Agentic-context-engine kayba-ace

Name: kayba-ace
Author: kayba-ai

ACE — Learn from Traces

install

source · Clone the upstream repo

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

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/kayba-ai/agentic-context-engine "$T" && mkdir -p ~/.claude/skills && cp -r "$T/examples/openclaw/kayba-ace" ~/.claude/skills/kayba-ai-agentic-context-engine-kayba-ace && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/kayba-ai/agentic-context-engine "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/examples/openclaw/kayba-ace" ~/.openclaw/skills/kayba-ai-agentic-context-engine-kayba-ace && rm -rf "$T"

manifest: examples/openclaw/kayba-ace/SKILL.md

source content

ACE — Learn from Traces

This skill ships

learn_from_traces.py

, a script that reads OpenClaw session transcripts, feeds them through the ACE learning pipeline, and writes an updated skillbook to disk.

Usage

python learn_from_traces.py [OPTIONS] [FILES...]

The script auto-discovers new sessions from

~/.openclaw/agents/<agent>/sessions/

and only processes files that haven't been processed before. Processed filenames are tracked in

ace_processed.txt

Options

Flag	Description
`--dry-run`	Parse sessions but skip the learning step (no LLM calls)
`--reprocess`	Ignore the processed log and reprocess all sessions
`--agent ID`	OpenClaw agent ID (default: `$OPENCLAW_AGENT_ID` or `main` )
`--output DIR`	Output directory for skillbook files (default: script directory)
`--opik`	Enable Opik observability logging

Pass one or more JSONL file paths as positional arguments to process specific files instead of auto-discovering sessions.

Examples

Learn from all new sessions

python learn_from_traces.py

Discovers unprocessed sessions under

~/.openclaw/agents/main/sessions/

, runs the learning pipeline, and writes the updated skillbook.

Dry run (no LLM calls)

python learn_from_traces.py --dry-run

Parses and validates sessions without calling the LLM. Useful for checking that session files are readable before committing to a full run.

Process a specific trace file

python learn_from_traces.py ~/.openclaw/agents/main/sessions/f967d602.jsonl

Skips auto-discovery and processes only the given file. The processed log is not updated when files are passed directly.

Process multiple files

python learn_from_traces.py session1.jsonl session2.jsonl session3.jsonl

Reprocess all sessions

python learn_from_traces.py --reprocess

Ignores

ace_processed.txt

and reprocesses every session file. Useful after upgrading ACE or when you want to rebuild the skillbook from scratch.

Use a different agent

python learn_from_traces.py --agent my-agent

Looks for sessions in

~/.openclaw/agents/my-agent/sessions/

instead of the default

main

Write output to a custom directory

python learn_from_traces.py --output /tmp/ace-out

Writes

ace_skillbook.json

ace_skillbook.md

, and

ace_processed.txt

/tmp/ace-out/

instead of the script's directory.

Enable Opik observability

python learn_from_traces.py --opik

Logs LLM calls and pipeline steps to Opik for debugging and monitoring.

Run from the ACE repo (host setup)

cd /path/to/agentic-context-engine
uv run python examples/openclaw/kayba-ace/learn_from_traces.py --dry-run

When running from a repo checkout,

uv run

ensures

ace

is importable.

Run inside Docker

# Dry run
docker run --rm -v ~/.openclaw:/home/node/.openclaw openclaw:local ace-learn --dry-run

# Full run with API key
docker run --rm \
  -v ~/.openclaw:/home/node/.openclaw \
  -e AWS_BEARER_TOKEN_BEDROCK="$AWS_BEARER_TOKEN_BEDROCK" \
  openclaw:local ace-learn

Inside the extended Docker image,

ace-learn

is a wrapper that calls this script with the correct paths.

Environment Variables

Variable	Default	Description
`ACE_MODEL`	`bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0`	LLM model for reflection and skill extraction
`OPENCLAW_AGENT_ID`	`main`	Default agent ID (overridden by `--agent` )
`OPENCLAW_HOME`	`~/.openclaw`	OpenClaw home directory

The script also loads

.env

files from

$OPENCLAW_HOME/.env

and

~/.env

. Set your API key in one of these variables:

```
AWS_BEARER_TOKEN_BEDROCK
```
```
ANTHROPIC_API_KEY
```
```
OPENROUTER_API_KEY
```
```
LITELLM_API_KEY
```
```
SPH_LITELLM_KEY
```

Output Files

File	Description
`ace_skillbook.json`	Machine-readable skillbook (persists across runs)
`ace_skillbook.md`	Human-readable skillbook loaded by the agent
`ace_processed.txt`	Tracks which sessions have been processed

Setup

Run the setup script from the ACE repo to copy this skill into your OpenClaw workspace:

python examples/openclaw/setup.py

Full guide: https://kayba-ai.github.io/agentic-context-engine/integrations/openclaw/