rsi-loop 0.1.0

Universal self-improvement loop for AI agents. Observe → Analyze → Fix → Verify.

🔄 RSI Loop

Every AI agent makes mistakes. RSI Loop makes them learn.

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The Problem

AI agents repeat the same failures. They hit rate limits, return empty responses, lose context, pick the wrong model — and do it all again next session. There's no feedback loop. No memory of what went wrong. No automatic improvement.

RSI Loop is the missing primitive: a universal recursive self-improvement loop that works with any agent framework.

How It Works

    ┌──────────┐     ┌──────────┐     ┌──────────┐     ┌──────────┐
    │ OBSERVE  │────▶│ ANALYZE  │────▶│   FIX    │────▶│  VERIFY  │
    │          │     │          │     │          │     │          │
    │ Record   │     │ Detect   │     │ Generate │     │ Check    │
    │ outcomes │     │ patterns │     │ & apply  │     │ health   │
    └──────────┘     └──────────┘     └──────────┘     └────┬─────┘
         ▲                                                   │
         └───────────────────────────────────────────────────┘

1. Observe — Record task outcomes: success/failure, quality, errors, model used, duration
2. Analyze — Detect patterns: recurring failures, error clusters, cross-source correlations
3. Fix — Auto-fix safe categories (routing, retries, thresholds); propose fixes for the rest
4. Verify — Track health score over time; confirm fixes reduced failure rates

Quick Start

uv add rsi-loop

Not using uv? uv add rsi-loop works too.

from rsi_loop import RSILoop

loop = RSILoop()
loop.observer.record_simple("code_generation", success=True, model="sonnet-4.6")
loop.observer.record_simple("api_call", success=False, error="429 Too Many Requests")
loop.observer.record_simple("api_call", success=False, error="429 Too Many Requests")
loop.observer.record_simple("api_call", success=False, error="429 Too Many Requests")

# Run improvement cycle
patterns = loop.run_cycle()
print(f"Health: {loop.health_score():.0%}")
print(f"Patterns found: {len(patterns)}")

Framework Integrations

Claude Code / OpenClaw

from rsi_loop.integrations.claude_code import ClaudeCodeAdapter

adapter = ClaudeCodeAdapter(data_dir="./rsi_data")
adapter.on_task_complete("code_review", success=True, model="claude-sonnet-4")
adapter.on_model_fallback("claude-opus-4", "claude-sonnet-4", "rate_limit")
adapter.on_session_reset()

Generic (File-Based)

Drop JSON files into a watch directory — works with any agent:

from rsi_loop.integrations.generic import GenericAdapter

adapter = GenericAdapter(watch_dir="./rsi_inbox")
# Your agent writes: {"task": "search", "success": false, "error": "timeout"}
# RSI picks it up automatically
outcomes = adapter.poll()

Webhook

from rsi_loop.integrations.webhook import WebhookAdapter

app = WebhookAdapter(data_dir="./rsi_data").create_app()
# POST /observe {"task": "code_gen", "success": true, "quality": 4}
# GET /health
# GET /patterns

Features

• Auto-classification — Categorizes errors automatically: rate_limit, empty_response, timeout, context_loss, etc.
• Recurrence detection — Flags issues that repeat 3+ times within the analysis window
• Health scoring — 0.0 (broken) to 1.0 (healthy), recency-weighted
• Cross-source correlation — Detects related issues across different agent sources
• Error clustering — Groups similar error messages even with different IDs/numbers
• Safe auto-fix — Automatically applies fixes for safe categories (routing, retries, thresholds)
• Fix proposals — Generates detailed proposals for unsafe categories, saved for human review
• Background loop — Run continuous improvement cycles in a background thread
• Framework-agnostic — Works with Claude Code, Cursor, Codex, or any custom agent
• Zero dependencies — Core package has no external dependencies (integrations optional)

Documentation

• Architecture — How RSI Loop works internally
• Source Taxonomy — Source classification and cross-source correlation
• Auto-Fix — How auto-fix works, safe categories, and fix templates

Examples

• Claude Code Setup
• Cursor Setup
• Generic Agent Setup
• Quick Start Script

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Built by ClawInfra — infrastructure for autonomous agents.