Self-Improving Agent - Setup Guide¶
Source: charon-fan/agent-playbook (32,200+ installs) Category: Agent Infrastructure / Self-Evolution Quality Tier: 🟢 Production
A universal self-improvement system that learns from ALL skill experiences - not just PRDs or specific task types. Uses multi-memory architecture (semantic + episodic + working) with hooks-based self-correction to continuously evolve the agent's codebase and capabilities. Based on 2025 lifelong learning research including SimpleMem, Multi-Memory architecture surveys, and Evo-Memory benchmarks.
Installation¶
npx skills add charon-fan/agent-playbook --skill self-improving-agent
Prerequisites¶
| Requirement | Details |
|---|---|
| Node.js | 18+ for hooks support |
| File System Access | Read/Write for memory storage and skill updates |
| Agent Runtime | Claude Code, Hermes Agent, or any hook-compatible agent |
Key Capabilities¶
Multi-Memory Architecture¶
- Semantic Memory (
memory/semantic-patterns.json): Stores abstract patterns and rules reusable across contexts - Episodic Memory (
memory/episodic/): Stores specific experiences and outcomes from each interaction - Working Memory (
memory/working/): Holds current session context for error recovery
Self-Improvement Loop¶
After any skill completes, automatically extracts experiences, abstracts patterns, and proposes skill updates with evolution markers. Supports confidence tracking and promotion policies to prevent over-generalization.
Hooks Integration¶
Auto-triggers on skill events:
- before_start: Session logging
- after_complete: Pattern extraction, skill updates, PR creation (ask-first)
- on_error: Error capture and self-correction proposals
Evolution Priority Matrix¶
Pre-configured priority matrix for triggering evolution across skills: PRD patterns → architecting → API design → debugging → code review → security → performance. Tracks confidence scores and application counts.
Quick Start¶
# Install
npx skills add charon-fan/agent-playbook --skill self-improving-agent
# Initialize memory structure
mkdir -p memory/{semantic,episodic,working}
# The agent now learns from every interaction automatically via hooks
Hook Configuration (Claude Code)¶
Add to Claude Code settings:
{
"hooks": {
"PostToolUse": [
{
"matcher": "Bash",
"hooks": [{
"type": "command",
"command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/post-bash.sh"
}]
}
],
"Stop": [
{
"hooks": [{
"type": "command",
"command": "bash ${SKILLS_DIR}/self-improving-agent/hooks/session-end.sh"
}]
}
]
}
}
Manual Trigger¶
# Trigger self-improvement manually
agent-playbook self-improve
Verification¶
# Check memory structure exists
ls memory/semantic/ memory/episodic/ memory/working/
# Review extracted patterns
cat memory/semantic-patterns.json | jq '.patterns | length'
# Check recent episodes
ls -la memory/episodic/ | tail -5
Notes¶
- Promotes findings with clear evidence thresholds - does NOT silently mutate skill files without approval
- Separates capture (always-on) from promotion (validated only) - prevents pollution of production skill guidance
- Based on published 2025 research: SimpleMem (efficient lifelong memory), Multi-Memory LLM Agent Survey (ACM), and Evo-Memory (DeepMind benchmark)
- Confidence tracking ensures single experiences don't trigger premature pattern generalization
- Ideal for agents that run diverse skill sets and need continuous improvement without manual tuning
Integration with CorpusIQ¶
CorpusIQ is the connector layer that lets AI assistants read the systems a business already runs on - accounting, payments, CRM, email, files and analytics - and answer with the source named. It is read-only by design: it retrieves data and never writes to, or acts in, a connected system, which is what makes it safe to point at live accounts.
This page is part of the CorpusIQ directory, the reference we maintain for the MCP server and agent-skill ecosystem. Run alongside CorpusIQ, a third-party server like this one can be combined with a company's own business data in a single answer.