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.

Example

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 Hardening (v0.3.10)

• 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

Self-Healing vs Self-Improvement