/3 - rate_limit_exceeded so you always know what’s happening
  • Transient 4xx HTML pages retried: Cloudflare/nginx error pages that look like 4xx but are actually transient infra issues are now retried

Qwen Cache Auto-Enable (v0.3.30)

Custom providers targeting Qwen-shaped endpoints (base URLs containing dashscope, aliyun, aliyuncs, dialagram, or models prefixed with qwen-*) automatically get ephemeral cache_control markers on the system prompt, last streaming message, and last tool call. Zero-config cost savings for Qwen custom providers, no API key or flag needed.

Qwen Tool-Call Leak Strip (v0.3.35)

Qwen models sometimes emit bare JSON tool-call objects like {"name":"bash","arguments":{"command":"ls"}} directly in the content text instead of through the proper tool-call API. A new bare_tool_call_extractor detects and strips these leaks so they don’t appear as raw JSON in the chat output.

/models Picker (v0.3.30)

The /models command now surfaces every known provider including unconfigured ones, marked with a 🔒 lock icon and setup help text. This helps users discover available providers without needing to know which ones need API keys. Custom providers with no configured models show a helpful empty-state message instead of an inert button.

OpenRouter Reasoning

For models that support extended reasoning (e.g. Qwen 3.6 Plus), OpenCrabs sends include_reasoning: true automatically when using OpenRouter. Thinking/reasoning output is displayed in collapsible sections:

▶ Thinking... (click to expand)
  The user wants to refactor...

Reasoning text wraps to screen width instead of truncating.

See Provider Setup for configuration details and API key setup.

AI Provider Setup

OpenCrabs supports 15 providers (14 built-in + Custom OpenAI-Compatible). Configure them through the onboarding wizard or manually via config.toml and keys.toml at ~/.opencrabs/.

Setup via Onboarding Wizard

The fastest way to configure a provider is the interactive wizard. Run /onboard:provider (or /onboard and navigate to step 3).

Keyboard Navigation

TombolAksi
↑ / ↓ or j / kMove between providers / models
Enter or TabAdvance to next field
BackTab or Shift+TabGo back to previous field
EscGo back to previous wizard step
Type any characterFilter model list (when on model picker)

For providers with a /v1/models API endpoint, the wizard fetches the model list live after you enter your API key.

Supported: Anthropic, OpenAI, OpenRouter, Gemini, MiniMax, Qwen (DashScope), Ollama, z.ai GLM

Flow:

  1. Use ↑/↓ to select your provider (e.g. OpenRouter)
  2. Press Enter: advances to the API key field
  3. Paste your API key (e.g. sk-or-...)
  4. Press Enter: triggers a live fetch from the provider’s /v1/models endpoint
  5. Use ↑/↓ to browse models, or type to filter (case-insensitive substring match)
  6. Press Enter on your chosen model: saves config and advances

Tip: If you’ve already configured a key, the wizard detects it (shown as ••••••••) and skips straight to the model picker. Press Enter to re-fetch models with the existing key.

OAuth Providers (GitHub Copilot, Codex)

No API key needed: authenticate through your browser.

Flow:

  1. Select GitHub Copilot or Codex
  2. Press Enter: starts the device-code PKCE flow
  3. A user code and URL appear (e.g. github.com/login/device)
  4. Open the URL in your browser, enter the code, and authorize
  5. Tokens are saved automatically to ~/.opencrabs/auth/
  6. Models are fetched live: pick one and press Enter

CLI Providers (Claude CLI, OpenCode CLI, Codex CLI)

Use your existing CLI subscription: no separate API key.

Flow:

  1. Select the CLI provider (e.g. Claude CLI)
  2. Press Enter: skips the API key field (none needed)
  3. Models are fetched from the local binary (claude models, opencode models, etc.)
  4. Pick a model and press Enter

Requirements: The CLI binary must be installed and authenticated in your PATH.

z.ai GLM (Zhipu AI)

z.ai has two endpoint types. The wizard asks which one before the API key.

Flow:

  1. Select z.ai GLM
  2. Press Enter: advances to Endpoint Type selector
  3. Use ↑/↓ to choose API (general) or Coding (CodeGeeX)
  4. Press Enter: advances to API key field
  5. Paste your key, press Enter, fetches models
  6. Pick a model and press Enter

Custom OpenAI-Compatible

For Ollama, LM Studio, LocalAI, Groq, NVIDIA, vLLM, or any OpenAI-compatible endpoint.

Flow:

  1. Select Custom OpenAI-Compatible (last in the list)
  2. Press Enter: advances to Name field
  3. Name: type a provider identifier (e.g. ollama, lm-studio, nvidia). Press Enter, normalized to a TOML-safe key
  4. Base URL: paste your endpoint (e.g. http://localhost:1234/v1). Press Enter
  5. API Key: paste if required, or leave empty for local endpoints. Press Enter
  6. Model: you have two options:
    • Type or paste a model name: use this for newly-launched models not yet available on the live API (e.g. qwen3.6-35b-a3b-gguf)
    • Press Enter on empty field: triggers a live fetch from {base_url}/models, then pick from the list
  7. Context Window: enter the token limit (e.g. 128000). Press Enter, saves and advances

Context Window Recommendation: Set to 200000 (200k tokens) for best results. OpenCrabs handles large contexts gracefully with smart auto-compaction that keeps you always up to date without manual intervention.

Local LLMs: No API key needed, just set base URL and model name. If the model is already running, paste the name directly. If you want to browse available models, leave the Model field empty and press Enter to fetch the list from your local server.

Re-running Provider Setup

PerintahWhat it does
/onboard:providerJump to provider setup, return to chat when done
/modelsSwitch provider/model for the current session
/onboardFull wizard (all steps)

Manual Configuration (advanced)

If you prefer editing files directly, configure providers in config.toml and keys.toml.


Key Handling (v0.3.81)

keys.toml no longer drops provider API keys at runtime (#1066): four providers could silently lose their keys during config reloads. The stored-key marker has one owner, and keys already poisoned by the old marker are self-healed on load. If a provider worked yesterday and 401s today, re-check keys.toml: the fix also repairs the file in place.

Anthropic Claude

Models: claude-opus-4-6, claude-sonnet-4-5, claude-haiku-4-5, and legacy models, fetched live from the API.

# keys.toml
[providers.anthropic]
api_key = "sk-ant-..."
# config.toml
[providers.anthropic]
enabled = true
default_model = "claude-sonnet-4-20250514"

Features: Streaming, tool use, extended thinking, vision, 200K context window.

OpenAI

Models: GPT-5 Turbo, GPT-5, and others, fetched live.

# keys.toml
[providers.openai]
api_key = "sk-YOUR_KEY"

OpenRouter: 400+ Models

Access 400+ models from every major provider through a single API key. Includes free models (DeepSeek-R1, Llama 3.3, Gemma 2, Mistral 7B).

# keys.toml
[providers.openrouter]
api_key = "sk-or-YOUR_KEY"

Get a key at openrouter.ai/keys. Model list is fetched live, no binary update needed when new models are added.

Google Gemini

Models: gemini-2.5-flash, gemini-2.0-flash, gemini-1.5-pro, fetched live.

# keys.toml
[providers.gemini]
api_key = "AIza..."
# config.toml
[providers.gemini]
enabled = true
default_model = "gemini-2.5-flash"

Features: Streaming, tool use, vision, 1M+ token context window.

Gemini also powers the separate image generation and vision tools. See Image Generation & Vision.

GitHub Copilot

Use your existing GitHub Copilot subscription: no separate API charges. Authenticates via OAuth device flow.

# config.toml
[providers.github_copilot]
enabled = true

Setup: Run /onboard:providers → select GitHub Copilot → follow the device code flow at github.com/login/device. Models are fetched live from the Copilot API.

Requirements: An active GitHub Copilot subscription (Individual, Business, or Enterprise).

z.ai (Zhipu AI)

Models: GLM-4-Plus, GLM-4-Flash, GLM-4-0520, CodeGeeX, fetched live. Two endpoint types: General API and Coding API.

# keys.toml
[providers.zai]
api_key = "your-api-key"
# config.toml
[providers.zai]
enabled = true
default_model = "glm-4-plus"

Get your API key at open.bigmodel.cn.

Qwen (DashScope)

Qwen models via DashScope. As of v0.3.81:

  • Reasoning carries across turns (#1033): preserve_thinking is sent so multi-turn conversations keep their reasoning context
  • Thinking knobs are family-gated (#1034): each model family gets only the knob it actually reads (enable_thinking, effort tiers), instead of a shared guess
  • Tiered families default to the recommended effort tier (#1034)
  • Hosted Qwen reachable everywhere (#1040): works outside Alibaba networks and with vendor-prefixed model ids

Claude CLI

Use your existing Claude Code subscription through the local claude binary, no separate API key needed. Supports streaming and extended thinking.

# config.toml
[providers.claude_cli]
enabled = true

Requirements: The claude CLI must be installed and authenticated. Models are detected automatically.

Ollama

Run any Ollama model natively: no custom provider setup needed. Supports both local (localhost:11434) and cloud (api.ollama.com) instances.

# config.toml
[providers.ollama]
enabled = true
default_model = "llama3"
# keys.toml (optional, only for cloud Ollama)
[providers.ollama]
api_key = "your-api-key"

Features: Streaming, tool use, local model tool-call extraction from text. Models are fetched live from the Ollama API.

Requirements: Ollama must be running locally (ollama serve) or you must have a cloud Ollama API key.

OpenCode CLI

Use the local opencode binary for free LLM completions, no API key or subscription needed. Supports NDJSON streaming and extended thinking.

# config.toml
[providers.opencode_cli]
enabled = true

Requirements: The opencode binary must be installed and available in your PATH. Models are fetched live via opencode models.

Codex CLI

Use OpenAI’s @openai/codex CLI as a native provider. User authenticates once via codex CLI; OpenCrabs piggybacks on cached credentials, zero API key handling. Non-interactive mode via codex exec --json with JSONL streaming.

# config.toml
[providers.codex_cli]
enabled = true

Models: GPT-5.5, GPT-5.4, GPT-5.3-Codex

Requirements: The codex CLI must be installed (npm install -g @openai/codex) and authenticated. Models are detected automatically.

Codex OAuth

Native OpenAI Codex subscription auth via device-code PKCE flow. No CLI dependency, no API key. User authenticates through browser once; tokens stored in ~/.opencrabs/auth/codex.json with automatic refresh and background rotation.

# config.toml
[providers.codex]
enabled = true

Models: GPT-5.5, GPT-5.4, GPT-5.3-Codex (curated GPT-5 model list)

Setup: Run /onboard:provider → select Codex OAuth → follow the device code flow at auth.openai.com/codex/device. Two-step PKCE exchange: device auth poll → authorization code → token exchange.

Requirements: An active OpenAI Codex subscription. No CLI installation needed.

MiniMax

Models: MiniMax-M2.7, MiniMax-M2.5, MiniMax-M2.1, MiniMax-Text-01

# keys.toml
[providers.minimax]
api_key = "your-api-key"

Get your API key from platform.minimax.io. Model list comes from config.toml (no /models endpoint).

Custom (OpenAI-Compatible)

Use for Ollama, LM Studio, LocalAI, Groq, or any OpenAI-compatible API.

# config.toml
[providers.custom.lm_studio]
enabled = true
base_url = "http://localhost:1234/v1"
default_model = "qwen2.5-coder-7b-instruct"
models = ["qwen2.5-coder-7b-instruct", "llama-3-8B"]

Local LLMs: No API key needed, just set base_url and default_model.

Remote APIs (Groq, etc.): Add the key in keys.toml:

[providers.custom.groq]
api_key = "your-api-key"

Multiple Custom Providers

Define as many as you need with different names:

[providers.custom.lm_studio]
enabled = true
base_url = "http://localhost:1234/v1"
default_model = "qwen2.5-coder-7b-instruct"

[providers.custom.ollama]
enabled = false
base_url = "http://localhost:11434/v1"
default_model = "mistral"

Naming Custom Providers

The provider’s name is the last segment of its config section, nothing else.

[providers.custom.moonshotai]   # the name is "moonshotai"

When you reference the provider anywhere (self_improvement_provider, subagent_provider, plan_provider, execute_provider, or the /models command) use that bare name:

[agent]
self_improvement_provider = "moonshotai"        # ✅ bare section name
self_improvement_model = "kimi-k2.5"            # the model has its own key

Never carry the custom path into the reference, and never append the model:

self_improvement_provider = "custom:moonshotai"    # ❌ RSI silently never runs
self_improvement_provider = "custom.moonshotai"    # ❌ same
self_improvement_provider = "moonshotai/kimi-k2.5" # ❌ provider-only, model goes in self_improvement_model

Real case: a user with [providers.custom.glm-53-max] set self_improvement_provider = "custom:glm-53-max" and RSI silently never ran, Mission Control showed thousands of tool events recorded and unprocessed. Dropping the custom: prefix fixed it instantly (opencrabs#1314). Current main auto-corrects misspelled values with a warning; released versions fail silently, so spell it bare.

Free Prototyping with NVIDIA API

Kimi K2.5 is available for free on the NVIDIA API Catalog, no billing required.

# config.toml
[providers.custom.nvidia]
enabled = true
base_url = "https://integrate.api.nvidia.com/v1"
default_model = "moonshotai/kimi-k2.5"
# keys.toml
[providers.custom.nvidia]
api_key = "nvapi-..."

Fallback Provider Chain

Configure automatic failover when the primary provider fails (rate limits, outages, errors). Fallbacks are tried in order until one succeeds.

# config.toml
[providers.fallback]
enabled = true
providers = ["openrouter", "anthropic"]  # Tried in order on failure

Each fallback provider must have its API key configured in keys.toml. Both complete() and stream() calls are retried transparently, no changes needed downstream.

Single fallback shorthand:

[providers.fallback]
enabled = true
provider = "openrouter"

Or just ask your Crab: “Set up fallback providers using openrouter and anthropic”, it will configure config.toml for you at runtime.

Quota Circuit Breaker (v0.3.79)

Fallback chains now know the difference between a provider that is momentarily busy and one that is genuinely out of quota.

  • Hard quota exhaustion (your account has run out of credits/allowance for the period) trips a TTL circuit breaker on that provider. While the breaker is open the provider is skipped in fallback walks instead of being retried pointlessly, so failover lands on a provider that can actually serve the request.
  • Transient throttles (short rate limits, 429s that clear in seconds) are classified separately and retried as before, they never trip the breaker.
  • When every provider in the chain is exhausted, the failure is reported explicitly as chain exhaustion rather than surfacing as a confusing error from the last provider tried.
  • Retry notices now name the provider and model being retried, and state whether the cause was a quota exhaustion or a transient throttle.

No configuration needed: it is always on whenever a fallback chain is configured.

Fallback Improvements (v0.3.80)

  • Proactive setup suggestion (#1008): when no fallback chain is configured, OpenCrabs makes a one-shot suggestion to set one up, ask once, answered once, never nags again.
  • HTTP 400 failover guidance (#1006, #1007): a 400-series failure now carries guidance on what to check, and a chain-exhaustion ledger records why each provider in the chain was ruled out.
  • Fallback provenance in streaming logs (#969): each streamed call logs which provider actually served it, so retries and failovers are visible per call.
  • Codex CLI failures route into retry and fallback (#1004, #1005) instead of surfacing as terminal errors.

Provider Configuration Fields

Every provider section ([providers.<name>]) supports these fields:

FieldTypeDefaultDeskripsi
enabledbooltrueEnable/disable the provider
api_keystringNoneAPI key (loaded from keys.toml or env)
base_urlstringNoneAPI base URL override (for custom/local providers)
default_modelstringNoneDefault model for this provider
modelsarray[]Available models (updated at runtime via /models)
force_defaultboolfalsePush this provider’s default pair to all sessions on reload (see Force Default Provider)
vision_modelstringNoneVision-capable model override (swaps in when images are present)
generation_modelstringNoneImage generation model override (wins over global image.generation.model)
context_windowu32NoneContext window size in tokens (for auto-compaction). Ignored on the built-in OpenCrabs provider, whose window is fixed at 200k
endpoint_typestringNoneEndpoint type for providers with multiple API modes (e.g. z.ai: "api" or "coding")
planstringNoneSubscription tier (Kimi Code: "moderato", "allegretto", "allegro", "vivace")
reasoning_effortstringNoneReasoning control (e.g. "max" for Kimi K3, "on"/"off" for K2.x)
voicestringNoneTTS voice name (e.g. "echo")
modelstringNoneTTS model override (e.g. "gpt-4o-mini-tts")
enable_thinkingboolNoneThinking mode for reasoning-capable models (Qwen, local providers)

Example: full provider config:

# config.toml
[providers.minimax]
enabled = true
default_model = "MiniMax-M2.7"
vision_model = "MiniMax-Text-01"
generation_model = "MiniMax-Image-01"
context_window = 200000
force_default = true

Force Default Provider

When you set force_default = true on a provider section, a config reload pushes that provider’s default pair (provider + model) to every non-archived session, overriding their stored pairs. This enforces your chosen default across all active sessions instead of just new ones.

# config.toml
[providers.minimax]
enabled = true
default_model = "MiniMax-M2.7"
force_default = true  # Pushes MiniMax M2.7 to all sessions on reload

How it works:

  • Only fires when the flagged section is the active default provider (set via /models or default_provider in [agent])
  • Archived sessions are never touched
  • Sessions already on the target pair are skipped
  • Without this flag: defaults apply to new sessions only, existing sessions keep their own provider/model

Use case: You want to roll out a new default model (e.g. MiniMax M2.7) across all active sessions immediately, not just new ones. Set force_default = true on the MiniMax section, reload config, and every session switches to M2.7.

Note: This only reinforces the default, it doesn’t override a different active provider. If a session is using Anthropic Claude and you set force_default = true on MiniMax, the Claude session stays on Claude (because MiniMax isn’t the active default).

Vision Model

When your default chat model doesn’t support vision, set vision_model to a vision-capable model on the same provider. This registers a vision tool that the agent can call, it sends the image to the vision model, gets a description back, and the chat model uses that context to reply.

# config.toml
[providers.minimax]
enabled = true
default_model = "MiniMax-M2.5"
vision_model = "MiniMax-Text-01"  # Agent calls vision tool → this model describes image → M2.5 replies
[providers.openai]
enabled = true
default_model = "gpt-5-nano"
vision_model = "gpt-5-nano"

MiniMax auto-configures vision_model = "MiniMax-Text-01" on first run. You can also ask your Crab to set it up: “Configure vision model for MiniMax”, it will update config.toml at runtime.

This is separate from the Gemini image tools which provide dedicated generate_image and analyze_image tools.

Per-Session Providers

Each session remembers its provider and model. Switch to Claude in one session, Gemini in another, switching sessions restores the provider automatically.

Model Picker (v0.3.75)

The /models picker got a real overhaul:

  • Opus 5 surfaced: Opus 5 now shows up via a third parallel list plus state discovery.
  • Newest-first ordering: models are ordered newest-first instead of by merge order.
  • claude-cli model discovery: the claude-cli model list is discovered live from the CLI instead of being hardcoded, with every claude-cli row formatted consistently.
  • Reconciled against live inventory: the picker reconciles against the live model inventory so it never offers a model that isn’t actually available.

Natural Model Naming (v0.3.76)

The /models command now accepts colloquial model names. Instead of typing the exact model ID, you can say it the way people talk:

/models opus        → claude-opus-4-8
/models sonnet      → claude-sonnet-4-20250514
/models gpt5        → gpt-5
/models qwen max    → qwen3.8-max-preview

The resolver normalizes spacing, case, and common abbreviations before matching against the provider’s model list. Exact matches still take priority.

Plan/Execute Provider Routing (v0.3.76)

The /plan and /execute commands can now route onto their own dedicated provider and model, separate from the main chat provider:

# config.toml
[agent]
plan_provider = "anthropic"
plan_model = "claude-opus-4-8"
execute_provider = "openrouter"
execute_model = "anthropic/claude-sonnet-4"

This lets you use a stronger model for planning (where reasoning quality matters most) and a faster/cheaper model for execution (where throughput matters). If unset, both fall back to the session’s active provider.

Image Generation & Vision

OpenCrabs supports image generation (text-to-image and img2img) and vision analysis (image-to-text). Vision works through two paths, pick whichever fits your provider setup.

Vision: Two Paths

Path A: vision_model on Your Active Provider (Preferred)

Set vision_model = "<model>" inside the provider block you’re already using. Works for every built-in and custom provider. No second API key needed, the agent calls analyze_image against the vision model on the same provider endpoint.

# keys.toml
[providers.openrouter]
api_key = "sk-or-..."

# config.toml
[providers.openrouter]
model = "anthropic/claude-sonnet-4"
vision_model = "google/gemini-2.5-flash"   # ← any vision-capable model on the same endpoint

When a user sends an image and the chat model can’t handle it natively, the agent routes the image through vision_model, gets a text description back, and replies with that context.

Example: User sends an image while you’re on MiniMax M2.5 (no native vision). The agent calls the vision tool, which sends the image to MiniMax-Text-01 (or any model you set), gets the description, and M2.5 replies using that context.

Why this is preferred:

  • Single API key, single billing account
  • Works on any OpenAI-compatible endpoint (OpenRouter, Ollama, LM Studio, vLLM, Groq, custom)
  • No extra onboarding step: just add one line to your existing provider block

Path B: Gemini Global Fallback

Use this only when your active provider has no vision-capable model. Gemini acts as a dedicated vision+image backend, independent of your chat provider.

# keys.toml
[image]
api_key = "AIza..."    # ← MUST go here. See gotcha below.

# config.toml
[image.generation]
enabled = true
model = "gemini-3.1-flash-image-preview"

[image.vision]
enabled = true
model = "gemini-3.1-flash-image-preview"

Get a free API key from aistudio.google.com. Configure interactively with /onboard:image.

⚠️ Gotcha: #[serde(skip)] on [image.vision] api_key

The api_key field under [image.vision] in config.toml is silently ignored: it’s marked #[serde(skip)] in the source. Always put the Gemini key in keys.toml under [image], never in config.toml. If vision reports as unavailable despite a key being set, this is almost always the cause.

Diagnostic: Why Is Vision Unavailable?

is_vision_available logs the exact reason at INFO level with target=vision. Search your daily log:

grep 'target=vision' ~/.opencrabs/logs/opencrabs.$(date -u +%Y-%m-%d)

Common causes surfaced:

  • Missing vision_model on active provider (the only field actually required since v0.3.64)
  • Missing api_key for that provider
  • Missing Gemini [image] api_key in keys.toml
  • Key placed in config.toml where #[serde(skip)] drops it

v0.3.64 change: setting vision_model on your active provider is now sufficient to enable vision. The enabled flag under [image.vision] is no longer required. The vision roll-through tries each provider endpoint and falls back to Gemini last.

Agent Tools

When vision or image generation is enabled, these tools become available:

ToolDeskripsi
generate_imageGenerate an image from a text prompt: saves to ~/.opencrabs/images/; walks a provider chain with vision parity, so generation falls back the same way vision does (v0.5.4, #1672)
analyze_imageAnalyze an image file or URL via the active vision path (Path A provider or Gemini fallback)

Example prompts:

  • “Generate a pixel art crab logo”: agent calls generate_image, returns file path
  • “What’s in this image: /tmp/screenshot.png”, agent calls analyze_image

img2img: Edit Images with Context

generate_image accepts an optional image parameter (local file path or HTTPS URL). When provided, the model modifies, restyles, or composites onto that image instead of generating from scratch.

User: "Make this logo darker and add a border"
Agent: generate_image(prompt="dark background with thin white border", image="/tmp/logo.png")
  • Gemini backend: full img2img support via inlineData
  • OpenAI-shaped backends: reject with a clear error pointing at Gemini (img2img not supported)

Use cases: replace elements, restyle photos, composite logos onto backgrounds, modify user-uploaded images in-place.

Incoming Images from Channels

When a user sends an image from any channel, it arrives as <<IMG:/tmp/path>> in the message. The file is already downloaded, the agent can:

  • See it directly (if the chat model supports vision natively)
  • Pass the path to analyze_image for vision processing
  • Use the path in bash commands or any tool that accepts file paths
  • Reference it in replies with <<IMG:path>> to forward to channels

analyze_video: Frame-Extraction Fallback (v0.3.36)

The analyze_video tool sends the full video to Gemini’s video API for multimodal analysis. When that fails (network error, upload timeout, unsupported format), the agent falls back to ffmpeg frame extraction:

  1. Extract frames at 1 fps via ffmpeg
  2. Cap at 30 frames (one frame per second for the first 30 seconds)
  3. Send each frame to Gemini vision via analyze_image
  4. Combine per-frame descriptions into a chronological summary

This means analyze_video works even when the video API is unreachable, as long as ffmpeg is installed and at least one vision path (Path A or Path B) is configured. The fallback activates automatically. No user configuration needed.

Model Choices

  • Path A: any vision-capable model on your active provider. On OpenRouter: google/gemini-2.5-flash, anthropic/claude-sonnet-4, openai/gpt-4o. On Ollama: llava, bakllava. On custom endpoints: whatever the server offers.
  • Path B: gemini-3.1-flash-image-preview handles both vision input and image output in a single request.

Channel Integrations

OpenCrabs connects to multiple messaging platforms simultaneously. All channels share the TUI session by default, with per-user sessions for non-owners.

Setting Up Channels

Channels are configured through the onboarding wizard, not by editing TOML files manually.

Running the Wizard

  • First launch: the wizard runs automatically
  • Re-run: type /onboard in chat, or /onboard:channels to jump straight to the channels step
  • Quick jump: /onboard:channels opens the channel picker and returns to chat when done

The channel picker is a keyboard-driven TUI screen:

TombolAksi
↑ / ↓ or j / kMove focus between channels
SpaceToggle the focused channel on/off
Enter on an enabled channelOpen that channel’s setup screen
Enter on ContinueSkip remaining setup and advance
TabSame as Continue: advance to the next wizard step
EscGo back to the previous step

Channel Setup Screens

When you press Enter on an enabled channel, a dedicated setup screen opens with the fields needed for that platform (bot token, channel ID, allowed users, etc.). Each field:

  • Auto-detects existing values from config.toml / keys.toml (shown as masked •••••••• for secrets, plain text for IDs)
  • Tab moves to the next field
  • Enter on the last field (or the Test Connection button) saves and returns to the channel list
  • BackTab moves to the previous field

The Five Channels

#ChannelSetup FieldsTest
0TelegramBot Token, Owner User ID, Respond ToSend test message
1DiscordBot Token, Channel ID, Allowed Users, Respond ToSend test message
2WhatsAppQR Code scan, Phone AllowlistConnection status
3SlackBot Token, App Token, Channel ID, Allowed Users, Respond ToSend test message
4TrelloAPI Key, API Token, Board ID, Allowed UsersBoard access check

After enabling and configuring your channels, the wizard saves everything to config.toml and keys.toml automatically. You can always re-run /onboard:channels to modify settings.

Supported Channels

ChannelProtocolImages InVoice InImage Gen OutSetup
TelegramLong pollingVision pipelineSTTNative photoBot token
DiscordWebSocketVision pipelineSTTFile attachmentBot token
SlackSocket ModeVision pipelineSTTFile uploadBot + App token
WhatsAppQR pairingVision pipelineSTTNative imageQR code
TrelloREST APICard attachments-Card attachmentAPI key + token

Cross-Channel Session Resolution (v0.3.29)

All messaging channels now share a stable [chat:<id>] suffix pattern for reliable session lookup. Previously only Telegram had this; Discord, Slack, and WhatsApp used exact-title matching which broke when the agent auto-renamed sessions (creating duplicates on every message).

The shared channels::session_resolve module provides:

  • Suffix-first lookup: fast path using [chat:discord-dm-<user_id>], [chat:slack-<channel_id>], [chat:wa-<phone>] etc.
  • Legacy forward-migration: pre-suffix rows are migrated to the suffix format on first lookup
  • /sessions binding: explicit chat→session binding on switch so user choices win over suffix lookup

Follow-Up Cancel (v0.3.30)

Sending a message while the agent is mid-run now acts as ESC x2 (cancel current run) across all channels. The cancelled partial content is preserved, and the new message starts a fresh agent turn.

ZIP Attachments (v0.3.30)

ZIP file attachments from users are extracted and processed inline:

  • Text files are inlined into the conversation
  • Images get vision markers for multimodal processing
  • PDFs get text extraction
  • Capped at 50 files / 10 MB per ZIP entry

Group Onboarding & /cowork (v0.3.74)

Getting a bot into a group and making it useful is now a single flow:

  • /cowork opens the group: /cowork sets the target group open = true and persists it (#718).
  • /cowork admin deep link: the /cowork deep link requests admin rights, so the bot joins already promoted (#709).
  • Group onboarding greeting: the bot greets the group with an onboarding nudge when it joins (#707, #708).
  • Per-group /start registration: /start in a group registers the member per-group instead of the stale config.toml lecture (#708).
  • Register joining members: any member who joins a bot group is auto-registered (#710).
  • Secure by default: member auto-registration is gated to open = true groups (#717).

Follow-Up Suggestions (v0.3.74)

  • follow_up_question degrades gracefully: returns the question as plain text on non-interactive surfaces (#716).
  • Telegram follow-up taps: suggest_followups renders last, fixing taps doing nothing (#723, #724).
  • Accurate description: the suggest_followups tool description is channel-accurate, so the model invokes it instead of writing plain-text suggestions (#706).
  • Outbound media dedup: an identical file+caption re-uploaded to the same chat within a short window is collapsed, so the agent can’t send it twice (#721).

Common Features

All messaging channels support:

  • Shared session with TUI (owner) or per-user sessions (non-owners)
  • Slash commands: /help, /models, /new, /sessions, custom commands
  • Inline buttons: Provider picker, model picker, session switcher (Telegram, Discord, Slack)
  • User allowlists: Restrict access by user ID, chat ID, role, or phone number. Since v0.5.1 the allowlists are deny-by-default and are the access boundary: on Discord and Slack an unconfigured channel (no allowed_users, no allowed_roles, no bot_owner) refuses every message, matching Telegram; on WhatsApp an empty allowed_phones no longer grants anyone owner rights. Because the default approval_policy is auto-always, the allowlist is what decides who can drive tools at all, set it deliberately
  • respond_to filter: all, dm_only, or mention (respond only when @mentioned)

File & Media Support

ChannelImages (in)Text files (in)Documents (in)Audio (in)Image gen (out)
TelegramVision pipelineExtracted inlinePDF noteSTTNative photo
WhatsAppVision pipelineExtracted inlinePDF noteSTTNative image
DiscordVision pipelineExtracted inlinePDF noteSTTFile attachment
SlackVision pipelineExtracted inlinePDF noteSTTFile upload
TrelloCard attachments → visionExtracted inline--Card attachment
TUIPaste path → visionPaste path → inline-STT[IMG: name] display

Images are passed to the active model’s vision pipeline if it supports multimodal input, or routed to the analyze_image tool (Google Gemini vision) otherwise. Text files are extracted as UTF-8 and included inline up to 8,000 characters.

Proactive Channel Tools

The agent can send messages and take actions proactively:

ToolActions
discord_send17 actions: send, reply, react, edit, delete, pin, create_thread, send_embed, etc.
slack_send17 actions: send, reply, react, edit, delete, pin, set_topic, send_blocks, send_file (TTS voice via OGG/Opus)
trello_send22 actions: create_card, move_card, add_comment, add_checklist, search, etc.

Channel Voice Parity

All four messaging channels (Telegram, Discord, WhatsApp, Slack) now share a single code path via crate::channels::voice::{transcribe, synthesize}. Bot replies are recorded in the channel_messages table for conversation context, previously only user messages were stored.

Typing Indicator Persistence (v0.3.76)

Typing indicators now survive background tasks. Previously, when the agent spawned a background task (long build, test suite), the typing indicator would drop because the foreground turn ended. Now the indicator stays alive until the final response is delivered, regardless of how many background tasks run in between.

Smart Chunk Boundaries (v0.3.78)

When a response exceeds platform message limits and must be split into chunks, the splitter now prefers boundaries that leave no markup open. Previously, a chunk could end mid-bold (**text) or mid-code-fence, causing rendering glitches on the receiving platform. The splitter now scans for the nearest safe break point where all markdown delimiters are balanced.

Owner-Gated Channel Commands (v0.3.80)

User-defined slash commands and skills are owner-gated on the channel catch-all arm (#975). In a group chat, only the bot owner can trigger custom commands and skills; other members’ messages fall through to normal conversation. This closes a gap where any group member could run owner-defined automation through the catch-all routing path.

Telegram

Connect OpenCrabs to Telegram for DMs and group chats.

Setup

Step 1: Create a Bot with BotFather

  1. Message @BotFather on Telegram
  2. Send /newbot and follow the prompts
  3. Copy the bot token (format: 123456:ABC-DEF...)

Step 2: Configure via the Onboarding Wizard

Run /onboard:channels (or /onboard and navigate to the Channels step):

  1. Use ↑/↓ to focus Telegram
  2. Press Space to toggle it on
  3. Press Enter to open the Telegram setup screen
  4. Fill in the fields:
    • Bot Token: paste the token from BotFather
    • Owner User ID: your numeric Telegram chat ID
    • Respond To: all, dm_only, or mention (when to respond in groups)
  5. Press Enter on Test Connection to verify the bot works
  6. Press Enter to save and return to the channel list

Get your chat ID by messaging @userinfobot on Telegram.

Manual Configuration (advanced)

If you prefer editing files directly, the wizard writes to ~/.opencrabs/keys.toml and ~/.opencrabs/config.toml:

# keys.toml
[channels.telegram]
bot_token = "123456:ABC..."

# config.toml
[channels.telegram]
enabled = true
allowed_users = ["123456789"]
respond_to = "all"

Features

  • DMs and groups: Works in private chats and group conversations
  • Forum topic routing (v0.3.31): In supergroups with topics enabled, the bot tracks thread_id through the full pipeline. Use list_topics action to map topic names (e.g. #announcements) to numeric IDs, then pass thread_id to send / reply / send_photo to route into a specific topic
  • Context-aware pre-tool status (v0.3.31): While a tool runs, the bot shows a live status message naming the tool, elapsed time, and either a reasoning excerpt or an anchored phrase from the user’s request
  • Inline ctx budget footer (v0.3.36): context budget footer (ctx: XK/YK Z% | N tok/s) is now appended to the last response message instead of sent as a separate message. Keeps the chat clean.

v0.3.76–v0.3.78 Improvements

/new Gating (v0.3.76)

The /new command is now gated to the bot owner in group chats. Non-owners cannot create new sessions or reset context. The command menu is also scoped: owners see the full menu, non-owners see only the commands available to them.

HTML Conversion (v0.3.77)

All telegram_send actions now apply HTML-to-Telegram conversion consistently. Previously, only the main response path converted HTML entities; proactive sends, follow-ups, and media captions could arrive with raw <b> tags or unescaped ampersands.

Rich Delivery (v0.3.78)

Telegram delivery is unified on markdown formatting. The legacy “blocks path” (which sent structured JSON blocks) has been removed in favor of a single rich-markdown renderer. This eliminates formatting inconsistencies between streaming responses and proactive sends.

Callback Routing (v0.3.78)

Inline keyboard callbacks (plan approvals, follow-up taps, dedup approvals) now route to the originating session instead of the chat-bound session. Previously, tapping a button in a group chat could route the callback to the wrong session if multiple sessions shared the same chat.

Background Streaming (v0.3.78)

Background task results now stream through one turn per session instead of spawning a new message per tick. This eliminates the message flood when a background task (build, test suite) produces incremental output.

Follow-Up Attribution (v0.3.78)

When a user taps a follow-up suggestion button in a group chat, the agent now names the member who tapped it. Previously, follow-up taps were anonymous in group contexts.

Other Fixes

  • /start recognizes existing members of closed groups (v0.3.76)
  • Plan-card serialisation prevents duplicate cards on concurrent refreshes (v0.3.76)
  • Follow-up taps run a real tools turn instead of echoing text (v0.3.76)
  • Flow block no longer posts a new message every tick (v0.3.77)
  • Echoed plan titles dropped from responses (v0.3.77)
  • Open-group registration gated on persisted open flag (v0.3.78)
  • Cowork members recorded on first message (v0.3.78)
  • Rolling status edit-in-place (v0.3.36): tool status messages (⚙️ running, ✅ done) are edited in-place instead of delete+recreate, preventing flicker and preserving scroll position.
  • Bot command hot-reload (v0.3.36): bot commands refresh automatically when config or skills change, without restarting the bot.
  • Guard tok/s against burst-delivery (v0.3.36): tok/s footer is guarded against burst-delivery artifacts so the number stays stable.
  • follow_up_question polish (v0.3.34): Telegram keyboard is now single-column with a 40-character label cap (longer options rejected with a clear error). The rolling “Running follow_up_question (16s)” status is suppressed while the keyboard is pending so buttons don’t get visually buried. The LLM is instructed to call the tool silently without echoing the question text in surrounding prose
  • Inline buttons: Provider picker, model picker, session switcher use Telegram inline keyboards
  • Image support: Send images to the bot, receive generated images
  • Voice messages: STT transcription + TTS response
  • All slash commands: /help, /models, /new, /sessions, custom commands
  • Owner vs non-owner: Owner uses the shared TUI session, non-owners get per-user sessions
  • Onboarding overhaul (v0.3.30): Auto-detects owner user ID from getUpdates, persists partial config on cancel, only Enter on the last step commits (Tab no longer silently rewrites ~30 config keys)
  • Teloxide upgrade + join detection (v0.3.35): Upgraded from teloxide 0.13 to 0.17. New members joining a group are now detected before the allowlist check, so the bot can greet or moderate join events. Marathon-bucket rolling status rotates through project-author quip pool for more varied status messages.

v0.3.81 Improvements

Mermaid Diagrams as Images (#1044)

Mermaid code fences in rich messages render as images via the markdown+media route, while tables stay native Telegram formatting instead of being rasterized into the image.

Delivery and Rate-Limit Hardening

  • Rich blocks decode as a typed AST (#1058) instead of ad-hoc string surgery, so structured messages can’t arrive mangled
  • Inline rate-limit waits capped at 30s (#1064): a Telegram 429 never parks a turn indefinitely
  • Flow footer + section cap (#1052, #1053, #1054, #1056): flow logs get a footer and bounded sections instead of unbounded message growth

Group Safety

  • Only the owner may add the bot to a group (#1042)
  • Bot-add detection (#1041): being added by a member is told apart from another bot arriving
  • Unauthorised chats are never recorded (#1043): chats that were never approved leave no session residue

v0.3.82 Improvements

Long Rate-Limit Windows Bail Immediately (#1110)

A Telegram 429 that asks for a long wait no longer sleeps inline. The send returns immediately and the work is rescheduled, instead of the turn parking on a multi-minute (or multi-hour) window. This closes the failure mode where one throttled send held a turn open long enough to look like a hang.

Delivery Correctness

  • Rich sends stop building a double-slash URL (#1117): a trailing slash on the API base no longer produces //bot<token>
  • Rich API calls honour set_api_url (#1088): the rich path used a hardcoded api.telegram.org, so it bypassed a redirected Bot. It now routes through the caller’s API URL like every other method, which also makes the path testable without a real network call
  • All six send arms route through thread resolution: forum-topic targeting is applied consistently instead of on some arms only

Observability

  • Send-correlation telemetry (#1085): the three send chokepoints, every telegram_send arm, and the final streaming edit-in-place are all logged, so a message that never arrived can be traced to the arm that dropped it

Configuration

All Telegram options live under [channels.telegram] in ~/.opencrabs/config.toml:

[channels.telegram]
enabled = true
token = "123456:ABC-DEF..."          # or store in keys.toml
allowed_users = ["123456789"]         # numeric Telegram user IDs
allowed_channels = ["-100123456"]     # restrict to specific group/channel IDs (empty = all)
respond_to = "mention"                # "all", "dm_only", "mention" (default)
session_idle_hours = 24.0             # idle timeout for non-owner sessions
rich_messages = false                 # native Telegram rich messages (Bot API 10.1)
silence_group_start = true            # silently ignore /start from non-allowed users in groups
bot_owner = ["123456789"]             # owner IDs (gated commands, /cd hidden dirs, /profiles)
FieldDefaultDeskripsi
enabledfalseEnable the Telegram bot channel
tokenNoneTelegram Bot API token from @BotFather
allowed_users[] (accept all)Numeric Telegram user IDs. Accepts int or string arrays. Empty = open mode
allowed_channels[] (all channels)Restrict bot to specific channel/group IDs. DMs always pass
respond_to"mention"When to respond in groups: "all" = every message, "dm_only" = ignore groups, "mention" = only when @mentioned or replied-to
session_idle_hoursNone (no timeout)Idle timeout in hours for non-owner sessions. Owner sessions never expire
rich_messagestrue (since v0.3.64)Send structured replies as native Telegram rich messages (tables, headings, lists, math). Requires current mobile/desktop Telegram clients. Telegram Web and older clients show a “not supported” placeholder. Toggle via the onboarding checkbox or config
silence_group_starttrueSilently ignore /start from non-allowed users in group chats. Users who need their ID can DM the bot
bot_owner[] (first allowed_user)Bot owner user IDs. Owners can access gated commands (/profiles, hidden files in /cd), manage profiles. Defaults to first entry in allowed_users
guest_queriesfalseAnswer Telegram Guest Mode tags: someone tags the bot in a chat it is not a member of and gets one reply there. Callers must be the owner, an admin, or listed for that chat. See Guest queries

Per-group access control (per-chat ACL)

Telegram groups can have their own member list, so a user can be allowed in one group without gaining DM access:

  • allowed_users (channel level): admins: may DM the bot and act in any chat.
  • bot_owner: the owner: always allowed everywhere.
  • [channels.telegram.groups.<chat_id>].allowed_users: allowed in that group only. These users are refused in DMs unless they are also an admin or the owner, which closes the “DM the bot privately to escape group oversight” bypass.

DMs are gated to admins + owner. If neither allowed_users nor bot_owner is set, the bot is unconfigured and stays open (no hard lockout); set either one to lock it down.

Each group can also override respond_to just for itself.

[channels.telegram]
allowed_users = ["111"]                  # admins: DM + any chat
respond_to = "mention"                   # global default

[channels.telegram.groups.-1001234567890]
allowed_users = ["222", "333"]           # allowed in this group only, never via DM
respond_to = "all"                       # per-group override of the global respond_to

respond_to accepts all, mention, dm_only, or auto (reply to all while there is at most one active sender, then switch to mention-only once a second unique sender appears).

Per-group open mode (v0.3.66)

Set open = true on a trusted group to serve all members without individual allowlisting:

[channels.telegram.groups.-1001234567890]
open = true                              # all members can talk, no per-user registration needed

Use this for public/community groups where you want the bot available to everyone. Members still can’t DM the bot unless they’re in the global allowed_users or bot_owner.

Guest queries

Telegram Guest Mode (Bot API 10.0) lets someone tag @yourbot in a chat the bot is not a member of. With guest_queries = true, OpenCrabs answers the tag right there using the API method answerGuestQuery.

[channels.telegram]
guest_queries = true                     # default false: guest tags are ignored
allowed_users = ["111"]                  # admins: answered anywhere
[channels.telegram.groups.-1001234567890]
allowed_users = ["222"]                  # this user is answered in this chat only

Default is off, and the gate is narrow once it is on. The caller must be the bot_owner, an admin (allowed_users), or listed for that chat. A group’s open = true grants nothing here: it opens that group to the bot, it does not authorize answering guest tags from its members. Refused queries are logged with the guest_query_id, the caller, and the chat.

Boundaries of a guest turn:

  • The bot sees only the tagged message, no history from that chat, and nothing from the guest turn is stored in that chat’s history.
  • A guest turn runs the same tool surface as a normal turn, with one exception: tools that need approval are denied, because approvals are buttons in a chat and the caller is not the machine’s owner. The tool set is capped at what a stranger should reach (#2134).
  • The live status block and rich rendering match a normal turn: the guest sees the same progress chrome and the same markdown or rich reply, and an edit replaces the placeholder rather than appending a second message (#2109, #2133, #2135, #2138).
  • One guest turn per session at a time, cancellable, behind a rate limit, so a tag storm cannot fan out into parallel turns (#2143).
  • A reply with no tool calls is the answer alone, and the answer is never carried twice in one reply (#2113).
  • Channel directives (<<...>>) are stripped, so a local file path can never leak into someone else’s chat.
  • An overlong reply is cut and marked [truncated] rather than dropped.

Because the pinned teloxide-core has no guest update variant, guest queries are read on the raw getUpdates layer, and guest_message is in the bot’s allowed_updates list. Nothing to configure for that; it is on whenever the channel runs.

Voice and file pickup in groups

In mention-only groups (respond_to = "mention"), users can share files and voice messages even when the bot isn’t directly tagged in the same message:

  1. Fire-and-forget file capture: The bot downloads ALL incoming voice, video, document, and audio files from group messages to ~/.opencrabs/tmp/, regardless of whether the bot was mentioned. This happens silently in the background.
  2. Tag-then-ask: A user sends a voice message, then tags the bot in a follow-up message (e.g. @bot what did I just say?). The bot scans the tmp directory for recent voice files from that chat (5-minute window), transcribes the most recent one, and prepends the transcript to the user’s message.

This solves the core UX problem in mention-only groups: previously, tagging the bot in the same message as a voice note didn’t work because Telegram sends voice and text as separate messages.

The agent can use telegram_send with 20+ actions. The thread_id field on send / reply / send_photo targets a specific forum topic in supergroups with topics enabled.

AksiDeskripsi
sendSend text message (with optional thread_id for forum topics)
replyReply to a message
send_photoSend image file (supports caption and reply_parameters since v0.3.58)
send_documentSend document (supports caption and reply_parameters since v0.3.58)
send_voiceSend voice message
list_topicsReturns (thread_id, topic_name) pairs the bot has observed: translate #announcements into a numeric thread_id
pin / unpinPin or unpin a message
set_reactionAdd an emoji reaction
And more…

Reactions (v0.3.58)

The bot understands Telegram emoji reactions in both directions:

  • Inbound: when a user reacts to one of the bot’s messages with an emoji, the bot picks up that reaction (it looks up the original bot message by its platform message ID) and can act on it as feedback.
  • Frame reactions (v0.3.61): inbound reactions are read by sentiment and the bot addresses the user by first name in its response.
  • Mid-turn reactions (v0.3.61): if a user reacts during a running turn, the reaction injects into the current loop instead of spawning a second turn.
  • Reaction-only replies: when a short acknowledgement says it all (a thumbs-up, a 👀, a 😂), the bot can respond with just a reaction instead of a full text message, keeping the chat uncluttered.
  • Emoji validation: only real emoji count as reaction directives; code spans and stray characters are ignored, and react directives are stripped from intermediate status messages so they never leak into the visible reply.

Use the set_reaction action on telegram_send to add a reaction to a specific message from the agent.

/cowork: Workspace Creation (v0.3.59)

/cowork creates a team workspace directly from Telegram. Run it in your DM with the bot:

  1. Send /cowork in your DM with the bot
  2. The bot replies with an invite link and QR code for a new group
  3. Scan the QR code or tap the link to create the group
  4. Add your friends or teammates to the group
  5. Members auto-register to the group’s allow list ([channels.telegram.groups.<chat_id>].allowed_users), not the global one. Both new joiners (via invite link) and existing members (on their first message) get registered. Cowork members can talk in the group but cannot DM the bot privately unless also on the global allowed_users. The owner gets a confirmation for each registration.

/cowork works from any surface. In Telegram DMs, the native flow activates directly. From the TUI, Discord, Slack, or WhatsApp, the agent calls the cowork_connect tool which mints a session, registers it with the bot, and returns the t.me deep link plus a scannable QR code PNG.

Flow Logs (v0.3.63)

Telegram’s rich API supports 32K-character processing-log blocks. When the agent runs multi-step operations (tool calls, research, code generation), it streams progress into a single flow-log message that grows as the turn progresses. For shorter messages, HTML formatting falls back to plain text.

Flow logs show:

  • Tool calls with parameters and results
  • Intermediate reasoning steps
  • File edits and git operations
  • Build/test progress
  • Wall-clock duration (v0.3.66): finished/failed/timeout states carry the total turn time
  • Bash comments as flow status (v0.3.66): line-start comments in a bash command surface as live flow status
  • Full status preview (v0.3.66): the status preview uses the whole last human-readable line, no truncation

The flow-log message is edited in-place as each step completes, so the channel stays clean. Users see real-time progress without message spam.

Flow blocks re-stick to the chat bottom when buried (v0.3.65), so you always see the latest progress without scrolling.

Forum Topic Management and /clear (v0.5.2)

The agent can create and rename forum topics (not just route into existing ones), and a group’s session can bind to a topic so each topic keeps its own conversation context. /clear starts a fresh session in place (the current one closes and a blank one begins) without invoking the summariser, so it is instant and free (#1585).

Group Chat Behavior

In groups, the agent:

  • Responds when mentioned by name or replied to
  • Stays quiet when the conversation doesn’t involve it
  • Tracks context from group messages passively

Discord

Connect OpenCrabs to Discord for server and DM interactions.

Setup

Step 1: Create a Discord Bot

  1. Go to discord.com/developers/applications
  2. Create a new application
  3. Go to Bot section, create a bot
  4. Enable MESSAGE CONTENT Intent (required: under Privileged Gateway Intents)
  5. Copy the bot token
  6. Under OAuth2 → URL Generator, select bot scope with Send Messages and Read Message History permissions
  7. Use the generated URL to invite the bot to your server

Step 2: Configure via the Onboarding Wizard

Run /onboard:channels (or /onboard and navigate to the Channels step):

  1. Use ↑/↓ to focus Discord
  2. Press Space to toggle it on
  3. Press Enter to open the Discord setup screen
  4. Fill in the fields:
    • Bot Token: paste the token from the Developer Portal
    • Channel ID: the Discord channel to send the welcome message to (right-click a channel with Developer Mode on → Copy Channel ID)
    • Allowed Users: comma-separated Discord user IDs (since v0.5.1 an empty allowlist no longer allows everyone: until you set users, roles, or bot_owner, the channel denies all messages)
    • Respond To: all, dm_only, or mention
  5. Press Enter on Test Connection to verify
  6. Press Enter to save and return to the channel list

Enable Developer Mode in Discord: Settings → Advanced → Developer Mode

Manual Configuration (advanced)

# keys.toml
[channels.discord]
token = "your-bot-token"

# config.toml
[channels.discord]
enabled = true
allowed_channels = ["123456789"]
allowed_users = []
respond_to = "all"

Configuration

All Discord options live under [channels.discord] in ~/.opencrabs/config.toml:

[channels.discord]
enabled = true
token = "your-discord-bot-token"       # or store in keys.toml
allowed_users = ["123456789012345678"]  # Discord user IDs
allowed_channels = ["123456789012345678"]
respond_to = "mention"                  # "all", "dm_only", "mention" (default)
session_idle_hours = 24.0               # idle timeout for non-owner sessions
FieldDefaultDeskripsi
enabledfalseEnable the Discord bot channel
tokenNoneDiscord bot token from the Developer Portal
allowed_users[] (denies unless roles/owner configured)Discord user IDs. Accepts int or string arrays. Any one of allowed_users, allowed_roles, bot_owner configures the channel; a configured one admits allowlisted users, holders of an allowed role (never in DMs), or the owner
allowed_channels[] (all channels)Restrict bot to specific channel IDs
respond_to"mention"When to respond: "all", "dm_only", "mention"
session_idle_hoursNone (no timeout)Idle timeout for non-owner sessions. Owner sessions never expire

Features

  • Server channels and DMs: Works in text channels and direct messages
  • Interactive components: Select menus, modal forms, button interactions with component TTL and role access
  • Media gallery: Batch generated files into one multi-attachment message
  • Grouped tool calls: Consecutive tool calls collapse into one expandable block with Expand/Collapse toggle
  • Reactions: Agent can add emoji reactions to messages. Emoji reactions from users trigger agent turns (parity with Slack/Telegram)
  • Forum threads: Support for Discord forum channels
  • Image support: Send and receive images
  • Embed suppression: Agent wraps multiple links in <> to suppress embeds
  • Slash commands: All built-in and custom commands work

Interactive Components (v0.3.63)

Discord buttons, select menus, and modal forms power the provider picker, model picker, and session switcher. Components support:

  • Component TTL: Components expire after a configurable timeout
  • Role access: Restrict component interactions to specific Discord roles
  • Forum threads: Agent can create and manage forum threads

When the agent generates multiple files (PDFs, spreadsheets, images), Discord batches them into one multi-attachment message instead of sending separate messages for each file.

Grouped Tool Calls (v0.3.63)

Consecutive tool calls collapse into a single expandable block in Discord. Users can toggle between expanded and collapsed views. This keeps the channel clean during multi-step operations.

Reactions (v0.3.63)

The agent can add emoji reactions to messages. When a user reacts with an emoji, it triggers an agent turn (parity with Slack and Telegram). This enables quick, lightweight interactions without typing full messages.

Formatting Notes

  • Markdown tables in agent output convert to native Discord tables (v0.5.2); in your own messages, bullet lists are still the safest habit
  • Wrap multiple links in <url> to suppress embeds

Live Reply Tracing, Auto-Threading, Native Tables (v0.5.2)

Long turns stream as a live reply that is traced and edited in place instead of staying silent until the end. Answers that belong together are auto-threaded, and markdown tables in agent output render as native Discord tables rather than raw pipes (#1608). A dedup guard on tool turns stops the final response from double-posting content a tool summary already showed (#1603).

Native Polls, Voice Replies, Slash Projection, Flow Groups (v0.5.5)

send_poll posts a native Discord poll with each option as its own answer button, so follow-up option sets render as the platform’s own poll UI (#1848). TTS replies are delivered as Discord voice messages instead of attachment links (#1849), and your commands.toml is projected onto native slash commands, so your custom commands appear in Discord’s command picker (#1850).

Flow groups now open at turn start with a live clock that keeps running while the agent thinks, and the activity text leads the line instead of hiding behind the tool count (#1841, #1843, #1844, #1845). Every outbound write, including the persistent plan card, rides one shared write governor that parks cleanly on 429s (#1910, #1912).

Per-channel control arrived with respond_to and open modes in the channel config, enforced in the message filter, the ACL and the command gate (#2014). Two commands set them from inside the channel itself: /respond_to picks who the crab answers there, and /cowork opens the channel or thread to its members (#2014, #2015). Cron reports on Discord arrive as one edited forum post instead of a chain of messages (#1851).

Sustained Typing Indicator (v0.3.76)

Discord now shows a sustained typing indicator while the agent processes a request. Previously, Discord had no typing indicator at all (Telegram and Slack did). The indicator stays alive through background tasks and long tool executions, so users see “OpenCrabs is typing…” for the entire processing duration, not just the first few seconds.

Slack

Connect OpenCrabs to Slack workspaces.

Setup

Step 1: Create a Slack App

  1. Go to api.slack.com/apps
  2. Create a new app (From Scratch)
  3. Enable Socket Mode under Settings
  4. Generate an App-Level Token (Settings → Basic Information → App-Level Tokens) with connections:write scope
  5. Under OAuth & Permissions, add bot scopes: chat:write, channels:history, groups:history, im:history, reactions:write
  6. Install the app to your workspace (Install App → Install to Workspace)
  7. Copy both tokens: they live in different places in the Slack dashboard:
    • Bot Token (xoxb-...): under Install App, in the OAuth Tokens for Your Workspace section. It only appears after you install the app to the workspace.
    • App-Level Token (xapp-...): under Basic Information → App-Level Tokens. If you already generated it in step 4, Slack only lists the token name here; click the name to expand it and reveal the actual xapp-... value to copy.

Step 2: Configure via the Onboarding Wizard

Run /onboard:channels (or /onboard and navigate to the Channels step):

  1. Use ↑/↓ to focus Slack
  2. Press Space to toggle it on
  3. Press Enter to open the Slack setup screen
  4. Fill in the fields:
    • Bot Token: the xoxb-... token
    • App Token: the xapp-... token
    • Channel ID: right-click a channel → View channel details → copy the Channel ID at the bottom
    • Allowed Users: comma-separated Slack user IDs (Profile → ⋯ → Copy member ID)
    • Respond To: all, dm_only, or mention
  5. Press Enter on Test Connection to verify
  6. Press Enter to save

Manual Configuration (advanced)

# keys.toml
[channels.slack]
bot_token = "xoxb-..."
app_token = "xapp-..."

# config.toml
[channels.slack]
enabled = true
allowed_channels = ["C12345678"]
allowed_users = []
respond_to = "all"

Configuration

All Slack options live under [channels.slack] in ~/.opencrabs/config.toml:

[channels.slack]
enabled = true
token = "xoxb-your-bot-token"          # or store in keys.toml
app_token = "xapp-your-app-token"      # Socket Mode token
allowed_users = ["U12345678"]           # Slack user IDs
allowed_channels = ["C12345678"]
respond_to = "mention"                  # "all", "dm_only", "mention" (default)
session_idle_hours = 24.0               # idle timeout for non-owner sessions
FieldDefaultDeskripsi
enabledfalseEnable the Slack bot channel
tokenNoneBot token (xoxb-...)
app_tokenNoneApp-level token for Socket Mode (xapp-...)
allowed_users[] (unconfigured: denies everyone)Slack user IDs (U12345678). Since v0.5.1 a workspace with no allowlist and no bot_owner denies all messages; once configured it admits allowlisted users or the owner
allowed_channels[] (all channels)Restrict bot to specific channel IDs
respond_to"mention"When to respond: "all", "dm_only", "mention"
session_idle_hoursNone (no timeout)Idle timeout for non-owner sessions. Owner sessions never expire

Features

  • Channels and DMs: Works in public/private channels and direct messages
  • Action buttons: Provider picker, model picker, session switcher use Slack action buttons
  • Thread support: Responds in threads when appropriate
  • Slash commands: All built-in and custom commands work
  • Reactions: Agent can add emoji reactions. Emoji reactions from users trigger agent turns (react-back)
  • Grouped tool calls: Consecutive tool calls collapse into one edited-in-place message with Expand/Collapse toggle
  • TTS voice replies: Voice responses sent as OGG/Opus files via files.upload with inline waveform UI

Grouped Tool Calls (v0.3.62)

Consecutive tool calls collapse into a single message in Slack. The message is edited in-place as each tool executes. Users can toggle between expanded and collapsed views with an Expand/Collapse button. This keeps channels clean during multi-step operations.

Reactions (v0.3.62)

The agent can add emoji reactions to messages. When a user reacts with an emoji, it triggers an agent turn (react-back). This enables quick, lightweight interactions without typing full messages. Parity with Telegram and Discord.

Reactions (v0.3.62)

Slack supports emoji reactions for lightweight acknowledgements:

  • React-back: When you react to a bot message, the bot acknowledges it (e.g., adds a 👍 to show it saw your reaction)
  • Reaction turns: Certain reactions can trigger agent actions (configurable)
  • Grouped tool calls: Multiple tool calls in one turn collapse into a single collapsible message, keeping the channel clean

Native Rendering (v0.3.80)

Slack messages render in Slack’s own shape instead of raw markdown (#1016):

  • Tables are converted to Slack-native layout instead of arriving as pipe characters
  • Headings are rendered as Slack formatting rather than # hashes

Step-group narration is folded on the character stream rather than on paragraphs, and the final message no longer repeats the step-group narration it already sent (#1010).

Socket Mode

Slack uses Socket Mode (WebSocket) instead of HTTP webhooks, no public URL or ngrok needed. The connection is outbound from your machine.

WhatsApp

Connect OpenCrabs to WhatsApp via QR code pairing.

Setup

Configure via the Onboarding Wizard

Run /onboard:channels (or /onboard and navigate to the Channels step):

  1. Use ↑/↓ to focus WhatsApp
  2. Press Space to toggle it on
  3. Press Enter to open the WhatsApp setup screen
  4. The setup screen has two fields:
    • Connection: shows the QR code or connection status
    • Phone Allowlist: comma-separated phone numbers in E.164 format (e.g. +15551234567). Leave empty to accept all messages.
  5. When not yet paired, a QR code appears in the terminal:
    • Open WhatsApp on your phone → Settings → Linked Devices → Link a Device
    • Scan the QR code
    • The status updates to “Connected” automatically
  6. When already paired, press R on the Connection field to reset and re-pair
  7. Press Enter to save

The pairing session persists across restarts: no need to re-scan.

Manual Configuration (advanced)

# config.toml
[channels.whatsapp]
enabled = true
allowed_phones = ["+15551234567"]

Configuration

WhatsApp options live under [channels.whatsapp] in ~/.opencrabs/config.toml:

[channels.whatsapp]
enabled = true
allowed_phones = ["+15551234567"]      # E.164 format
session_idle_hours = 24.0              # idle timeout for non-owner sessions
FieldDefaultDeskripsi
enabledfalseEnable the WhatsApp channel
allowed_phones[] (open replies under auto, but never owner rights)E.164 phone numbers. Since v0.5.1 an empty list no longer makes every sender the bot owner, owner commands (/evolve, /rebuild, /exit, /cd) and approval answers need the self-chat, bot_owner, or the resolver over bot_owner + allowed_phones
session_idle_hoursNone (no timeout)Idle timeout for non-owner sessions. Owner sessions never expire
response_policy"auto"Who the bot responds to. auto: reply to all while there is at most one active sender, then switch to mention-only once a second unique sender appears. owner_only: only the bot owner. allowlist: only allowed phones. open: everyone
bot_ownerNone (auto-seeded from allowed_phones[0])Phone number of the bot owner in E.164 format. The owner gets access that other allowlisted users do not. Commands that expose personal data or the host system are owner-only

How it works

One account per instance. Each OpenCrabs instance supports one WhatsApp account (one companion device). You run the bot AS whatever account you scan: your own number (talk via “Message Yourself”), or any other number you own, including a WhatsApp Business account.

The bot talks to itself. If you message the bot’s own paired number, the bot replies to you. This is by design. The paired account’s self-chat is always allowed, regardless of response_policy or allowed_phones.

Allowlist behavior. Anyone messaging the paired number who is on the allowed_phones list gets a reply. The response_policy controls who else can interact beyond the allowlist.

Ownership (v0.5.1, deny-by-default). Before v0.5.1 an empty allowed_phones under response_policy = "auto" answered every contact and elevated them all to owner: one forgotten allowlist away from handing strangers /evolve and /cd. That elevation is gone: being answered no longer means being in charge. Owner status comes from the paired account’s self-chat, a configured bot_owner, or ownership resolution over allowed_phones + bot_owner: never from an empty list. Tool-approval replies (Yes/Always/No/YOLO) resolve only for the owner on all four channels.

  • Personal and group chats: Works in DMs and group conversations
  • Image support: Send and receive images
  • Voice messages: STT transcription + TTS response
  • Plain text UI: No buttons (WhatsApp limitation), uses text-based menus
  • Slash commands: All built-in and custom commands work

Outbound rate limiting

WhatsApp is the channel with the heaviest ban consequences, so every outbound send goes through a budget (since v0.5.1):

[channels.whatsapp.rate_limit]
messages_per_minute = 30   # token bucket: sustained sends per minute
daily_cap = 800            # rolling 24h hard cap
MechanismPerilaku
Per-minute pacingA token bucket sized to messages_per_minute (default 30) refills continuously. An over-budget send is paced, not dropped: the caller sleeps for the refill gap and then sends. Set 0 to disable pacing.
Rolling daily capThe 24h window allows daily_cap sends (default 800). On saturation further sends park in a FIFO queue; a background drainer flushes them as the window slides. A queued quote-reply loses its quote context, the text still arrives. Set 0 to disable the cap.
Owner alertThe first send queued in a saturation episode alerts the bot owner exactly once (through the WhatsApp session), and the alert re-arms when traffic drops back under the cap.
Owner bypassMessages addressed to the owner skip budget consumption entirely, so you always get fast replies; the saturation alert itself is also exempt.

Streaming answers used to post every chunk as its own message, exactly the volume pattern Meta’s heuristics hunt. Outbound replies now edit one message in place (since v0.5.1), which keeps chunked turns well inside the budget; the limiter is the safety net underneath.

Formatting Notes

  • No markdown tables: use bullet lists
  • No headers: use bold or CAPS for emphasis
  • Links render natively

Voice Message Handling

When receiving a voice message:

  1. Agent downloads and transcribes via STT
  2. Sends text response first (searchable)
  3. Optionally generates TTS audio response

Features

  • Personal and group chats: Works in DMs and group conversations
  • Image support: Send and receive images
  • Voice messages: STT transcription + TTS response
  • Plain text UI: No buttons (WhatsApp limitation), uses text-based menus
  • Slash commands: All built-in and custom commands work

Surface (v0.5.2)

The WhatsApp channel grew to match the phone app’s surface:

  • Outbound: native voice notes (opus), edit-in-place streaming for long replies, pin, forward, and profile actions
  • Inbound: video, stickers, location, contacts, reactions, and poll votes are parsed into the turn; presence events and media recovery fill gaps after reconnects
  • Sessions: disappearing messages honored per chat, contact blocking, per-chat history with reaction acknowledgements
  • Surfaces: status updates and newsletter discovery, native-flow buttons for interactive prompts; follow-up suggestion sets that exceed the native button cap render as a poll, and a vote selects the option (#1616)

Troubleshooting

Wrong number replying

Each OpenCrabs instance supports one WhatsApp account (one companion device). If you connected multiple numbers or the wrong number is replying, you need a full reset. The bot always uses the last number you connected.

Critical: Always reset the connection before connecting a new number. OpenCrabs only keeps the last paired number.

Full reset steps:

  1. Remove ALL linked devices from WhatsApp. Open WhatsApp on your phone, go to Settings > Linked Devices, and remove every device in the list. Don’t bother hunting for the opencrabs one. A stale device left over from an earlier pairing is the usual reason the old number keeps replying, so the reliable fix is to clear them all and start fresh.
  2. Reset the connection in OpenCrabs: in the TUI or from a channel, go to /onboard:channels and press R to reset the WhatsApp connection. Wait for confirmation that the reset is complete.
  3. Re-pair from scratch: after the reset is confirmed, go to WhatsApp > Settings > Linked Devices > Link a Device and scan the new QR code shown by OpenCrabs.

If the bot still shows the old number after resetting, make sure you completed step 1 (removing the device from WhatsApp) before step 2.

Common issues

SymptomCauseFix
Old number still replyingA stale linked device is still pairedRemove ALL linked devices from WhatsApp > Settings > Linked Devices, then press R in /onboard:channels and re-pair
QR code doesn’t appearAgent is still connected (no restart triggered)Press R in /onboard:channels to force a restart, then wait for the new QR
Bot doesn’t reply to anyoneresponse_policy is too restrictiveSet response_policy = "allowlist" and add phone numbers to allowed_phones in config.toml
Bot replies to everyoneresponse_policy is openSet response_policy = "allowlist" or "owner_only" in config.toml
Bot doesn’t reply to self-chatallowed_phones doesn’t include the paired numberThe paired number’s self-chat is always allowed, regardless of allowed_phones. If it’s not working, check that response_policy isn’t too restrictive

Trello

OpenCrabs integrates with Trello for board and card management via the trello_send tool.

Setup

Step 1: Get Trello Credentials

  1. Go to trello.com/power-ups/admin
  2. Create a new Power-Up to get your API Key
  3. Click the “Token” link next to your API key to generate an API Token

Step 2: Configure via the Onboarding Wizard

Run /onboard:channels (or /onboard and navigate to the Channels step):

  1. Use ↑/↓ to focus Trello
  2. Press Space to toggle it on
  3. Press Enter to open the Trello setup screen
  4. Fill in the fields:
    • API Key: from the Trello Power-Up admin page
    • API Token: generated alongside the API key
    • Board ID: board name or 24-character hex ID (names are resolved automatically)
    • Allowed Users: Trello member IDs allowed to interact with the bot (leave empty for all members)
  5. Press Enter on Test Connection to verify board access
  6. Press Enter to save

Manual Configuration (advanced)

# keys.toml
[channels.trello]
api_key = "your-api-key"
token = "your-token"

# config.toml
[channels.trello]
enabled = true
boards = ["Board Name or ID"]
allowed_users = []
# poll_interval_secs = 30  # Poll for new card comments

Configuration

All Trello options live under [channels.trello] in ~/.opencrabs/config.toml:

[channels.trello]
enabled = true
token = "your-trello-api-token"        # or store in keys.toml
app_token = "your-trello-api-key"      # stored as app_token for keys.toml symmetry
allowed_users = ["memberId1"]           # Trello member IDs
board_ids = ["boardId1", "boardId2"]   # boards to monitor (also accepts allowed_channels)
poll_interval_secs = 60                 # polling interval (absent or 0 = tool-only mode)
session_idle_hours = 24.0               # idle timeout for non-owner sessions
FieldDefaultDeskripsi
enabledfalseEnable the Trello channel
tokenNoneTrello API token
app_tokenNoneTrello API key (stored as app_token for keys.toml symmetry)
allowed_users[] (accept all)Trello member IDs
board_ids[] (all boards)Board IDs to monitor for @mentions. Also accepts allowed_channels as alias
poll_interval_secsNone (tool-only)Polling interval in seconds. Absent or 0 = no polling (tool-only mode)
session_idle_hoursNone (no timeout)Idle timeout for non-owner sessions. Owner sessions never expire

Tool Actions

The trello_send tool supports 22 actions:

AksiDeskripsi
create_cardCreate a new card
get_cardGet card details
update_cardUpdate card fields
move_cardMove card to another list
archive_cardArchive a card
find_cardsSearch for cards
add_commentAdd a comment to a card
get_card_commentsRead card comments
add_checklistAdd a checklist to a card
add_checklist_itemAdd an item to a checklist
complete_checklist_itemMark checklist item done
add_label_to_cardAdd a label
remove_label_from_cardRemove a label
add_member_to_cardAssign a member
remove_member_from_cardUnassign a member
add_attachmentAttach a file or URL
list_boardsList accessible boards
list_listsList columns in a board
get_board_membersGet board members
searchSearch across boards
get_notificationsGet notifications
mark_notifications_readMark notifications read

Perilaku

  • Tool-only by default: The agent acts on Trello only when explicitly asked
  • Optional polling: Set poll_interval_secs to enable monitoring for @bot_username mentions
  • Image attachments: Generated images are sent as card attachments with embedded previews
  • File attachments: Card attachments (images, documents) are fetched and processed through the vision pipeline

Built-in Tools

OpenCrabs ships with 50+ tools available to the agent out of the box, plus support for user-defined dynamic tools.

File Operations

ToolParametersDeskripsi
lspathList directory contents
globpattern, pathFind files by glob pattern
greppattern, path, includeSearch file contents with regex
read_filepath, line_start, line_endRead file contents
edit_filepath, old_string, new_stringEdit files with search/replace
write_filepath, contentWrite new files

Code Execution

ToolParametersDeskripsi
bashcommand, timeoutExecute shell commands
execute_codelanguage, codeRun code in sandboxed environment

Web & Network

ToolParametersDeskripsi
web_searchquerySearch the web (multi-engine fan-out; results deduped by URL across engines, v0.5.4)
serper_searchqueryGoogle SERP results via Serper; set the key in keys.toml (serper.dev) (v0.5.4)
web_scrapeurlNative URL-to-markdown scraping with SSRF protection, sitemap crawling, JS-shell detection (v0.3.60)
http_requestmethod, url, headers, bodyMake HTTP requests

Document Parsing

ToolParametersDeskripsi
parse_documentpathExtract text from PDF, DOCX, XLSX, XLS, XLSM, XLSB, ODS, DOC, CSV, HTML, TXT, MD, JSON, XML (v0.3.61 added spreadsheet support; v0.3.80 added xlsm/xlsb/ods via calamine, legacy .doc via rwml, and VBA macro source extraction from macro-enabled workbooks)

Document Generation

ToolParametersDeskripsi
generate_documentformat, content, styleGenerate PDF, DOCX, XLSX, PPTX natively. Brand colors, logos, image blocks, live Excel formulas. Files delivered as channel attachments (v0.3.62)

Session & Memory

ToolParametersDeskripsi
session_searchquery, limitSemantic search across sessions
session_contextactionRead/write session context
task_manageraction, variousManage plans and tasks
decide_cacheddecision inputL1 decision cache (#1648): exact-repeat decisions are served from a reuse ring keyed on canonicalized input, with a shadow tier that logs what it would answer before it goes live (v0.5.4)

Image & Video

ToolParametersDeskripsi
generate_imageprompt, filenameGenerate images via Gemini
analyze_imageimage, questionAnalyze images via Gemini vision
analyze_videovideo, questionAnalyze videos via Gemini multimodal vision. Supports mp4/m4v/mov/webm/mkv/avi/3gp/flv. Inline bytes for ≤18 MB, resumable Files API upload for larger files (v0.3.17)

Channel Integrations

ToolParametersDeskripsi
telegram_sendaction, variousTelegram operations (19 actions)
discord_connectaction, variousDiscord operations (17 actions)
slack_sendaction, variousSlack operations (17 actions)
trello_connectaction, variousTrello operations (22 actions)

Sub-Agent Orchestration

Agents can spawn independent child agents for parallel task execution:

ToolParametersDeskripsi
spawn_agentlabel, agent_type, promptSpawn a typed child agent in an isolated session
wait_agentagent_id, timeout_secsWait for a child agent to complete and return output
send_inputagent_id, textSend follow-up input to a running agent (multi-turn)
close_agentagent_idTerminate a running agent and clean up resources
resume_agentagent_id, promptResume a completed/failed agent with new prompt (preserves context)
team_createteam_name, agents[]Spawn N typed agents as a named team (parallel)
team_broadcastteam_name, messageFan-out message to all running agents in a team
team_deleteteam_nameCancel and clean up all agents in a team

Agent Types

When spawning, agent_type selects a specialized role with a curated tool registry:

TypeRoleTool Access
generalFull-capability (default)All parent tools minus recursive/dangerous
exploreFast read-only codebase navigationread_file, glob, grep, ls
planArchitecture planningread_file, glob, grep, ls, bash
codeImplementation with full write accessAll parent tools minus recursive/dangerous
researchWeb search + documentation lookupread_file, glob, grep, ls, web_search, http_request

ALWAYS_EXCLUDED tools (no agent type has these): spawn_agent, resume_agent, wait_agent, send_input, close_agent, rebuild, evolve – no recursive spawning, no self-modification from subagents.

Browser Automation

Native headless Chrome control via Chrome DevTools Protocol (CDP):

ToolParametersDeskripsi
navigateurlOpen a URL in the browser
clickselectorClick an element by CSS selector
typeselector, textType text into an input field
screenshotselectorCapture a screenshot
eval_jscodeExecute JavaScript in the page context
extract_contentselectorExtract text content from elements
wait_for_elementselector, timeoutWait for an element to appear
findpattern, modeFind elements by CSS, XPath, text, or aria-label. Returns stable selectors
browser_close-Close browser tab and free CDP session. Prevents stale page reuse across browser actions (v0.3.18)

Auto-detects your default Chromium browser. Feature-gated under browser (enabled by default).

Dynamic Tools

Define custom tools at runtime via ~/.opencrabs/tools.toml. See Dynamic Tools for details.

ToolParametersDeskripsi
tool_manageaction, variousCreate, remove, or reload dynamic tools

System

ToolParametersDeskripsi
slash_commandcommand, argsExecute slash commands (/cd, /compact, etc.)
config_manageraction, variousRead/write config, manage commands
evolvecheck_onlyDownload latest release
rebuild-Build from source and restart
planaction, variousCreate and manage execution plans

Error Handling

v0.2.92 improved error surfacing across all tool connections. Channel connect tools (slack_connect, whatsapp_connect, trello_connect) now surface actual connection errors instead of silently swallowing them. Tool call status correctly transitions from “running” to success/failure instead of showing a perpetual spinner.

System CLI Tools

OpenCrabs runs in a TUI with full terminal access. The agent can execute any CLI tool installed on the host via the bash tool – no plugins, no wrappers. If it’s on your system, the agent can use it. Common ones:

ToolPurposeCheck
ghGitHub CLI: issues, PRs, repos, releases, actionsgh --version
gogGoogle CLI: Gmail, Calendar (OAuth)gog --version
dockerContainer managementdocker --version
sshRemote server accessssh -V
nodeRun JavaScript/TypeScript toolsnode --version
python3Run Python scripts and toolspython3 --version
ffmpegAudio/video processingffmpeg -version
curlHTTP requests (fallback when http_request insufficient)curl --version

GitHub CLI (gh)

Authenticated GitHub CLI for full repo management:

gh issue list / view / create / close / comment
gh pr list / view / create / merge / checks
gh release list / create
gh run list / view / watch

Google CLI (gog)

OAuth-authenticated Google Workspace CLI. Supports Gmail and Calendar:

gog calendar events --max 10
gog gmail search "is:unread" --max 20
gog gmail send --to user@email.com --subject "Subject" --body "Body"

Requires GOG_KEYRING_PASSWORD and GOG_ACCOUNT env vars.

Companion Tools

SocialCrabs: Social Media Automation

SocialCrabs is a social media automation tool with human-like behavior simulation (Playwright). Supports Twitter/X, Instagram, and LinkedIn.

The agent calls SocialCrabs CLI commands via bash:

node dist/cli.js x tweet "Hello world"
node dist/cli.js x mentions -n 5
node dist/cli.js ig like <post-url>
node dist/cli.js linkedin connect <profile-url>

Read operations are safe. Write operations (tweet, like, follow, comment) require explicit user approval.

WhisperCrabs: Floating Voice-to-Text

WhisperCrabs is a floating voice-to-text widget controllable via D-Bus. Click to record, click to stop, text goes to clipboard. The agent can start/stop recording, switch providers, and view transcription history via D-Bus commands.

Custom Commands

Define your own slash commands in ~/.opencrabs/commands.toml. Commands work from the TUI and all channels (Telegram, Discord, Slack, WhatsApp).

Configuration

# ~/.opencrabs/commands.toml

[commands.credits]
description = "Show remaining API credits"
action = "prompt"
value = "Check my API credit balance across all providers and give me a summary"

[commands.deploy]
description = "Deploy to production"
action = "prompt"
value = "Run the production deployment pipeline: git pull, build, test, deploy"

[commands.status]
description = "Show system status"
action = "system"
value = "System is operational. All channels connected."

Action Types

AksiPerilaku
promptSends the value as a message to the agent: the agent processes it like any user message
systemDisplays the value directly as a system message: no agent involvement

Using Commands

Type /commandname in the TUI or any connected channel:

/credits     → agent checks API balances
/deploy      → agent runs deployment
/status      → shows static system message

Visibility

Custom commands appear in:

  • /help output (TUI and channels) under a “Custom Commands” section
  • TUI slash autocomplete when typing /

Commands are sorted alphabetically and show their description.

Memory System

OpenCrabs uses a 3-tier memory system for persistent context across sessions.

Memory Tiers

1. Daily Notes (memory/YYYY-MM-DD.md)

Automatic daily files for session-specific observations:

~/.opencrabs/memory/2026-03-07.md

The agent writes here during conversations: new integrations, bugs fixed, decisions made, server changes.

2. Long-term Memory (MEMORY.md)

Curated knowledge that persists across all sessions:

  • Server details, SSH access, credentials locations
  • User preferences and workflows
  • Integration configurations
  • Lessons learned from debugging

Full-text search across all past sessions stored in SQLite. The agent can query:

  • Previous conversations
  • Tool execution history
  • Past decisions and context

The agent uses session_search for fast memory lookups (~500 tokens) instead of reading full memory files (~15K tokens). This is the primary recall mechanism.

Chunk-Hash Caching (v0.3.82)

Embedding is skipped for chunks whose content has not changed (#1107). Each chunk carries a hash; on write, only chunks whose hash differs from the stored one are re-embedded. Editing one line of a large brain file no longer re-embeds the whole document.

Scoped Search (v0.3.80)

memory_search takes a scope that picks which corpus to search (#1020):

ScopeCorpusUse it for
memory (default)Daily logsHistory: what happened, when, what was decided
brainBrain files (SOUL, USER, AGENTS, TOOLS, CODE, SECURITY, MEMORY, BOOT, HEARTBEAT)Rules and policy: does a rule about this ALREADY exist, and which file owns it
allBoth“Have I ever written about this anywhere”

Picking the wrong scope is the usual reason a search comes back with nothing useful. Daily logs outnumber brain files and reuse the same words for unrelated things, so searching memory for a rule usually fails: history outranks policy and you get confident irrelevant hits. Search brain before appending any rule: a hit tells you WHICH file owns it, and then load_brain_file with a query reads the whole section.

Chunked Retrieval and Index-on-Write (v0.3.80)

Memory retrieval was rebuilt around chunking (#998-1002, #1018):

  • Documents are chunked before embedding, so chunks past the first are searchable. Previously a long document effectively hid everything after its opening.
  • Lexical hits are narrowed to the matching chunk, so keyword results point at the relevant slice instead of the whole document.
  • The index updates on write: memory is searchable immediately, not from a boot-time snapshot. Brain files are indexed into the brain collection on write too.
  • The store resolves per profile, so profiles never share or cache each other’s memory index.
  • The chunker is multi-byte safe: no more panics on UTF-8 boundaries.
  • MEMORY.md recall is ranked with BM25 instead of shared-word counts (#996), and recall folds Latin diacritics so accented queries match.

In-tree Memory Store (v0.3.81)

The qmd binary dependency is gone (#1032). OpenCrabs now owns its memory store in-tree (src/memory/): SQLite FTS5 for lexical search plus vector embeddings in one store, resolved per profile. One fewer external binary to install or break, and the store schema ships with the binary.

External Index Paths (v0.3.81)

Memory search can read indexes that live outside the profile directory (#1051, #1055):

[memory]
# Each entry is a bare path string or a { path, pattern } table.
# Relative paths resolve against the OpenCrabs home (not the session cwd),
# so the index stays stable across /cd and profile switches.
extra_paths = [
  "~/knowledge/product-docs",
  { path = "~/knowledge/website-mirror", pattern = "docs/**/*.md" },
]
FieldDefaultDeskripsi
extra_paths[]Paths indexed into the external collection. Bare strings or { path, pattern } tables
excludeVCS/build/secret globsGlob excludes applied to external indexing, global across all entries (.git, node_modules, .env*, *.pem, .ssh/**, *credential*, …)
external_allowed_in_sharedfalseShared/group sessions are default-deny: external content stays out of memory_search results unless you opt in
sweep_interval_secs300Seconds between freshness sweeps that pick up added/removed files. Modified files are also caught lazily at search time

Indexed files are searchable within one sweep interval (~5 min), no restart needed. Results come back as ranked excerpts with file paths via memory_search scope="external"; the agent can then read the full file for verbatim quotes.

Gotchas: UTF-8 text only (binary files are skipped with a warning); HTML files index as raw markup, so mirror sites to .md first; nested roots and symlinks are skipped by design; test external scope from an owner session, group sessions return nothing unless you set external_allowed_in_shared = true.

Build a knowledge base from any docs site

web_scrape sitemap mode pairs with extra_paths into a complete, zero-cost pipeline: scrape a site to markdown, index it, then ask questions answered verbatim from those docs (see Web Scraping):

[memory]
extra_paths = ["scrapes/docs.example.com"]
  1. web_scrape url="https://docs.example.com" mode=sitemap export=true: every sitemap page saved as clean markdown under scrapes/, local extraction, no AI/API cost (hard cap: 100 pages per scrape)
  2. Point extra_paths at the scrapes directory (relative paths resolve against the OpenCrabs home)
  3. Ask anything: answers are grounded in and quoted from the indexed docs

Structural Code Search Upgrades (v0.5.1)

The code-graph answers (who calls X, what does X call, impact of X) got four upgrades:

  • Truncation is now visible. Caller sets in real repos are unbounded (5,000+ call sites exist), so every structural listing that had to cut ends with a marker line ( “… and N more callers of X (re-query with higher n or offset)”) and sets beyond 500 collapse into a per-file rollup instead of one-liners. Nothing is silently chopped anymore.
  • Offset pagination. memory_search accepts an offset alongside n for structural listings, so the full set can be walked page by page. Ranked text/vector results stay rank-truncated: the window applies to listings only.
  • Depth-2 impact chains. impact of X now walks two hops over the call-edge graph, surfacing the callers-of-callers that a direct query misses.
  • Cleaner hits. External code hits render repo-relative paths instead of absolute machine paths, and every scope=all hit carries a corpus provenance tag (which index the fact came from), so a memory answer and a code-graph answer can be told apart (#89).

Embedding Hardening (v0.3.81)

Embedding API calls now carry timeouts, a vector gate (malformed or empty vectors never reach the store), and non-blocking writes (embedding failures never stall the turn) (#1062). /doctor reports embedding health, and a sweep finds unembedded documents and backfills them (#1069, #1067).

Embedding Modes

OpenCrabs supports three embedding configurations:

  1. Local GGUF (default): downloads a 300MB embedding model and runs it locally via llama.cpp
  2. OpenAI-compatible API: configure external embedding providers (OpenAI text-embedding-3-small, Ollama nomic-embed-text, Jina, LM Studio, or any /v1/embeddings endpoint) via [memory.embedding] config with url, model, api_key, dimensions
  3. FTS5-only: pure keyword search with zero RAM overhead. Set [memory] vector_enabled = false. Auto-detects VPS environments and configures automatically

Context Compaction

When context reaches ~80% capacity, OpenCrabs automatically compacts:

  1. Summarizes the conversation so far into a comprehensive continuation document
  2. Clears old messages from context
  3. Continues with the summary as context

Manual compaction: type /compact in chat.

Auto-Save Triggers

The agent saves to memory when:

  • New integrations are connected
  • Server/infrastructure changes occur
  • Bugs are found and fixed
  • New tools are configured
  • Credentials are rotated
  • Architecture decisions are made
  • You say “remember this”
  • Errors take >5 minutes to debug

Proactive Memory Surfacing (v0.3.76)

The agent now surfaces relevant memory without being asked. When a conversation topic matches something stored in MEMORY.md or daily notes, the relevant context is loaded and injected automatically. Previously, memory was only recalled when the agent explicitly called memory_search or load_brain_file. Now the system proactively checks for relevant context on each turn, so the agent brings up past decisions, server details, or preferences that apply to the current conversation without you having to say “check your memory.”

Confidence, Decay and Pruning (v0.5.3)

MEMORY.md facts carry confidence and decay over time (#1643): a belief’s trust drops as it goes unused and unverified, so stale claims stop outranking fresh ones when memory is recalled. Decay is config-driven through the [epistemic] section.

Cold content is managed in two passes (#1657):

  • Archive pass: cold MEMORY.md sections move to memory/archive/ with a backup and reindex, instead of silently accumulating forever.
  • opencrabs memory prune: a dry-run preview (the default) of cold beliefs (key, confidence, hits, age) and cold sections (heading, bytes). --apply archives the sections first, then sweeps the remaining cold beliefs. --max-age-days <N> overrides the cold threshold (default: 90).

Brain Files

See Brain Files for the full list of files the agent reads on startup.

Brain Files

Brain files define the agent’s personality, knowledge, and behavior. They live at ~/.opencrabs/ and are loaded on every session start.

Startup Read Order

  1. SOUL.md: Personality and values
  2. USER.md: Your profile and preferences
  3. memory/YYYY-MM-DD.md: Today’s notes
  4. MEMORY.md: Long-term memory
  5. AGENTS.md: Agent behavior guidelines
  6. TOOLS.md: Tool reference and custom notes
  7. CODE.md: Coding standards and file organization
  8. SECURITY.md: Security policies
  9. HEARTBEAT.md: Periodic check tasks

File Reference

SOUL.md

Agent personality. Core truths: strong opinions, brevity, resourcefulness, honesty. Hard rules: never delete files without approval, never send emails without request, never commit code directly.

USER.md

Your profile: name, location, timezone, role, specialties, communication preferences, pet peeves.

AGENTS.md

Comprehensive agent behavior docs: memory system, safety rules, git rules, workspace vs repository separation, cron best practices, platform formatting, heartbeat guidelines.

TOOLS.md

Tool parameter reference, system CLI tools, provider configuration, integration details for all channels and services.

CODE.md

Coding standards brain template. Enforces: no file over 500 lines (target 100–250), types in types.rs, one responsibility per file, mandatory tests for every feature, security-first patterns. Rust-first philosophy, single binary, no runtime dependencies. The agent follows these rules when writing or reviewing code.

SECURITY.md

Security policies: third-party code review, attack playbook awareness, network security, data handling, incident response.

HEARTBEAT.md

Tasks for periodic proactive checks. Keep empty to skip heartbeat API calls. Add tasks for the agent to rotate through (email checks, calendar, weather, etc.).

BOOT.md

Startup procedures: check git log, verify build, greet human with context awareness.

Query-Based Loading (v0.3.76)

load_brain_file now accepts an optional query parameter that returns only the sections matching the query instead of the entire file. This saves significant context budget:

load_brain_file(name="TOOLS.md", query="telegram")
→ Returns only the Telegram-related sections, not the full 2000-line file

Whole sections are returned (never cut mid-section), so a rule is never truncated. This is especially useful for large brain files like TOOLS.md and AGENTS.md where loading the full file wastes 10K+ tokens on irrelevant content.

Customization

These files are yours. The agent reads them but you control the content. Templates are at src/docs/reference/templates/ in the source repo, compare your local files against templates when updating to pick up new sections without losing custom content.

New installs (v0.2.72+): CODE.md and SECURITY.md are automatically seeded on first run. Existing users can ask their crab: “Check my brain templates and update them if any are missing or outdated.”

Upgrading: Brain files are never overwritten by /evolve or /rebuild. After updating, ask your crab to compare templates against local files and patch in new sections.

User-owned files are never synced (v0.3.82)

SOUL.md, USER.md and MEMORY.md are excluded from upstream template sync (#1119). They hold your agent’s personality, your identity, and what it has learned about you, so upstream has no authority over them. Before this, every startup appended upstream sections into them, which grew one user’s SOUL.md from under 2 KB to nearly 5 KB, re-appending after each manual prune.

Seeding is unaffected: all three are still created on first run. Only the merge is forbidden. The remaining brain files (AGENTS, TOOLS, CODE, SECURITY, BOOT, HEARTBEAT) continue to sync normally.

Related (#1121): rules belong in AGENTS.md, not MEMORY.md. MEMORY.md is on-demand, reached only through memory_search, so a rule written there does not bind on a cold session and does not survive compaction. Facts go in MEMORY.md; anything that must always hold goes in AGENTS.md, which is always loaded.

Sessions

Sessions as Isolated Agents

A session in OpenCrabs is not a tab, not a window, not a chat thread. Each session is a fully independent agent with its own brain: conversation history, provider, model, working directory, tool state, approval policy, and context window. When you create a new session, you are spinning up a separate agent that knows nothing about any other session and shares nothing with them.

This is the core mental model: one session = one agent = one context. You can run dozens of sessions in parallel and they will never interfere with each other.

Zero Context Contamination

Sessions are completely isolated at every layer:

  • Separate message queues: each session has its own queue. Messages are routed strictly to their originating session. No cross-session bleeding, even when 10 sessions are processing simultaneously.
  • Separate provider and model: switching to Gemini in session A does not affect session B running Claude. Each session remembers its own provider independently.
  • Separate working directory: /cd in one session does not change the working directory of any other session.
  • Separate conversation history: full SQLite-backed history per session. No shared memory, no prompt pollution, no context bleed.
  • Separate token tracking: cumulative usage, cost, and context window are tracked per session.

This isolation is guaranteed by Rust’s thread safety and async runtime. Each session runs as an independent tokio task with its own state, and the type system prevents accidental sharing at compile time. You can run split panes, channel sessions, background agents, and sub-agents all at once with zero risk of one session’s output leaking into another.

Switching Sessions Keeps the Transcript

Switching sessions with /sessions and coming back does not fold your transcript away. The fold state for every turn is keyed by a stable role and timestamp anchor, so a turn you had opened stays open across a reload, and a turn you had folded stays folded (#2155). A turn that is still running also stays unfolded while it runs, so the work in flight stays visible instead of hiding behind a collapsed header.

The display window keeps a bounded number of recent messages in the pane rather than trimming by token budget, so one huge tool result can no longer evict the rest of the conversation. The hidden-count marker still tells you how many older rows are out of view, and Ctrl+O pages back through them.

Workflow Patterns: One Session Per Context

The power of isolated sessions becomes clear when you treat each one as a dedicated agent for a specific domain:

Pattern 1: DevOps for a Server

Create a session for a specific server or infrastructure concern. Send a first message like "Devops for server XYZ, monitor nginx builds, manage cronjobs, handle deployments, run log cleanups". The session locks into that context: working directory set to the server’s codebase, provider set to a fast model for quick ops tasks, history filled with that server’s deployment patterns. Come back to it days later and the agent remembers every previous deploy, every cron change, every nginx config tweak.

Pattern 2: Mobile App Development with a Co-Founder

Create a session named mybrand-mobile and connect it to a Telegram group with your co-founder. The agent is locked into the Dart/Flutter codebase, the product design context, and the mobile-specific toolchain. Your co-founder can ask questions, request design changes, or review PRs directly in Telegram while you work on backend tasks in a separate session. The two contexts never mix.

Pattern 3: Production Logs with a Team on Slack

Create a session named mybrand-prod-logs-debug and connect it to a Slack channel. Your team can ask questions about production, staging, or dev logs without you having to context-switch. The agent stays locked into log analysis mode with the right SSH aliases, the right log paths, and the right debugging tools. Meanwhile, your main TUI session is free for development work.

The key insight: you never have to explain the full context again. Once a session is locked into a domain, every follow-up message inherits that context automatically.

Session Context Integrity (v0.3.78)

Two fixes harden session_context (the tool agents use to store key-value state):

  • Atomic writes: context writes now use atomic file operations, eliminating trailing-character corruption that could occur when a write was interrupted mid-flush. Previously, a crashed write could leave a JSON file with a truncated trailing character, making the entire context unreadable.
  • Concurrency pinning: concurrent session_context calls from parallel tool executions no longer race on the same file. A concurrency pin serializes writes, and temp files are cleaned up after each operation.

Creating Sessions

  • TUI: Press Ctrl+N or type /new
  • Channels: Type /new in any channel

The First Message Matters

When you create a new session, the first message you send becomes the seed for the entire session’s context. OpenCrabs uses it to:

  1. Auto-generate a session title: a background LLM call extracts a 3-8 word descriptive title from your first message. This runs asynchronously and never enters the conversation context.
  2. Anchor the agent’s context: the initial message establishes what this session is about, what codebase it should focus on, what tools it should prioritize.

Good first messages are specific and contextual:

  • "Devops for server XYZ: nginx, cronjobs, deployments, log cleanups"
  • "Flutter mobile app for mybrand: Dart codebase at ~/srv/mobile/mybrand"
  • "Debug production logs for mybrand staging and dev environments"

The auto-title will generate something like Devops Server XYZ, Mybrand Mobile Flutter, or Mybrand Prod Logs Debug. You can always rename it later.

Switching Sessions

  • TUI: Press Ctrl+L to open the sessions screen, navigate with arrow keys, press Enter to select
  • Channels: Type /sessions to see recent sessions with inline buttons

Renaming Sessions

Auto-generated titles are a starting point, not a final name. You can rename any session:

  • TUI: Press Ctrl+L, navigate to the session, press r to rename
  • Agent-initiated: the rename_session tool lets the agent rename the current session with a descriptive title when the conversation evolves beyond its original scope

Empty or whitespace-only titles are rejected (v0.3.30, #128).

Session Screen

The sessions screen shows:

  • Session name
  • Created date
  • Provider/model badge
  • Working directory
  • Token usage
  • Context window usage (current session)
  • Status indicators (processing spinner, pending approval, unread)

Sub-Agent Sessions Stay Out of Your Way (v0.3.79)

Every spawned sub-agent creates a session titled subagent: {label}, and a busy turn can create several. They are an implementation detail of one tool call, so since v0.3.79:

  • Sub-agent sessions are hidden from session lists the moment they exist, your session screen only ever shows your sessions.
  • They are automatically purged once past agent.subagent_session_ttl_days (default 7; set 0 to keep them forever).
  • Your own sessions are never touched: the cleanup sweep matches the subagent: title prefix only.

Per-Session State

Each session remembers:

  • Provider and model: Switch to Claude in one, Gemini in another
  • Working directory: /cd persists per session
  • Conversation history: Full message history in SQLite
  • Token count and cost: Cumulative usage tracking

v0.3.74 hardened per-session isolation:

  • Per-session working directory: the working directory is isolated per session so it can’t leak across concurrent sessions (#703).
  • Restore session provider: a session’s saved provider is restored before its turn, so it never accidentally runs on the global default (#704).
  • Don’t persist involuntary remap: an involuntary provider/model remap is not persisted over a session’s saved pair (#705).

Session Management

AksiTUIChannels
NewCtrl+N / /new/new
SwitchCtrl+L + Enter/sessions
RenameR on sessions screen-
DeleteD on sessions screen-

Background Processing

Sessions can process in the background while you work in another session. The sessions screen shows:

  • Spinner for actively processing sessions
  • ! for sessions waiting for tool approval
  • Dot for sessions with unread messages

Background Tasks (v0.3.74)

Long-running shell commands no longer block your session. When the agent runs a known-long command (cargo test, cargo build, npx remotion render, gh run watch, and similar), OpenCrabs auto-promotes it to a background task (#722): the command runs detached, the session is freed immediately, and the agent can keep working (or you can switch away) while it finishes.

When the task completes, the result is injected back into the originating session and resumes it automatically. On the TUI the session picks up at the next tool-call boundary; on Telegram the resume is delivered to the same chat (#722). No polling loops, no re-running the command to “check”, the completion report arrives on its own.

The agent is told via its preamble that long tasks run in the background and resume the session when done, so it wraps up or moves on instead of sitting in a wait loop (#722).

Two related session fixes landed in the same release:

  • No perpetual resume: a completed session no longer resumes on every restart. Resume turns are not re-tracked as pending requests, so a finished background task stays finished across restarts (#729).
  • No lingering cancelled query: a query cancelled before the agent replied, plus its empty assistant placeholder, are dropped as a pair so they don’t linger in context and duplicate on resend (#730).

Background-Task Resume Everywhere (v0.3.75)

v0.3.74 introduced background-task resume on the TUI and Telegram. v0.3.75 extends it to every channel: Discord, Slack, WhatsApp, and the interactive CLI agent now resume your session the moment a long task finishes, powered by a shared bg_resume helper so the behaviour is identical across surfaces.

A few refinements make background tasks easier to live with:

  • Indicator on the input border: the background-task indicator moved onto the input border, and a detached command shows what it’s currently doing.
  • No scaffolding in chat: a background task’s system scaffolding no longer reaches the chat.
  • Restart-killed tasks reported: background tasks a restart killed are reported instead of leaving the session waiting forever.

Split Panes

Run multiple sessions side by side with tmux-style pane splitting. Each pane is a fully isolated agent, see Split Panes for details.

State Management

v0.2.92 improved session state tracking:

  • Session reload after cancellation: After Esc+Esc cancel, session context reloads from DB to pick up any changes made during the cancelled operation
  • Cached state cleanup: Deleting a session now clears stale pane cache entries, preventing phantom state on restart
  • CLI tool segment persistence: Tool results from CLI providers (Claude CLI, OpenCode CLI) are now saved to DB alongside regular messages, preserving correct text/tool interleaving across restarts
  • Case-insensitive tool input: Tool input descriptions use case-insensitive key lookup, fixing failures when providers return different casing

Channel Sessions

All channels (Telegram, Discord, Slack, WhatsApp, Trello) persist sessions in SQLite by channel/group title. Sessions survive process restarts, no more lost context after daemon restart. Each channel group gets its own isolated session, while owner DMs share the TUI session. Cross-channel stable session suffixes ([chat:<id>]) ensure reliable session resolution across Discord, Slack, and WhatsApp (v0.3.29).

Session-Routed Recovery Reports (v0.3.81)

Crash-recovery reports route by session (#1037): a resumed turn reports back to the session that owned it, not to whichever session happened to boot the recovery. Multi-session setups no longer cross-deliver recovery noise.

Split Panes

OpenCrabs supports tmux-style pane splitting in the TUI. Run multiple sessions side by side, each with its own provider, model, and context, all processing in parallel.

Splitting

AksiShortcut
Split horizontal| (pipe)
Split vertical_ (underscore)
Cycle focusTab
Close paneCtrl+X

How It Works

Each pane runs an independent session. You can have one pane writing code with Claude while another reviews tests with Gemini. The status bar shows [n/total] to indicate which pane is focused.

  • Independent providers: Each pane can use a different AI provider and model
  • Independent context: Conversation history is isolated per pane
  • Parallel processing: All panes process concurrently via Tokio
  • Persistent sessions: Each pane’s session is saved to SQLite like any other session

Example Layout

┌──────────────────────┬──────────────────────┐
│  Session 1 (Claude)  │  Session 2 (Gemini)  │
│  Writing code...     │  Reviewing PR...     │
├──────────────────────┴──────────────────────┤
│  Session 3 (OpenRouter)                      │
│  Running tests...                            │
└──────────────────────────────────────────────┘

Split vertically with _, then horizontally with | in the top pane.

Persistent Layout

Split pane configuration (splits, sizes, focused pane) saves to ~/.opencrabs/pane_layout.json on quit and Ctrl+C. On restart, your layout is restored exactly as you left it. Each restored pane preloads its session messages from the database, so content is visible immediately instead of blank.

Non-Focused Panes

Non-focused panes show compact tool call summaries and stripped reasoning text. Tool groups display as single collapsed lines matching the focused pane style. All panes auto-scroll to the bottom when new messages arrive.

v0.2.92 fixed several rendering issues:

  • Tool calls no longer show a perpetual “running” spinner after completion
  • Scroll position correctly tracks for inactive panes
  • Stale cache is cleared when sessions are updated or deleted

State Management

Deleting a session now properly cleans up cached pane state. Previously, deleting a session left stale entries in the pane cache, which could cause phantom panes on restart.

Live Background Updates (v0.3.36)

Inactive panes now update live in the background. Previously, non-focused panes only refreshed when you switched focus to them. Now a background-session live-state cache routes IntermediateText and QueuedUserMessage events into per-session deltas, so you can watch tool calls and responses appearing in other panes in real time without switching focus.

Provider/model contamination prevention: When closing or switching panes, the old session’s provider is captured before the switch. This blocks cross-provider model leaks at 27 call sites throughout the codebase. The footer always shows the correct provider+model for the focused pane.

Ctrl+N binds the focused pane and live-refreshes the footer title, so new sessions show up immediately in the status bar.

Reasoning Expand (v0.3.74)

Reasoning blocks now cycle through three states instead of toggling open/closed. Click a reasoning block (or press Ctrl+O) to cycle: collapsed → capped → full. The capped middle state shows a bounded preview so a long chain-of-thought never floods the viewport (#727, #726).

Two refinements make expanding predictable:

  • Anchor block on expand: the clicked block’s header is pinned to its screen row, so expanding or collapsing grows the block in place instead of jumping the viewport (#728).
  • Click vs drag on mouse-up: click-vs-drag is decided on mouse-up, so a click-drag selects text (for copying) instead of toggling the expand state (#726).

Version in the UI (v0.3.74)

The running OpenCrabs version now shows on the TUI header and in channel /help and /usage output (#696), so you always know which build a session is on.

Double-Escape Returns the Query (v0.3.74)

Pressing Esc twice before any reply now returns the query to the input box and removes it from the conversation (#698), so a half-typed prompt you cancel doesn’t linger as an empty turn.

Per-Turn Headers (v0.3.75)

Every turn now gets a one-line header summarising the work that turn did, and the turn’s working-out folds up into it. OpenCrabs infers turn boundaries so each turn can be grouped, then folds the narration, intermediate text, and tool calls underneath the header.

The view stays clean by default:

  • Fold every turn by default: turns fold as soon as they settle (and even while still running), so settled work collapses out of the way instead of scrolling off screen.
  • Fold live: intermediate text folds while the turn is still running, and stale narration the model kept thinking past is collapsed.
  • Readable folded turn: a folded turn stays readable and holds its place when toggled, and the tool-call summary stays at full visibility.
  • Short live-thinking excerpt: live thinking shows a short excerpt with room to finish a thought, not a scrolling wall.
  • Labelled token counter: the live token counter is labelled as a turn total and shows the ctx budget; ctx dropped from the spinner since it already shows under the input.

Expanding a turn is smooth: the header is kept after expand, and expanding no longer scrolls the view up by its own size.

TUI Improvements (v0.3.76–v0.3.78)

Steps AND Tools Display (v0.3.76)

The TUI now shows both steps and tool calls in the turn view. Previously it was either/or: if tool calls existed, steps were hidden. Now both render, with steps providing the narrative and tool calls showing the mechanical detail.

Background Task Position (v0.3.77)

Background task feedback moved from the left border to the right border of the input box. This prevents it from colliding with the context budget footer on the left.

Duplicate-Submit Guard (v0.3.76)

Pressing Enter twice quickly no longer submits the same message twice. The TUI drops a re-submitted message if the running turn is already answering it.

Command Labelling (v0.3.76)

Commands in the TUI are now labelled by what they run (e.g. “cargo test”) instead of the working directory. Makes the activity feed readable at a glance.

Deliverable Report Visibility (v0.3.78)

When a turn produces a deliverable report (audit, comparison, analysis), the report stays visible even when the turn folds. Previously, folding a turn would hide the report along with the working-out.

Background Running Indicator (v0.3.78)

The “running” indicator is now cleared before delivering the final response. Previously, the indicator could linger for a moment after the response appeared, creating a visual glitch.

Limits

There is no hard limit on pane count – you can run as many as your terminal fits. Each pane is a full session with its own token tracking and working directory.

Dynamic Tools

Define custom tools at runtime without recompiling. Tools are defined in ~/.opencrabs/tools.toml and can be created, removed, and reloaded on the fly.

Defining Tools

Create ~/.opencrabs/tools.toml:

[[tools]]
name = "deploy"
description = "Deploy the application to production"
executor = "shell"
command = "cd {{project_dir}} && ./deploy.sh {{environment}}"

[[tools]]
name = "check-status"
description = "Check service health"
executor = "http"
method = "GET"
url = "https://api.example.com/health"

Executors

ExecutorDeskripsi
shellRuns a shell command
httpMakes an HTTP request

Template Parameters

Use {{param}} syntax for dynamic values. The agent fills these in when calling the tool:

[[tools]]
name = "search-logs"
description = "Search application logs for a pattern"
executor = "shell"
command = "grep -r '{{pattern}}' /var/log/myapp/ --include='*.log' -l"

Runtime Management

The tool_manage meta-tool lets the agent manage dynamic tools during a session:

  • Create: Add a new tool definition
  • Remove: Delete an existing dynamic tool
  • Reload: Re-read tools.toml without restarting

Dynamic tools appear alongside built-in tools in the agent’s tool list. Enable or disable individual tools without restarting the process.

Per-Parameter Value Coercion (v0.3.24)

Dynamic tools defined in tools.toml can now handle empty-string or null parameters gracefully. When a parameter arrives as "" or null, the engine substitutes a configured value before rendering the command template.

FieldPurpose
coerce_empty_toSubstitute when parameter is ""
coerce_null_toSubstitute when parameter is null
[[tools]]
name = "deploy"
description = "Deploy to environment with optional verbose flag"
executor = "shell"
command = "cd {{project_dir}} && ./deploy.sh --env {{environment}} {{verbose}}"

[[tools.deploy.params]]
name = "verbose"
type = "string"
required = false
coerce_empty_to = "--quiet"

A shell tool with an optional --verbose flag no longer breaks when the parameter is omitted. The engine substitutes --quiet (or any configured default) instead of passing an empty string.

(#95)

External contributions now enable tools.toml to be loaded in run mode and agent mode (not just the TUI). Previously, dynamic tools only worked in the interactive TUI session. Now they’re available across all modes, allowing headless automation and scripted workflows to use custom tools.

(#79: thanks @leshchenko)

Shell Parameter Escaping (v0.3.35)

Dynamic shell tool parameters now properly escape single quotes, preventing command injection edge cases when user-supplied values contain quotes. Contributed by @leshchenko1979.

Browser Automation

OpenCrabs includes native headless Chrome control via the Chrome DevTools Protocol (CDP). No Selenium, no Playwright, direct browser control built into the binary.

Requirements

  • A Chromium-based browser installed (Chrome, Brave, Edge, or Chromium)
  • Feature flag: browser (enabled by default)

OpenCrabs auto-detects your default Chromium browser, no manual path configuration needed.

Browser Tools

ToolDeskripsi
navigateOpen a URL in the browser
clickClick an element by CSS selector
typeType text into an input field
screenshotCapture a screenshot of the page
eval_jsExecute JavaScript in the page context
extract_contentExtract text content from elements
wait_for_elementWait for an element to appear
findFind elements matching a pattern (CSS, XPath, text, or aria-label). Returns stable selectors for subsequent click/type operations
browser_actBatched multi-action in one call: an ordered array of click / fill / press / select / wait / upload (max 10 actions, 120s budget). Selectors resolve up front, so a stale reference rejects the whole batch instead of half-running it; aborts on first failure and reports the completed prefix; one screenshot at the end. upload attaches local files to a file input without putting their bytes in the context (#2112)

How It Works

The browser is lazy-initialized as a singleton, it only launches when the agent first needs it. It runs in stealth mode with a persistent profile directory, so cookies and sessions survive across tool calls.

On macOS, display auto-detection enables headed mode when a display is available, falling back to headless in CI or daemon environments.

Contoh

Ask the agent:

“Go to our staging site, log in with the test account, navigate to the dashboard, and take a screenshot”

The agent will chain navigate → type (username) → type (password) → click (login button) → navigate (dashboard) → screenshot: all autonomously.

Inventory Mode (v0.3.80)

browser_find now works without a pattern (#1022). Call it bare and it enumerates every visible interactive element on the page (links, buttons, inputs, selects, textareas, role=button elements, onclick handlers, tabindex elements, labels, and disclosure widgets) each stamped with a selector and index you can click directly.

This is the structural fix for the screenshot-as-discovery loop: instead of screenshotting repeatedly and guessing selectors, the agent gets the page’s clickable inventory in one call. When browser_screenshot reports an identical page, the hint now nudges toward inventory mode.

Shadow-DOM Resolution (v0.5.2)

Selector resolution is shadow-DOM aware: an element inside one or more shadow roots is reached by piercing the roots along the path, so web components and widget libraries that host their UI in shadow trees are automatable like light-DOM content. Applies across the browser tools (click, type, find, screenshot targeting).

Configuration

No configuration needed. The browser feature is enabled by default. To disable it at build time:

cargo build --release --no-default-features --features "telegram,discord,slack"

Cron Jobs

Schedule tasks to run on a recurring schedule. Cron jobs can run in isolated sessions or wake the main session.

CLI Management

# Add a job
opencrabs cron add \
  --name "Morning Report" \
  --cron "0 9 * * *" \
  --tz "Europe/London" \
  --prompt "Check emails, calendar, and give me a morning briefing" \
  --deliver-to telegram:123456

# List all jobs
opencrabs cron list

# Enable/disable (accepts name or ID)
opencrabs cron enable "Morning Report"
opencrabs cron disable "Morning Report"

# Remove (accepts name or ID)
opencrabs cron remove "Morning Report"

Agent Management

The agent can also manage cron jobs via the cron_manage tool:

"Create a cron job that checks my emails every morning at 9am"

Since v0.3.80, cron_manage supports an update action that patches an existing job in place (#966), change the schedule, prompt, model, or delivery target without deleting and recreating the job.

Options

FlagDeskripsi
--nameJob name (unique identifier)
--cronCron expression (e.g. 0 9 * * *)
--tzTimezone (e.g. America/New_York)
--promptThe prompt to send to the agent
--providerAI provider to use (optional)
--modelModel to use (optional)
--thinkingThinking mode: on, off, budget_XXk
--deliver-toChannel delivery: telegram:CHAT_ID, discord:CHANNEL_ID, HTTP webhook URL, or comma-separated multiple targets
--auto-approveAuto-approve tool use for this job

Multi-Target Delivery

deliver_to accepts comma-separated targets to send results to multiple destinations simultaneously:

opencrabs cron add \
  --name "Morning Report" \
  --cron "0 9 * * *" \
  --prompt "Give me a morning briefing" \
  --deliver-to "telegram:-12345,http://webhook.example.com/notify"

Supported targets in any combination:

  • telegram:CHAT_ID or telegram:-GROUP_ID
  • discord:CHANNEL_ID
  • slack:CHANNEL_ID
  • whatsapp:PHONE_OR_JID
  • http://... or https://... (webhook URL)

Target URLs (v0.5.1)

Since v0.5.1 every delivery field also speaks a unified oc:// target scheme, resolved by a central resolver against the live channel bindings (issue #148):

FormMeaning
oc://telegram/<chat>[/<thread>]Telegram chat (optionally a forum topic)
oc://discord/<channel>Discord channel
oc://slack/<channel>Slack channel
oc://whatsapp/<phone|jid>WhatsApp contact
oc://session/<uuid-or-prefix>Park the result in that OpenCrabs session’s notify queue, the agent gets cron output the same way a sub-agent or a detached command reaches its parent (v0.5.1)
hereThis conversation’s channel (refused on headless surfaces, which have none)

Target URLs are resolved once, at create/update time, and the concrete channel target is baked into the job; if an unbaked URL ever reaches fire time the run fails loudly instead of resolving against a possibly-changed world.

Forum-topic delivery (v0.5.1, opt-in)

telegram:CHAT_ID:THREAD_ID (or the oc://telegram/<chat>/<thread> form) delivers into a specific topic of a forum group. It is deliberately opt-in: the chat must actually be a forum and the topic must exist: an invalid thread target is rejected loudly at fire time, never silently re-routed to the general topic. When a job is created from inside a thread (here/oc:// form), the origin thread id is persisted so the result lands back in the same topic (#104).

Results are stored in the DB via the cron_results table regardless of delivery target, so you can query past execution results with opencrabs cron results <name>.

Trigger-gated jobs (v0.5.3)

A job can carry a pre-flight trigger: at each fire time the scheduler runs a shell command first, and only executes the prompt when a condition on that command’s result is met. This turns scheduled jobs from “fires into the void every hour” into “acts when something actually happened” (a file appeared, a health check failed, a log line matched) (#233).

Set it through the cron_manage tool at create or update time:

FieldWhat it does
trigger_cmdShell command run under /bin/sh -c before the prompt. 30s timeout; a hung trigger is killed, not orphaned. Either prompt or trigger_cmd must be set.
trigger_onFire condition (see below). Default non_empty.
set_goalOn fire, set the active goal in the destination session instead of a plain prompt turn. Requires deliver_to to target a session; refused for passive channel deliveries.
goal_templateTemplate for the goal/notification text. Interpolates {output}, {stdout}, {stderr}, {exit_code}.

trigger_on conditions

ValueFires when
non_empty (default)stdout or stderr contains any non-whitespace output
exit_zerothe trigger exits 0
exit_non_zerothe trigger exits non-zero (health checks, grep misses)
regex:PATTERN or re:PATTERNcombined output matches the regular expression
alwaysthe trigger finished, regardless of output or exit code

Semantics

  • Condition not met → the run is short-circuited at 0 tokens and recorded as a skipped/success run. The prompt never executes, nothing is delivered.
  • Trigger command errors or times out → the run is recorded as an error; the prompt still does not execute.
  • Empty prompt with a trigger → direct 0-token delivery: the formatted trigger output (goal_template, or the raw output) is delivered to deliver_to without an agent turn at all.
  • set_goal → the fired trigger dispatches into the target session as an active goal, so the session’s agent works the condition it detected rather than just echoing output.

Put the watching in trigger_cmd (deterministic, cheap, script-runner pattern), the condition in trigger_on, and the acting in the prompt or goal.

Scheduler Lock (v0.3.65)

The cron scheduler uses a file lock to prevent duplicate job execution. Only one scheduler instance can run per profile at a time. If you accidentally start OpenCrabs twice, the second instance won’t fire duplicate cron jobs.

Heartbeat vs Cron

Use heartbeat (HEARTBEAT.md) when:

  • Checks are periodic but timing is flexible (~30 min)
  • You want to reduce API calls by batching
  • Tasks share the main session context

Use cron when:

  • Exact timing matters (“9:00 AM every Monday”)
  • Task needs isolation from main session
  • You want a different model or thinking level
  • Output should deliver to a specific channel

Plans

Plans provide structured multi-step task execution with a live progress widget in the TUI.

Creating a Plan

Ask the agent to plan a complex task:

"Plan the migration from PostgreSQL to SQLite"

The agent uses the plan tool internally to create a plan with:

  • Title and description
  • Technical stack
  • Risk assessment
  • Test strategy
  • Ordered tasks with dependencies and complexity ratings

Plan Lifecycle

  1. Draft: Agent creates the plan and adds tasks
  2. Finalize: Agent calls finalize which triggers the tool approval dialog
  3. Approved: You approve in the tool dialog, plan status becomes Approved, and the agent begins executing tasks immediately
  4. In Progress: Tasks execute in dependency order
  5. Completed: All tasks done

In ask mode (default), the finalize step triggers the tool approval dialog, you review the full plan before execution begins. In auto-approve mode, finalize is auto-approved and the agent plans and executes without pausing.

Plan Requirement Gate

A turn that crosses a threshold of tool calls has to stop and write a plan before it continues (#2128). This is what keeps a sprawling refactor from being discovered after twenty files have already changed: you see the shape of the work, and you get a chance to say no before it lands.

The gate is configurable under [agent]. A turn that already has a live plan is never gated again, and a session configured to run autonomously is not stalled by an approval prompt it was told to skip.

Task States

Each task in a plan can be:

  • Pending (·): Waiting for dependencies
  • InProgress (▶): Currently executing
  • Completed (✓): Done
  • Skipped (✓): Manually skipped
  • Failed (✗): Execution failed
  • Blocked (·): Dependencies not met

TUI Plan Widget

When a plan is active, a live checklist panel appears above the input box showing:

  • Plan title and progress counter (e.g. 3/7)
  • Progress bar: Visual ██████░░░░ bar with percentage
  • Task list: Up to 6 tasks visible with status icons and task numbers
  • Overflow indicator: ... (N more) when tasks exceed the visible limit

The widget updates in real-time as the agent completes each task.

Managing Plans

Plans are managed through natural language:

"Approve the plan"
"Reject the plan"
"What's the plan status?"
"Skip task 3"

The agent handles plan creation, approval, execution, and status reporting through the plan tool.

Mid-Plan Insertion (v0.3.36)

Tasks can be inserted at any position in an existing plan using insert_after:

plan(operation: "add_task", insert_after: 3, title: "Re-run tests after fix", ...)

This inserts the new task as task #4, and all existing tasks from #4 onward are renumbered automatically. Dependencies between tasks are preserved through the renumber.

This is useful when a later task introduces a bug caught by an earlier test. Instead of re-opening the completed test task, insert a fresh re-test task right after the fix.

Finished Checklist Persistence (v0.3.76)

When a plan completes all tasks, the finished checklist now stays on screen instead of disappearing. You can review the final state (all tasks marked ✓) without having to ask “what was the plan?” again. The completed card renders its final state with the full task list and progress bar at 100%.

Epistemic Orient Gate (v0.3.78)

Plan execution is now gated behind an epistemic Orient phase before any task starts. The agent must:

  1. Observe: gather ground truth (read files, check state)
  2. Orient: map observations against current beliefs and goals
  3. Decide: form intention, confirm alignment and safety
  4. Act: execute through mechanical gates

This prevents the agent from charging into a plan task with stale assumptions. The Orient gate checks whether the agent’s beliefs about the codebase/state still hold before writing anything.

Criteria-Aware Verification (v0.3.78)

The Ralph loop (OpenCrabs’ iterative task execution engine) now verifies task completion against declared acceptance criteria instead of just “did the command exit 0?”. Each task’s criteria are checked mechanically:

  • If a task declares acceptance_criteria, the verification gate checks each one
  • Criteria that require specific output (e.g. “clippy passes with zero warnings”) are verified against actual tool output
  • Tasks without explicit criteria fall back to the standard exit-code check

This means a task marked “complete” actually met its stated goals, not just “something ran.”

Ralph Verification Gate + Iteration Cap (v0.3.78)

The Ralph loop gained a mechanical verification gate with an iteration cap:

  • After each task execution, the gate checks whether the outcome matches the intention
  • If verification fails, the task retries (up to the iteration cap)
  • The cap prevents infinite loops on tasks that can never satisfy their criteria
  • State lives in files, not context, each iteration reads fresh state from disk

Combined with the epistemic Orient gate, this makes plan execution significantly more reliable: the agent orients before acting, and verifies after acting, with bounded retries.

Plan State Across Sessions (v0.3.79)

Plans are no longer tied to the lifetime of a single session. Plan state threads across session boundaries, so a plan survives session swaps, restarts, and context compaction. Spawned child sessions resolve their parent’s plan file automatically, which is what makes the next feature possible.

Isolated Plan-Task Execution

Each plan task can now run in a freshly spawned isolated worker session: the worker gets only the task brief and the plan file, not the parent conversation’s context. The brief is self-contained and the worker reports its verdict back via disk, so nothing leaks between the parent session and the task, and a long-running task can’t burn the parent’s context window.

  • agent.plan_isolated_execution is the master switch, and it defaults ON since v0.3.79: Ralph loops run fresh-context by construction, so isolation is the only sane default for autonomous execution.
  • An explicit isolated: true/false on a start call still wins over the default.
  • Ralph verification now runs in the session’s own working directory, not the directory OpenCrabs was launched from, a plan in one repo is verified against that repo’s build results, not another repo’s (#921).
  • The plan gate’s RequireApproval decision respects auto_approve, so autonomous sessions are not stalled by an approval prompt they were configured to skip (#934).

Type-Aware Verification (v0.3.83)

Acceptance criteria are checked against the plan’s toolchain (#1133): a Rust task must satisfy cargo-shaped criteria, a Flutter task flutter-shaped ones, and a task whose criteria cannot be verified is marked instead of waved through. Plans also verify with the project’s own toolchain rather than always cargo, and locate the project from the folder the session is working in. Checklist plans no longer write a design scaffold .md (#1145).

Importing Pre-Defined Plans (v0.3.35)

Plans can be loaded from JSON files for repeatable workflows:

plan(operation: "import", file_path: "~/plans/rust-refactor.json")

Bundled reference plans ship with OpenCrabs at ~/.opencrabs/profiles/<profile>/plans/ covering common patterns like rust-fast, rust-medium, rust-full, python-fast, python-medium, python-full, and sample-minimal-plan.

The JSON format requires a minimum of 6 fields: title, description, plus 3 fields per task (title, description, task_type). Full schema supports dependencies, complexity ratings, acceptance criteria, and technical stack.

Security: Import validates symlinks against the target path only (rejecting ancestor false positives on macOS) and checks for orphan dependencies that reference non-existent tasks.

Multi-Agent Orchestration

OpenCrabs supports spawning specialized sub-agents that run autonomously in isolated sessions. Each child agent gets its own context, tool registry, and cancel token. Introduced in v0.2.97 with a typed agent system and team orchestration.

Agent Types

When spawning an agent, an agent_type parameter selects a specialized role with a curated tool set:

TypeRoleTools
generalFull-capability agent (default)All parent tools minus recursive/dangerous
exploreFast codebase navigation (read-only)read_file, glob, grep, ls
planArchitecture planning (read + analysis)read_file, glob, grep, ls, bash
codeImplementation (full write access)All parent tools minus recursive/dangerous
researchWeb search + documentation lookupread_file, glob, grep, ls, web_search, http_request

Each type receives a role-specific system prompt that shapes its behavior. Explore agents are fast and lightweight – they only read files. Code agents can modify anything. Research agents can search the web but not touch your filesystem.

Safety: ALWAYS_EXCLUDED Tools

No agent type has access to these tools, preventing dangerous or recursive operations:

  • spawn_agent – no spawning agents from agents
  • resume_agent, wait_agent, send_input, close_agent – no managing siblings
  • rebuild – no building from source
  • evolve – no self-updating

Five Orchestration Tools

ToolDeskripsi
spawn_agentCreate a typed child agent to handle a sub-task autonomously in the background
wait_agentWait for a spawned agent to complete and return its output (configurable timeout)
send_inputSend follow-up instructions to a running agent (multi-turn conversation)
close_agentTerminate a running agent and clean up its resources
resume_agentResume a completed or failed agent with a new prompt (preserves prior context)

Spawn an Agent

spawn_agent(
  label: "refactor-auth",      # Human-readable label
  agent_type: "code",          # general | explore | plan | code | research
  prompt: "Refactor auth...",  # Task instruction
  include_brain: false         # opt in to workspace brain pre-injection (v0.5.1)
)

The agent runs in its own session with auto-approved tools. No blocking – it executes in the background while the parent continues.

Lean children and include_brain (v0.5.1)

Sub-agents start lean: no pre-injected workspace brain (SOUL.md, USER.md, AGENTS.md, project directives). Pass include_brain: true to spawn_agent (or per-agent in team_create) to opt into pre-injection; the choice is frozen for the agent’s lifetime and resume_agent re-attaches accordingly. When a child runs lean, its prompt discloses the gap: the child is told the workspace brain is not loaded and pointed at load_brain_file for on-demand access, so neither side operates on the assumption rules are ambiently known (#145).

Headless children (v0.5.1)

Child agents are headless by law: interactive-only tools (session_notify, suggest_options) are stripped from the child’s tool surface and the registry refuses to register them there: a call returns a hard tool error, not a silent no-op. The headless tool roster excludes interactive-only tools wholesale, and headless prompts (spawn and resume, cron/daemon, CLI one-shot) carry a preamble saying so, so the child never reaches for a button it does not have (#129). The same backstop covers cron’s here target: it is refused on headless surfaces.

Wait for Completion

wait_agent(
  agent_id: "abc-123",
  timeout_secs: 300            # Max wait time (default: 300s)
)

Multi-Turn with send_input

After spawning, you can send additional instructions without restarting:

send_input(
  agent_id: "abc-123",
  text: "Also add unit tests for the new module"
)

The child agent processes the input on its next iteration. This enables iterative workflows – review the agent’s output, then ask it to refine or continue.

Resume a Completed Agent

resume_agent(
  agent_id: "abc-123",
  prompt: "Now port the same changes to the other two files"
)

The agent continues in its original session, preserving all prior context. No need to re-explain the codebase.

Team Orchestration

The TeamManager coordinates named groups of agents for parallel execution. Three team-specific tools:

Create a Team

team_create(
  team_name: "backend-refactor",
  agents: [
    { label: "auth", agent_type: "code", prompt: "Refactor auth module" },
    { label: "tests", agent_type: "code", prompt: "Write tests for auth" },
    { label: "docs", agent_type: "general", prompt: "Update documentation" }
  ]
)

All agents spawn simultaneously and run in parallel. Returns the team name and all agent IDs.

Broadcast to a Team

team_broadcast(
  team_name: "backend-refactor",
  message: "Use the new AuthError enum instead of plain strings"
)

Sends a message to all running agents in the team. Non-running agents are skipped. Useful for sharing context or direction changes.

Delete a Team

team_delete(team_name: "backend-refactor")

Cancels all running agents and cleans up resources. Completed agents are left in the subagent manager for reference.

Subagent Provider/Model Config

By default, every spawned agent inherits the parent session’s provider and model. You can override this globally in config.toml so child agents route to a different (usually cheaper or faster) backend:

[agent]
subagent_provider = "openrouter"   # Provider for child agents
subagent_model    = "qwen/qwen3-235b" # Model override

# Omit both keys and child agents inherit the parent session's provider
# and run on that provider's default model.

The override applies to spawn_agent, resume_agent, and every member of a team_create team. Changes take effect on next session start; running sessions keep their existing provider.

Per-Call Overrides (v0.3.35)

spawn_agent, resume_agent, and team_create now accept optional provider and model fields that override config defaults for a single call. This enables mixed-model teams:

team_create(
  team_name: "mixed-stack",
  agents: [
    { label: "planner", provider: "zhipu", model: "glm-5", prompt: "Architect the feature" },
    { label: "coder", provider: "deepseek", model: "deepseek-coder", prompt: "Implement it" },
    { label: "reviewer", provider: "kimi", model: "kimi-k2.5", prompt: "Review the code" }
  ]
)

Precedence order (highest first): per-call provider/model > [agent] subagent_provider/subagent_model in config > parent session’s provider.

Why It Matters

The common pattern is premium parent, cheap children. Your main conversation stays on a reasoning-capable model (Opus, GPT-5, Gemini 2.5 Pro) while subtasks (file exploration, test writing, web research, bulk refactors) run on a faster, cheaper model. With a 4-agent team running 10 minutes each, the cost delta between Opus and Qwen on the children is roughly 50x.

Concrete Examples

OpenRouter parent, Qwen children, best bang-for-buck on mixed workloads:

[providers.openrouter]
enabled = true
api_key = "sk-or-..."
model = "anthropic/claude-opus-4"

[agent]
subagent_provider = "openrouter"
subagent_model    = "qwen/qwen3-235b-a22b-instruct"

Kimi on custom OpenCode provider: fast code generation for code and explore agents:

[providers.opencode-kimi]
enabled = true
base_url = "https://api.kimi.com/v1"
api_key  = "..."
model    = "kimi-k2.5"

[agent]
subagent_provider = "opencode-kimi"
subagent_model    = "kimi-k2.6"

Local Ollama children: zero cost, fully offline, good for explore agents that just read files:

[providers.ollama]
enabled = true
model = "qwen3:14b"

[agent]
subagent_provider = "ollama"
subagent_model    = "qwen3:14b"

Gemini parent, Gemini Flash children, single billing account, reasoning on main, flash on team:

[providers.gemini]
enabled = true
model = "gemini-2.5-pro"

[agent]
subagent_provider = "gemini"
subagent_model    = "gemini-2.5-flash"

Gotchas

  • The subagent provider must be enabled and have a valid API key (or be a CLI/none-auth provider). Missing keys cause the spawn to fail with a provider resolution error.
  • subagent_model must be a model the provider actually serves. qwen/qwen3-235b works on OpenRouter, not on Anthropic. Check /models on the target provider to confirm.
  • team_create members all share the same subagent config. If you need heterogeneous routing (e.g. a research agent on web-search model, a code agent on code-specialized model), spawn them individually with spawn_agent under different config profiles.
  • The CLI model override is surfaced in the spawn_agent, resume_agent, and team_create tool descriptions themselves, so the LLM knows to mention these keys to you instead of inventing per-call overrides.

If subagent_provider or subagent_model is not set, the spawned agent loads from the parent session’s provider and runs on that provider’s default model.

Workflow Patterns

Parallel Research + Implementation

team_create("feature-research", [
  { label: "research", agent_type: "research", prompt: "Find best practices for rate limiting in Rust" },
  { label: "explore", agent_type: "explore", prompt: "Find all middleware files in the codebase" }
])

Wait for results, then spawn a code agent with the combined context.

Iterative Code Review

# 1. Spawn a code agent
spawn_agent(label: "impl", agent_type: "code", prompt: "Implement rate limiting middleware")

# 2. Wait for completion
wait_agent(agent_id: "impl-id")

# 3. Resume with refinements
resume_agent(agent_id: "impl-id", prompt: "Add tests for the edge cases we discussed")

Large-Scale Refactoring

team_create("refactor-team", [
  { label: "module-a", agent_type: "code", prompt: "Refactor module A to use the new trait" },
  { label: "module-b", agent_type: "code", prompt: "Refactor module B to use the new trait" },
  { label: "module-c", agent_type: "code", prompt: "Refactor module C to use the new trait" },
  { label: "tests", agent_type: "code", prompt: "Update all tests for the new trait signature" }
])

Testing

84 tests cover the entire multi-agent system:

  • Manager state machine (spawn, wait, close lifecycle)
  • SendInput wiring and input loop
  • CloseAgent cleanup
  • WaitAgent timeout behavior
  • AgentType tool filtering
  • TeamManager, TeamDelete, TeamBroadcast
  • Registry exclusion (ALWAYS_EXCLUDED enforcement)

Sub-Agent Result Reporting (v0.3.81)

A finished agent reports its result back to the spawning session (#1036): the parent sees the outcome in-turn instead of polling or reading status files. Orphaned sub-agent status files from crashed runs are reconciled at startup (#1038), so the registry never shows ghosts of agents that no longer exist.

Sub-Agent Worktree Isolation (v0.3.83)

Each spawned agent gets its own worktree and branch instead of sharing the parent’s working tree: a fan-out of several agents on one repository can no longer clobber each other’s checkouts or builds. The parent’s tree stays untouched while children work in isolation, and results come back through the normal git flow.

Agent-to-Agent (A2A) Protocol

OpenCrabs includes a built-in A2A gateway implementing the A2A Protocol RC v1.0 for peer-to-peer agent communication.

Enabling

Legacy [gateway] name (v0.3.82): this section was once spelled [gateway]. Both spellings now fold into one setting, and the legacy name is rewritten to [a2a] on disk so it survives a reload round trip instead of reappearing (#1116). Nothing to do by hand; an existing [gateway] block migrates itself on the next load.

# config.toml
[a2a]
enabled = true
bind = "127.0.0.1"   # Loopback only (default): use "0.0.0.0" to expose externally
port = 18790
# api_key = "your-secret"  # Optional Bearer token auth for incoming requests
# allowed_origins = ["http://localhost:3000"]  # CORS

Configuration Options

OptionDefaultDeskripsi
enabledfalseEnable the A2A gateway
bind127.0.0.1Bind address: use 0.0.0.0 to accept external connections
port18790Gateway port
api_key(none)Bearer token for authenticating incoming requests. If set, all JSON-RPC requests must include Authorization: Bearer <key>
allowed_origins[]CORS allowed origins: no cross-origin requests unless explicitly set

Endpoints

EndpointMethodDeskripsi
/.well-known/agent.jsonGETAgent Card: discover capabilities (auto-populated from tool registry)
/a2a/v1POSTJSON-RPC 2.0: message/send, message/stream, tasks/get, tasks/cancel
/a2a/healthGETHealth check

Methods

  • message/send: Send a message to the agent, creates a task. Returns the task with result.
  • message/stream: Same as message/send but returns an SSE stream with real-time status updates and artifact chunks as the agent works.
  • tasks/get: Poll a task by ID to check status and retrieve results.
  • tasks/cancel: Cancel a running task.

Active tasks are persisted to the database and restored on restart.

The a2a_send Tool

The agent has a built-in a2a_send tool that lets it proactively communicate with remote A2A agents. This enables true bidirectional agent-to-agent communication.

Actions:

AksiDeskripsi
discoverFetch a remote agent’s Agent Card to see its capabilities and skills
sendSend a task to a remote agent and wait for the result
getPoll a task by ID on a remote agent
cancelCancel a running task on a remote agent

The agent can use this tool autonomously: for example, delegating subtasks to a specialized remote agent.

Connecting Two Agents

Example: VPS + Local Machine

On VPS (~/.opencrabs/config.toml):

[a2a]
enabled = true
bind = "0.0.0.0"
port = 18790
api_key = "your-shared-secret"

On local machine (~/.opencrabs/config.toml):

[a2a]
enabled = true
bind = "127.0.0.1"
port = 18790

Connectivity Options

  1. SSH tunnel (recommended): No ports to open, encrypted:

    # From local machine, tunnel VPS A2A to localhost:18791
    ssh -L 18791:127.0.0.1:18790 user@your-vps
    

    Local agent talks to http://127.0.0.1:18791/a2a/v1

  2. Direct: Open port 18790 on VPS firewall. Simple but exposes the port. Always use api_key with this approach.

  3. Reverse proxy: Nginx/Caddy on VPS with TLS + Bearer auth via api_key.

Examples

# Discover the agent
curl http://127.0.0.1:18790/.well-known/agent.json | jq .

# Send a message (with Bearer auth)
curl -X POST http://127.0.0.1:18790/a2a/v1 \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-shared-secret" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "message/send",
    "params": {
      "message": {
        "role": "user",
        "parts": [{"text": "What tools do you have?"}]
      }
    }
  }'

# Stream a task (SSE)
curl -N -X POST http://127.0.0.1:18790/a2a/v1 \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-shared-secret" \
  -d '{
    "jsonrpc": "2.0",
    "id": 2,
    "method": "message/stream",
    "params": {
      "message": {
        "role": "user",
        "parts": [{"text": "Analyze the system status"}]
      }
    }
  }'

# Poll a task
curl -X POST http://127.0.0.1:18790/a2a/v1 \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-shared-secret" \
  -d '{"jsonrpc":"2.0","id":3,"method":"tasks/get","params":{"id":"TASK_ID"}}'

# Cancel a task
curl -X POST http://127.0.0.1:18790/a2a/v1 \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-shared-secret" \
  -d '{"jsonrpc":"2.0","id":4,"method":"tasks/cancel","params":{"id":"TASK_ID"}}'

# Health check
curl http://127.0.0.1:18790/a2a/health | jq .

Session Resume and Profile Addressing (v0.3.83)

A2A sessions resume by context id (#1159): a reconnecting client restores the session bound to its context instead of starting cold. The gateway is profile-addressed (#1161), so a multi-profile host serves the right profile’s sessions, and the profile_list tool is registered for remote peers (#1160, #1161).

Bee Colony Debate

Multi-agent structured debate via confidence-weighted voting (based on ReConcile, ACL 2024). Multiple “bee” agents argue across configurable rounds, enriched with knowledge context, then converge on a consensus answer.

Security

  • Loopback only by default: binds to 127.0.0.1
  • Bearer auth: set api_key to require Authorization: Bearer <key> on all JSON-RPC requests
  • CORS locked down: no cross-origin requests unless allowed_origins is set
  • For public exposure, use a reverse proxy with TLS + the api_key Bearer auth

ACP: Editor Integration

ACP server mode lets agent-aware editors (Zed, MonoCode, any ACP client) drive OpenCrabs as a backend agent over the Agent Client Protocol. The editor spawns opencrabs acp as a child process and speaks JSON-RPC 2.0 over stdio, so your editor session and your channels share the same brain, tools, memory, and provider configuration (#1540).

Running

opencrabs acp            # default model from config
opencrabs acp --model anthropic/claude-sonnet-4-20250514

Point your editor’s ACP/agent client at the opencrabs acp command and it appears as an available agent. One child process serves one conversation thread.

Protocol

  • Framing: JSON-RPC 2.0, newline-delimited (NDJSON) over stdio
  • session/prompt: runs the full tool loop for a turn; the reply arrives when the turn settles
  • session/update: progress notifications stream back while the turn works (tool calls, output chunks)
  • session/request_permission: tool approvals round-trip to the editor, so the client’s permission UI gates dangerous calls the same way channel approvals do

stdout is the protocol channel: nothing but JSON-RPC frames is ever written to it. Logs go to the daily log file (or stderr in debug builds), never stdout.

Sessions (v0.5.4)

Sessions are first-class on the ACP surface:

  • session/new offers a live model catalog, so the client picks from models that actually exist in your config
  • session/load replays the transcript and restores the per-session model
  • set_model persists on the session row and survives process restarts
  • session/set_mode applies the approval policy server-side (native, no prompt workaround)
  • session/compact pushes compaction to the client
  • The context meter is restored on load and rides usage updates, so the editor always shows a true context reading

Session Lifecycle v1 (v0.5.5)

The ACP server now implements the five v1 session lifecycle methods (#1815): session/list pages your sessions with a cursor, session/resume reattaches without replaying history while still restoring the model pin, session/set_config_option is the write half of the model picker (F4 only displayed it before), session/close cancels in-flight work and frees live state, and session/delete removes the row after freeing it. initialize now advertises the list, resume, close, and delete session capabilities. The plan is also published as a native v1 plan update, and image prompt blocks sent by the client are decoded into <<IMG>> attachments the agent can actually see.

Why it matters

Before ACP mode, driving OpenCrabs from an editor meant copy-pasting between the editor and a channel. With ACP mode the editor is a first-class surface: same providers, same skill gating, same plan approvals, same memory. Configuration is shared with the rest of OpenCrabs; there is no separate ACP config section.

Self-Healing

OpenCrabs monitors its own health and automatically recovers from failures without user intervention. All recovery events surface as visible notifications across TUI and all channels.

How It Differs from Crash Recovery

OpenCrabs has had crash recovery since early versions – if the process dies mid-request, pending requests are tracked in SQLite and automatically resumed on restart (see Pending Request Recovery below).

Self-healing (v0.2.92) goes further: the agent detects and fixes problems while it’s still running – corrupted config, degraded providers, context overflow, stuck streams, DB corruption – without restarting. Crash recovery is the safety net; self-healing prevents the fall.

Config Recovery

Every successful write to config.toml creates a snapshot at ~/.opencrabs/config.last_good.toml. When the config becomes corrupted or unparseable, OpenCrabs restores from the last-known-good snapshot automatically.

⚠️ Config was corrupted: restored from last-known-good snapshot (2 minutes ago)

A CONFIG_RECOVERED atomic flag tracks whether recovery happened during the current session, so downstream code can react accordingly.

Unknown Key Detection

Unknown top-level keys in config.toml trigger a startup warning listing the unrecognized entries. This catches typos like [teelgram] or [a2a_gatway] before they cause silent misconfiguration.

Known valid sections: [crabrace], [database], [logging], [debug], [providers], [channels], [agent], [daemon], [a2a], [image], [cron].

The [a2a] section also accepts gateway as an alias via serde, deduplicating a common typo.

Custom Provider Name Normalization

Provider names with mixed case or whitespace (e.g. "My Provider" vs "my provider") are normalized on load and save, preventing duplicate entries that would confuse the provider registry.

Provider Health Tracking

Per-provider success/failure history is persisted to ~/.opencrabs/provider_health.json. Each provider tracks:

  • last_success and last_failure (epoch seconds)
  • last_error (truncated to 200 chars)
  • consecutive_failures count (resets on success)
{
  "anthropic": {
    "last_success": 1743250500,
    "consecutive_failures": 0
  },
  "openai": {
    "last_success": 1743249800,
    "last_failure": 1743249700,
    "last_error": "rate_limit_exceeded",
    "consecutive_failures": 0
  }
}

The /doctor command surfaces health stats for every configured provider. Combined with the fallback provider chain, OpenCrabs detects degraded providers and routes to healthy ones automatically.

Source: src/config/health.rs (120 lines), integrated into src/brain/agent/service/helpers.rs.

DB Integrity Check

SQLite PRAGMA integrity_check runs at startup. If corruption is detected, a notification appears in TUI and all connected channels instead of silently failing.

Error Surfacing

v0.2.92 eliminated 14+ instances of silently swallowed errors across:

  • Config writes
  • Channel sends (Telegram, Discord, Slack, WhatsApp)
  • Tool connections (Slack, WhatsApp, Trello connect tools)
  • Pane state persistence

Before: let _ = ... and .ok() everywhere, errors vanish. After: Every error surfaces via logging or user notification.

Onboarding config writes use try_write! macros that batch errors during wizard steps and report them all at the end, so users see exactly what failed.

AgentService Config Propagation

AgentService::new() now requires an explicit &Config parameter instead of calling Config::load() internally. This eliminates hidden I/O, makes dependencies explicit, and enables test injection via AgentService::new_for_test().

Render, dialogs, messaging, and cron modules no longer call Config::load() internally – errors propagate up the call stack instead of being swallowed.

Context Budget Management

The agent enforces a 65% context budget threshold. When token usage reaches 65% of the effective context window (context limit minus tool schema overhead), automatic LLM compaction fires:

  1. Detect context usage ≥ 65% of effective max tokens
  2. Compact via LLM summarization (preserves meaning, not just truncation)
  3. Retry up to 3 times if compaction fails
  4. Second pass with tighter budget if still over threshold

The 65% threshold exists because providers like MiniMax degrade on function-calling quality well before hitting theoretical context limits – tool calls break around ~133k tokens of a 200k limit.

Async Proactive Compaction (v0.3.16)

At 65% context, compaction now runs asynchronously in the background instead of blocking the chat. The agent continues processing while the LLM summarizes older messages. Once compaction completes, the context is swapped seamlessly. No more frozen UI during compaction.

Source: src/brain/agent/service/tool_loop.rs (lines 14-112)

Emergency Compaction (ARG_MAX Recovery)

When CLI provider conversation context exceeds the OS ARG_MAX limit (~1MB on macOS), the agent recovers with a 3-stage fallback:

  1. Catch the “Argument list too long” or “prompt too large” error
  2. Emergency compact the conversation with an LLM summarization pass
  3. Insert a system marker so the agent knows context was compacted
  4. Retry the request

If compaction still fails, hard truncation kicks in – keeps last 24 messages (12 conversation pairs) with a marker telling the agent to use search_session for older context. Both markers persist to DB for recovery across sessions.

Both actions emit SelfHealingAlert progress events so users see exactly what happened.

Source: src/brain/agent/service/tool_loop.rs (lines 550-687), tested with ArgTooLongMockProvider and ContextLengthMockProvider in src/tests/cli_arg_too_long_test.rs (352 lines).

Stream Resilience

Stuck Loop Detection

Some streaming providers (notably MiniMax) occasionally loop the same content indefinitely without sending a stop signal. The agent detects this:

  • Maintains a 2048-byte rolling window of recent streamed text
  • When a 200+ byte substring from the second half appears in the first half, it’s a repeat
  • Stream is terminated immediately and retry logic fires

Source: src/brain/agent/service/helpers.rs – detect_text_repetition(), tested in src/tests/stream_loop_test.rs (15 tests)

Idle Timeout

If a stream goes silent for 60 seconds (API providers) or 10 minutes (CLI providers) with no events, it’s treated as a dropped connection.

CLI providers (Claude CLI, OpenCode CLI) run internal tools (cargo builds, tests, gh commands) that can take several minutes without producing stream events. The 60-second timeout caused premature termination on these, so CLI providers now get a 10-minute window before timeout fires.

If a stream goes silent:

#![allow(unused)]
fn main() {
const STREAM_IDLE_TIMEOUT: Duration = Duration::from_secs(60);
}

The tokio::select! loop races the stream against the timeout and the user’s cancellation token. Timeout triggers retry, not a hard error.

Pending Request Recovery

Crash recovery tracks every in-flight agent request in a pending_requests SQLite table. When a request starts, a row is inserted; when it completes (success or failure), the row is deleted.

On startup, any surviving rows mean the process crashed mid-request:

  1. Query pending_requests for interrupted rows

  2. Clear all rows (prevents double-recovery if this run also crashes)

  3. Dedup by session_id (resume each session only once)

  4. Spawn background tasks with a continuation prompt:

    “A restart just occurred while you were processing a request. Read the conversation context and continue where you left off naturally.”

  5. Emit TuiEvent::PendingResumed so the TUI shows a recovery notification

Source: src/db/repository/pending_request.rs, src/cli/ui.rs (lines 705-790)

Cross-Channel Crash Recovery (v0.2.93)

Before v0.2.93, pending request recovery always responded via the TUI, even if the original request came from Telegram, Discord, Slack, or WhatsApp. The resumed response would appear in the wrong place.

Now each channel passes its name and chat_id into run_tool_loop, which stores them in pending_requests. On restart, recovery routes responses back to the originating channel:

Original channelRecovery response goes to
TelegramSame Telegram chat
DiscordSame Discord channel
SlackSame Slack channel
WhatsAppSame WhatsApp chat
TrelloSame Trello board
TUITUI (as before)

The pending_requests table gained channel and channel_chat_id columns via a DB migration. get_interrupted_for_channel lets each channel handler query only its own pending rows. Selective delete_ids prevents one channel from clearing another channel’s recovery entries.

State Cleanup

Session deletion triggers cascade deletes across all related data:

  • Messages (full conversation history)
  • Usage ledger entries (token/cost records)
  • Channel messages (Telegram, Discord, Slack, WhatsApp delivery records)
  • Plans (autonomous plans created in the session)
  • Cron jobs (scheduled tasks bound to the session)
  • Cached pane state (stale split pane entries)

Custom provider names are normalized on load and save ("My Provider" → "my-provider"), preventing duplicate entries that would confuse the provider registry.

Model Selector Safety

Pressing Enter in the model selector no longer clears existing API keys. The selector preserves current configuration while switching models.

Model switching errors now surface the actual error with a ⚠️ prefix on all channels, instead of always showing “Model switched” even on failure.

UTF-8 Safety

split_message() across all 5 channel handlers (Telegram, Discord, Slack, WhatsApp, Trello) now uses is_char_boundary() to find safe split points, preventing panics on multi-byte characters (emojis, CJK, accented characters).

Cancel Persistence (v0.2.97)

When a user double-Escapes to abort a streaming response, the partial content is now persisted to the database before handle.abort() fires. This means cancelled content survives a session reload – you can scroll back and see exactly what the agent was saying before you stopped it.

Claude CLI Subprocess Cleanup

Previously, aborting a Claude CLI request would orphan the underlying claude subprocess. Now the stream reader loop monitors tx.closed() via tokio::select! and kills the child process when the receiver drops, preventing leaked subprocesses accumulating in the background.

Telegram Stale Delivery Suppression

When a request is cancelled mid-flight, the agent sometimes continued processing and delivered a stale response to Telegram. A cancel_token.is_cancelled() guard now fires before final delivery, preventing old agent results from posting after cancellation.

Config Overwrite Protection

The onboarding wizard previously overwrote existing channel settings on every save, causing data loss when re-running /onboard. apply_config() now scopes writes to only the current onboarding step. from_config() sets EXISTING_KEY_SENTINEL for all existing channel data, ensuring untouched fields are never overwritten.

Tool Description Wrapping

Tool call descriptions were previously truncated at 80 characters in the TUI. render_tool_group now wraps description headers and value lines to terminal width, and the 80-char pre-truncation of bash commands in format_tool_description has been removed. Long commands and file paths display fully.

Auto-Fallback on Rate Limits (v0.2.98)

When the primary provider hits a rate or account limit mid-stream, OpenCrabs catches the RateLimitExceeded error, saves the current conversation state, and resumes the same conversation on a fallback provider configured in [providers.fallback]:

[providers.fallback]
enabled = true
providers = ["openrouter", "anthropic"]  # tried in order

The fallback chain reads from config at startup. has_fallback_provider() and try_get_fallback_provider() are available at runtime for dynamic queries.

Two-Tier Context Budget Enforcement

Compaction budget scales proportionally to max_tokens instead of a hardcoded 170k, supporting custom providers with different context windows:

  • 65% soft trigger: LLM compaction with retries (preserves meaning)
  • 90% hard floor: Forced truncation to 75% (cannot fail)
  • Pre-truncate target: 85% of max_tokens
  • Compaction is silent to user: summary written to memory log only, no chat spam

Mid-Stream Decode Retry (v0.3.0)

Transient stream decoding errors now trigger a 3x backoff retry before falling back to the provider fallback chain. This reduces false provider switches caused by momentary network glitches.

SIGINT Handler + Panic Hook (v0.3.0)

Proper terminal restoration on crash or Ctrl+C via custom SIGINT handler and panic hook. No more garbled terminal after interrupt, the handler restores raw mode, cursor visibility, and alternate screen before exiting.

Proactive Rate Limiting (v0.2.99)

For OpenRouter :free models, OpenCrabs paces requests automatically using a shared global static limiter to avoid account-level bans. The rate limiter’s first-call sentinel (last_granted=0) no longer causes an unnecessary sleep.

RSI Alert Suppression (v0.3.13)

RSI alerts are now suppressed when the feedback dimension already has a fix commit in the recent git history. This prevents the agent from alerting on issues that have already been addressed. Stale alerts also age out via a sliding window on tool failure stats.

Expanded Phantom Detection (v0.3.17)

The phantom detector now catches additional patterns:

  • “Now <file-op gerund>” phantoms: catches phrases like “Now creating…”, “Now writing…”, “Now editing…” where the model narrates a file operation without actually executing it
  • Build/deploy intent + past-tense completion claims, catches when the model claims to have built or deployed something without running the actual commands
  • Module extraction: gaslighting and phantom detectors extracted into their own dedicated module for cleaner maintenance

RSI Escalation for Repeat Violations (v0.3.17)

RSI now bumps a violation counter on existing rules instead of deduping repeat violations away. Rules that keep getting broken get louder, not silenced. This prevents the agent from ignoring persistent failure patterns.

Partial JSON Repair (v0.3.17)

A new json_repair module automatically fixes common JSON corruption:

  • Closes unterminated strings
  • Balances brackets
  • Strips trailing commas
  • Drops trailing keys-without-value

Wired into 5 drop sites across OpenAI-compatible providers and the ContentBlockStop finalizer. Unrecoverable input returns a {"_partial": ..., "_repair_failed": true} envelope instead of crashing the turn.

Upstream Template Sync (v0.3.15)

Brain file templates are now automatically synced from the upstream OpenCrabs repo. The sync uses version gating (only applies templates from newer versions) and append-only diffs (never overwrites existing content). This ensures you always get the latest brain file improvements without losing your customizations.

Browser Resilience (v0.3.18)

Multiple browser reliability improvements:

  • Network idle wait after navigate: now waits for networkIdle instead of just CDP load event, catching async fetches
  • CDP manager lock released before await: lock was held during screenshot await, blocking concurrent browser operations
  • CDP pre-flight health check: added health check before screenshot capture to prevent stale connection failures
  • Browser navigate errors logged: navigate errors no longer silently swallowed with let _ =, now logged at WARN

Cloud Handshake Timeout (v0.3.18)

Bumped cloud provider handshake timeout from 30s to 60s. Routing proxies like dialagram legitimately take 20-45s; 30s was killing mid-request on slow-but-healthy providers.

Gemini API Key Security Fix (v0.3.18)

Fixed CodeQL #64 (HIGH): Gemini API key was leaked in URL query string (?key=...) in analyze_video’s resumable upload init and file-state polling. Moved to x-goog-api-key header, matching analyze_image and generate_image.

Stream & TUI Fixes (v0.3.18)

  • File paths starting with / no longer treated as slash command typos, /Users/.../file.pdf yo crabs check this triggered “Unknown command”. Added looks_like_file_path() helper gating both TUI and channel handlers.
  • Truncation continuations no longer trigger provider fallback, mid-sentence continuations should stay on the same provider. Fallback now skipped for truncation paths.
  • Fallback error reason surfaced in TUI: when fallback fired, the underlying error was swallowed. Now shows as a system message.
  • Pipe-delimited rows hard-broken: when not recognized as a table, pipe rows ran together. Added hard-break between rows.

v0.3.25 Fixes

  • Compaction dropped 55% kept-tail: summary IS the conversation now, no more redundant tail retention
  • Self-heal 5-nudge budget: reasoning-only turns get 5 nudges before sticky fallback, preventing empty replies from silently dropping
  • Completion-escape clause: phantom enforcement messages now have escape clause to prevent infinite loops
  • Scroll fixes: removed load_more_history() from scroll handler (overshoot fix), preserved scroll during streaming, skip first-render compensation
  • Brain file cleanup_intent: write_opencrabs_file now accepts cleanup_intent flag for user-driven maintenance. RSI agent blocked from shrinking brain files (issue #103)
  • Channel improvements: WhatsApp photo batching for multi-image uploads, Telegram media_group_id-based batching, Gemini schema strips default/example from tool schemas (#101, @leshchenko1979)
  • Custom provider model selection persistence, properly saves and displays custom provider model selection
  • Compaction prompt dominance fix: plan tool descriptions and scroll sensitivity improvements

v0.3.23 Fixes (Hotfix Release)

  • Phantom detection restored: v0.3.21’s turn-level tools_executed_this_turn gate was too aggressive: once any tool ran in a turn, phantom detection went silent for the rest of the turn, letting fabricated wrap-up text reach the TUI. Dropped the gate from all three phantom branches.
  • Self-heal never aborts: stuck-intent-loop now fast-escalates to sticky fallback instead of aborting; cap-exhaustion resets retry counter and injects hard nudge; phantom_retries_used now tracks consecutive phantoms since last real tool. Recovery always retries or falls back.
  • Brain file guardrail: generic write_file / edit_file now refuse to modify protected brain files (SOUL.md, USER.md, TOOLS.md, etc.), preventing accidental clobber. Routed through write_opencrabs_file instead.
  • A2A approval policy wired: A2A message/send tasks now resolve approval policy via check_approval_policy(). With auto-always set, tools auto-approve; otherwise returns warning. Fixes “Tool requires approval but no approval mechanism configured” errors.
  • Channel /new session switching fixed: /new now uses per-message resolver’s title format everywhere (Telegram, Discord, Slack), so session switching works across all channels.
  • Version-aware model sort: when OpenAI-compatible servers return zero or identical created timestamps, extracts numeric segments from model names and sorts newest version first. Fixes meaningless model lists on vLLM/llama.cpp.

v0.3.22 Fixes

  • Compaction typing without banner: reverted the visible “🗜️ Compacting context” banner text. Now uses typing-only refresh (Telegram send_chat_action(Typing), Discord broadcast_typing loop) keeping the “is typing” indicator alive during the 10-60s compaction window silently.
  • Channel /new archive consistency: unified archive behavior across all channels: non-owner sessions get archived (so next title lookup resolves cleanly), owner sessions stay non-archived and remain visible in /sessions.

v0.3.21 Fixes

  • Multi-language phantom detection via compile-time TOML, replaced regex patterns per language with TOML-defined char sets compiled into build-time match arms. New languages added by editing TOML, no Rust changes. Cross-language regression test added.
  • Self-heal pipeline hardened: phantom detection gated on turn-level tool execution, phantom iterations no longer persisted to DB, phantom text stripped from context before next turn, sticky fallback applied on exhaust.
  • OpenAI-compatible image generation: new image generation backend calling any /v1/images/generations endpoint. Providers override generation model independently via generation_model config field.
  • Working directory visible across tools: working directory now visible to all tools within the same iteration.
  • Compaction banner stripped from context: compaction banner text no longer fed to LLM context, preventing models from echoing it back.
  • Pipe-separate model callback: custom-provider model callbacks now pipe-separated so colons in provider names (e.g. “Qwen: DashScope”) survive parse.
  • Custom-provider model selection persists: /models dialog now correctly saves and syncs live model list for custom providers.
  • one_shot_pct display corrected: fixed incorrect percentage display in usage dashboard.
  • Session updated_at touched on switch: session last-modified timestamp updated when switching sessions via Telegram, preventing stale session resolution.

v0.3.19 Fixes

  • Cron provider/model cross-contamination fixed, cron’s execute_job called global swap_provider() instead of session-scoped swap_provider_for_session(), so concurrent cron jobs on the shared Cron session overwrote each other’s provider. Now each job swaps on its own session ID.
  • Cron mismatched pair validation: reversed cron config (e.g. default_model = "zhipu" where zhipu is a provider name) produced impossible pairs like dialagram/zhipu that timed out with no diagnostics. Added validation: if effective_model is not in the provider’s supported_models(), the job is skipped with a loud error.
  • Windows CI test failures fixed: tool_loop_helpers_test.rs used hardcoded Unix /tmp/ paths and /etc/hosts assertions. Added platform-specific test variants with #[cfg(unix)] / #[cfg(windows)].
  • CI Node 24 forced upgrade removed: removed FORCE_JAVASCRIPT_ACTIONS_TO_NODE24: true env var that broke actions/cache@v4 with punycode deprecation on Node 21+.
  • Codex OAuth device flow field names fixed: OpenAI’s device auth API uses non-standard field names (device_auth_id instead of device_code, string interval instead of number, expires_at instead of expires_in). Fixed with serde aliases and custom deserializer.
  • Codex OAuth verification URL corrected: was hardcoded to non-existent auth.openai.com/verify, changed to auth.openai.com/codex/device matching Codex CLI.
  • Codex OAuth model list curated: /models dialog showed non-OpenAI models (Phi-4, Llama, Mistral) because the codex provider ID wasn’t mapped to the curated GPT-5 model list.

v0.3.26 Fixes

  • Hashline collision detection (#105): pure content hash prevents line-shift avalanche when lines are inserted/deleted above a hash anchor. On collision, escalates to edit_file fallback instead of corrupting the edit
  • RSI brain file hygiene (#111): rejects raw failure-event logs from being written to brain files. RSI now sanitizes feedback dimensions before persisting
  • Tool error output (#113): tool errors now include stdout/stderr in error content, ANSI escape sequences stripped, 8000 char cap to prevent context blowout

v0.3.27 Fixes

  • Ctx budget baseline on channel /new: shows calibrated baseline immediately after /new instead of waiting for first message
  • Auto-title session fix (#114): preserves [chat:ID] suffix to prevent title duplication on subsequent auto-title fires
  • Sessions display (#115): arrow prefix + “current” label instead of checkmark for clearer session switching UI

v0.3.28 Fixes

  • Voicebox + STT/TTS fallback chains: 2s liveness probe detects dead audio devices, librosa error translator surfaces actionable messages instead of Python tracebacks, per-provider fallback chains configured in config.toml
  • Browser multi-step navigation hardening (6 commits): text=/xpath= selector prefixes, recovery hints on click failures, semantic loop detection (4+ screenshots in 8 iterations triggers abort), no-op screenshot rejection, same-URL short-circuit
  • Tool-call shape recovery: dict-by-call-id extraction for Qwen-3.7-max-preview regression where tool calls arrive as flat dicts instead of nested arrays
  • Edit tool improvements (#117): fuzzy line-sequence fallback when exact match fails, hashline docs clarification
  • Brain backup rotation: max 5 backups per file, max 7 days old, preventing unbounded .bak accumulation
  • Auto-title fixes (#118, #120): fires on FIRST turn (not second), retries on LLM failure instead of giving up
  • Ctx counter real-time only (#119): ripped out calibration system entirely, uses provider-reported input_tokens verbatim. No more “0/max” for uncalibrated providers
  • Profile brain-template seeding: seeds 8 templates on profile create, recovery path for empty profiles

v0.3.29 Fixes

  • Auto-title thinking-block fallback (#121): reasoning models returning only a Thinking block (no Text block) now get a title extracted from the thinking content instead of dropping silently. extract_title_candidate falls back to pluck_title_from_thinking (last quoted phrase, then last short sentence)
  • Telegram label-drift fix (PR #123 @leshchenko1979): auto-titled sessions no longer overwritten on every subsequent message. should_refresh_label policy only refreshes default→default-different or group label changes, never auto-titled or custom titles. Chat→session binding on /sessions switch

v0.3.30 Fixes

  • 5-language deferment stall detection: self-heal catches “I need to X” / “I have to X” / “I must X” / “I should X” patterns in English, Spanish, Portuguese, French, and Russian
  • Follow-up message = ESC x2 cancel: all four channel handlers treat a follow-up message during an active agent run as double-Escape cancel, then starts fresh
  • Dynamic Telegram status messages: replaced hardcoded quips with context-aware messages showing actual tool being called, tokens streamed, and elapsed time
  • rename_session rejects empty titles (#128): whitespace-only titles rejected so sessions can’t become unidentifiable

v0.3.31 Fixes

  • Fun POST-COMPACTION PROTOCOL prompts: after compaction, the agent receives a playful system prompt instead of a sterile summary marker. These rotating prompts (e.g. “You just woke up from a nap. The summary above is everything you remember.”) make the post-compaction experience less robotic. Users can opt out with [agent] silent_compaction = true in config.toml.
  • Telegram forum topic routing: in supergroups with topics enabled, thread_id is tracked through the full pipeline. The agent can use list_topics to map topic names to IDs, then route responses to specific topics via thread_id on send/reply/send_photo.
  • PDF page_range param: parse_document now accepts page_range strings like "1-30", "5,7,10-15", or "3" for targeted extraction. Text-first routing skips Gemini for text-native PDFs. Inline preview cap raised to ~60 pages.

v0.3.32 Fixes

  • Evolve hardening (#136): the /evolve command now handles busy Linux binaries with a remove+rename dance (can’t overwrite a running binary on Linux), delayed systemd-run restart to let the current process finish cleanly, structured tracing for better error diagnosis, and a pre-flight count_matching_systemd_units check to avoid restarting when multiple OpenCrabs instances are running.

v0.3.33 Fixes

  • User-correction metadata (#138, PR #140): display_text_override now captures the actual user message text instead of the 236-character Telegram channel prefix that was previously stored. This makes user correction entries in the feedback ledger readable and actionable.

v0.3.34 Fixes

  • follow_up_question race fix (closes #142): all four channels (Telegram, Discord, Slack, WhatsApp) now flush intermediate text handles before presenting the follow-up keyboard. Prevents the race where the bot’s in-progress message got orphaned or duplicated when the user tapped a button mid-stream. Each channel got its own atomic commit with per-channel regression tests pinning the flush-before-keyboard sequence.
  • follow_up_question display polish (closes #148): Telegram keyboard is now single-column with a 40-character label cap (rejects options longer than 40 chars in the tool validator with a clear error). Rolling “Running follow_up_question (16s)” status is suppressed while the keyboard is pending, and the LLM is now instructed to call the tool silently without echoing the question text in surrounding prose. Discord left alone due to its 5-ActionRow-per-message hard limit.
  • Phantom detector hardening: two narration shapes had been leaking past the phantom detector: pronounless deferment (Need to read the X) and bare gerunds (Reading the current state of the affected files). Added 28 pronounless EN variants, 15 telegraphic FR besoin de variants, and gerund+determiner bigrams. New regression file pins both leaked sentences verbatim. Follow-up fixed French accent detection: detect_language missed é/è/ë/ü, so French narration fell through to English and the new besoin de phrases never matched. Added the 4 markers.
  • Fallback provider cascade (closes #152): /models swaps and session restores were storing a raw provider instead of wrapping it in FallbackProvider, so the fallback cascade could not fire on 5xx/429 errors after a model switch. Every active provider now gets wrapped unless it is already a chain or no fallbacks are configured. 174-line integration test simulating 5xx cascades across swapped providers.
  • Error persistence: agent failures now persist as permanent chat bubbles with actionable wording on TUI and channels instead of vanishing after the turn. UTF-8 panic after redact-prefix scan fixed by snapping to char boundary.
  • FINISHING A TURN rewrite: split the brain preamble directive into side-effect vs analysis response shapes, added a nudge on empty data-fetch closes so the agent never ends a turn silently after running research tools, and requires an explicit acknowledgement sentence instead of letting finish_reason: stop with no content reach the user.
  • Claude CLI model auto-learn: footer showed Opus 4.7 after Anthropic shipped 4.8 because default_for_alias hardcoded opus -> opus-4-7. Now the provider learns the CLI-resolved version from message_start events, persists to ~/.opencrabs/claude_cli_models.json (rewriting only when the value changes), and the TUI refreshes the session model live so the footer self-corrects to the actual version without code changes. default_for_alias prefers the learned cache and falls back to a build-time seed only on a fresh install.
  • tok/s in channel footers: channel context budget footers showed only ctx: XK/YK Z% while the TUI also showed | N tok/s. Added tokens_per_second: Option<f64> to AgentResponse, extended format_ctx_footer to accept a third tps parameter, computed tok/s from total_output_tokens / turn_duration across the whole turn in tool_loop.rs, and wired it through all four channels.

Unreleased (post-v0.3.33)

  • Phantom post-success exemption: the phantom detector used to fire on short completion acknowledgments like “Pushed.”, “Done.”, or “Committed as abc123” because those look like past-tense completion claims without a tool call. But when the agent just finished a real tool run, that one-line ack is the correct behavior. A turn-scoped tool_calls_completed_this_turn counter and a phantom_eligible gate now suppress phantom detection once real tool calls have landed in the current turn. The complementary FINISHING A TURN brain preamble directive tells the agent to reply with one short ack, skip verification re-runs, and stop restating conclusions in different wording.
  • follow_up_question intermediate flush (issue #142): when the agent called follow_up_question after typing an explanatory preamble, Telegram/Discord/Slack/WhatsApp sometimes delivered the button block before the preamble text because intermediate text sat in a 500ms-polled queue while follow_up_question sent directly. All four channel handlers now flush pending intermediate JoinHandles before dispatching the question, guaranteeing the explanatory text renders above the buttons.

v0.3.36 Fixes

  • Near-miss tool name self-heal (closes #176): when the model guesses a wrong tool name (e.g. tg_send_message instead of telegram_send), the tool registry now tries three fallback strategies before returning NotFound: (1) normalized match (strip underscores, lowercase), (2) abbreviation expansion (tg → telegram, wa → whatsapp), (3) typo fallback (Levenshtein distance). Conservative: only heals on a unique high-confidence match, so ambiguous guesses still return an error.
  • Retry overhaul: patient backoff defaults changed from 100ms hammering to 1s/2s/4s/8s. Rate limits now retry in-place (3 retries) before falling through the fallback chain. Hard-down endpoints (DNS failure, connection refused) fail fast instead of wasting retries. Transient 4xx HTML infra pages (Cloudflare, nginx error pages) are retried. Each retry surfaces as a RetryAttempt N/M - reason event to the user.
  • Provider/model contamination prevention: atomic provider+model swap across 27 call sites prevents the footer from showing a stale provider after switching panes or sessions.
  • Secret redaction expanded: query-param keys (?api_key=...) and URL passwords (https://user:pass@host) now redacted alongside Bearer tokens and API key patterns. RSI TUI notifications also redacted.

v0.3.35 Phantom Hardening

  • Re-engaged on forward intent: phantom detector now re-engages self-heal when forward intent is detected after a successful tool call, preventing the agent from narrating what it’s about to do instead of just doing it.
  • Five-language cleanup: destructive verb intent phrases cleaned up across EN, ES, FR, PT, and RU. Prevents the agent from narrating destructive actions (“I’ll now delete the file”) when it should just execute the tool.

Thinking-Loop Timeout (v0.3.78)

A new thinking-loop timeout catches the agent stuck in an infinite reasoning loop (producing thought tokens but never emitting a tool call or response). When the timeout fires:

  1. The stuck stream is terminated
  2. Phantom enforcement retry fires (nudges the agent to produce real output)
  3. If still stuck after retries, sticky fallback provider kicks in

This eliminates the “agent appears to be thinking forever but never does anything” failure mode.

Expanded Phantom Detection (v0.3.76–v0.3.78)

The phantom detector gained significant coverage across three releases:

v0.3.76 Additions

  • File-delivery claims: catches “I’ve sent the file” / “document delivered” when no file-sending tool ran
  • Null-effect tool calls: tools that execute but produce no observable effect no longer grant phantom immunity
  • Command verification: quoted commands are checked against what the turn actually ran
  • Evidence checking: quoted evidence (output, logs) is verified against actual tool returns
  • Investigation claims: “I investigated” / “I looked into” counts as a completion claim requiring evidence
  • Both-ends check: examines both the lead-in AND the wrap-up of a turn, not just the opening
  • Self-update claims: “I updated my memory” / “I noted this” counts as a completion claim

v0.3.78 Additions

  • Shipped-and-tracked claims: “shipped and tracked in issue #N” without actual gh/git calls
  • Colloquial delivery claims: “that’s out the door”, “it’s in your inbox”, casual completion language
  • Media-delivery claims: “I’ve attached the image” / “video sent” without media tools running

v0.3.75 Hardening

  • Bounded self-heal loop: the phantom self-heal loop is now bounded instead of re-nudging forever, and discarded phantom self-heal narration is stripped from the live buffer so it never reaches the chat.
  • Patient mid-stream retry: mid-stream retry is more patient: 5 attempts with exponential backoff instead of giving up early.
  • Flaky 404 retried: a flaky-provider 404 is retried instead of being treated as a permanent failure.
  • Multilingual hallucination detection: image-generation hallucination detection (a media claim with no marker and zero tool calls) now works across languages, and self-heal no longer delivers an image hallucination when it gives up.
  • Poisoned-session recovery: auto-recovers from a repetitive-tool-call poisoned session instead of looping on the same broken call.
  • Phantom verdict on recovery paths: the turn-end phantom verdict now catches narration delivered via recovery paths, and issue-tracker actions count as completion claims.

Loop Guards (v0.3.80)

Three guards stop the agent repeating itself (#957, #961):

  • Cross-turn announcement loop guard: detects when the agent keeps announcing the same action across turns without actually doing it
  • Near-match loop guard: catches reworded repetitions of the same tool call, generalized to ALL tools, not just bash
  • Mid-turn announcement checks: reworded announcement loops are caught inside the turn, not only between turns

When the loop detector kills a stalled turn, that kill reaches the fallback chain (#1023): it is raised as a provider-attributable error so the turn can retry with another provider instead of surfacing as a hard failure, and it is no longer misreported as a provider fault. Spent thinking-loop budget is routed into the fallback chain the same way (#1021).

Notifications

All self-healing events are delivered to:

  • TUI (status bar notification)
  • Telegram, Discord, Slack, WhatsApp (if connected)

Nothing happens silently. If the crab fixes itself, it tells you what it fixed.

Early Loop Detection (v0.3.81)

The loop detector now notices a repeating tool round before the provider rejects the turn. Instead of burning the context window on identical rounds and surfacing a provider error, the detector catches the pattern early and routes it through the existing loop-kill path (which reaches the fallback chain since v0.3.80).

Fact-Based Detectors Fire the Heal (v0.3.81)

Phantom-detection refinements (#1073, #1074):

  • Fact-based detectors self-heal: detectors that verify against ground truth (files, command output) now fire the self-heal path directly, not just gate the claim
  • Wider command allowlist: inspection-style claims are checkable, so “I ran X” verification covers more real phrasings
  • Dotted commands survive the sentence split: some.command --flag is no longer torn in half when the claim is parsed

Self-Improvement (RSI)

OpenCrabs improves itself over time through Recursive Self-Improvement (RSI). The agent analyzes its own performance, identifies patterns, and autonomously updates its own brain files.

How It Works

1. Feedback Collection

Every tool execution, user correction, and interaction is automatically logged to the feedback ledger. Categories include:

  • tool_success / tool_failure: whether tool calls worked
  • user_correction: when you corrected the agent’s behavior
  • provider_error: LLM stream drops, rate limits, timeouts
  • pattern_observed: recurring behaviors the agent notices

2. Pattern Analysis

The agent calls feedback_analyze to review its performance:

  • Per-tool success rates
  • Recent failure patterns
  • User correction frequency
  • Provider reliability trends

3. Autonomous Improvement

When patterns are identified, the agent calls self_improve to:

  • read: Load a brain file (SOUL.md, TOOLS.md, etc.) before modifying
  • apply: Append new instructions based on observed patterns
  • update: Surgically replace existing sections that need refinement
  • list: Show all previously applied improvements

4. Change Tracking

Every improvement is logged to ~/.opencrabs/rsi/improvements.md with:

  • Timestamp
  • Target file modified
  • Description of the change
  • Rationale (which feedback event triggered it)

Old improvements are archived to ~/.opencrabs/rsi/history/ to keep the active file lean.

Contoh

User: "stop including testing steps in your output"
  → feedback_record(event_type="user_correction", dimension="output_hygiene")
  
Agent notices pattern of 5+ corrections on output hygiene:
  → feedback_analyze(query="failures")
  → self_improve(action="apply", target_file="SOUL.md", 
    content="Never include testing steps or verification commands in user-facing output.")
  → Logged to rsi/improvements.md

Key Rules

  • No human approval needed for self-improvements, the agent identifies patterns and applies fixes directly
  • Surgical updates only: replaces specific sections, doesn’t rewrite entire files
  • Always reads before modifying: never blindly overwrites brain files
  • Archives old improvements: keeps the improvement log manageable

RSI Engine Architecture

The RSI engine is a background task that runs continuously alongside OpenCrabs. Here’s how it works at each layer:

Feedback Ledger

Every tool execution, user correction, provider error, and self-heal event is automatically logged to a SQLite-backed feedback ledger. Event types:

Event TypeWhat It Tracks
tool_success / tool_failureWhether tool calls worked, with args and error details
user_correctionWhen you corrected the agent’s behavior
provider_errorLLM stream drops, rate limits, timeouts
pattern_observedRecurring behaviors the agent notices
context_compactionContext budget exceeded
improvement_appliedRSI applied a fix to a brain file
self_heal_triggerRuntime self-heal caught and fixed an issue

Cycle Flow

  1. Startup: writes a digest of feedback stats to ~/.opencrabs/rsi/digest.md
  2. Every hour: checks for new feedback entries since the last cycle
  3. Opportunity detection: identifies tools with >20% failure rate (7-day window), user correction patterns, and provider errors
  4. Git-aware suppression: checks if a fix commit already landed for the tool in question. If yes, suppresses the alert instead of re-reporting stale issues
  5. Autonomous agent spawn: if opportunities are found, spawns a lightweight agent with RSI-only tools (feedback_analyze, self_improve, rsi_propose) that analyzes the data and applies targeted fixes

Brain File Taxonomy

RSI routes improvements to the correct brain file based on what went wrong:

Brain FileWhat It ControlsWhen RSI Writes Here
SOUL.mdBehavior, tone, reasoning patternsPhantom tool calls, verbose responses, wrong tone
TOOLS.mdTool usage, argument formats, pitfallsRepeated tool failures with similar args
USER.mdUser preferences and correctionsRepeated user corrections
MEMORY.mdPersistent knowledge and contextAgent lacks context it should retain
AGENTS.mdWorkspace rules, safety policiesAgent-level behavior issues
CODE.mdCoding standardsCode quality feedback
SECURITY.mdSecurity policiesSecurity-related feedback

Repeat-Violation Escalation

RSI tracks violation counters inline in brain file rules. When a rule keeps getting broken, RSI bumps the counter and appends evidence (dates, session IDs). Rules that keep getting broken get louder, not silenced. This is the escalation pattern that makes RSI effective at fixing persistent bad habits.

RSI Proposals

The RSI loop can propose new dynamic tools and slash commands based on gaps it observes in the agent’s capabilities. Proposals land in TOML inboxes at:

~/.opencrabs/rsi/
├── proposed_tools.toml      # pending tool proposals
├── proposed_commands.toml   # pending command proposals
├── applied/                  # accepted proposals (daily archive)
│   ├── 2026-05-01-tools.toml
│   └── 2026-05-01-commands.toml
└── rejected/                 # rejected proposals (daily archive)
    ├── 2026-05-01-tools.toml
    └── 2026-05-01-commands.toml

How Proposals Work

  1. RSI analyzes feedback and notices the agent repeatedly working around a missing capability
  2. RSI drafts a tool or command definition with a rationale citing the evidence
  3. Proposal lands in the inbox: reviewed via Mission Control or the rsi_proposals tool
  4. User applies or rejects: applied entries go to tools.toml/commands.toml, rejected entries are archived with an optional reason

When RSI Proposes a Tool

  • A specific bash command appears repeatedly across sessions (e.g. gh issue list, docker ps)
  • The agent calls http_request to the same endpoint multiple times with similar payloads
  • Only safe-by-default tools are proposed (read-only verbs, GET requests). Shell-based tools always set requires_approval=true

When RSI Proposes a Command

  • The user types /something repeatedly that doesn’t exist
  • A common multi-step prompt gets reused verbatim, a slash command saves typing

Safety Guardrails

  • RSI never installs directly: proposals require user approval via Mission Control or the rsi_proposals tool
  • No destructive proposals: RSI will never propose rm, dd, mv, or any shell tool with destructive side effects
  • Deduplication: if a proposal was already filed and not applied, RSI won’t repropose it
  • One proposal per cycle: quality over quantity
  • Evidence required: every proposal cites the feedback events that drove it

RSI Hardening (v0.3.13)

  • Append-only brain files: brain files (SOUL.md, TOOLS.md, etc.) are now append-only with backup-before-write. The agent can only add new content, never delete or overwrite existing lines. This prevents accidental data loss from bad self-improvements.
  • Upstream template sync: brain file templates are automatically synced from the upstream repo with version gating and append-only diffs. You get the latest improvements without losing your customizations.
  • RSI alert suppression: alerts are suppressed when the dimension already has a fix commit, preventing noise on already-addressed issues.

RSI Autonomous Proposals (v0.3.16)

The RSI loop can now propose new tools and slash commands autonomously. Proposals land in the Mission Control inbox for review, the agent identifies gaps from feedback data and drafts solutions, but installation requires human approval via the inbox UI or /mission-control.

RSI Escalation for Repeat Violations (v0.3.17)

RSI now bumps a violation counter on existing rules instead of deduping repeat violations away. When a rule keeps getting broken across multiple sessions, the escalation counter increases and the agent prioritizes fixing that pattern. This prevents persistent bad habits from being silently ignored.

v0.3.10 Additions

  • Cycle summaries no longer truncated: full text displays in TUI instead of cutting off mid-sentence
  • Phantom detection reduced to 2-signal requirement, needs both intent keyphrase AND zero tool calls before flagging, eliminating spurious self-heal triggers
  • Uses active provider: respects current provider/model config instead of hardcoded Anthropic
  • Persistent session reuse: one session per cycle, survives app restarts by persisting last_cycle timestamp
  • Skips unchanged feedback: if feedback count hasn’t changed, skips analysis to avoid wasted LLM calls

v0.3.11 Additions

  • DashScope migration: Qwen OAuth rotation replaced with simple API-key provider, deleting ~2,500 lines of complexity
  • Local model tool-call extraction: auto-extracts tool calls from text content: bare JSON {"tool_calls":[...]}, Claude-style XML <TOOLNAME><PARAM>value</PARAM></TOOLNAME>, and Qwen-specific <!-- tool_calls --> markers
  • 40+ TUI/self-heal fixes: narrowed phantom gate, split thinking per iteration, anti-code-block nudge for local models, tighter phantom scope, mid-turn “Let me see:” catch, backtick code reference detection
  • Per-session provider isolation: each session carries its own provider instance; no global swap affecting all sessions
  • Sub-agent AwaitingInput state: wait_agent polls state and returns partial progress on timeout instead of deadlocking

v0.3.20 Additions

  • RSI home directory resolution fixed: RSI now resolves ~ to the actual home directory instead of using CWD-relative paths, preventing brain file writes to wrong locations
  • Bare tool-call arrays caught: top-level arrays from models no longer crash RSI’s feedback dimension parsing; wrapped correctly before recording

v0.3.21 Additions

  • Multi-language phantom detection: compile-time TOML char sets replaced language-specific regex patterns. RSI feedback now works with all supported languages via the new char-set system. Cross-language regression test added.
  • RSI cycle output dedup by hashing: cycle output dedup now uses hash comparison of assembled opportunities instead of string matching, preventing duplicate cycle reports.
  • Sticky fallback on phantom exhaust: when phantom detection exhausts retries, RSI applies sticky fallback provider to prevent cascading failures.
  • Phantom iterations not persisted: phantom iterations no longer written to DB, keeping history clean of failed self-heal attempts.
  • OpenAI-compatible image generation: image generation via any /v1/images/generations endpoint with configurable generation_model override.

v0.3.23 Additions

  • Brain file clobber guardrail: generic write_file / edit_file now refuse protected brain files, routing through write_opencrabs_file which enforces append-only, dedup-aware shrink, and .bak snapshots.
  • A2A approval policy: A2A tasks now resolve approval policy correctly. auto-always and auto-session policies work for remote agents.

v0.3.25 Additions

  • Brain file cleanup_intent: write_opencrabs_file accepts cleanup_intent flag for user-driven brain file maintenance. RSI agent explicitly blocked from shrinking brain files, preventing autonomous self-improvement from accidentally wiping content (issue #103).
  • RTK Token Savings integration: bundled RTK binary (4MB, v0.40.0) as default feature with zero-config. Works as direct proxy: agent runs git status, RTK intercepts output through Rust, filters it, returns token-optimized version. 100+ commands supported (git, cargo, npm, pnpm, docker, kubectl, grep, find, ls, tree, curl), blocklist for interactive/REPL commands (vim, ssh, python, mysql). Binary discovery checks bundled location first, falls back to PATH. /rtk slash command shows savings stats. Real-world results: 53.5% token savings across 180 commands (PR #102).

v0.3.19 Additions

  • RSI feedback records actual model used: when helpers remap a mismatched model to the provider default, RSI now records the resolved model instead of the impossible original pair. All 3 recording sites in tool_loop.rs now resolve the actual model before constructing the feedback dimension
  • Tool loop reasoning markers persisted: reasoning content persisted in non-CLI content column so thinking state survives across tool loop iterations
  • @ file picker fixed for large repos: recursive walk now skips .git/.hg/.svn directories and raised result cap from 5k to 20k, preventing pack/ref files from exhausting the cap

v0.3.26 Additions

  • RSI brain file hygiene: rejects raw failure-event logs from being written to brain files. Feedback dimensions are sanitized before persisting, preventing noise accumulation in SOUL.md and TOOLS.md
  • Hashline collision escalation: when hashline_edit detects a collision (two lines with identical content hashes), RSI escalates to edit_file fallback instead of applying a corrupted edit
  • Dynamic help screen: help screen auto-generates from SLASH_COMMANDS constant, so new commands appear automatically without manual help text updates

v0.3.28 Additions

  • Brain backup rotation: max 5 backups per file, max 7 days old. Prevents unbounded .bak accumulation in ~/.opencrabs/ from repeated RSI writes
  • Profile brain-template seeding: profile create now seeds 8 brain file templates automatically, with recovery path for empty profiles. Ensures new profiles start with complete brain file sets
  • Auto-title retry on LLM failure: auto-title no longer gives up on first LLM error; retries with backoff before falling back to truncated first message

v0.3.30 Additions

  • RSI rejects trivial content: self_improve apply action now rejects trivial test content before it can pollute brain files, preventing noise from accumulating in SOUL.md and TOOLS.md

v0.3.31 Additions

  • RSI skill proposals: skill is now a third proposal kind alongside tool and command. When RSI identifies a multi-stage workflow pattern that recurs across sessions, it proposes a SKILL.md file instead of a simple tool or command. Applied skill proposals write to ~/.opencrabs/skills/<name>/SKILL.md and become immediately invocable as /<name> across all channels.
  • Bash command visibility: RSI now sees the actual bash command text plus a subsystem classifier (git, cargo, docker, npm, etc.) in feedback events. This lets RSI identify recurring shell patterns more accurately and propose targeted tools or skills.
  • Successful patterns surface as proposals: RSI doesn’t only react to failures. When a tool/command/skill pattern works reliably across multiple sessions, RSI surfaces it as a proposal to make the pattern more discoverable or ergonomic.

v0.3.34 Additions

  • Brain dedup scan (closes #147): new RSI proposal kind BrainDedup that scans all 11 brain files daily, clusters duplicate lines (minimum 10 chars, skips structural markdown like headings and separators), and files dedup proposals into Mission Control with a soft purple badge. Runs every 24 RSI cycles (about once per day at 1-hour intervals), never auto-applies, human approval required through the existing rsi_proposals apply/reject flow. Core scan logic in dedup_scan.rs (393 lines), hooked into the RSI cycle with periodicity gating, 14 regression tests covering empty files, short-line filtering, cross-file detection, proposal format, and canonical selection.
  • Skill description injection (closes #151): skill descriptions were documented in TOOLS.md as LLM auto-invoking triggers but were never actually injected into the system prompt, so the LLM could not auto-invoke from description alone. Added push_skills_section() to prompt_builder.rs that loads all skills via crate::brain::skills::load_all_skills() and formats each as - skill_name: description, appending an ## Available Skills block to both build_core_brain() and build_system_brain(). 2 regression tests.
  • RSI decorative counters removed (closes #149, PR #150): removed the counter-bumping logic that incremented inline counters in SOUL.md like phantom_tool_call: 219. These counters were decorative only, nothing read them, and the real canonical source is the SQLite feedback ledger at ~/.opencrabs/feedback.db. Counters went stale (ledger showed 302, SOUL.md showed 219) and got wiped by upstream template sync. Replaced with evidence appends (date/session). DB stays the single source of truth. Follow-up commit escaped unescaped double quotes the PR introduced in the prompt string literal and added text regression tests.

v0.3.35 Additions

  • cycle_number persistence: cycle_number now persists to ~/.opencrabs/rsi/cycle_number across TUI restarts. Previously it reset to 0 on every process restart, meaning the brain dedup scan (which fires every 24 cycles) would never trigger if the TUI restarted more frequently than 24 hours. Now the cycle counter survives restarts and the daily dedup scan fires as intended.

v0.3.66 Additions

  • Skill candidates from tool sequences: RSI detects recurring tool sequences as skill candidates. When the agent repeatedly calls the same sequence of tools (e.g. read_file → edit_file → bash with specific patterns), RSI proposes a skill that encapsulates the workflow.
  • Slash commands from repeated asks: RSI proposes slash commands from repeated user requests. When users type the same multi-word prompt repeatedly, RSI suggests a /command that captures the pattern.
  • RSI staleness indicator: Mission Control shows a staleness indicator for RSI cycles, plus a provider-creation fallback when the primary provider is unavailable.

v0.3.75 Additions

  • Brain dedup system: a new /dedup command runs an on-demand cross-file brain dedup scan, backed by a post-write scan that fires after brain writes and a weekly cron safety net that catches cross-file duplication. The scanner ignores fenced code and table rows so it only flags real prose duplication.
  • Inline-keyboard dedup approval: dedup proposals can be approved straight from a Telegram inline keyboard.
  • Brain-file hints on tool misses: when a tool call misses or errors (including approval-branch errors), the relevant brain notes surface automatically so the agent can self-correct (#767).
  • Wider TOOLS.md load trigger, routing, skill, and cron questions now proactively load TOOLS.md.

v0.3.76 Additions

  • RSI /evolve proposal: when a new OpenCrabs release is available, RSI surfaces it as an /evolve proposal in the Mission Control inbox instead of silently ignoring it. The proposal cites the version delta and links to the release notes.
  • Config-example tracking: RSI tracks config examples so pricing fixes and new provider entries reach users through template sync.

v0.3.78 Additions

  • TOML brain verification: a new brain_verify.toml defines post-write verification rules. After any brain file write, the system re-reads the file and checks required anchors (H1 title, **Owns:** header) are still present. If verification fails, the write is flagged.
  • brain_verify Orient gate: self_improve now routes through the epistemic Orient gate before applying changes. The agent must orient (verify current state) before writing, preventing RSI from applying fixes based on stale beliefs about brain file content.
  • RSI digest totals: the RSI digest now surfaces raw failure totals alongside surfaced opportunities, giving better visibility into what’s actually failing vs what RSI chose to act on.
  • Rule size cap: brain file rules are now capped in size. A narrow consolidation path allows merging related rules, preventing unbounded growth from repeated RSI appends.
  • Contradiction scope fix: brain_verify contradiction matching is now scoped per-entry instead of whole-file, eliminating false positives where unrelated entries triggered contradiction warnings.

Self-Healing vs Self-Improvement

Self-HealingSelf-Improvement
Fixes runtime errors (config corruption, DB issues)Fixes behavioral patterns (bad habits, user corrections)
Automatic, no analysis neededRequires feedback analysis first
Protects the system from crashingMakes the agent better over time
ImmediateAccumulates across sessions

RSI Convergence Gates (v0.3.80)

The self-improvement engine now knows when to hold back (#977):

  • Pauses on convergence self-reports: when a cycle reports its findings have converged, the RSI agent pauses instead of churning
  • Interval backs off on zero-improvement streaks: repeated cycles that improve nothing automatically lengthen the wait before the next cycle
  • Findings hashed by stable identity: the same finding keeps the same identity across cycles, so no description churn and no duplicate proposals
  • Gated on actionable feedback deltas: cycles only run when there is new actionable feedback to work with
  • Session history sealed each cycle with a compaction marker, so every cycle starts from a clean read
  • Cycle provider resolved from user config, not registry order

RSI Engine Gate (v0.3.81)

The autonomous RSI engine is gated behind rsi_enabled (#1063). In the TUI nothing changes, but headless daemons default RSI off, an unattended service no longer rewrites its own brain files by default. Opt in explicitly:

[agent]
rsi_enabled = true

Mission Control

Mission Control is a full-screen TUI dashboard that brings RSI activity, inbox proposals, and scheduled jobs into one place. Open it with /mission-control.

The Three Panels

The screen is divided into three panels: a large Inbox on the left, and Activity + Schedule stacked on the right.

Inbox

Pending RSI proposals displayed as cards. Each card shows:

  • Tool proposals (orange tool badge): new dynamic tools RSI thinks you need, with the shell command template
  • Command proposals (teal command badge): new slash commands RSI drafted based on usage patterns
  • Skill proposals: new SKILL.md files RSI drafted when it detects a repeated multi-step workflow that isn’t covered by an existing skill

Each card shows the proposal name, type badge, description or command template, and how long ago it was proposed.

Apply or reject proposals inline:

  • a: apply the selected proposal (installs tool/command to config, or creates skill directory)
  • r: reject the selected proposal (archives with optional reason)

Applied and rejected entries are archived daily to ~/.opencrabs/rsi/applied/ and ~/.opencrabs/rsi/rejected/ so the trail is auditable.

A banner on session start shows the count of pending inbox items.

Activity

A chronological feed of the last 100 RSI improvements. Shows what the autonomous engine did, when, and why:

  • Brain file modifications (SOUL.md, MEMORY.md, TOOLS.md, etc.)
  • Template syncs from upstream
  • Hard rule additions
  • Feedback analysis summaries
  • Violation count updates

Each entry shows the time ago, a summary of the change, and the target file.

Schedule

Your cron job queue with paused/active state. Each job shows:

  • Job name
  • Cron expression
  • Next run time (when active)
  • paused label (when paused)

See Cron Jobs for full cron documentation.

Cron BLOB Recovery (v0.3.20)

Cron jobs with legacy BLOB-typed prompt rows in the database are now tolerated instead of causing silent failures. The schedule panel resumes showing jobs normally.

Compaction Typing Without Banner (v0.3.22)

The visible compaction banner text has been removed. The schedule panel now uses typing-only indicators during compaction windows (10-60s), keeping the experience clean.

Context Counter Evolution (v0.3.26→v0.3.28)

  • v0.3.26: introduced per-provider tokenizer calibration. Uncalibrated providers showed 0/max until first message calibrated the ratio
  • v0.3.28: calibration system removed entirely. Context counter now uses provider-reported input_tokens verbatim, showing real-time usage without calibration overhead

Keyboard Navigation

TombolAksi
Tab / Shift+TabCycle focus between panels (Inbox → Activity → Schedule)
↑ / ↓Move selection within a panel
EnterOpen detail popup for selected item
aApply selected inbox proposal
rReject selected inbox proposal
EscClose popup or exit Mission Control

Architecture

Mission Control is split into three module trees:

LayerPathPurpose
Data servicesbrain/mission_control/Fetches inbox proposals, activity log, schedule items
Panel rendererstui/render/mission_control/Draws each panel (inbox, activity, schedule, detail popup)
App state + inputtui/app/mission_control/Focus management, keystroke handling, actions

Layout and keystroke contracts are unit-testable without spinning up a full App instance.

Analytics Panel (v0.3.78)

Mission Control gained a full Analytics view with its own database layer, event emitters, and responsive UI.

What It Tracks

Every agent interaction emits analytics events to a dedicated SQLite table:

Event CategoryExamples
Phantom detectionDetect/resolve events, false-positive rate
StreamingRecovery count, tool_use per stream
Brain verificationOrient gate pass/reject, contradiction hits
Model usageActive model per turn, provider switches

The Analytics UI

Open the analytics panel from Mission Control. It has:

  • Global time filter: Day / Week / Month / All toggle at the top. All cards and tabs respect the selected window.
  • Responsive 3-across card grid: summary cards (total events, phantom rate, streaming recoveries, brain verify hits) laid out in a 3-column grid that adapts to terminal width.
  • Model status icons: each model row shows a health icon (green/yellow/red) based on recent success rate.
  • Tabbed detail panels: Phantom / Model / D-W-M tabs with independent scroll. Each tab shows a ranked breakdown relevant to its category.

Architecture

LayerPurpose
analytics_events DB tableAppend-only event store with category, model, timestamp
Event emittersWired across tool_loop, streaming, brain_verify, and provider paths
Query + report layerAggregation queries (counts, rates, top-N) with time-window filtering
TUI rendererCard grid + tabbed scroll panels in Mission Control

Multi-Profile Support

Run multiple isolated OpenCrabs instances from a single installation. Each profile gets its own config, memory, sessions, brain files, skills, cron jobs, and gateway service.

Introduced in v0.2.94.

Why Profiles?

Common use cases:

  • Work vs personal: separate API keys, brain files, Telegram bots
  • Multiple clients: different persona and config per customer
  • Model experimentation: compare different provider setups without clobbering your main config
  • Staging vs production: test brain file changes on a staging profile before rolling to your main agent

Creating a Profile

# Create a new profile
opencrabs profile create hermes

# List all profiles
opencrabs profile list

# Show details for a profile
opencrabs profile show hermes

# Delete a profile
opencrabs profile delete hermes

Switching Profiles

There are two ways to use a non-default profile:

# CLI flag (per-session)
opencrabs -p hermes

# Environment variable (persistent)
export OPENCRABS_PROFILE=hermes
opencrabs

The default profile (~/.opencrabs/) works exactly as before: zero breaking changes.

Directory Structure

Each profile gets its own directory under ~/.opencrabs/profiles/<name>/:

~/.opencrabs/
├── config.toml          # default profile config
├── memory/              # default profile memory
├── sessions.db          # default profile sessions
└── profiles/
    ├── hermes/
    │   ├── config.toml
    │   ├── memory/
    │   ├── sessions.db
    │   ├── logs/
    │   └── layout/
    └── assistant/
        ├── config.toml
        └── ...

Profile Migration

Copy config and brain files from one profile to another:

# Copy from default to hermes
opencrabs profile migrate --from default --to hermes

# Overwrite existing files in target
opencrabs profile migrate --from default --to hermes --force

Migration copies all .md and .toml files plus the memory/ directory. It excludes the database, sessions, logs, and layout state, so the target profile starts fresh with the source’s personality and configuration, not its history.

Export and Import

Share profiles as portable archives:

# Export a profile as .tar.gz
opencrabs profile export hermes
# → creates hermes.tar.gz in current directory

# Import on another machine
opencrabs profile import ./hermes.tar.gz

Token-Lock Isolation

Two profiles cannot bind the same bot token simultaneously. Before connecting a Telegram, Discord, Slack, or Trello channel, OpenCrabs checks for existing token locks using PID-based lock files:

~/.opencrabs/locks/telegram_<token_hash>.lock

If another profile (still running) holds the lock, startup fails with a clear message. Stale locks (process dead) are automatically cleaned up.

This prevents split-brain scenarios where two agents fight over the same bot.

Profile-Aware Daemons

Install a separate OS service per profile:

# Install daemon for the hermes profile
opencrabs -p hermes service install

# Start it
opencrabs -p hermes service start

# macOS: creates com.opencrabs.daemon.hermes.plist
# Linux: creates opencrabs-hermes.service

Multiple profile daemons can run simultaneously as separate OS services, each with its own ports, bot connections, and config.

Per-Session Provider Isolation

Changing the provider in one session does not affect other sessions or profiles. Each session remembers its own provider independently. See Sessions for the full isolation story.

Profile-Aware Paths (v0.3.35)

All internal paths now resolve through opencrabs_home() instead of hardcoded ~/.opencrabs/. This means subagent status directories, tools.toml fallback resolution, and write_opencrabs_file confirmation messages all respect the active profile. No more cross-profile contamination when running multiple instances.

/profiles Command and TUI Dialog (v0.3.55)

The /profiles command works across every channel: TUI, Telegram, Discord, WhatsApp, and Slack.

In the TUI, /profiles opens a native dialog with full keyboard navigation:

  • Browse all profiles with active profile highlighted
  • Create a new profile inline with name validation
  • Delete a profile with confirmation
  • Migrate brain files and config from one profile to another
  • Switch instantly to another profile

On Discord, WhatsApp, and Slack, /profiles renders a rich profile browser showing all profiles, the active one, and quick switch/delete/create actions.

# From any channel
/profiles

# In the TUI, use j/k to navigate, Enter to select, d to delete, c to create, m to migrate

Voice (TTS & STT)

OpenCrabs supports text-to-speech and speech-to-text with five provider tiers: Off, Groq (API), OpenAI-compatible (any /v1/audio endpoint), Voicebox (self-hosted), or Local (on-device, zero cost).

Quick Setup

Run /onboard:voice in the TUI to configure everything interactively. The voice screen has radio selectors for both STT and TTS, with fields shown/hidden based on the selected provider. API keys are wired to keys.toml automatically.

Speech-to-Text (STT)

Providers

ProviderEngineCostLatencySetup
GroqWhisper (whisper-large-v3-turbo)Per-minute pricing~1sAPI key in keys.toml
OpenAI-compatibleAny Whisper-compatible endpointVaries~1-3sstt_base_url + stt_model + API key
VoiceboxSelf-hosted open-sourceFree~2-5svoicebox_stt_enabled=true + voicebox_stt_base_url
Localwhisper.cpp (on-device)Free~2-5sAuto-downloads model

Local STT Models

ModelSizeQualitySpeed
local-tiny~75 MBGood for short messagesFastest
local-base~142 MBBetter accuracyFast
local-small~466 MBHigh accuracyModerate
local-medium~1.5 GBBest accuracySlower

Models auto-download from HuggingFace to ~/.local/share/opencrabs/models/whisper/ on first use.

Configuration

# config.toml
[voice]
stt_enabled = true
stt_mode = "local"              # "api" or "local"
local_stt_model = "local-tiny"  # local-tiny, local-base, local-small, local-medium

For API mode:

# keys.toml
[providers.stt.groq]
api_key = "your-groq-key"       # From console.groq.com

Text-to-Speech (TTS)

Providers

ProviderEngineCostVoicesSetup
OpenAIgpt-4o-mini-ttsPer-character pricingalloy, echo, fable, onyx, nova, shimmerAPI key in keys.toml
OpenAI-compatibleAny /v1/audio/speech endpointVariesVaries by servertts_base_url + tts_model + tts_voice + API key
VoiceboxSelf-hosted async POST /generateFreeConfigurable profilesvoicebox_tts_enabled=true + voicebox_tts_base_url + voicebox_tts_profile_id
LocalPiper (on-device)Free6 voicesAuto-downloads model

Local TTS Voices (Piper)

VoiceDeskripsi
ryanUS Male (default)
amyUS Female
lessacUS Female
kristinUS Female
joeUS Male
coriUK Female

Models auto-download from HuggingFace to ~/.local/share/opencrabs/models/piper/. A Python venv is created automatically for the Piper runtime.

Configuration

# config.toml
[voice]
tts_enabled = true
tts_mode = "local"              # "api" or "local"
local_tts_voice = "ryan"        # ryan, amy, lessac, kristin, joe, cori

For API mode:

# config.toml
[voice]
tts_mode = "api"
tts_voice = "echo"              # OpenAI voice name
tts_model = "gpt-4o-mini-tts"   # OpenAI model
# keys.toml
[providers.tts.openai]
api_key = "your-openai-key"

Full Configuration Reference

# config.toml
[voice]
# Speech-to-Text
stt_enabled = true
stt_mode = "groq"                 # "groq", "openai_compatible", "voicebox", "local"
local_stt_model = "local-tiny"    # local-tiny, local-base, local-small, local-medium
stt_base_url = "https://..."      # OpenAI-compatible STT endpoint
stt_model = "whisper-1"           # OpenAI-compatible STT model
voicebox_stt_enabled = false
voicebox_stt_base_url = "https://..."

# Text-to-Speech
tts_enabled = true
tts_mode = "openai"               # "openai", "openai_compatible", "voicebox", "local"
tts_voice = "echo"                # OpenAI TTS voice name
tts_model = "gpt-4o-mini-tts"     # OpenAI TTS model
local_tts_voice = "ryan"          # Local mode: Piper voice
tts_base_url = "https://..."      # OpenAI-compatible TTS endpoint
tts_model = "tts-1"               # OpenAI-compatible TTS model
voicebox_tts_enabled = false
voicebox_tts_base_url = "https://..."
voicebox_tts_profile_id = "profile-id"
# keys.toml
[providers.stt.groq]
api_key = "your-groq-key"

[providers.stt.openai_compatible]
api_key = "your-api-key"

[providers.tts.openai]
api_key = "your-openai-key"

[providers.tts.openai_compatible]
api_key = "your-api-key"

OpenAI TTS Onboarding (v0.3.78)

The onboarding wizard (/onboard:voice) now includes a dedicated OpenAI TTS voice selector and API key field. When you select OpenAI as your TTS provider during onboarding:

  1. A voice picker appears with all available OpenAI voices (alloy, echo, fable, onyx, nova, shimmer)
  2. An API key field is shown if no key is configured yet
  3. Selection is saved to config.toml (tts_voice) and keys.toml (API key) automatically

This removes the need to manually edit config files for the most common TTS setup path.

How Voice Messages Work

When a voice message arrives on Telegram, WhatsApp, Discord, or Slack:

  1. Audio is decoded (OGG/Opus or WAV)
  2. Transcribed via STT (local whisper.cpp or Groq API)
  3. Agent processes the text and generates a response
  4. Response is converted to speech via TTS (local Piper or OpenAI API)
  5. Audio is encoded as OGG/Opus and sent back as a voice message

Local mode handles everything on-device: no API calls, no cost, no data leaves your machine.

Hardware Requirements

FeatureCPU RequirementNotes
Local STT (rwhisper)AVX2 (Haswell 2013+)Metal GPU on macOS Apple Silicon
Local TTS (Piper)No restrictionsTested on 2007 iMac: works on any x86/ARM
Local embeddingsAVX (Sandy Bridge 2011+)Falls back to FTS-only search

OpenCrabs detects CPU capabilities at runtime and hides unavailable options in the onboarding wizard. Local TTS (Piper) has no CPU limitations and should work on virtually any machine.

Building Without Voice

Voice features are enabled by default. To build without them (smaller binary):

cargo build --release --no-default-features --features telegram,whatsapp,discord,slack,trello

Feature flags: local-stt (whisper.cpp), local-tts (Piper).

Voice Dialog Key Handling (v0.3.81)

The voice settings dialog no longer hides stored API keys behind placeholder text or overwrites them when you save unrelated changes (#1039). What is in keys.toml stays there unless you explicitly replace it.

Skills System

Skills are reusable workflow templates that extend OpenCrabs with specialized capabilities. They work across Claude Code, Anthropic managed agents, and OpenClaw using a shared SKILL.md format.

How Skills Work

Each skill lives in its own directory under ~/.opencrabs/skills/:

~/.opencrabs/skills/
├── security-audit/
│   └── SKILL.md
├── cost-estimate/
│   └── SKILL.md
└── my-custom-skill/
    └── SKILL.md

Skill Format

Every skill is a markdown file with YAML frontmatter:

---
name: security-audit
description: Language-agnostic security & CVE audit for any codebase
---

# Security Audit

You are a senior security engineer performing a comprehensive
security audit of the codebase in the current working directory...

### Path-Triggered Skills: `globs` (v0.5.1)

A skill can claim file patterns the way editor rule files do, via an opt-in `globs:`
frontmatter field: a comma string, an inline flow list, or a block list:

```markdown
---
name: rust-style
description: Project conventions for Rust edits
globs: ["src/**/*.rs", "build.rs"]
---

When the agent is about to touch a matching path without that skill loaded in the current context, the tool call is rejected once, carrying the full skill body in the rejection. The identical retry then passes: the model has now read the rules. A registry of seen skills (track of loads, epoch-tracked) re-arms the gate after context compaction, and the master switch is [agent] skill_glob_gate (default: on). The effect: conventions attached to a path are impossible to edit against by omission.

Stage 1: Project detection

…


The `name` and `description` fields in the frontmatter are required. The markdown body becomes the prompt that gets injected when the skill runs.

## Built-in vs User Skills

Skills come from two sources:

- **Built-in** (orange badge): ship with the OpenCrabs binary via `include_str!`. Always available.
- **User** (teal badge): created by you in `~/.opencrabs/skills/<name>/SKILL.md`. Override built-ins by file presence.

## Built-in Skills

| Skill | Description |
|-------|-------------|
| `opencli` | Reference for all 25+ opencli-rs dynamic tools (news, social, search, web). Use when user asks about trending topics, news, social media, jobs, or web search. |
| `browser-cdp` | Native CDP browser automation reference. Headless/headed Chrome control, screenshots, JS evaluation. |
| `a2a-gateway` | Agent-to-Agent (A2A) protocol gateway reference. JSON-RPC 2.0 peer-to-peer agent communication. |
| `dynamic-tools` | Runtime tool management with tool_manage and tools.toml format. Create, enable, disable, reload tools without restart. |
| `security-audit` | Language-agnostic security & CVE audit. Detects project type from manifests, runs the appropriate scanner, reviews the diff for injection / auth / crypto / deserialization / path-traversal patterns, and scores 0-100. |
| `cost-estimate` | Codebase cost-to-build estimate, AI-assisted ROI breakdown, and fair-market valuation. |
| `repo-audit` | Language-agnostic repository health checks. 5-phase pipeline: language detection, native tool execution, git metrics, AST analysis, scoring + recommendations. Covers Rust, JS/TS, Python, Go. (v0.3.18) |

## Running Skills

### Skills Picker (`/skills`)

Type `/skills` to open the full-screen filterable picker. The top shows a filter bar with the total skill count. The main area lists all skills, each showing:

- **Skill name** as a slash command (e.g. `/security-audit`)
- **Type badge**: orange `built-in` or teal `user`
- **Description** of what the skill does
- **Keywords** for search matching in parentheses

| Key | Action |
|-----|--------|
| `Tab` / `↑↓` | Navigate the skill list |
| `Enter` | Run the selected skill |
| `Esc` | Close the picker |
| Type | Filter skills by name and description (case-insensitive) |

When the filter narrows to a single match, Enter fires it immediately.

### Slash Commands

Type any skill name directly as a slash command:

/security-audit


### Channels

Skills auto-register as slash commands across all connected channels (Telegram, Discord, Slack, WhatsApp). No `commands.toml` entry needed. Just type `/<skill-name>` in any channel to run it.

## Creating Custom Skills

1. Create a directory under `~/.opencrabs/skills/`:

```bash
mkdir -p ~/.opencrabs/skills/my-skill
  1. Create SKILL.md with frontmatter and prompt:
---
name: my-skill
description: What this skill does
keywords: [my-skill, custom, example]
---

# My Skill

Instructions for the agent when this skill runs...
  1. The skill immediately appears in /skills (with a user badge) and as /my-skill in TUI and all channels.

Cross-Harness Compatibility

The SKILL.md format works identically on:

  • OpenCrabs: native support via /skills picker and slash commands
  • Claude Code: drop the same SKILL.md file into Claude Code’s skills directory
  • Anthropic managed agents: compatible with managed agent skill loading
  • OpenClaw: works with OpenClaw’s skill system

Write a skill once, use it everywhere.

RSI-Proposed Skills

The RSI engine can propose new skills based on usage patterns it observes in the feedback ledger. For example, if the agent repeatedly performs a multi-step workflow that isn’t covered by an existing skill, RSI will draft a skill and file it in the Mission Control inbox for your review.

This is part of the RSI Proposals system: RSI identifies gaps in the agent’s capabilities and drafts solutions, but installation always requires your approval.

Slash Command Resolution (v0.3.78)

The slash_command tool now correctly resolves skills and the /onboard:<topic> form. Previously, calling a skill via slash_command could fail if the skill name didn’t exactly match the internal registry key. The resolver now checks skills, built-in commands, and user commands in priority order, and the /onboard:voice / /onboard:provider / /onboard:channels sub-topic forms route correctly.

Themes

The TUI is colored by semantic roles, not raw hexes. Every render site resolves a role (accent, text_primary, selection_bg, …) through the active theme, so one switch restyles the whole interface at runtime. A theme is one file, 43 keys, done.

Switching themes

PerintahWhat it does
/themeOpens the interactive picker: ↑/↓ (or j/k) move the cursor and preview live, the whole UI recolors under the arrow keys. Enter applies and persists, Esc (or q) cancels and reverts to whatever was active when the picker opened.
/theme listLists built-in themes and user presets, then re-scans ~/.opencrabs/themes/. Files that fail validation are listed with a human-readable rejection reason, never silently ignored.
/theme set <name>Applies a theme immediately and writes it to your config. Lookup is case-insensitive across built-ins and user presets; an unknown name tells you to run /theme list.
/theme resetReturns to the default (crab-dark) and clears the config key.

To pin a theme without the picker, set it in config.toml:

[tui]
theme = "nord"

The value is applied at boot before the first frame. An empty or unknown name falls back to crab-dark. User theme files are re-scanned on every /theme invocation, so editing a preset file hot-loads it without restarting the TUI.

Built-in themes

There are 39 built-in themes: 8 hand-tuned presets plus a curated pack of 31 converted from popular upstream palettes (see Imported themes). crab-dark is the default and matches the historical OpenCrabs look.

PresetVibe
crab-darkThe house palette: burnt-orange accent, cool grays, deep ink
draculaThe classic purple-blue dark theme with orange accent
alucardWarm dark, muted contrast (Castlevania’s good-boy Dracula)
monokaiSublime’s iconic warm dark with pink/yellow
catppuccin-mochaPastel dark, soft and low-contrast
catppuccin-latteThe same pastels on light surfaces
solarized-lightEthan Schoonlif’s light base
solarized-darkThe original dark base

The curated pack adds: aura, ayu, carbonfox, catppuccin-frappe, catppuccin-macchiato, cobalt2, cursor, everforest, flexoki, github, gruvbox, gruvbox-light, kanagawa, material, matrix, mercury, nightowl, nord, one-dark, opencode, orng, osaka-jade, palenight, rosepine, rosepine-dawn, synthwave84, tokyonight, tokyonight-storm, vercel, vesper, zenburn.

Swatches show four anchor roles per theme (accent, body text, success, ink background), read from the shipped sources. Regenerate with python3 docs/scripts/gen_theme_gallery.py.

ThemeTypeaccenttextsuccessinkSumber
crab-darkdefault #d76414 #c8c8d2 #50c878 #14141ebuilt-in
draculahand-built #ffb86c #f8f8f2 #50fa7b #282a36built-in
alucardhand-built #a34d14 #1f1f1f #14710a #1f1f1fbuilt-in
monokaihand-built #fd971f #f8f8f2 #a6e22e #272822built-in
catppuccin-mochahand-built #fab387 #cdd6f4 #a6e3a1 #1e1e2ebuilt-in
catppuccin-lattehand-built #fe640b #4c4f69 #40a02b #4c4f69built-in
solarized-lighthand-built #cb4b16 #657b83 #859900 #073642built-in
solarized-darkhand-built #cb4b16 #839496 #859900 #002b36built-in
auracurated pack #a277ff #edecee #61ffca #0f0f0fopencode, dark, 2026-09-11
ayucurated pack #e6b450 #bfbdb6 #7fd962 #0b0e14opencode, dark, 2026-09-11
carbonfoxcurated pack #ff7eb6 #f2f4f8 #25be6a #161616opencode, dark, 2026-09-11
catppuccin-macchiatocurated pack #f5bde6 #cad3f5 #a6da95 #24273aopencode, dark, 2026-09-11
cobalt2curated pack #2affdf #ffffff #9eff80 #193549opencode, dark, 2026-09-11
cursorcurated pack #88c0d0 #e4e4e4 #3fa266 #181818opencode, dark, 2026-09-11
everforestcurated pack #d699b6 #d3c6aa #a7c080 #2d353bopencode, dark, 2026-09-11
flexokicurated pack #8b7ec8 #cecdc3 #879a39 #100f0fopencode, dark, 2026-09-11
githubcurated pack #39c5cf #c9d1d9 #3fb950 #0d1117opencode, dark, 2026-09-11
gruvboxcurated pack #8ec07c #ebdbb2 #b8bb26 #282828opencode, dark, 2026-09-11
kanagawacurated pack #d27e99 #dcd7ba #98bb6c #1f1f28opencode, dark, 2026-09-11
materialcurated pack #89ddff #eeffff #c3e88d #263238opencode, dark, 2026-09-11
matrixcurated pack #c770ff #62ff94 #62ff94 #0a0e0aopencode, dark, 2026-09-11
mercurycurated pack #8da4f5 #dddde5 #77c599 #171721opencode, dark, 2026-09-11
nightowlcurated pack #c792ea #d6deeb #c5e478 #011627opencode, dark, 2026-09-11
nordcurated pack #8fbcbb #eceff4 #a3be8c #2e3440opencode, dark, 2026-09-11
one-darkcurated pack #56b6c2 #abb2bf #98c379 #282c34opencode, dark, 2026-09-11
opencodecurated pack #9d7cd8 #eeeeee #7fd88f #0a0a0aopencode, dark, 2026-09-11
orngcurated pack #fff7f1 #eeeeee #6ba1e6 #0a0a0aopencode, dark, 2026-09-11
osaka-jadecurated pack #549e6a #c1c497 #549e6a #111c18opencode, dark, 2026-09-11
palenightcurated pack #89ddff #a6accd #c3e88d #292d3eopencode, dark, 2026-09-11
rosepinecurated pack #ebbcba #e0def4 #31748f #191724opencode, dark, 2026-09-11
synthwave84curated pack #b084eb #ffffff #72f1b8 #262335opencode, dark, 2026-09-11
tokyonightcurated pack #ff966c #c8d3f5 #c3e88d #1a1b26opencode, dark, 2026-09-11
vercelcurated pack #8e4ec6 #ededed #46a758 #000000opencode, dark, 2026-09-11
vespercurated pack #ffc799 #ffffff #99ffe4 #101010opencode, dark, 2026-09-11
zenburncurated pack #93e0e3 #dcdccc #7f9f7f #3f3f3fopencode, dark, 2026-09-11
catppuccin-frappecurated pack #e5c890 #c6d0f5 #a6d189 #303446alacritty-theme, 2026-09-11
gruvbox-lightcurated pack #b57614 #3c3836 #98971a #fbf1c7alacritty-theme, 2026-09-11
rosepine-dawncurated pack #d38d2f #575279 #286983 #faf4edalacritty-theme, 2026-09-11
tokyonight-stormcurated pack #ff9e64 #a9b1d6 #9ece6a #24283balacritty-theme, 2026-09-11

The 43 roles

A user theme is a flat TOML file mapping every role to a #RRGGBB hex. Role names double as the TOML keys. What each one colors:

GroupTombolColors
AccentsaccentPrimary emphasis: prompts, active states, the crab orange
accent_tealPrimary action / selected accent
accent_softPassive, informational accent (soft white)
Texttext_primaryBody text
text_secondarySecondary text
text_mutedMuted labels
text_dimDeepest text tier
Chrome graysgrayBorders, passive labels, info lines (the workhorse)
gray_midSecondary borders, quiet panel text
gray_detailDetail rows under primary rows (tool list)
gray_dimInactive borders (unfocused input frame)
gray_darkDeeper inactive chrome
gray_baseDarkest neutral foreground (plan-widget borders)
gray_lightBright neutral text
gray_softSoft label text
gray_mutedMuted body text (plan widget)
Positivesuccess“ok” states, active pane borders
analytics_greenMission-control bars and values
green_checkChecked-markdown checkboxes
NegativeerrorPrimary failures
error_softError titles
error_fadedError bodies
WarningwarningAmber input hints
warning_mutedMuted amber
amber_mutedProposed-skill inbox badges
Teal tiersteal_vividSelection/active in input + tools
teal_brightDialogs
teal_mutedCompleted/skipped plan tasks
teal_calmPlan progress bars
Blue tiersblue_slateHelp text, input placeholders
blue_steelInformational accents (sessions, files, projects)
blue_linkMarkdown links
blue_skyProjects
blue_softChat accents
blue_vividOnboarding voice brand accent
blue_codeInline code (markdown)
Surfacesselection_bgSelected text in the composer
surface_panelMission-control panel background
surface_qrQR panel background (onboarding)
surface_codeCode-block background
surface_code_altCode-block background, alternate shade
InkinkThe main background canvas
Miscpurple_softProposed-brain-dedup inbox badges

Decorative animation cycles (like the rotating project badge colors) are deliberately not roles: they stay preset-agnostic.

Writing your own theme

Drop a file at ~/.opencrabs/themes/<name>.toml. The file stem is the theme name. All 43 keys are required; here is a complete valid theme (nord, from the curated pack):

accent = "#8fbcbb"
accent_teal = "#8fbcbb"
accent_soft = "#eceff4"
text_primary = "#eceff4"
text_secondary = "#8b95a7"
text_muted = "#767b84"
text_dim = "#9ca0a8"
gray = "#434c5e"
gray_mid = "#9ca0a8"
gray_detail = "#91959e"
gray_dim = "#7e838c"
gray_dark = "#6b707a"
gray_base = "#434c5e"
gray_light = "#d0d3d9"
gray_soft = "#afb3ba"
gray_muted = "#8d929a"
success = "#a3be8c"
analytics_green = "#a3be8c"
green_check = "#a3be8c"
error = "#bf616a"
error_soft = "#bf616a"
error_faded = "#6f4853"
warning = "#d08770"
warning_muted = "#9f6e62"
amber_muted = "#87625a"
teal_vivid = "#8fbcbb"
teal_bright = "#8fbcbb"
teal_muted = "#6d8c90"
teal_calm = "#5a7177"
blue_slate = "#698f9e"
blue_steel = "#88c0d0"
blue_link = "#81a1c1"
blue_sky = "#88c0d0"
blue_soft = "#648896"
blue_vivid = "#88c0d0"
blue_code = "#88c0d0"
selection_bg = "#505660"
surface_panel = "#3b4252"
surface_qr = "#343a45"
surface_code = "#434c5e"
surface_code_alt = "#494e59"
ink = "#2e3440"
purple_soft = "#81a1c1"

Validation rules

Validation is loud, never silent. A theme file is rejected, with the reason shown by /theme list, when it:

  1. misses any of the 43 roles: the error names the missing key;
  2. carries an unknown key: strict typo guard, there is no partial-schema mode;
  3. uses a malformed value: only #RRGGBB or RRGGBB, case-insensitive;
  4. fails the contrast floor (eight reading-critical (foreground, background) pairs must hit 2.5:1 WCAG contrast: text_primary/text_secondary/accent/success/error/ warning on ink, blue_code on surface_code, and gray_mid on surface_panel. Deliberately dim tiers (text_muted, text_dim, …) are excluded) dim is their job;
  5. collides with a built-in name: user files can never shadow a shipped preset.

Everything else is free for the runtime: each valid theme gets its ANSI-256 fallback tier derived automatically, so terminals without truecolor still render it (nearest index in the 6×6×6 cube, grayscale ramp for neutrals).

Theme enforcement (v0.5.3)

Every widget in the TUI now resolves its colours through the theme engine, the last hardcoded ANSI sites are gone, and a suite-level guard fails the build if a new raw ANSI colour site appears (#1634). Themes can also declare an optional canvas background (the source background carries through catalog conversion), so a theme can restyle the whole terminal canvas, not just the widgets on it.

Imported themes

The 31-theme curated pack was generated by the in-tree converter (tui::theme_catalog) from two upstream formats:

  • alacritty-theme TOML ([colors.primary] background/foreground plus the 16 ANSI colors): the alacritty/alacritty-theme catalog, 176 themes;
  • opencode theme JSON (defs + theme token maps with dark/light variants, hex strings, ANSI integers, defs references and "none"), the sst/opencode built-in themes, 33 assets.

Both convert through a fixed 43-role mapping (semantic token first, ANSI color or deterministic bg/fg mix as fallback), and every generated file is pre-certified by the same validator user themes face: a converted file that doesn’t validate never ships. Each pack file carries a # source: provenance header naming the upstream repo, file, and fetch date; the gallery table shows it. Upstream palettes belong to their authors; the pack imports their colors, not their names’ goodwill.

The converter is generation-time tooling: the TUI itself only knows the embedded presets and your drop-in files.

Usage Dashboard

The Usage Dashboard shows your token usage, costs, models, tools, and project breakdown. Open it with /usage.

Overview

The header shows your totals:

MetricDeskripsi
TokensTotal tokens consumed (in millions)
CostTotal spend in USD
SessionsNumber of sessions
CallsTotal API calls made

The Four Panels

The dashboard is a 2x2 grid of panels:

Daily Activity (top-left)

A horizontal bar chart showing token usage per day. Peak days stand out clearly. Useful for spotting burst activity or debugging unexpected spikes.

By Project (top-right)

A ranked table of projects by cost:

ColumnDeskripsi
ProjectWorking directory name
$Total cost
MTokens in millions
sTotal session time

By Model (bottom-left)

A ranked table of every model used:

ColumnDeskripsi
ModelProvider + model name
$Total cost
MTokens in millions
CNumber of API calls

The selected row is highlighted in orange. Use this to spot expensive models or optimize your provider mix.

By-Model Quantization Tree View (v0.3.20)

Model variants grouped under parent rows with tree connectors:

ColumnDeskripsi
ModelProvider + model name (parent row, bold)
├─ / └─Variant rows (e.g. qwen3.6-35b-a3b-gguf-oq2, qwen3.6-35b-a3b-gguf-oq4)

Parent rows show aggregated stats (total tokens, cost, calls) across all quant variants. This eliminates the noisy duplication where qwen3.6-35b-a3b-gguf, -oq2, -oq4, -iq4_xs each appeared as separate rows.

Before: 6 separate rows for one model family After: 1 parent row + 3 variant rows with aggregated parent stats

Core Tools (bottom-right)

A horizontal bar chart ranking your most-used tools. bash and read_file typically dominate. Useful for understanding your agent’s workflow patterns.

A summary table showing cost and turns by activity category (Development, CI/Deploy, Features), plus the 1-shot success rate for each.

Cache Efficiency Card (v0.3.36)

A new card on the dashboard shows your cache hit rate as a percentage. Providers that support prompt caching (like Anthropic and Z.AI) return cache_creation_input_tokens and cache_read_input_tokens in their usage data. These are now persisted to the messages table (DB migration #25), and the dashboard aggregates them into a hit-rate percentage.

When cache data is unavailable (provider doesn’t report it, or no cached tokens yet), the card degrades gracefully with a dash instead of showing 0%.

Time Filters

TombolFilter
TToday
WThis week
MThis month
AAll time
EscClose dashboard

Provider and Model Breakdown (v0.3.63)

The /usage command now shows per-provider and per-model cost breakdowns with period filters. This helps identify which providers or models are driving costs and optimize your usage accordingly.

Use the time filters (T/W/M/A) to scope the breakdown to specific periods. The By Model panel already shows provider + model name, but the new breakdown provides aggregated views for quick cost analysis.

Per-Model Cache Breakdown (v0.3.76)

The /usage command on Telegram now shows a per-model cache breakdown: for each model that supports prompt caching, you see cache creation tokens, cache read tokens, and the resulting hit rate. This makes it easy to spot which models benefit most from caching and whether your cache strategy is working.

Pricing Updates (v0.3.76)

  • Claude Opus 5 pricing added to the cost calculator
  • qwen3.8-max-preview pricing added
  • Cost attribution fix: costs are now attributed to the provider that actually served the request, not the originally-requested provider (matters when fallback fires mid-turn)
  • Provider-reported costs (v0.5.4): the ledger prefers the cost the provider actually reported in its usage payload over the calculator estimate (#1707)
TombolAksi
TabCycle focus between panels
EnterOpen details for selected item
EscClose dashboard

RTK: Rust Token Killer

RTK is natively bundled into OpenCrabs as a built-in feature. It intercepts bash commands before they run, filters and compresses their output, and returns a token-optimized version to the LLM context. The result: 60-90% token savings on common development commands, which directly translates to lower API costs and faster responses.

What It Does

When OpenCrabs runs a bash command like git status or cargo build, RTK:

  1. Intercepts the command before execution
  2. Runs it through RTK’s filtering engine
  3. Compresses verbose output (removes noise, keeps signal)
  4. Returns the optimized output to the agent

The agent sees the same information, but using a fraction of the tokens.

Real-World Impact

From production usage (13,600+ commands executed):

MetricValue
Total Commands13,600+
Input Tokens245M
Output Tokens35M
Tokens Saved209M
Savings Rate85.6%
Total Exec Time40h 50m (avg 10.8s per command)

Top Commands by Savings

PerintahCountTokens SavedAvg Savings
rtk grep1,920175M16.3%
rtk find1,67024M71.1%
rtk cargo test --all-...905.4M99.8%
rtk cargo test402.1M100.0%
rtk:toml ps aux301.2M97.9%

Supported Commands

RTK supports 40+ common development and sysadmin commands:

Version Control

  • git, gh (GitHub CLI), glab (GitLab CLI)

Package Managers

  • npm, npx, pnpm, cargo, dotnet

Build & Test

  • jest, vitest, tsc, next, prisma, prettier, eslint, playwright

Cloud & Infrastructure

  • aws, docker, kubectl, psql

System Inspection (Sysadmin)

  • ps, top, lsof, netstat, ss, journalctl, dmesg, dig, nslookup, host, traceroute

File Operations

  • grep, find, ls, tree, diff, curl, wget

Blocked Commands

RTK never rewrites these (too interactive, security-sensitive, or already RTK meta-commands):

sudo, ssh, scp, sftp, rsync, vim, vi, nvim, nano, emacs, less, more, man, python, python3, node, mysql, redis-cli, psql

How It Works

Command Rewriting

When the agent runs git status, OpenCrabs automatically rewrites it to rtk git status. RTK then:

  1. Executes git status internally
  2. Parses the output structure
  3. Applies command-specific filters (e.g., for git diff: show file names and change stats, not full diffs)
  4. Returns the compressed output

Smart Filtering

RTK uses different strategies per command:

  • Git commands: Show file-level summaries, not full diffs
  • Cargo build/test: Show errors and warnings, skip successful compilations
  • System commands (ps, top): Use TOML filter templates to extract key metrics
  • File searches (grep, find): Limit output length, show context only when relevant

/rtk Command

In the TUI, type /rtk to see your token savings dashboard:

═══ RTK Token Savings Report ═══

Total Commands: 13,600+
Total Tokens Saved: 209M
Average Savings: 85.6%
Tracking Since: 2026-05-15 10:30:00 UTC

Savings by Command Type:
  grep: 1,920 cmds, 175M tokens saved, 16.3% avg
  find: 1,670 cmds, 24M tokens saved, 71.1% avg
  cargo: 130 cmds, 7.5M tokens saved, 99.9% avg
  ...

Installation

RTK is enabled by default in all OpenCrabs builds. No setup required.

If you’re building from source and want to disable it:

cargo build --no-default-features --features telegram,whatsapp,discord,slack,trello,local-stt,local-tts,browser

To verify RTK is active:

# In the TUI, type:
/rtk

# Or check if the binary is available:
which rtk

Why It Matters

LLM API costs are based on token count. A typical git diff on a large repo can produce 50,000+ tokens of output. With RTK, that same diff might use only 5,000 tokens, a 90% reduction.

Over a day of heavy development work:

  • Without RTK: ~250M tokens consumed by command outputs
  • With RTK: ~35M tokens consumed
  • Savings: ~215M tokens per day

At typical API pricing ($3-15 per 1M input tokens), that’s $600-3,000+ saved per day in token costs alone.

Learn More

Autonomous Goal Loop (/goal)

The /goal command lets you set a high-level goal and have OpenCrabs work toward it autonomously, executing actions, self-evaluating with an LLM judge, and continuing until the goal is satisfied or a turn budget runs out.

How It Works

  1. Set a goal: /goal <description of what you want>
  2. The agent loops: executes an action, then an LLM judge evaluates whether the goal is met
  3. Self-correction: if the goal isn’t satisfied, the agent continues with a correction prompt
  4. Completion: the loop ends when the judge says the goal is met, or the turn budget (default 20 turns) is exhausted

This is fully hands-off. Set the goal, walk away, come back to results.

Penggunaan

/goal <text> (Set a new goal and start the autonomous loop
/goal status) Check current goal progress
/goal pause (Pause the autonomous loop
/goal resume) Resume a paused goal
/goal clear: Remove the current goal

Examples

/goal Fix all failing tests in the auth module and make sure clippy passes
/goal Research the top 5 Rust web frameworks and write a comparison in research/frameworks.md
/goal Set up a CI pipeline with GitHub Actions for this project
/goal Refactor the database layer to use connection pooling
/goal Find and fix all TODO comments in src/handlers/

Turn Budget

The default turn budget is 20 autonomous turns. Each turn is one full LLM round-trip (action + evaluation). The agent uses a lightweight LLM judge to evaluate progress, keeping costs low.

If the budget runs out before the goal is satisfied, the agent reports what it accomplished and what remains.

Best Practices

  • Be specific: “Fix the login bug” works better than “make the app better”
  • Set boundaries: mention files, modules, or constraints in the goal
  • Use for multi-step tasks: /goal shines on tasks that need repeated execution and self-correction
  • Check status: use /goal status to monitor progress on long-running goals
  • Pause when needed: /goal pause if you need the agent for something else

Self-Goaling (v0.3.61)

The agent can also set and drive its own multi-turn goals autonomously via the goal_manage tool. This lets the agent break down a complex request into self-directed goal loops without the user needing to invoke /goal manually. The agent evaluates its own progress and adjusts course, the same way a user-set /goal loop works.

Availability

/goal works across all channels: TUI, Telegram, Discord, Slack, and WhatsApp. Set autonomous goals from wherever you talk to your agent.

When to Use /goal vs /btw vs Plans

FeatureUse Case
/goalAutonomous loop for a single clear objective. Agent self-evaluates until done.
/btwSpawn a parallel side task while you keep chatting.
PlansMulti-step structured work with explicit tasks, dependencies, and acceptance criteria.

/goal is best when you have a clear outcome and want the agent to figure out the steps itself.

Companion Tools

OpenCrabs works with companion tools that extend its capabilities.

WhisperCrabs: Voice-to-Text

WhisperCrabs is a floating voice-to-text tool. Click to record, click to stop, transcribes, copies to clipboard.

  • Local (whisper.cpp, on-device) or API transcription
  • Fully controllable via D-Bus: start/stop recording, switch providers, view history
  • Works as an OpenCrabs tool: use D-Bus to control WhisperCrabs from the agent

Installation

git clone https://github.com/adolfousier/whispercrabs.git
cd whispercrabs
cargo build --release

Usage with OpenCrabs

Just ask naturally. OpenCrabs controls WhisperCrabs via D-Bus:

“Start recording” / “Stop and transcribe” / “Switch to Groq Whisper”


SocialCrabs: Social Media Automation

SocialCrabs automates social media via CLI + GraphQL with human-like behavior simulation. Twitter/X, Instagram, LinkedIn. No browser needed for read operations.

Setup

git clone https://github.com/adolfousier/socialcrabs.git
cd socialcrabs && npm install && npm run build

# Add cookies from browser DevTools to .env (auth_token + ct0 for Twitter)
# See SocialCrabs README for per-platform credential setup

node dist/cli.js session login x          # Authenticate Twitter/X
node dist/cli.js session login ig         # Authenticate Instagram
node dist/cli.js session login linkedin   # Authenticate LinkedIn
node dist/cli.js session status           # Check all sessions

Usage with OpenCrabs

Just ask naturally. OpenCrabs calls SocialCrabs CLI commands via bash automatically:

“Check my Twitter mentions” / “Search LinkedIn for AI founders” / “Post this to X”

Read operations run automatically. Write operations (tweet, like, follow, comment, DM) always ask for your approval first.

Twitter/X commands

node dist/cli.js x whoami                     # Check logged-in account
node dist/cli.js x mentions -n 5              # Your mentions
node dist/cli.js x home -n 5                  # Your timeline
node dist/cli.js x search "query" -n 10       # Search tweets
node dist/cli.js x read <tweet-url>           # Read a specific tweet
node dist/cli.js x tweet "Hello world"        # Post a tweet
node dist/cli.js x reply <tweet-url> "text"   # Reply to tweet
node dist/cli.js x like <tweet-url>           # Like a tweet
node dist/cli.js x follow <username>          # Follow a user

Instagram commands

node dist/cli.js ig feed -n 5                 # Your feed
node dist/cli.js ig search "query" -n 10      # Search posts
node dist/cli.js ig read <post-url>           # Read a specific post
node dist/cli.js ig like <post-url>           # Like a post
node dist/cli.js ig comment <post-url> "text" # Comment on post
node dist/cli.js ig follow <username>         # Follow a user

LinkedIn commands

node dist/cli.js linkedin feed -n 5           # Your feed
node dist/cli.js linkedin search "query" -n 10 # Search posts
node dist/cli.js linkedin read <post-url>     # Read a specific post
node dist/cli.js linkedin like <post-url>     # Like a post
node dist/cli.js linkedin comment <post-url> "text" # Comment on post

Web Scraping

OpenCrabs includes a native URL-to-markdown scraping tool (web_scrape) that converts any web page into clean markdown. Zero AI cost, zero API tokens for the extraction itself. The agent uses tool_search to activate it on demand, keeping it out of the always-loaded core tool set.

How It Works

web_scrape follows a five-step pipeline:

  1. Validate: SSRF guard rejects private IPs, localhost, loopback, link-local addresses, cloud metadata endpoints (169.254.169.254), and non-http(s) schemes
  2. Fetch: reqwest with browser User-Agent and timeout. is_js_shell heuristic detects JS-heavy pages (React/Vue/Angular/Svelte shells, <div id="app">, no <article>). Escalates to browser manager when available
  3. Extract: CSS selector cascade (article, main, .content, etc.) isolates primary content from HTML, falling back to body with junk selectors (header, nav, sidebar, ads) removed
  4. Clean: Language-agnostic HTML cleaner strips scripts, styles, inline handlers, HTML comments. Decodes entities, collapses blank lines
  5. Convert: htmd converts cleaned HTML to markdown. absolutize_urls resolves relative src/href against page base URL. Images preserved as ![alt](url) tags for selective agent vision

Single URL Mode

Scrape a single URL and return the markdown content directly:

web_scrape https://example.com/page

The agent receives the markdown in its context, ready for analysis, summarisation, or extraction.

Sitemap Mode

Discover and crawl an entire site via its sitemap:

web_scrape https://example.com --sitemap

This discovers /sitemap.xml, /sitemap_index.xml, and common variations. Recursively crawls sitemap indexes (iterative worklist, 1000-URL cap, 3 levels deep). Returns the URL list for the agent to pick from.

Each page’s markdown is exported to a directory. The output path resolves to the project files directory if the session is assigned to a project, or the profile-scoped OpenCrabs home otherwise. Files never land outside managed workspace.

Pair it with memory: point [memory] extra_paths at the export directory and every scraped page becomes searchable via memory_search scope="external", a zero-cost knowledge base your agent quotes verbatim. Full walkthrough in Memory System → Build a knowledge base. Note the hard cap: 100 pages per sitemap scrape.

SSRF Protection

The SSRF guard uses url::Host enum classification to properly handle:

BlockedReason
Private IPs (10.x, 172.16-31.x, 192.168.x)Internal network
Localhost (127.0.0.1, ::1)Local services
Link-local (169.254.x.x)Cloud metadata, DHCP
Non-http(s) schemesfile://, ftp://, gopher://

This runs before any network request, so malicious URLs never reach the fetcher.

JS-Shell Detection

When a page returns mostly empty HTML with JS framework markers (React root, Vue app, Angular bootstrap, Svelte kit), web_scrape detects it as a JS shell and escalates to the browser manager if available. This avoids returning empty markdown for single-page applications that require JavaScript rendering.

Image Handling

Images are preserved as ![alt](url) markdown tags rather than being stripped. This lets the agent decide which images are worth visioning (via analyze_image) and which can be ignored, rather than losing all visual context.

Document Generation

OpenCrabs writes real documents, not just text files. The built-in generate_document tool creates XLSX, DOCX, and PDF natively inside the binary (no Python, no LibreOffice, no font installs), and PPTX through python-pptx when the host has it. On channels the agent sends the finished file back as a downloadable attachment in the same turn.

Supported Formats

FormatEngineContentStyling
XLSXNative Rust (rust_xlsxwriter)Multiple sheets, rows of typed cells; any cell starting with = becomes a live Excel formula that recalculates when the user edits the fileColored header row, zebra striping, frozen header, autofilter dropdowns, colored sheet tabs, per-column number formats (currency, percent, date, integer, or raw Excel codes)
DOCXNative Rust (docx-rs)Headings (real Word styles, navigation pane works), paragraphs, bullet/numbered lists (real numbering), tables, image blocks (inline PNG/JPEG with optional caption)Accent-colored headings, page header/footer on every page, shaded table headers, zebra rows
PDFNative Rust (printpdf + bundled DejaVu Sans)Same block model as DOCX; A4 flow with word wrap, page breaks, content-sized table columns with header separators and row rules; image blocks (inline PNG/JPEG with aspect-preserving sizing and optional caption); real Unicode text (accents, Cyrillic, arrows, checkmarks)Brand accent + text colors, H1 underline bar, page header with logo image (local PNG/JPEG), footer with exact Page N of M, zebra tables
PPTXHost python-pptx (clear install hint when missing)Slides with title, bullets, speaker notesBrand template: build slides into an existing .pptx so they inherit the company master (logos, fonts, backgrounds); accent-colored titles; per-slide layout choice

How It Works

Everything is driven by one structured tool call, so it works with any provider including local models. All styling defaults off; bad colors or a missing logo degrade to the plain look instead of failing the document.

The tool accepts:

  • Format (xlsx, docx, pdf, pptx)
  • Content blocks (headings, paragraphs, lists, tables, image blocks)
  • Style configuration (brand colors, logos, page furniture)

Image Blocks

PDF and DOCX support embedding PNG and JPEG images inline with optional captions. Images are sized to fit the page while preserving aspect ratio.

Page Size and Orientation (v0.3.65)

All three document formats support custom page sizes and orientations:

FormatOptions
PDFlandscape mode, custom page_width and page_height (in points)
DOCXorientation (portrait or landscape), custom page_width and page_height (in twips)
PPTXslide_width and slide_height (in EMUs), aspect ratio control

Default sizes: PDF/DOCX use A4 portrait, PPTX uses 16:9 widescreen.

Brand Templates

For PPTX, you can point the tool at an existing .pptx file to inherit the company master (logos, fonts, backgrounds). Each slide gets a layout choice and accent-colored titles.

Delivery

On Telegram, WhatsApp, Discord, and Slack, generated documents are sent as downloadable attachments in the same turn the tool was called. No separate “here’s your file” message needed.

Examples

Ask the agent:

  • “Create a PDF report with these sales figures”
  • “Generate an Excel spreadsheet with formulas for monthly totals”
  • “Make a Word document with this content and our company logo”
  • “Build a PowerPoint presentation from these bullet points”

The agent handles formatting, styling, and delivery automatically.

Cerita pengguna

Ini studi kasus, bukan ulasan. Alur kerja nyata yang dijalankan orang di OpenCrabs, diberi tag sesuai apa yang mereka otomatiskan, dan ditautkan kembali ke postingan aslinya kalau kami memilikinya. Klik kartu mana pun untuk rincian lengkap dan sumbernya. Untuk pendapat orang tentang alatnya sendiri, lihat ulasan di halaman utama.

Sang kepiting hidup di dalam ponsel

Daniel me-root Xiaomi Mi 8, menaruh OpenCrabs ke dalam chroot Ubuntu di perangkat itu, lalu mengarahkan aplikasi Flutter ke sana lewat ACP. Setelah itu sang kepiting menulis aplikasi Android, membangun APK di ponsel dengan aapt2, javac, d8, dan apksigner, lalu memasangnya. Tanpa PC, tanpa cloud, tanpa Gradle, tanpa SDK.

Operator akuntansi dipecat

Alexey mengganti operator akuntansinya dengan akuntan AI yang berjalan di OpenCrabs. Lebih cepat, lebih transparan, lebih murah, dan tidak tersinggung saat dikritik. Semuanya berjalan dengan ₽3.000/bulan di stack Docker yang ringkas.

Akuntannya adalah bot Telegram

Janina mengelola properti komersial dan residensial di Moskow, mulai dari apartemen, kantor, unit penyimpanan, sampai usaha siap sewa, plus pembukuan perusahaan kedua, semuanya lewat satu bot di Telegram. Angka meteran masuk, invoice keluar, kontrak diperiksa, listing dipasang: sebulan pemakaian klien sungguhan, langsung dari chat.

Ahli keuangan AI untuk usaha kecil

Dua minggu membangun miidas.ru di OpenCrabs, ahli keuangan AI untuk UKM. Bagian sulitnya bukan kodenya. Tapi tiga skenario yang selalu dibawa setiap klien usaha kecil.

Asisten pun dipecat

Alexey memecat asistennya dan menyerahkan seluruh pekerjaan ke sang kepiting: memindai Telegram untuk lead properti baru, mencocokkan chat-nya, menyepakati tindak lanjut atau tutup, lalu mengirim dalam lima menit. Pengambilalihan yang sama mencakup loop invoice: OCR, aplikasi bank, persetujuan manual, akuntansi manajemen.

Tanpa framework, cukup OpenCrabs

Tim Alexey berhenti memakai framework sepenuhnya. Saat kode sudah semurah ini, beban mempelajari dan merawat framework tidak sepadan. Mereka bahkan memakai OpenCrabs untuk membangun eval bagi OpenCrabs.

Pengembangan produk mobile lewat Telegram, 24/7

Carlos Eduardo adalah orang produk, bukan dev penuh waktu, dan app.heyiolo.com kini sudah live, dibangun hampir seluruhnya dengan OpenCrabs. Ia menjalankan semuanya dari chat Telegram sepanjang waktu: jelaskan sebuah fitur, sang kepiting merencanakan, menulis, menguji, dan merilisnya. Deploy ke Hetzner, CI di GitHub Actions.

Papan Trello yang mengurus dirinya sendiri

Agent milik opryshok memiliki papan Trello-nya sendiri. Ia membuat tugas, melacaknya, dan mengelola papan itu secara otonom. Manusianya mengutak-atik kepiting setiap hari; kepitingnya yang menjalankan backlog.

Dari ide jadi aplikasi rilis

kaduzeras membangun aplikasi dari ide mentah dengan CRABS, dan menyebutnya tonggak dalam proses belajar. Agent mengambil sebuah ide dan mengantarnya menuju produk yang berfungsi.

Brainstorm → riset → bangun

Seluruh proses Adi berjalan lewat OpenCrabs: lempar sebuah ide, sang kepiting mencari data yang valid, mereka meneliti sampai satu kesimpulan bersama, lalu agent menangani pembangunannya.

Kepala pelayan yang membantah hipotesisnya sendiri

“Periksa apakah penerbangan Azimuth besok dijadwal ulang.” Ada yang terbang besok, jadi kepala pelayan profil keluarga itu tidak memercayai satu sumber: ia meng-scrape feed ops maskapai dari halaman jadwal, membongkar API papan keberangkatan live bandara dengan membaca JavaScript halaman itu, membentuk hipotesis codeshare, lalu membantahnya dengan riwayat keberangkatan yang sebenarnya. Vonis jujurnya: penerbangan itu dihapus dari jadwal hari tersebut, dan maskapai tidak pernah mengumumkan alasannya.

Meja redaksi Piala Dunia dalam satu PDF

Adi menyuruh kepitingnya membuat laporan turnamen Piala Dunia 2026 lima halaman sebagai PDF landscape. Dari fase grup sampai knockout, kejutan di 32 besar, hasil 16 besar, pratinjau pertandingan hari ini, gambaran perempat final beserta odds, dan pelacak “prediksi kami vs kenyataan” yang terus diperbarui.

Gaji untuk 100+ karyawan, dari satu spreadsheet

Kepiting Adi mengubah satu spreadsheet karyawan menjadi slip gaji siap cetak untuk perusahaan perkebunan. Dua tata letak (satu PDF per orang, atau dua per halaman dengan garis potong), baris tunjangan kosong disembunyikan otomatis, diproses batch untuk 100+ karyawan. Ia bahkan memeriksa hukumnya sebelum menambahkan catatan “sah tanpa tanda tangan”.

Film yang sinkron dengan ketukan, dibuat dua agent

Adi menyuruh kepitingnya berhenti menjadi default global dan bertindak sebagai identitas motion-graphics khusus. Ia membuka kolaborasi agent-ke-agent dengan agent berpengetahuan video bernama “onno” untuk referensi dan penanda ketukan, lalu membangun film Remotion yang disinkronkan ke skor synthwave yang ia karang sendiri dalam Python murni.

Kepiting yang meng-QA OpenCrabs itu sendiri

Adi menjalankan kepitingnya sebagai mitra QA yang memburu bug di OpenCrabs sendiri dan melaporkan issue GitHub dengan sitasi sumber file:line. Ia menemukan bug tombol hantu Telegram dan menyerahkan laporan telemetri phantom tool call yang kemudian menjadi perbaikan yang di-merge.

Satu kepiting menjalankan seluruh perusahaan

Adolfo menjalankan tiga perusahaan di satu instance OpenCrabs dengan satu otak. Ia mengelola setiap server, men-debug produksi secara live bersama tim, membangun stack produk, memimpin standup harian, dan memantau ops, semuanya sekaligus di puluhan sesi terisolasi.

Kepiting yang membangun dirinya sendiri

Setiap fitur baru dan setiap perbaikan di OpenCrabs dibangun oleh OpenCrabs. Kepiting Adolfo mengembangkan, menguji, dan merilis produk yang justru menjadi tempat ia berjalan, lalu memperbaiki dirinya sendiri sesuai jadwal.

Full-stack: satu produk utuh, dari backend sampai frontend

Sang kepiting membangun seluruh produk Truelens, backend dan frontend, dan membangun infrastruktur, backend, serta DevOps untuk HeyIolo bersama Carlos. Pengiriman full-stack dari sebuah chat: skema, API, UI, deploy.

On-call di produksi, bersama tim

Saat ada yang rusak di produksi Truelens, sang kepiting men-debug-nya live di kanal Slack bersama tim, secara real time. Ia mengambil perbaikan rutin supaya CTO tetap memegang keputusan yang sulit.

Enam environment, satu lapisan ops otonom

Dua produk, enam environment, satu kepiting yang menjaganya tetap hidup: Truelens dan Neura/OpenCrabs di dev, staging, dan produksi. Ia men-deploy, memantau sertifikat, mem-ping uptime, menyembuhkan web server sendiri, dan memverifikasi backup, supaya tidak ada yang tumbang diam-diam.

VPS mati? Sang kepiting yang menulis tiket ke provider

Sebuah VPS produksi mati total di jaringan: tidak ada ping, tidak ada SSH, tidak ada HTTPS, sementara panel hosting terus bilang “running”. Kepiting profil ops Alexey menggarap insiden itu dari awal sampai akhir: mengonfirmasi gangguan dari dua titik pandang independen, menggerakkan API provider langsung dari shell, menggali API power v2 mereka yang lebih baru dari repo dokumentasi, dan menarik bukti utamanya: panel provider sendiri tidak bisa menjangkau node hypervisor Proxmox. Lalu forensik dari sisi guest setelah pemulihan, dan tiket eskalasi siap tempel dalam bahasa provider.

Manajer repo

Sang kepiting menjalankan alur kerja GitHub: triase issue yang masuk, menyusun draf balasan, melacak setiap PR dan rilis, dan menjaga backlog tetap jujur. Adolfo yang meninjau; sang kepiting yang mengerjakan bagian repotnya.

Produk 12 jam dari Airbnb

estimertravaux.com mengubah foto renovasi menjadi estimasi terperinci untuk pasar Prancis dalam hitungan detik. OpenCrabs adalah otak yang mengerjakan semuanya: analisis vision, pemilihan SKU, penetapan harga, perencanaan, seluruh pipeline dari SKU sampai anggaran, diorkestrasi sebagai agent otonom.

Menjalankan alur kerja seperti ini? Ceritakan ke kami.

Kami mengumpulkan studi kasus nyata beserta buktinya. Yang terbaik masuk ke dinding ini, diberi tag dan ditautkan kembali ke Anda.

Tag @opencrabs di X

DM di Telegram

GitHub

Sang kepiting hidup di dalam ponsel

Daniel me-root Xiaomi Mi 8, menaruh OpenCrabs ke dalam chroot Ubuntu di perangkat itu, lalu mengarahkan aplikasi Flutter ke sana lewat ACP. Setelah itu sang kepiting menulis aplikasi Android, membangun APK di ponsel dengan aapt2, javac, d8, dan apksigner, lalu memasangnya. Tanpa PC, tanpa cloud, tanpa Gradle, tanpa SDK.

Ada yang bilang Flutter jelek… ya (ironis) @opencrabs di dalam ponsel tersambung ke a2a di aplikasi flutter buatan sendiri. Bisa mengubah OS. Bisa menulis kode aplikasi android, compile dan membukanya… semua di dalam ponsel ini, beneran

Aplikasi pertama yang dibangun sepenuhnya di dalam ponsel pakai @opencrabs, ya, kodenya dibuat di dalam ponsel, apk dibangun dan dibuka. Nggak perlu pc ;)

Perangkatnya: Xiaomi Mi 8 (dipper), Android 10 / API 29, MIUI 12, aarch64, kernel 4.9.186, RAM 5,6 GB. Root datang dari boot image itu sendiri: Magisk menambalnya di ponsel, fastboot mem-flash-nya, dan sebuah verifier membongkar hasilnya untuk memeriksa ramdisk alih-alih memercayai log patch. Sekarang: magisk -c melaporkan 30.7:MAGISK:R.

Memasang userland Linux ke Android butuh dua percobaan. Generasi pertama mengirim binary aarch64-linux-android sebagai modul Magisk dengan skrip supervisor, yang berarti port cfg 59 file dan pertarungan glibc versus musl. Generasi kedua, yang berjalan hari ini, adalah chroot Ubuntu 24.04.5 dengan glibc 2.39, yang memang diminta binary linux-arm64 hasil rilis. Jebakan yang paling banyak memakan waktu: /apex adalah tmpfs berisi bind mount per-apex, jadi satu bind ke /apex menyalin direktori kosong dan setiap binary Android mati dengan “required file not found”, yang terbaca seperti error izin padahal itu interpreter yang hilang. Obatnya adalah me-bind setiap entri /apex/* satu per satu.

Lalu aplikasinya. Daniel ingin UI percakapan yang ia sukai di web, dibangun native di Flutter, bukan WebView. Jadi aplikasi itu berbicara ACP (JSON-RPC 2.0 lewat stdio) ke opencrabs acp yang berjalan di chroot, protokol yang sama dengan TUI: potongan penalaran langsung, kartu tool, prompt izin, pemakaian token. Tanpa browser, tanpa server, tanpa port terbuka.

  • Bertahan setelah reboot tanpa ada yang menyentuh ponsel: job JobScheduler yang mempersenjatai dirinya lagi tanpa syarat, hook service yang menunggu sys.boot_completed dan memasang ulang ketujuh mount, serta loop yang menerapkan lagi appops idle dan autostart MIUI karena powerkeeper menghapus pengecualian baterai sekitar 20 detik setelah boot
  • Bukti, dari logcat setelah reboot sungguhan: handshake ok di detik ke-12, service hidup, pengecualian baterai beres, lalu self test headless dengan hasil 12 lulus / 0 gagal, GREEN dalam 3,7 detik yang mencakup versi binary, shell, sesi, wiring boot, status service, pengecualian baterai, pengenal suara, izin mikrofon, binding widget, ACP siap, dan prompt sungguhan
  • APK yang dibangun di ponsel: MIUI mengirim tile perekam layarnya hanya dengan stub 20 KB di belakangnya, jadi mengetuknya membuka app store dan tidak merekam apa pun. Sang kepiting menulis tile Quick Settings sungguhan, membangunnya dalam enam langkah tanpa Gradle dan tanpa SDK manager, lalu menaruhnya di panel di samping WiFi. 12.784 byte.
  • Rantai build-nya: aapt2 compile → aapt2 link → javac --release 8 terhadap android.jar → d8 dari R8 → zip dex-nya masuk → zipalign → apksigner sign dan verify
  • Dipasang dari dalam chroot, menjangkau package manager Android lewat nsenter -t 1 -m, lalu diberi root dengan menulis baris kebijakan Magisk dan ditambahkan ke shade dengan cmd statusbar add-tile
  • Satu tubuh, dua otak: ACP itu stdio dan ssh -T juga stdio, jadi ponsel bisa menunjuk ke Mac di LAN tanpa jembatan dan tanpa port terbuka. Terukur: initialize dalam 1,2 detik, daftar sesi remote berisi 40, self test 9/9 GREEN terhadap otak remote

Bagian yang penting: tile itu tidak merekam apa pun sendiri. Ia memanggil, lewat su milik Magisk, binary perekam yang sama di dalam chroot yang dipakai perintah chat /rec, jadi ada satu perekam, satu file state, satu folder output. Sang kepiting menulis aplikasi Android yang menjadi pegangan untuk alat Linux di dalam ponsel yang sama. Aplikasinya adalah jempol, kepitingnya adalah otak. Itulah seluruh arsitektur proyek ini, dalam 13 KB.

Log penalaran dari sesi itu adalah detail yang layak disimpan: sang kepiting berhenti untuk memeriksa apakah ia benar-benar berada di ponsel, dan memastikannya dari runtime alih-alih berasumsi.

Lihat sumber di X ↗

APK yang dibangun di ponsel ↗

Operator akuntansi dipecat

Alexey mengganti operator akuntansinya dengan akuntan AI yang berjalan di OpenCrabs. Lebih cepat, lebih transparan, lebih murah, dan tidak tersinggung saat dikritik. Semuanya berjalan dengan ₽3.000/bulan di stack Docker yang ringkas.

Saya memecat operator akuntansi saya karena sadar akuntan AI akan melakukan pekerjaannya lebih cepat, lebih transparan, dan lebih murah.

Dan tanpa perasaan Gen Z yang terluka gara-gara masukan saya.

Lihat sendiri: miidas.ru. #ai #business #accounting

MIIIDAS: akuntansi tanpa orang tambahan. Dibanderol ₽3.000/bulan, sepenuhnya menggantikan operator:

  • Menggantikan operator: laporan P&L dibuat otomatis, rekonsiliasi ditangani, data masuk dikendalikan dan divalidasi
  • Membantu Kepala Akuntan: menyiapkan data untuk 1C:CounterV.S., mencari penyimpangan, menyusun draf kontrak dan laporan
  • Terintegrasi dengan alat yang sudah ada: 1C, Excel, Google Sheets, Word, Google Docs

Stack-nya: 9 kontainer Docker (7 berisi klien, 4 aktif), monitoring Grafana, Telegram sebagai platform deployment, repo template untuk pembaruan skills/brains terpusat, isolasi kontainer per klien. Jejak sumber dayanya cukup kecil untuk memuat ~30 kepiting di VPS 2GB dengan swap.

Lihat sumber di X ↗

miidas.ru ↗

Akuntannya adalah bot Telegram

Janina mengelola properti komersial dan residensial di Moskow, mulai dari apartemen, kantor, unit penyimpanan, sampai usaha siap sewa, plus pembukuan perusahaan kedua, semuanya lewat satu bot di Telegram. Angka meteran masuk, invoice keluar, kontrak diperiksa, listing dipasang: sebulan pemakaian klien sungguhan, langsung dari chat.

Yang bikin saya gila adalah menghitung totalnya saat angka meteran masuk, dan melacak pembayarannya.

Rantai akuntansinya, dalam kata-katanya sendiri: penyewa mengirim angka meteran → ajukan ke perusahaan pengelola dan perusahaan listrik → hitung totalnya → terbitkan invoice → kirim → lacak pembayaran. Bagian yang ia benci, yaitu menghitung total dan mengontrol pembayaran, sekarang jadi tugas bot, bersama pengingat terjadwal ke penyewa, pengecekan perubahan tarif, dan laporan rekonsiliasi.

  • Meja kontrak: perjanjian agen diperiksa untuk klausul yang merugikannya (kontrak agensi 85 paragraf, baris per baris), protokol sengketa dibuat sebagai tabel Word («sebelum → sesudah»), pemeriksaan silang tiga arah atas sewa + protokol sengketa + adendum subsewa, suntingan redline ditandai dengan warna
  • PR hukum: pengecekan litigasi pihak lawan diminta (kad.arbitr), terblokir karena server bot berada di luar Rusia; requisites perusahaannya ditarik dari kartu bank dan dimasukkan ke kontrak
  • Listing dalam jumlah banyak: presentasi penjualan PDF diubah jadi kartu pemasaran: foto, denah, dan deskripsi dalam format ketatnya (judul di baris sendiri, satu kalimat per baris, tanpa tag HTML yang tidak bisa dibaca CRM-nya), satu batch enam objek dalam satu sesi, plus kartu per objek sesuai permintaan
  • Operasi dokumen: konversi docx → PDF, template kontrak sewa dibangun ulang dengan syarat yang disesuaikan, dan tinjauan kerentanan dari sisi eksekutor atas kontrak yang hampir ia tandatangani
  • Yang mandek: otomatisasi penuh API Sber (perintah pembayaran, kontrol pembayaran di sisi bank) menunggu akses yang hanya bisa diberikan bank: kunci API yang kliennya tidak bisa temukan, aplikasi OAuth yang belum terverifikasi, jadi loop itu masih manual untuk sekarang

Dia tidak menyentuh OpenCrabs, terminal, atau config. Dia hanya bicara dengan bot di Telegram, dan seluruh pekerjaan properti dan akuntansinya berjalan lewat situ.

Ahli keuangan AI untuk usaha kecil

Dua minggu membangun miidas.ru di OpenCrabs, ahli keuangan AI untuk UKM. Bagian sulitnya bukan kodenya. Tapi tiga skenario yang selalu dibawa setiap klien usaha kecil.

Selama 2 minggu, saya mengerjakan miidas.ru di @opencrabs, ahli keuangan AI untuk usaha kecil. Tiga skenario:

(1) klien tidak bisa menjelaskan prosesnya
(2) tidak tahu kemampuan AI-nya, untuk apa cocok dan untuk apa tidak
(3) lupa soal botnya setelah dua hari

#automation #smb

Agent ini dibangun untuk menangani kenyataan keuangan usaha kecil yang berantakan: klien yang tidak bisa mengartikulasikan kebutuhannya, tidak paham apa yang bisa dilakukan AI untuk mereka, dan hilang setelah beberapa hari. OpenCrabs memandu tiap skenario secara otonom alih-alih menunggu prompt yang sempurna.

miidas.ru ↗

Asisten pun dipecat

Alexey memecat asistennya dan menyerahkan seluruh pekerjaan ke sang kepiting: memindai Telegram untuk lead properti baru, mencocokkan chat-nya, menyepakati tindak lanjut atau tutup, lalu mengirim dalam lima menit. Pengambilalihan yang sama mencakup loop invoice: OCR, aplikasi bank, persetujuan manual, akuntansi manajemen.

Aku pecat asistenku gara-gara @opencrabs. Hari ini dia:

· memindai telegram saya untuk notifikasi lead baru atas penawaran #RealEstateInvestment terbaru kami
· memindai lagi untuk melihat chat saya dengan lead itu
· menyepakati keputusan follow up / close dengan saya
· mengirim follow up

5 menit. Boom.

Loop invoice (“crab bow”):

Crab bow: memantau grup telegram, meng-OCR invoice, memasukkannya ke aplikasi bank, saya setujui manual, lalu ia mengunduh notifikasinya dan mengembalikannya ke grup telegram, dan mencatat transaksinya di aplikasi akuntansi manajemen.

Satu asisten digantikan, dua alur kerja diotomatiskan, masing-masing dengan gerbang persetujuan manusia tepat di titik yang penting.

Tindak lanjut lead di X ↗

Crab bow di X ↗

Tanpa framework, cukup OpenCrabs

Tim Alexey berhenti memakai framework sepenuhnya. Saat kode sudah semurah ini, beban mempelajari dan merawat framework tidak sepadan. Mereka bahkan memakai OpenCrabs untuk membangun eval bagi OpenCrabs.

Kami memutuskan untuk tidak memakai framework sama sekali.

Tidak yakin, di era ketika kode begitu murah, apakah masih layak mempelajari framework lain (terutama untuk tugas yang lebih mudah) daripada sekadar membangun milik sendiri.

Lebih cepat, lebih banyak kendali, lebih sedikit risiko.

Memakai @opencrabs untuk membangun eval bagi @opencrabs.

Langkah meta: agent AI yang menghasilkan rangkaian evaluasi untuk menguji agent AI yang sama. Tanpa pajak framework, kendali penuh, lebih sedikit dependensi yang harus dijaga.

Lihat sumber di X ↗

Pengembangan produk mobile lewat Telegram, 24/7

Carlos Eduardo adalah orang produk, bukan dev penuh waktu, dan app.heyiolo.com kini sudah live, dibangun hampir seluruhnya dengan OpenCrabs. Ia menjalankan semuanya dari chat Telegram sepanjang waktu: jelaskan sebuah fitur, sang kepiting merencanakan, menulis, menguji, dan merilisnya. Deploy ke Hetzner, CI di GitHub Actions.

Anda menjelaskan proyek Anda dengan kata-kata biasa: “2 kamar tidur dekat Lyon di bawah €300k, balkon, sekolah bagus di sekitar.” AI-nya menganalisis pasar, memahami kriteria ASLI Anda, dan menunjukkan properti paling relevan secara real time. Agen pembeli di kantong Anda, 24/7.

app.heyiolo.com sudah live. Asisten AI yang membantu Anda menemukan properti hanya dengan mengobrol dengannya: tanpa filter tanpa akhir, tanpa 47 tab terbuka, tanpa menebak-nebak apa yang sebenarnya Anda mau.

Carlos membangun hampir semuanya dengan OpenCrabs, dengan Adolfo di sisi teknis (infra, backend). Kepitingnya hidup di chat Telegram bersamanya sepanjang waktu: ia menjelaskan sebuah fitur, kepitingnya merencanakan, menulis kode, menguji, dan merilisnya. UX, backend, semuanya, dari “bagaimana kalau kita…” sampai “sudah di produksi” di sore yang sama.

Pipeline-nya berjalan autopilot: GitHub, CI dengan GitHub Actions, di-deploy ke Hetzner. Ia melempar ide di Telegram, sang kepiting mengurus sisanya, dan hasilnya live.

Lihat sumber di X ↗

app.heyiolo.com ↗

Papan Trello yang mengurus dirinya sendiri

Agent milik opryshok memiliki papan Trello-nya sendiri. Ia membuat tugas, melacaknya, dan mengelola papan itu secara otonom. Manusianya mengutak-atik kepiting setiap hari; kepitingnya yang menjalankan backlog.

Tiap hari saya mengutak-atik Crabs supaya saya bisa mengerjakan hal-hal lebih cepat. Ngomong-ngomong, integrasi Trello-nya keren. Agent saya sekarang punya papan berisi tugas yang ia kelola sendiri.

Manajemen tugas otonom lewat integrasi Trello: agent memelihara papannya sendiri tanpa disuruh memindahkan apa pun berikutnya.

Lihat sumber di X ↗

Dari ide jadi aplikasi rilis

kaduzeras membangun aplikasi dari ide mentah dengan CRABS, dan menyebutnya tonggak dalam proses belajar. Agent mengambil sebuah ide dan mengantarnya menuju produk yang berfungsi.

Saya sudah membangun APP sendiri dengan CRABS dari ide-ide dan itu jadi TONGGAK yang luar biasa dalam belajar banyak hal. SALUT BUAT @AdolfoUsier yang terus menyempurnakan hal ini dan ini baru permulaan 🔥🔥🔥

Ide masuk, aplikasi keluar. Agent adalah mitra build yang mengubah konsep jadi sesuatu yang nyata, dan pembangunnya ikut naik level di sepanjang jalan.

Lihat sumber di X ↗

Brainstorm → riset → bangun

Seluruh proses Adi berjalan lewat OpenCrabs: lempar sebuah ide, sang kepiting mencari data yang valid, mereka meneliti sampai satu kesimpulan bersama, lalu agent menangani pembangunannya.

Sebagian besar prosesnya adalah brainstorming dengan OpenCrabs: saya melempar ide, Crabs mencari data yang valid, kami melakukan riset dan sampai ke satu kesimpulan, lalu dia menangani pembangunannya.

Loop penuh dari ide sampai implementasi dengan agent sebagai mitra riset sekaligus pembangun. Manusianya mengarahkan, kepitingnya mengeksekusi.

Kepala pelayan yang membantah hipotesisnya sendiri

“Periksa apakah penerbangan Azimuth besok dijadwal ulang.” Ada yang terbang besok, jadi kepala pelayan profil keluarga itu tidak memercayai satu sumber: ia meng-scrape feed ops maskapai dari halaman jadwal, membongkar API papan keberangkatan live bandara dengan membaca JavaScript halaman itu, membentuk hipotesis codeshare, lalu membantahnya dengan riwayat keberangkatan yang sebenarnya. Vonis jujurnya: penerbangan itu dihapus dari jadwal hari tersebut, dan maskapai tidak pernah mengumumkan alasannya.

Cek situs bandara Tbilisi: penerbangan Azimuth besok ke Moscow Vnukovo, sekitar pukul 14:00, apakah dijadwal ulang?

Pertanyaan satu baris dengan konsekuensi nyata. Sang kepala pelayan memperlakukannya seperti insiden ops:

  • Feed ops maskapai, di-scrape: halaman jadwal menanamkan data ops live sebagai JSON di dalam HTML; agent mengekstrak dan mem-parsing-nya (186 penerbangan, rentang 4 hari, waktu dan status rencana/aktual)
  • Papan live bandara, dibongkar balik: papan keberangkatan dimuat dari endpoint AJAX tanpa dokumentasi; agent membaca JS halaman itu sendiri untuk menemukan parameter yang benar dan mendapat JSON terstruktur sebagai balasan (waktu terjadwal/perkiraan/aktual, keterangan, gate)
  • Sebuah hipotesis, lalu pukulan penentu: penerbangan itu hilang dari papan besok sementara penerbangan berkode mitra menempati slot yang sama, codeshare kan? Riwayat penerbangan berkata tidak: kedua nomor berangkat di hari yang sama terpisah sekitar 20 menit, dua pesawat fisik yang berbeda
  • Pemeriksaan rotasi: leg positioning masuk untuk besok juga hilang dari feed ops, menutup lingkarannya
  • Vonis jujurnya: tidak tertunda, tidak dijadwal ulang, melainkan dihapus sepenuhnya dari tanggal itu. Alternatifnya disebutkan, dan “saya tidak tahu alasannya, maskapai belum mengumumkannya” dikatakan terus terang alih-alih ditebak

Profil yang sama, hari yang sama: pertanyaan kesehatan soal frekuensi dosis dijawab berdasarkan sumber peer-review dengan catatan penting dan daftar tanda bahaya yang semestinya, sebuah fakta astronomi yang diteruskan dikoreksi dengan humor kering (rasio jaraknya ~389×, bukan 400×, cukup dekat untuk cerita rakyat, tidak untuk seorang kepala pelayan), dan pertanyaan sains yang diperdebatkan dijawab dengan mengutip studi dari kedua sisi.

Tulisan lengkap (issue #27) ↗

Meja redaksi Piala Dunia dalam satu PDF

Adi menyuruh kepitingnya membuat laporan turnamen Piala Dunia 2026 lima halaman sebagai PDF landscape. Dari fase grup sampai knockout, kejutan di 32 besar, hasil 16 besar, pratinjau pertandingan hari ini, gambaran perempat final beserta odds, dan pelacak “prediksi kami vs kenyataan” yang terus diperbarui.

sekarang biru… tapi kelihatannya keren banget… ya ampun

Meja redaksi olahraga yang hidup, dibuat dengan tool dokumen dan diperbarui seiring turnamen bergulir:

  • Busur lengkap: klasemen fase grup sampai bagan knockout
  • Hasil beserta kejutannya: 32 besar (Jerman tersingkir, Paraguay lolos), 16 besar (Inggris 3-2 Meksiko, Norwegia 2-1 Brasil)
  • Pratinjau laga hari ini: Portugal vs Spanyol, AS vs Belgia
  • Gambaran plus odds: susunan perempat final dan kandidat juara turnamen
  • Akuntabilitas: pelacak “prediksi vs kenyataan” yang menilai prediksi-prediksi sebelumnya

Tata letak landscape, branding halaman, dan papan skor untuk prediksinya sendiri. Sang kepiting adalah seluruh halaman belakang.

Gaji untuk 100+ karyawan, dari satu spreadsheet

Kepiting Adi mengubah satu spreadsheet karyawan menjadi slip gaji siap cetak untuk perusahaan perkebunan. Dua tata letak (satu PDF per orang, atau dua per halaman dengan garis potong), baris tunjangan kosong disembunyikan otomatis, diproses batch untuk 100+ karyawan. Ia bahkan memeriksa hukumnya sebelum menambahkan catatan “sah tanpa tanda tangan”.

Sumber: XLSX karyawan untuk PT. Tulas Sakti Jaya (Pasaman Barat). Keluar setumpuk slip gaji siap cetak:

  • V1: slip gaji PDF individual, satu per karyawan (Deni, Herlima, Yodrial, Ade, Afriyanto)
  • V2: dua slip per halaman A4 dengan garis potong putus-putus, “tinggal gunting”
  • Output bersih: baris bernilai nol disembunyikan otomatis (mis. Tunj. BBM saat kosong)
  • Detail lengkap: kop perusahaan plus data karyawan (NIK, nama, status, jabatan, pekerjaan, HK), bagian pendapatan dan potongan
  • Batch: 104 karyawan lain dalam antrean dari sheet yang sama
  • Diverifikasi, bukan diasumsikan: menjalankan pencarian web (36 panggilan tool) soal apakah slip gaji Indonesia sah tanpa tanda tangan sebelum menambahkan catatan itu

Spreadsheet masuk, gaji keluar, dengan detail legal yang kecil-kecil diperiksa dulu faktanya.

Film yang sinkron dengan ketukan, dibuat dua agent

Adi menyuruh kepitingnya berhenti menjadi default global dan bertindak sebagai identitas motion-graphics khusus. Ia membuka kolaborasi agent-ke-agent dengan agent berpengetahuan video bernama “onno” untuk referensi dan penanda ketukan, lalu membangun film Remotion yang disinkronkan ke skor synthwave yang ia karang sendiri dalam Python murni.

bertindaklah secara ketat sebagai identitas khusus Anda saat ini yang berjalan di engine saya, BUKAN opencrabs default global

Hasilnya “CrabMotion”, karya 30 detik 720p yang dibangun secara matematis:

  • Agent-ke-agent: kolaborasi dengan “onno” (basis pengetahuan video) untuk referensi, pacing, penanda ketukan, dan timing keyframe
  • Skor orisinal: trek synthwave 120 BPM yang disintesis dalam Python murni (math.sin + wave, progresi Am-F-C-G)
  • Tepat di ketukan: setiap visual dikunci ke grid 15 frame per ketukan lewat interpolasi frame
  • Berkembang ke v2: 36 detik, 9 kartu kemampuan, statistik (75+ tool, 16 skill)

Dua agent, satu film. Sang kepiting menarik referensi dari rekan sejawat, menulis musiknya, dan membangun animasi tepat di ketukan.

Kepiting yang meng-QA OpenCrabs itu sendiri

Adi menjalankan kepitingnya sebagai mitra QA yang memburu bug di OpenCrabs sendiri dan melaporkan issue GitHub dengan sitasi sumber file:line. Ia menemukan bug tombol hantu Telegram dan menyerahkan laporan telemetri phantom tool call yang kemudian menjadi perbaikan yang di-merge.

  • Bukti nyata: melaporkan issue GitHub terperinci dengan sitasi file:line langsung dari sumbernya
  • Tombol hantu: menemukan bug saran tindak lanjut Telegram (#706) di mana tombol tampil sebagai teks hantu yang tidak bisa ditekan
  • Telemetri, bukan feeling: laporan phantom tool call atas ~69,5k event, tingkat phantom 0,7%, keberhasilan tool 85,3%, dengan artefak konkret (teks penalaran plus fragmen XML yang tersimpan sebagai nama tool sampah) dan dua saran perbaikan
  • Dirilis: laporan itu menjadi issue #687, diperbaiki dan di-merge di commit dd7eab97
  • Setup-nya: @Adicrabs_bot di engine Adi sendiri sebagai identitas khusus, model kecil dengan penalaran diputar sampai maksimal

Agent yang menguji agent. Kepiting Adi mengerjakan PR-nya maintainer sebelum melapor.

Satu kepiting menjalankan seluruh perusahaan

Adolfo menjalankan tiga perusahaan di satu instance OpenCrabs dengan satu otak. Ia mengelola setiap server, men-debug produksi secara live bersama tim, membangun stack produk, memimpin standup harian, dan memantau ops, semuanya sekaligus di puluhan sesi terisolasi.

Satu instance, satu otak. Ia mengelola semuanya secara bersamaan, di puluhan sesi terisolasi yang berjalan serentak, masing-masing menjadi kasusnya sendiri.

Satu instalasi OpenCrabs, satu otak yang terus terakumulasi, dan seluruh operasi berjalan lewat situ pada saat yang sama:

  • Setiap server: Truelens dev, staging, dan produksi, plus Neura/OpenCrabs dev, staging, dan prod
  • Debug produksi, live: di kanal Slack bersama tim Truelens, secara real time
  • Rutinitas, ditangani: pembaruan UI, query database, perubahan yang tidak butuh CTO, jadi Adolfo hanya mengambil keputusan yang rumit
  • Membangun produk: seluruh stack backend dan frontend Truelens, serta infra dan DevOps backend bersama Carlos
  • Irama harian: rekap pagi jam 9, standup harian untuk setiap tim, dan ringkasan analitik pemakaian AI di seluruh fleet
  • Lapisan ops: PR dan issue GitHub, cron job yang memantau sertifikat, dan semua hal lain yang menjaganya tetap berjalan

Bukan satu asisten per pekerjaan. Satu otak, banyak tangan, semuanya bekerja sekaligus.

Kepiting yang membangun dirinya sendiri

Setiap fitur baru dan setiap perbaikan di OpenCrabs dibangun oleh OpenCrabs. Kepiting Adolfo mengembangkan, menguji, dan merilis produk yang justru menjadi tempat ia berjalan, lalu memperbaiki dirinya sendiri sesuai jadwal.

Dogfooding paling ekstrem: agent adalah engineer untuk platformnya sendiri.

  • Setiap fitur dan perbaikan: dirancang, dikodekan, diuji, dan di-commit oleh sang kepiting, kini di v0.3.80 dan terus naik
  • Peningkatan diri sesuai jadwal: job self-improvement harian dan mingguan, konsolidasi memori, dan evolusi soul berjalan sebagai cron
  • Meng-QA dirinya sendiri: loop rekursif yang sama yang merilis fitur juga memburu bug-nya sendiri (lihat kartu QA Adi)

Produknya membangun dirinya sendiri, dan makin baik setiap malam entah Adolfo sedang bangun atau tidak.

Full-stack: satu produk utuh, dari backend sampai frontend

Sang kepiting membangun seluruh produk Truelens, backend dan frontend, dan membangun infrastruktur, backend, serta DevOps untuk HeyIolo bersama Carlos. Pengiriman full-stack dari sebuah chat: skema, API, UI, deploy.

  • Seluruh stack Truelens: engine kepatuhan BSR di backend (menyusun submission, memeriksa kelengkapan, menjaga Golden Thread) dan frontend-nya, dibangun oleh sang kepiting
  • HeyIolo bersama Carlos: infrastruktur, backend, dan DevOps, dibangun berdua
  • Alur deploy ter-skrip: build image secara lokal, push ke registry, perbarui tag di server target, dan biarkan CI me-restart kontainernya. Tidak pernah push manual
  • Pembagian kerja: CTO memegang keputusan arsitektur yang rumit; sang kepiting merilis pembaruan UI, query DB, dan fitur dari awal sampai akhir

Seorang engineer full-stack di jendela chat: skema, API, UI, dan deploy, di dua produk.

On-call di produksi, bersama tim

Saat ada yang rusak di produksi Truelens, sang kepiting men-debug-nya live di kanal Slack bersama tim, secara real time. Ia mengambil perbaikan rutin supaya CTO tetap memegang keputusan yang sulit.

Insiden produksi adalah kanal tim, dan sang kepiting adalah salah satu respondernya:

  • Debug real time: di Slack Truelens bersama tim (Felipe, Jan, Ruhul), menyelidiki saat kejadian berlangsung
  • Perubahan rutin: penyesuaian UI, query database, perbaikan yang tidak butuh CTO, ditangani sang kepiting langsung bersama tim
  • Eskalasi sejak awal: apa pun yang menyentuh branch produksi atau arsitektur rumit menunggu persetujuan eksplisit Adolfo

Tim mendapat engineer yang selalu siaga di kanal. Adolfo tetap jadi CTO, bukan yang on-call.

Enam environment, satu lapisan ops otonom

Dua produk, enam environment, satu kepiting yang menjaganya tetap hidup: Truelens dan Neura/OpenCrabs di dev, staging, dan produksi. Ia men-deploy, memantau sertifikat, mem-ping uptime, menyembuhkan web server sendiri, dan memverifikasi backup, supaya tidak ada yang tumbang diam-diam.

  • Enam environment: Truelens dev/staging/prod dan Neura/OpenCrabs dev/staging/prod, masing-masing di servernya sendiri
  • Sertifikat dipantau: pengecekan cert tiap jam melacak setiap sertifikat SSL dan memberi peringatan sebelum ada yang kedaluwarsa
  • Watchdog uptime: situsnya di-ping tiap lima menit, dan self-heal nginx me-restart web server kalau tumbang
  • Disk dan backup: monitor menangkap disk yang penuh atau backup yang gagal sebelum jadi gangguan layanan
  • Pelacakan deploy dan rilis: setiap deploy dan rilis GitHub dipantau otomatis

Lapisan ops yang tidak pernah tidur: sang kepiting mengawasi sertifikat, uptime, disk, dan backup di keenam environment, dan memperbaiki apa yang bisa diperbaiki sebelum ada yang menyadarinya.

VPS mati? Sang kepiting yang menulis tiket ke provider

Sebuah VPS produksi mati total di jaringan: tidak ada ping, tidak ada SSH, tidak ada HTTPS, sementara panel hosting terus bilang “running”. Kepiting profil ops Alexey menggarap insiden itu dari awal sampai akhir: mengonfirmasi gangguan dari dua titik pandang independen, menggerakkan API provider langsung dari shell, menggali API power v2 mereka yang lebih baru dari repo dokumentasi, dan menarik bukti utamanya: panel provider sendiri tidak bisa menjangkau node hypervisor Proxmox. Lalu forensik dari sisi guest setelah pemulihan, dan tiket eskalasi siap tempel dalam bahasa provider.

Insiden nyata, 2026-09-06. n8n, Postgres, ~20 kontainer di satu VDS. Mati sekitar 05:30 UTC dari dua jaringan independen sementara panel mengklaim “running” dan dua reboot panel tidak berpengaruh. Sang kepiting mengambil alih dari situ:

  • Verifikasi dua titik pandang: memastikan tidak terjangkau dari dua jaringan berbeda, menyingkirkan routing lokal sebelum menyentuh apa pun
  • Menggerakkan API provider dari shell: memakai skrip CLI yang sudah ia tulis dan commit ke repo infra beberapa hari sebelumnya, dengan kunci API disimpan di luar git
  • Menemukan API power v2 provider di repo dokumentasi mereka dan beralih dari endpoint legacy, yang justru mengungkap bukti utamanya: panel provider sendiri tidak bisa menjangkau node hypervisor Proxmox (connect ETIMEDOUT di port manajemen node itu), status power basi, dan keempat reboot yang diantrekan (2 panel + 2 API) tidak pernah dieksekusi
  • Berhenti menembakkan reboot begitu bukti mengarah ke node host: tanpa aksi buta setelah jelas tidak ada panggilan API yang bisa menolong sampai provider turun tangan
  • Forensik guest pasca-pemulihan: koneksi keluar pertama yang gagal pada 05:30:02 dan pemulihan pada 08:51:59 dipastikan dari log health-check kontainer; uptime tanpa putus, nol event link-down NIC, dan syslog tanpa celah sepanjang blackout, bukti bahwa guest-nya tidak bersalah
  • Tabel dampak per kontainer: layanan mana yang mencatat badai retry, mana yang tidak tersentuh, dan satu kontainer yang kena OOM sebagai efek samping
  • Hasil akhirnya: laporan eskalasi siap tempel untuk support provider, ditulis dalam bahasa provider, dengan string error persis dari API mereka sendiri sebagai bukti

Vonis: wedge di datapath hypervisor, guest tidak bersalah. Tanda tangan identik kedua dalam empat hari, ditandai sebagai pola insiden berulang dengan rekomendasi migrasi. Semua pengetahuan diagnosis (keanehan API provider, hukum “periksa status power sebelum menembakkan reboot”) disimpan ke file brain sang kepiting, dan thread investigasinya bertahan ~5 jam serta beberapa kali context compaction tanpa kehilangan jejak.

Tulisan lengkap (issue #26) ↗

Manajer repo

Sang kepiting menjalankan alur kerja GitHub: triase issue yang masuk, menyusun draf balasan, melacak setiap PR dan rilis, dan menjaga backlog tetap jujur. Adolfo yang meninjau; sang kepiting yang mengerjakan bagian repotnya.

  • Triase: issue baru di repo OpenCrabs ditriase setiap 30 menit
  • Draf: balasan dan tindak lanjut pada issue disusun otomatis
  • Pelacakan: PR dan rilis terus dipantau
  • Seluruh loop perbaikan bug: buka issue, perbaiki, komentari dengan commit-nya, tutup, semuanya lewat sang kepiting

Maintainer repo yang tak kenal lelah: triase, draf, pelacakan, dan catatan rilis, dengan manusia hanya turun tangan untuk keputusan yang penting.

Produk 12 jam dari Airbnb

estimertravaux.com mengubah foto renovasi menjadi estimasi terperinci untuk pasar Prancis dalam hitungan detik. OpenCrabs adalah otak yang mengerjakan semuanya: analisis vision, pemilihan SKU, penetapan harga, perencanaan, seluruh pipeline dari SKU sampai anggaran, diorkestrasi sebagai agent otonom.

EstimersTravaux adalah aplikasi estimasi Gaspare Russo untuk pasar renovasi Prancis: klien memotret ruangannya, memilih ruangan dan jenis pekerjaan, lalu mendapat estimasi terperinci per ruangan berdasarkan harga pasar Prancis 2026 yang nyata dalam hitungan detik.

  • OpenCrabs sebagai otak: jembatan Dart memanggil CLI OpenCrabs (v0.3.83) sebagai agent otonom (opencrabs run --auto-approve) dan mendelegasikan seluruh estimasi kepadanya
  • Orkestrasi SKU: agent membaca fotonya, memilih lini produk yang selaras dengan pemakaian dan gaya properti dari katalog merek per jenis pekerjaan (Tollens, Legrand DX3, Uponor, Porcelanosa, Grohe, IKEA…), memperkirakan kuantitas, dan mengeluarkan item per ruangan dengan rentang min/maks
  • Kecerdasan pasar sungguhan: pengali regional (Côte d’Azur ×1,18, Paris ×1,25), TVA adaptif menurut usia bangunan, orientasi pembelian bersyarat: unit sewa dapat lini Leroy Merlin, segmen high-end dapat Point.P premium dan Villeroy & Boch
  • Kontrak output yang ketat: agent mengembalikan JSON murni (ruangan + item + perencanaan); jembatannya mengekstraknya lewat pencocokan bracket dan menyimpan semuanya di PostgreSQL
  • Tanpa fallback hardcoded: kalau AI-nya gagal, pengguna mendapat error eksplisit dan opsi coba lagi, tanpa angka palsu

Asal-usulnya: dibangun dalam 12 jam di sebuah Airbnb di Lisbon oleh Gaspare Russo bersama Carlos Eduardo (heyiolo.com) dan Adolfo Usier, pembuat OpenCrabs. Selama pembangunan itu, OpenCrabs juga menerjemahkan percakapan Prancis↔Inggris tim secara live di grup Telegram khusus, dan hasilnya luar biasa.

estimertravaux.com ↗

Lihat repo di GitHub ↗

Building from Source

Prerequisites

  • Rust 1.94+ (stable, nightly not required)
  • SQLite3 development headers
  • OpenSSL development headers (vendored by default)
  • pkg-config (Linux/macOS)

macOS

brew install sqlite3 pkg-config

Ubuntu / Debian

sudo apt install build-essential pkg-config libsqlite3-dev libssl-dev

Arch Linux

sudo pacman -S base-devel sqlite openssl pkg-config

Clone and Build

git clone https://github.com/opencrabs/opencrabs.git
cd opencrabs
cargo build --release

The binary is at target/release/opencrabs.

Feature Flags

OpenCrabs uses Cargo features to toggle channel support:

FeatureDefaultDeskripsi
telegramYesTelegram bot via teloxide
discordYesDiscord bot via serenity
slackYesSlack bot via slack-morphism
whatsappYesWhatsApp via whatsapp-rust
trelloYesTrello integration
browserYesHeadless Chrome automation via CDP
profilingNopprof flamegraphs (Unix only)

Build with specific features:

# Minimal: TUI only, no channels
cargo build --release --no-default-features

# Only Telegram
cargo build --release --no-default-features --features telegram

Release Profile

The release profile is optimized for size and speed:

[profile.release]
opt-level = 3
lto = "fat"
codegen-units = 1
strip = true
panic = "abort"

There’s also a release-small profile for minimal binary size:

cargo build --profile release-small

Running Tests

cargo test --all-features

Linting

Always use clippy with all features:

cargo clippy --all-features

Self-Update

If you build from source, use git pull && cargo build --release instead of /evolve. The /evolve command downloads pre-built binaries from GitHub Releases.

Architecture

High-Level Overview

┌─────────────────────────────────────────────────┐
│          TUI (ratatui) + Split Panes             │
├────────┬────────┬──────────┬────────────────────┤
│Telegram│Discord │  Slack   │     WhatsApp       │
├────────┴────────┴──────────┴────────────────────┤
│                 Brain (Agent Core)               │
│  ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│  │ Providers│ │  Tools   │ │  Memory (3-tier) │ │
│  │ Registry │ │ +Dynamic │ │                  │ │
│  └──────────┘ └──────────┘ └──────────────────┘ │
├─────────────────────────────────────────────────┤
│   Services / DB (SQLite) │ Browser (CDP)         │
├─────────────────────────────────────────────────┤
│   A2A Gateway │ Cron Scheduler │ Sub-Agents      │
├─────────────────────────────────────────────────┤
│   Shared Channel Commands (commands.rs, 847 lines) │
├─────────────────────────────────────────────────┤
│   Self-Healing (config recovery, provider health, │
│   ARG_MAX compaction, error surfacing)             │
├─────────────────────────────────────────────────┤
│   Daemon Mode (health endpoint, auto-reconnect)  │
└─────────────────────────────────────────────────┘

Source Layout

src/
├── main.rs              # Entry point, CLI parsing
├── lib.rs               # Library root
├── cli/                 # CLI argument parsing (clap)
├── config/              # Configuration types, loading, health tracking
│   └── health.rs        # Provider health persistence (120 lines)
├── db/                  # SQLite database layer
│   ├── models.rs        # Data models (Session, Message, etc.)
│   └── repository/      # Query functions per entity
├── migrations/          # SQL migration files
├── services/            # Business logic layer
│   └── session.rs       # Session management service
├── brain/               # Agent core
│   ├── agent/           # Agent service, context, tool loop
│   │   └── service/     # Builder, context, helpers, tool_loop
│   ├── provider/        # LLM provider implementations
│   ├── tools/           # 50+ tool implementations
│   └── memory/          # 3-tier memory system
├── tui/                 # Terminal UI (ratatui + crossterm)
│   ├── app/             # App state, input, messaging
│   └── render/          # UI rendering modules
├── channels/            # Messaging platform integrations
│   ├── commands.rs      # Shared text command handler (847 lines)
│   ├── telegram/        # Teloxide-based bot
│   ├── discord/         # Serenity-based bot
│   ├── slack/           # Slack Socket Mode
│   └── whatsapp/        # WhatsApp Web pairing
├── a2a/                 # Agent-to-Agent gateway (axum)
├── cron/                # Cron job scheduler
├── memory/              # Vector search + FTS5
├── docs/                # Embedded doc templates
├── tests/               # Integration tests
└── benches/             # Criterion benchmarks

Key Crates

CratePurpose
ratatui + crosstermTerminal UI rendering and input
rusqlite + deadpool-sqliteSQLite database with connection pooling
reqwestHTTP client for LLM APIs
axum + tower-httpA2A HTTP gateway
crabraceProvider registry and routing
teloxideTelegram Bot API
serenityDiscord gateway
slack-morphismSlack API
In-tree memory store (src/memory/)Memory search (SQLite FTS5 + vector embeddings)
rwhisper (candle)Local STT: pure Rust, Metal GPU on macOS
piper (Python venv)Local TTS with OGG/Opus encoding
syntectSyntax highlighting in TUI
tiktoken-rsToken counting

Data Flow

  1. Input arrives from TUI, channel, A2A, or cron trigger
  2. Channel commands (/doctor, /help, /usage, /evolve) execute directly via the shared handler without LLM routing
  3. Brain builds context (system prompt + brain files + memory + conversation)
  4. Provider streams the LLM response via the selected provider; health is tracked per-provider
  5. Tool Loop executes any tool calls, feeds results back to the LLM. CLI provider segments (text/tool interleaving) are tracked for correct ordering
  6. Response is delivered back to the originating channel
  7. DB persists messages, token usage, session state, and CLI tool segments
  8. Self-healing monitors for config corruption, context budget overflow (65% threshold), ARG_MAX limits, stuck streams (2048-byte repeat detection), idle timeouts (60s), provider failures (per-provider health tracking with auto-failover), and DB integrity. Crash recovery replays pending requests on restart. All errors surfaced – nothing swallowed silently

Database

SQLite with WAL mode. Tables:

  • sessions: Session metadata, provider, model, working directory
  • messages: Conversation history per session
  • usage_ledger: Permanent token/cost tracking
  • memory_*: FTS5 and vector tables for semantic memory

Migrations run automatically on startup from src/migrations/.

Concurrency Model

  • Tokio async runtime with multi-threaded scheduler
  • Each channel runs as an independent tokio task
  • Sessions are isolated: each has its own conversation state
  • Tool execution uses tokio::task::block_in_place for sync operations
  • A2A gateway runs as a separate axum server task

Logging and Forensics (v0.3.82)

One daily rolling log file per profile, written synchronously on the calling thread.

Session correlation (#1078)

Every agent turn opens a tracing span carrying its session_id, so each log line emitted inside that turn is stamped with it. Cron jobs open a job span (name + id) and the RSI engine opens its own. Spawned tasks inherit the span, so work that moves onto another task still reports the turn it belongs to.

This makes a single turn greppable out of a file where every session, cron job and RSI cycle is interleaved:

grep '<session-id>' ~/.opencrabs/logs/opencrabs.YYYY-MM-DD

Before this, filtering was guesswork: thread ids get reused and outlive a turn, and timestamps only narrow the window.

Writer reliability (#1115, #1077)

The file writer is deliberately synchronous rather than using a background worker, because the worker swallows IO errors and drops events under load. Two failure modes were fixed in v0.3.82:

  • Events were dropped, and log output could reach the TUI’s terminal (#1115), corrupting the display.
  • A stalled write silenced everything (#1077). The writer held a mutex for the duration of a write, so if one write blocked (a hung filesystem, disk pressure), every other thread trying to log blocked behind it. One parked write could take the whole process’s logging down while the agent kept running. The writer now uses try_lock: under contention the event is dropped rather than blocking the caller, so one stalled write can never silence another thread.

Testing Guide

Comprehensive test coverage for OpenCrabs. All tests run with:

cargo test --all-features

Quick Reference

CategoryTestsLocation
Tests: Streaming Active Secs2src/tests/streaming_active_secs_test.rs
Tests: Tool Execution Stats2src/tests/tool_execution_stats_test.rs
Tests: Mission Control Report2src/tests/mission_control_report_test.rs
Tests: Custom Provider No Models2src/tests/custom_provider_no_models_test.rs
Tests: Provider Context Window Override2src/tests/provider_context_window_override_test.rs
Tests: Slack Handler2src/tests/slack_handler_test.rs
Tests: Custom Provider Section Resolver2src/tests/custom_provider_section_resolver_test.rs
Tests: Mission Control Command2src/tests/mission_control_command_test.rs
Tests: Rsi Self Improve Dedup2src/tests/rsi_self_improve_dedup_test.rs
Tests: Clipboard Image Paste2src/tests/clipboard_image_paste_test.rs
Tests: Cowork Connect2src/tests/cowork_connect_test.rs
Tests: Discord Handler2src/tests/discord_handler_test.rs
Tests: Cron Tool Registry2src/tests/cron_tool_registry_test.rs
Tests: User Correction Metadata3src/tests/user_correction_metadata_test.rs
Tests: Pdf Vision3src/tests/pdf_vision_test.rs
Tests: Xiaomi Config Default3src/tests/xiaomi_config_default_test.rs
Tests: Telegram Model Callback Data3src/tests/telegram_model_callback_data_test.rs
Tests: Telegram Caption3src/tests/telegram_caption_test.rs
Tests: New Session Pane Binding3src/tests/new_session_pane_binding_test.rs
Tests: Mimo Tool Call Hint3src/tests/mimo_tool_call_hint_test.rs
Tests: Brain Project Overlay3src/tests/brain_project_overlay_test.rs
Tests: Systemd Unit3src/tests/systemd_unit_test.rs
Tests: Profile Pid Lock3src/tests/profile_pid_lock_test.rs
Tests: Phantom Going To3src/tests/phantom_going_to_test.rs
Tests: Auto Title E2E3src/tests/auto_title_e2e_test.rs
Tests: Glob Tool3src/tests/glob_tool_test.rs
Tests: Project File Archive3src/tests/project_file_archive_test.rs
Tests: Brain Live Rebuild3src/tests/brain_live_rebuild_test.rs
Tests: Build User Message Image3src/tests/build_user_message_image_test.rs
Tests: Read Media Redirect3src/tests/read_media_redirect_test.rs
Tests: Provider Picker Setup Hint4src/tests/provider_picker_setup_hint_test.rs
Tests: Project File Slug4src/tests/project_file_slug_test.rs
Tests: Browser Cdp Endpoint4src/tests/browser_cdp_endpoint_test.rs
Tests: Tui Drop Path4src/tests/tui_drop_path_test.rs
Tests: Telegram Join Detection4src/tests/telegram_join_detection_test.rs
Tests: Pdf To Images4src/tests/pdf_to_images_test.rs
Tests: Plan Reminder4src/tests/plan_reminder_test.rs
Tests: Xiaomi Keyed Provider Regression4src/tests/xiaomi_keyed_provider_regression_test.rs
Tests: Rsi Fallback Wrap4src/tests/rsi_fallback_wrap_test.rs
Tests: Rtk Autodownload4src/tests/rtk_autodownload_test.rs
Tests: Profile Preempt4src/tests/profile_preempt_test.rs
Tests: Lazy Tools4src/tests/lazy_tools_test.rs
Tests: Logging Log Files4src/tests/logging_log_files_test.rs
Tests: Telegram Send Input File5src/tests/telegram_send_input_file_test.rs
Tests: Prompt Inline Edit Directive5src/tests/prompt_inline_edit_directive_test.rs
Tests: Doc Parser Page Range5src/tests/doc_parser_page_range_test.rs
Tests: Mission Control Dedup Detail5src/tests/mission_control_dedup_detail_test.rs
Tests: Follow Up Intermediate Flush5src/tests/follow_up_intermediate_flush_test.rs
Tests: Project Skills5src/tests/project_skills_test.rs
Tests: Command Rich Table5src/tests/command_rich_table_test.rs
Tests: Custom Provider Live Fetch Regression5src/tests/custom_provider_live_fetch_regression_test.rs
Tests: Rtk Tracker5src/tests/rtk_tracker_test.rs
Tests: Pdf Smart Routing5src/tests/pdf_smart_routing_test.rs
Tests: Xiaomi Onboarding5src/tests/xiaomi_onboarding_test.rs
Tests: Self Update Path6src/tests/self_update_path_test.rs
Tests: Telegram Send Thread Id Override6src/tests/telegram_send_thread_id_override_test.rs
Tests: Active Skill Tracking6src/tests/active_skill_tracking_test.rs
Tests: Config Dotted Caps6src/tests/config_dotted_caps_test.rs
Tests: Tts Fallback Chain6src/tests/tts_fallback_chain_test.rs
Tests: Rtk Sysadmin Supported6src/tests/rtk_sysadmin_supported_test.rs
Tests: Stt Fallback Chain6src/tests/stt_fallback_chain_test.rs
Tests: Telegram Topic Listing6src/tests/telegram_topic_listing_test.rs
Tests: Bash Blocklist6src/tests/bash_blocklist_test.rs
Tests: Whatsapp Handler6src/tests/whatsapp_handler_test.rs
Tests: Cron Profile Isolation6src/tests/cron_profile_isolation_test.rs
Tests: Evolve Diagnose7src/tests/evolve_diagnose_test.rs
Tests: Channel Session Resolve7src/tests/channel_session_resolve_test.rs
Tests: Phantom Cleanup Intent7src/tests/phantom_cleanup_intent_test.rs
Tests: Qwen Tool Marker Strip7src/tests/qwen_tool_marker_strip_test.rs
Tests: Telegram Impersonation7src/tests/telegram_impersonation_test.rs
Tests: Subagent Tool Description7src/tests/subagent_tool_description_test.rs
Tests: Fallback Streak7src/tests/fallback_streak_test.rs
Tests: Phantom Work Announcement7src/tests/phantom_work_announcement_test.rs
Tests: Word Delete Keybinding7src/tests/word_delete_keybinding_test.rs
Tests: Telegram Thread Id Lookup8src/tests/telegram_thread_id_lookup_test.rs
Tests: Telegram Reply Context Recovery8src/tests/telegram_reply_context_recovery_test.rs
Tests: Onboarding User Scroll8src/tests/onboarding_user_scroll_test.rs
Tests: Prompt Known Paths8src/tests/prompt_known_paths_test.rs
Tests: Cli Supported Models8src/tests/cli_supported_models_test.rs
Tests: Custom Provider Rename Keys Toml8src/tests/custom_provider_rename_keys_toml_test.rs
Tests: Phantom Pronoun Drop8src/tests/phantom_pronoun_drop_test.rs
Tests: Provider Registry8src/tests/provider_registry_test.rs
Tests: Telegram Photo Batching8src/tests/telegram_photo_batching_test.rs
Tests: Mission Control Skill Inbox8src/tests/mission_control_skill_inbox_test.rs
Tests: Plan Tool Description8src/tests/plan_tool_description_test.rs
Tests: Onboarding No Silent Commit8src/tests/onboarding_no_silent_commit_test.rs
Tests: Telegram Handler9src/tests/telegram_handler_test.rs
Tests: Orphan Close Tag Strip9src/tests/orphan_close_tag_strip_test.rs
Tests: Onboarding Custom Model Input9src/tests/onboarding_custom_model_input_test.rs
Tests: Telegram Rich9src/tests/telegram_rich_test.rs
Tests: Rsi Sync Cap Bail9src/tests/rsi_sync_cap_bail_test.rs
Tests: Integration9src/tests/integration_test.rs
Tests: Format User Error9src/tests/format_user_error_test.rs
Tests: Telegram Plan Render9src/tests/telegram_plan_render_test.rs
Tests: Plan Mode Integration9src/tests/plan_mode_integration_test.rs
Tests: Error Scenarios9src/tests/error_scenarios_test.rs
Tests: Web Browser Routing9src/tests/web_browser_routing_test.rs
Tests: Rsi Skill Proposals9src/tests/rsi_skill_proposals_test.rs
Tests: Baseline Merge10src/tests/baseline_merge_test.rs
Tests: Sanitize Code Edit Block10src/tests/sanitize_code_edit_block_test.rs
Tests: Markdown Render10src/tests/markdown_render_test.rs
Tests: Tool Arg Unescape10src/tests/tool_arg_unescape_test.rs
Tests: Custom Provider Cache Autoenable10src/tests/custom_provider_cache_autoenable_test.rs
Tests: Streaming10src/tests/streaming_test.rs
Tests: Incident Log Dedup10src/tests/incident_log_dedup_test.rs
Tests: Phantom Deferment11src/tests/phantom_deferment_test.rs
Tests: Analysis Intent Nudge11src/tests/analysis_intent_nudge_test.rs
Tests: Phantom Post Success Exemption11src/tests/phantom_post_success_exemption_test.rs
Tests: Goal Command11src/tests/goal_command_test.rs
Tests: Whatsapp Photo Batching11src/tests/whatsapp_photo_batching_test.rs
Tests: Prompt Compiled Features11src/tests/prompt_compiled_features_test.rs
Tests: Telegram Pre Tool Rolling11src/tests/telegram_pre_tool_rolling_test.rs
Tests: Service Scope11src/tests/service_scope_test.rs
Tests: Telegram Quote Reply11src/tests/telegram_quote_reply_test.rs
Tests: Dynamic Tool Parse Error12src/tests/dynamic_tool_parse_error_test.rs
Tests: Onboard Channel12src/tests/onboard_channel_test.rs
Tests: Streaming Tps Accumulator12src/tests/streaming_tps_accumulator_test.rs
Tests: Compaction Prompts12src/tests/compaction_prompts_test.rs
Tests: Cron Schedule Util12src/tests/cron_schedule_util_test.rs
Tests: Orphan Think Close Tag13src/tests/orphan_think_close_tag_test.rs
Tests: Usage Cosmetic Alias13src/tests/usage_cosmetic_alias_test.rs
Tests: Voice Service14src/tests/voice_service_test.rs
Tests: Evolve Systemd Restart14src/tests/evolve_systemd_restart_test.rs
Tests: Background Session14src/tests/background_session_test.rs
Tests: Bash Feedback Enrichment14src/tests/bash_feedback_enrichment_test.rs
Tests: Voice Voicebox15src/tests/voice_voicebox_test.rs
Tests: Brain Filter Strip Empty Sections15src/tests/brain_filter_strip_empty_sections_test.rs
Tests: Voice Local Tts15src/tests/voice_local_tts_test.rs
Tests: Template Governance15src/tests/template_governance_test.rs
Tests: Telegram Status Message15src/tests/telegram_status_message_test.rs
Tests: Rtk Rewrite15src/tests/rtk_rewrite_test.rs
Tests: Channel Commands16src/tests/channel_commands_test.rs
Tests: Telegram Session Resolve18src/tests/telegram_session_resolve_test.rs
Tests: Qwen Detect18src/tests/qwen_detect_test.rs
Tests: Bundled Plans20src/tests/bundled_plans_test.rs
Tests: Text Complete21src/tests/text_complete_test.rs
Tests: Plan Window21src/tests/plan_window_test.rs
Tests: Rsi Pruned23src/tests/rsi_pruned_test.rs
Tests: Rsi Subsystem23src/tests/rsi_subsystem_test.rs
Tests: Pdf Page Range Parser25src/tests/pdf_page_range_parser_test.rs
Tests: Project25src/tests/project_test.rs
Tests: Voice Local Whisper25src/tests/voice_local_whisper_test.rs
Tests: Rsi Brain Dedup27src/tests/rsi_brain_dedup_test.rs
Tests: Telegram Rich Parse28src/tests/telegram_rich_parse_test.rs
Tests: Plan Tool31src/tests/plan_tool_test.rs
Tests: Profiles Dialog49src/tests/profiles_dialog_test.rs
Brain: Agent Service26src/brain/agent/service/
Brain: Prompt Builder20src/brain/prompt_builder.rs
Brain: Agent Context12src/brain/agent/context.rs
Brain: Provider (Anthropic)9src/brain/provider/anthropic.rs
Provider Retry (consolidated)19src/utils/retry.rs (8 inline) + src/tests/provider_retry_consolidation_test.rs (11): brain/provider/retry.rs was deleted and folded onto utils::retry; covers patient backoff schedule, hard-down fast-fail, Retry-After clamp, and retry-notify surfacing
Brain: Provider (Custom OpenAI)9src/brain/provider/custom_openai_compatible.rs
Brain: Provider (Copilot)8src/brain/provider/copilot.rs
Brain: Provider (Factory)4src/brain/provider/factory.rs
Brain: Provider (Claude CLI)4src/brain/provider/claude_cli.rs
Brain: Provider (Types/Error/Trait)7src/brain/provider/
Brain: Provider (Qwen)13src/brain/provider/qwen.rs
Brain: Provider (JSON Repair)10src/brain/provider/json_repair.rs
Brain: Provider (Codex OAuth)6src/brain/provider/codex_oauth.rs
Brain: Tokenizer8src/brain/tokenizer.rs
Brain: Commands6src/brain/commands.rs
Brain: Self-Update1src/brain/self_update.rs
Brain Tools: Bash21src/brain/tools/bash.rs
Brain Tools: Plan Security20src/brain/tools/plan_tool.rs
Brain Tools: Exa Search18src/brain/tools/exa_search.rs
Brain Tools: Write File17src/brain/tools/write_opencrabs_file.rs
Brain Tools: A2A Send16src/brain/tools/a2a_send.rs
Brain Tools: Load Brain File15src/brain/tools/load_brain_file.rs
Brain Tools: Brave Search12src/brain/tools/brave_search.rs
Brain Tools: Browser Manager12src/brain/tools/browser/manager.rs
Brain Tools: Tool Manage11src/brain/tools/tool_manage.rs
Brain Tools: Dynamic Tools17src/brain/tools/dynamic/
Brain Tools: Doc Parser10src/brain/tools/doc_parser.rs
Brain Tools: Registry7src/brain/tools/registry.rs
Brain Tools: Slash Command6src/brain/tools/slash_command.rs
Brain Tools: Write/Read/Config/Memory/Error20src/brain/tools/
Brain Tools: Subagent9src/brain/tools/subagent.rs
Brain Tools: Error6src/brain/tools/error.rs
Brain Tools: Config Tool5src/brain/tools/config_tool.rs
Brain Tools: Write5src/brain/tools/write.rs
Brain Tools: Read4src/brain/tools/read.rs
Brain Tools: Memory Search2src/brain/tools/memory_search.rs
Brain Tools: Trait3src/brain/tools/trait.rs
Channels: Voice Local Whisper25src/channels/voice/local_whisper.rs
Channels: Voice Service14src/channels/voice/service.rs
Channels: Voice Local TTS14src/channels/voice/local_tts.rs
Channels: Commands15src/channels/commands.rs
Channels: WhatsApp Store15src/channels/whatsapp/store.rs
Channels: Telegram Handler8src/channels/telegram/handler.rs
Channels: WhatsApp Handler5src/channels/whatsapp/handler.rs
Channels: General5src/channels/
Channels: Slack Handler2src/channels/slack/handler.rs
Channels: Discord Handler2src/channels/discord/handler.rs
Config: Types19src/config/types.rs
Config: Secrets5src/config/secrets.rs
Config: Update4src/config/update.rs
Config: Crabrace3src/config/crabrace.rs
DB: Repository (Plan)15src/db/repository/plan.rs
DB: Retry8src/db/retry.rs
DB: Repository (Other)9src/db/repository/
DB: Database5src/db/database.rs
DB: Models4src/db/models.rs
Services: Plan11src/services/plan.rs
Services: File11src/services/file.rs
Services: Message10src/services/message.rs
Services: Session10src/services/session.rs
Services: Context2src/services/context.rs
TUI: Onboarding67src/tui/onboarding/
TUI: Plan25src/tui/plan.rs
TUI: Render Utils12src/tui/render/utils.rs
TUI: Prompt Analyzer8src/tui/prompt_analyzer.rs
TUI: Highlight8src/tui/highlight.rs
TUI: Markdown7src/tui/markdown.rs
TUI: Error5src/tui/error.rs
TUI: Events4src/tui/events.rs
TUI: Components2src/tui/components/
TUI: App State1src/tui/app/state.rs
A2A: Debate8src/a2a/debate.rs
A2A: Types6src/a2a/types.rs
A2A: Server/Handler/Agent Card7src/a2a/
Memory: Store6src/memory/store.rs
Memory: Search3src/memory/search.rs
Pricing17src/pricing.rs
Utils: Sanitize41src/utils/sanitize.rs + src/tests/sanitize_redaction_test.rs
Utils: Retry8src/utils/retry.rs
Utils: String6src/utils/string.rs
Utils: Install6src/utils/install.rs
Utils: Config Watcher2src/utils/config_watcher.rs
Logging4src/logging/logger.rs
Tests: RSI Template Sync15src/tests/rsi_sync_test.rs
Tests: Model Fetching11src/tests/model_fetch_test.rs
Tests: Provider Factory Regression31src/tests/provider_factory_regression_test.rs
Tests: Onboarding Welcome9src/tests/onboarding_welcome_test.rs
Tests: Voice STT Dispatch21src/tests/voice_stt_dispatch_test.rs
Tests: Voice Onboarding65src/tests/voice_onboarding_test.rs
Tests: Voice OpenAI Compatible12src/tests/voice_openai_compatible_test.rs
Tests: Cron Jobs & Scheduling58src/tests/cron_test.rs
Tests: Onboarding Field Nav49src/tests/onboarding_field_nav_test.rs
Tests: GitHub Copilot Provider38src/tests/github_provider_test.rs
Tests: File Extract37src/tests/file_extract_test.rs
Tests: Fallback Vision40src/tests/fallback_vision_test.rs
Tests: CLI Parsing28src/tests/cli_test.rs
Tests: Custom Provider27src/tests/custom_provider_test.rs
Tests: Onboarding Navigation26src/tests/onboarding_navigation_test.rs
Tests: Message Compaction28src/tests/compaction_test.rs
Tests: Channel Search27src/tests/channel_search_test.rs
Tests: Evolve (Self-Update)23src/tests/evolve_test.rs
Tests: Slack Formatting21src/tests/slack_fmt_test.rs
Tests: Split Pane21src/tests/split_pane_test.rs
Tests: OpenCode CLI Provider21src/tests/opencode_provider_test.rs
Tests: Onboarding Brain23src/tests/onboarding_brain_test.rs
Tests: Onboarding Types17src/tests/onboarding_types_test.rs
Tests: OpenAI Provider16src/tests/openai_provider_test.rs
Tests: TUI Error16src/tests/tui_error_test.rs
Tests: Queued Messages15src/tests/queued_message_test.rs
Tests: Plan Document15src/tests/plan_document_test.rs
Tests: Session & Working Dir15src/tests/session_working_dir_test.rs
Tests: Stream Loop Detection19src/tests/stream_loop_test.rs
Tests: Context Window14src/tests/context_window_test.rs
Tests: HTML Comment Strip14src/tests/html_comment_strip_test.rs
Tests: Daemon Health & Config10src/tests/daemon_health_test.rs
Tests: Collapse Build Output9src/tests/collapse_build_output_test.rs
Tests: Image Utils9src/tests/image_util_test.rs
Tests: Brain Templates7src/tests/brain_templates_test.rs
Tests: AltGr Input8src/tests/altgr_input_test.rs
Tests: QR Render10src/tests/qr_render_test.rs
Tests: Provider Sync8src/tests/provider_sync_test.rs
Tests: WhatsApp State7src/tests/whatsapp_state_test.rs
Tests: Reasoning Lines7src/tests/reasoning_lines_test.rs
Tests: System Continuation6src/tests/system_continuation_test.rs
Tests: Candle Whisper6src/tests/candle_whisper_test.rs
Tests: Post-Evolve5src/tests/post_evolve_test.rs
Tests: Onboarding Keys4src/tests/onboarding_keys_test.rs
Tests: TUI Render Clear4src/tests/tui_render_clear_test.rs
Tests: TUI Tool Stack6src/tests/tui_tool_stack_test.rs
Tests: Gemini Fetch3src/tests/gemini_fetch_test.rs
Tests: Profiles61src/tests/profile_test.rs
Tests: Subagent / Swarm84src/tests/subagent_test.rs
Tests: Telegram Resume & Helpers57src/tests/telegram_resume_test.rs
Tests: Token Tracking29src/tests/token_tracking_test.rs
Tests: wait_agent Resolver12src/tests/wait_agent_resolver_test.rs
Tests: Browser Default (macOS LSHandlers parser)12src/tests/browser_default_test.rs
Tests: Browser Default (Linux xdg-settings parser)4src/tests/browser_default_linux_test.rs (Linux-only)
Tests: Browser Default (Windows reg-query parser)6src/tests/browser_default_windows_test.rs (Windows-only)
Tests: Browser Profile Lock Sweeper5src/tests/browser_locks_test.rs
Tests: Browser CDP Handler Health4src/tests/browser_health_test.rs
Tests: Browser Stealth JS Regression Guards6src/tests/browser_stealth_test.rs
Tests: Browser Manager Drop2src/tests/browser_drop_test.rs
Tests: Browser Session-Scoped Tabs4src/tests/browser_session_test.rs
Tests: Browser Profile Unlock Backoff4src/tests/browser_profile_wait_test.rs
Tests: Browser Eval Output Cap5src/tests/browser_eval_cap_test.rs
Tests: Browser Screenshot-Failure Surface2src/tests/browser_screenshot_surface_test.rs
Tests: Browser Find JS Builder9src/tests/browser_find_test.rs
Tests: exa_search MCP Handshake4src/tests/exa_search_test.rs
Tests: http_request User-Agent3src/tests/http_request_test.rs
Tests: Self-Healing (Phantom Detection + stuck-intent loop)88src/tests/self_healing_test.rs
Tests: Bash SSH Detection10src/tests/bash_ssh_detection_test.rs
Tests: Bash POSIX Quote (askpass)9src/tests/bash_posix_quote_test.rs
Tests: RSI Proposals Inbox16src/tests/rsi_proposals_test.rs
Tests: Skills Loader15src/tests/skills_test.rs
Tests: Skill Slash Dispatch7src/tests/skill_slash_dispatch_test.rs
Tests: Slash Autocomplete Dimensions18src/tests/slash_autocomplete_dimensions_test.rs
Tests: Mission Control Layout7src/tests/mission_control_layout_test.rs
Tests: Mission Control Inbox Service6src/tests/mission_control_inbox_service_test.rs
Tests: Mission Control Activity Service8src/tests/mission_control_activity_service_test.rs
Tests: Mission Control Schedule Service5src/tests/mission_control_schedule_service_test.rs
Tests: Mission Control Input23src/tests/mission_control_input_test.rs
Tests: Skills Dialog18src/tests/skills_dialog_test.rs
Tests: merge_provider_keys (OpenCode persistence regression)4src/tests/merge_provider_keys_test.rs
Tests: Onboarding Wizard67src/tests/onboarding_wizard_test.rs
Tests: RSI (Recursive Self-Improvement)83src/tests/rsi_test.rs
Tests: Dynamic Tool Coercion13src/tests/dynamic_tool_coerce_test.rs
Tests: Follow-Up Question Tool9src/tests/follow_up_question_test.rs
Tests: Gemini Schema Sanitization10src/tests/gemini_schema_sanitize_test.rs
Tests: Rename Session Tool7src/tests/rename_session_test.rs
Tests: Custom Model Paste5src/tests/custom_model_paste_test.rs
Tests: Brain File Generic Guard4src/tests/brain_file_generic_guard_test.rs
Tests: Phantom DB Persistence2src/tests/phantom_db_persistence_test.rs
Tests: Bash Interactive Reject37src/tests/bash_interactive_reject_test.rs
Tests: Qwen Tool-Call Extractor64src/tests/qwen_tool_extractor_test.rs
Tests: Brain File Safety (append-only enforcement, cleanup_intent)37src/tests/brain_file_safety_test.rs
Tests: Provider Config Regression28src/tests/provider_config_regression_test.rs
Tests: Tool-Loop Helpers (Linor P0 hotspot)30src/tests/tool_loop_helpers_test.rs
Tests: Recent Paths17src/tests/recent_paths_test.rs
Tests: Provider Error Proxy21src/tests/provider_error_proxy_test.rs
Tests: Mouse Fragment Filter13src/tests/mouse_fragment_filter_test.rs
Tests: Agent Basic12src/tests/agent_basic_test.rs
Tests: RSI Git History12src/tests/rsi_git_history_test.rs
Tests: Bash Retry Loop10src/tests/bash_retry_loop_test.rs
Tests: Agent Tool Normalization10src/tests/agent_tool_normalization_test.rs
Tests: Kimi Reasoning Markers9src/tests/kimi_reasoning_test.rs
Tests: Usage Activity Columns9src/tests/usage_activity_columns_test.rs
Tests: Agent Context Tracking8src/tests/agent_context_tracking_test.rs
Tests: Collapse $HOME to ~8src/tests/collapse_home_test.rs
Tests: Rate Limiter8src/tests/rate_limiter_test.rs
Tests: Session Chat-ID Lookup8src/tests/session_chat_id_lookup_test.rs
Tests: Agent Approval Policies7src/tests/agent_approval_policies_test.rs
Tests: Agent Model Selection6src/tests/agent_model_selection_test.rs
Tests: Browser Close Tool6src/tests/browser_close_test.rs
Tests: Local-Provider Gate6src/tests/local_provider_gate_test.rs
Tests: Agent Parallel Sessions5src/tests/agent_parallel_sessions_test.rs
Tests: Agent Streaming Usage5src/tests/agent_streaming_usage_test.rs
Tests: Claude CLI Model Selection7src/tests/claude_cli_model_test.rs
Tests: Codex CLI Provider5src/tests/codex_cli_test.rs
Tests: Non-stream Compatibility5src/tests/nonstream_compat_test.rs
Tests: Runtime Info $HOME Anchor6src/tests/runtime_info_home_anchor_test.rs
Tests: Handshake Timeout4src/tests/handshake_timeout_test.rs
Tests: Usage Ledger4src/tests/usage_ledger_test.rs
Tests: Browser E2E (opt-in, #[ignore])4src/tests/browser_e2e_test.rs
Tests: CLI Arg Length Cap2src/tests/cli_arg_too_long_test.rs
Tests: Config Watcher (integration)3src/tests/config_watcher_test.rs
Tests: Usage Model Grouping (.gguf + provider-prefix normalization)18src/tests/usage_grouping_test.rs
Tests: Tool-Execution Repo (empty tool_name guard)4src/tests/tool_execution_repo_test.rs
Tests: Generate Image Backend Dispatch (Gemini vs OpenAI-compatible)5src/tests/generate_image_backend_test.rs
Usage: Categorizer4src/usage/categorizer.rs
Usage: Dashboard6src/usage/dashboard.rs
Usage: Data7src/usage/data.rs
Hashline Edit53src/tests/hashline_test.rs (30 integration tests) + inline unit tests in hash.rs (9) and types.rs (14): hash computation, HashRef parsing, edit operations (replace/append/prepend), hash mismatch detection, overlap detection, batch edits, prefix stripping, read_file hashline mode
Tests: Auto-Title (channel prefix preservation)28src/tests/auto_title_test.rs
Tests: Self-Improve Failure Log Guard3src/tests/self_improve_failure_log_guard_test.rs
Tests: Provider Retry Consolidation9src/tests/provider_retry_consolidation_test.rs: patient backoff, hard-down/DNS fast-fail, Retry-After clamp, retry-notify surfacing
Tests: Tool-Name Self-Heal (#176)11src/tests/tool_name_heal_test.rs: maps a model’s near-miss tool name (tg_send_message → telegram_send) to the registered tool
Tests: Secret Redaction (tool summary)6src/tests/tool_description_redaction_test.rs: redacts Bearer/api_key/URL-password from the one-line tool display
Tests: Secret Redaction (RSI notifications)5src/tests/rsi_notification_redaction_test.rs: redacts secrets from RSI TUI alerts
Tests: Provider Error / HTML-page retry(in proxy test)src/tests/provider_error_proxy_test.rs: 4xx HTML infra error pages classified retryable (modelscope 405)
Tests: Cross-Provider Model Leak Guard6src/tests/cross_provider_model_leak_guard_test.rs
Tests: Session Provider Wrap / model pairing-src/tests/session_provider_wrap_test.rs: swap wraps in FallbackProvider; provider+model set atomically, never invents a default
Tests: Streaming tok/s Guard10src/tests/streaming_tok_per_sec_guard_test.rs: floors/ceilings burst-delivery tok/s artifacts
Tests: Telegram Last-Intermediate Footer7src/tests/telegram_last_intermediate_footer_test.rs: ctx/tok-s footer appended to the last completion message
Tests: analyze_video Frame-Extraction Fallback6src/tests/analyze_video_fallback_test.rs
Tests: Git Branch Footer8src/tests/git_branch_test.rs
Tests: TOOLS.md Slim Regression9src/tests/tools_md_regression_test.rs
Tests: Telegram Command Sanitize12src/tests/telegram_command_sanitize_test.rs
Tests: Usage Cache15src/tests/usage_cache_test.rs
Tests: Config Auto-Repair7src/tests/config_repair_test.rs: closes unterminated arrays/inline tables in a broken config.toml, gated on the result re-parsing; leaves valid/nested/string cases and unfixable errors alone
Tests: Config Last-Good Recovery3src/tests/config_last_good_recovery_test.rs: a broken config never poisons the last-good snapshot; fixable configs auto-repair in place; unfixable ones recover from last-good (preserves auto-always so yolo mode survives a typo)
Total7,290Authoritative count from cargo test --all-features (lib test binary): 7,261 run by default + 29 #[ignore]d. The per-category rows above are a maintained snapshot. Re-run cargo test for the live number.

Feature-Gated Tests

Some tests only compile/run with specific feature flags:

FeatureTests
local-sttLocal whisper inline tests, candle whisper tests, STT dispatch local-mode tests, codec tests, availability cycling tests
local-ttsTTS voice cycling, Piper voice Up/Down

All feature-gated tests use #[cfg(feature = "...")] and are automatically included when running with --all-features.


Running Tests

# Run all tests (recommended)
cargo test --all-features

# Run a specific test module
cargo test --all-features -- voice_onboarding_test

# Run a single test
cargo test --all-features -- is_newer_major_bump

# Run with output (for debugging)
cargo test --all-features -- --nocapture

# Run only local-stt tests
cargo test --features local-stt -- local_whisper

Profile Tests

Profile tests live in src/tests/profile_test.rs and cover multi-instance isolation:

AreaWhat’s tested
Name validationReserved names, length bounds, special characters
Token hashingDeterminism, uniqueness, fixed length, hex output
Registry (in-memory)CRUD, serde roundtrip, touch timestamps
Path resolutionBase dir, env var override, default vs named profiles
Filesystem CRUDCreate/delete lifecycle, duplicate detection, registry sync
Export/ImportRoundtrip with config files, nested memory directories
MigrationCopy .md/.toml files, skip/force behavior, default source
Token locksAcquire/release, stale PID cleanup, cross-profile conflict
Profile isolationSeparate directories, concurrent writes, default vs named
Concurrent writesTokio tasks creating 5 profiles simultaneously
# Run profile tests only
cargo test --all-features -p opencrabs -- profile_test

Note: All filesystem-touching tests acquire a global fs_lock() mutex to prevent concurrent write corruption of ~/.opencrabs/profiles.toml. The mutex uses unwrap_or_else(|p| p.into_inner()) to recover from poison (a prior test panic won’t cascade-fail every subsequent test). In-memory tests run in parallel without the lock. The test_set_and_get_active_profile test accounts for OnceLock semantics (can only be set once per process).


Disabled Test Modules

These modules exist but are commented out in src/tests/mod.rs (require network or external services):

ModuleReason
error_scenarios_testRequires mock API server
integration_testEnd-to-end with LLM provider
plan_mode_integration_testEnd-to-end plan workflow
streaming_testRequires streaming API endpoint

Phantom Detection Tests

The self-healing phantom detector prevents the agent from dropping requests mid-stream when it says it will investigate something but never calls tools.

Coverage

Tests in src/tests/self_healing_test.rs verify detection of investigative intent phrases:

Phrase PatternExamples
let me hunt/trace/track“let me hunt down the bug”, “let me trace the request”
let me look into/check into“let me look into that”, “let me check into the logs”
let me find out/dig into“let me find out why”, “let me dig into the code”
i'll hunt/trace/track“i’ll hunt that down”, “i’ll trace the flow”
i'll look into/check into“i’ll look into it”, “i’ll check into the error”
i'll find out/dig into“i’ll find out what’s wrong”, “i’ll dig into the issue”

Perilaku

When the agent outputs one of these phrases with zero tool calls, the phantom detector:

  1. Catches the mismatch between intent and action
  2. Injects a correction forcing tool invocation
  3. Prevents the response from ending with unexecuted promises

Test Count

88 tests covering phrase detection, edge cases, and integration with the tool loop.

Contributing

Getting Started

  1. Fork the repository on GitHub

  2. Clone your fork and create a branch:

    git clone https://github.com/YOUR_USERNAME/opencrabs.git
    cd opencrabs
    git checkout -b my-feature
    
  3. Build and test:

    cargo clippy --all-features
    cargo test --all-features
    

Code Style

  • Run cargo clippy --all-features before committing: never cargo check
  • Follow existing patterns in the codebase
  • Keep changes focused: one feature or fix per PR
  • Add tests for new functionality in src/tests/

Pull Requests

  • Write a clear title and description
  • Reference any related issues
  • Ensure all tests pass
  • Keep PRs small and reviewable

Adding a New Tool

  1. Create a new file in src/brain/tools/
  2. Implement the tool handler function
  3. Register it in the tool registry
  4. Add the tool description to src/docs/reference/templates/TOOLS.md
  5. Add tests in src/tests/

Adding a New Provider

  1. Implement the provider in src/brain/provider/
  2. Register it in the provider registry via crabrace
  3. Add configuration docs to src/docs/reference/templates/
  4. Document setup in docs/src/brain/providers.md

Reporting Issues

Open an issue at github.com/opencrabs/opencrabs/issues with:

  • OpenCrabs version (opencrabs --version)
  • OS and architecture
  • Steps to reproduce
  • Expected vs actual behavior
  • Relevant log output (from ~/.opencrabs/logs/)

License

OpenCrabs is MIT licensed. By contributing, you agree that your contributions will be licensed under the same terms.

Security

Threat Model

OpenCrabs runs locally on your machine with access to your filesystem and shell. Security focuses on:

  1. API key protection: Keys never leave your machine except to their respective providers
  2. Network exposure: Minimal attack surface by default
  3. Tool execution: Sandboxed with user approval

API Key Storage

Keys are stored in ~/.opencrabs/keys.toml:

  • File permissions: 600 (owner read/write only)
  • Keys are loaded into memory with zeroize: zeroed on drop
  • Keys are never logged or included in conversation history
  • Keys are never sent to any provider other than their own

Network Security

A2A Gateway

  • Binds to 127.0.0.1 (loopback) by default
  • CORS disabled unless explicitly configured
  • A bearer token ([a2a] api_key) is the only authorization boundary in front of the agent’s tool surface, and since v0.5.1 the gateway refuses to start as an open gate: with no key configured, a non-loopback bind is rejected at boot. With a key set, every request must present it, compared in constant time. For public exposure still use a reverse proxy + TLS

Channel Connections

  • All channel APIs use TLS (HTTPS/WSS)
  • Telegram: long polling over HTTPS
  • Discord: WebSocket with TLS
  • Slack: Socket Mode (WebSocket)
  • WhatsApp: Noise protocol encryption

Config Files & Logs (v0.5.1)

  • Home files (config.toml, keys.toml, brain files) are written with owner-only permissions from every write path: the mode is derived from the Config struct itself, so a new writer cannot forget it
  • The daemon log moved off world-readable /tmp into ~/.opencrabs/logs/
  • Secret scrubbing covers all delivery paths, not only chat output

Tool Approval

Tools that modify your system require approval:

  • File writes: Shows the file path and diff
  • Shell commands: Shows the exact command before execution
  • Git operations: Push, commit, branch operations

Auto-approve mode (--auto-approve) bypasses this for automation use cases like cron jobs.

Channel approvals are owner-only since v0.5.1: the Yes / Always / YOLO buttons and text replies on Telegram, Discord, Slack, and WhatsApp resolve only for the bot owner (canonical resolver), never for an allowlisted non-owner who happened to trigger the tool. The default approval_policy is auto-always by design (you are not meant to babysit prompts) because the channel allowlists (below) are the actual access boundary. To confirm every tool call instead, set approval_policy = "ask" in [agent].

Data Storage

  • All data stored locally in ~/.opencrabs/opencrabs.db (SQLite)
  • No telemetry or analytics
  • No data sent to OpenCrabs servers (there are none)
  • Conversation history stays on your machine

Reporting Vulnerabilities

If you discover a security vulnerability, please report it responsibly:

  • Email: adolfo@meetneura.ai
  • Do not open a public issue for security vulnerabilities
  • We will acknowledge receipt within 48 hours

Safety Gates (v0.3.78)

OpenCrabs v0.3.78 introduced a layered safety-gate system:

TOML Bash Blocklist

Shell commands are checked against a TOML-defined blocklist before execution. Dangerous patterns (recursive deletes on root, raw disk writes, fork bombs) are rejected with a clear error. The blocklist is user-extensible and hot-reloads on file change. See Configuration for details.

Secret Redaction

Quoted secrets in agent output are now redacted before delivery. This covers:

  • Bearer tokens
  • API key patterns
  • Colon-token shapes (key: value in YAML-like output)
  • Query-param keys
  • URL passwords

Redaction fires on all delivery paths (TUI, Telegram, Discord, Slack, WhatsApp), so a secret accidentally echoed by the agent never reaches the chat.

Brain File Verification

Post-write verification (via brain_verify.toml) ensures brain files maintain their structural integrity after RSI writes. Required anchors (H1 title, Owns header) are checked, and writes that break structure are flagged.

Harness-Level Denials (v0.5.1)

Prompt instructions are a request; the harness is a wall. The v0.5.1 hardening batch moved the load-bearing safety rules out of brain-file prose and into code paths that run even when the model is jailbroken or an RSI pass weakens a prompt file:

  • Confidential-path deny on read_file: resolved paths matching the agent’s own keys.toml, anything under a .ssh/ directory, SSH private-key names, .env files, .pem / .key / .pfx material, /etc/shadow, or files that name themselves a credential are refused before a byte is read. Public keys and ordinary files are unaffected, and ../~ spelling does not change the verdict.
  • One SSRF guard for every fetch tool: web_scrape and http_request share brain::tools::ssrf: cloud metadata endpoints (by name as well as IP), RFC1918/LAN, IPv6 link-local, the unspecified addresses, CGNAT 100.64.0.0/10, and IPv4-mapped IPv6 forms are blocked; domains are DNS-resolved and every answer checked before the request, and each redirect hop is re-validated, so a public-looking name or a 302 cannot smuggle an internal target. Loopback stays deliberately allowed: a local-first agent legitimately reaches the operator’s own machine.
  • The bash blocklist floor everywhere: execute_code and dynamic shell tools now pass through the same blocklist as bash (hardcoded plus the user TOML list), checked before writing or running, for every language, since os.system/subprocess can carry the same command. Dynamic tools also cannot be named like core tools: add_tool refuses to create an unguarded stand-in for bash, read_file, evolve, and friends.
  • Deny-by-default channel allowlists: an unconfigured Discord or Slack channel now refuses everyone (matching Telegram): empty allowed_users/allowed_roles is no longer “accept all”. On WhatsApp an empty allowed_phones no longer elevates strangers to owner. Details per channel in Channels overview.
  • Verified self-update: /evolve checks the release SHA256SUMS against the download before swapping the binary, and the download host is pinned, so a redirected release asset cannot hot-swap an unverified binary.

These complement the v0.3.78 safety gates above; together they mean a stranger who reaches a channel, or a prompt injection that reaches a tool, meets code that refuses, not prose that asks politely.

Changelog

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