specs/001-openclaw-integration/quickstart.md

Quick Start: OpenClaw Integration

Feature: 001-openclaw-integration | Date: 2026-02-27

Prerequisites

• Python 3.12+
• An OpenClaw agent that has completed at least one session
• An Anthropic API key (or any LiteLLM-supported provider)

Setup (3 steps)

1. Install

git clone https://github.com/kayba-ai/agentic-context-engine.git
cd agentic-context-engine
uv sync

2. Configure

export ANTHROPIC_API_KEY="your-api-key"

Optional overrides:

export OPENCLAW_AGENT_ID="main"           # which agent to learn from
export OPENCLAW_HOME="~/.openclaw"        # OpenClaw home directory
export ACE_MODEL="anthropic/claude-sonnet-4-20250514"  # LLM model

3. Run

# Learn from all past sessions
uv run python examples/openclaw/kayba-ace/learn_from_traces.py

# Preview what would be processed (no LLM calls, no file changes)
uv run python examples/openclaw/kayba-ace/learn_from_traces.py --dry-run

# Reprocess everything (ignore what's already been learned)
uv run python examples/openclaw/kayba-ace/learn_from_traces.py --reprocess

What Happens

1. Discovers session transcripts from ~/.openclaw/agents/<id>/sessions/
2. Parses JSONL files into structured traces (user messages, reasoning, tool calls)
3. Learns by running ACE's Reflect → Tag → Update → Apply pipeline
4. Saves strategies to ~/.openclaw/ace_skillbook.json
5. Syncs strategies into ~/.openclaw/workspace/AGENTS.md
6. Your OpenClaw agent reads the updated AGENTS.md on its next session

Automate (optional)

Run every 30 minutes via cron:

crontab -e
# Add:
*/30 * * * * cd /path/to/agentic-context-engine && uv run python examples/openclaw/kayba-ace/learn_from_traces.py >> /tmp/ace-openclaw.log 2>&1

Verify

After running, check the output:

# View learned strategies
cat ~/.openclaw/ace_skillbook.json | python -m json.tool | head -50

# View what was injected into your agent
grep -A 20 "ACE:SKILLBOOK:START" ~/.openclaw/workspace/AGENTS.md