agent-learner 0.3.1

Reusable self-learning engine for agent workflows

agent-learner

Reusable learning control plane for coding-agent workflows.

agent-learner helps you:

• capture learned rules from agent work
• keep project-local and global brain knowledge separate
• review candidates and promote useful rules
• use a dashboard UI for history, rules, and promotions

It is designed to layer onto existing agent environments rather than replace them.

Start here

If you want the shortest one-line setup, use one of these:

pipx install "agent-learner[web]" && agent-learner dashboard --project-root "$PWD" --open
npx @cafitac/agent-learner@latest dashboard --project-root "$PWD" --open

If you want a preflight check first, run doctor before dashboard.

The dashboard defaults to 127.0.0.1:8766 to avoid common local MCP/gateway ports such as 8765.

doctor tells you whether the dashboard can run now, what is missing, and the next command to use.

Choose your path

1. Published Python package

pipx install "agent-learner[web]" && agent-learner dashboard --project-root "$PWD" --open

2. npm / npx wrapper

npx @cafitac/agent-learner@latest dashboard --project-root "$PWD" --open

3. Source checkout

./bin/dashboard.sh doctor
./bin/dashboard.sh --open

4. Optional Docker path

docker compose up --build

Docker is optional convenience only. It is not the primary OSS install path.

Typical workflow

1. Point agent-learner at a project
2. Run doctor
3. Open the dashboard
4. Review rules, candidates, and history
5. Promote reusable knowledge to the global brain when appropriate

Core concepts

Project brain vs global brain

• project-local knowledge lives under <project>/.agent-learner/
• reusable shared knowledge lives under ~/.agent-learner/global/
• retrieval is local-first, then global

Main runtime

The primary UI/runtime path is:

• FastAPI backend
• built React dashboard frontend

Static dashboard generation and stdlib-only serving still exist, but they are secondary paths.

Key commands

agent-learner doctor --project-root /path/to/repo
agent-learner dashboard --project-root /path/to/repo --open
agent-learner bootstrap --target /path/to/repo
agent-learner review-candidates --project-root /path/to/repo
agent-learner history --project-root /path/to/repo --latest-per-rule --last 10
agent-learner history-summary --project-root /path/to/repo --by adapter-decision
agent-learner overview --project-root /path/to/repo --format json

Repository shape

• src/agent_learner/ — Python core
• frontend/ — React + Vite dashboard UI
• bin/ — shell / wrapper entrypoints
• tests/ — CLI, lifecycle, wrapper, and dashboard tests
• docs/ — install, architecture, release, and smoke docs

Docs

• Start here:

  • docs/install.md — install and run paths
  • docs/quickstart.md — shortest command sequences

• Release and publish:

  • docs/publish-smoke-checklist.md — post-publish smoke matrix
  • docs/release-process.md — tag order and release flow
  • docs/distribution.md — Python core vs npm wrapper strategy

• Architecture:

  • docs/architecture.md
  • docs/adapter-convergence.md

• docs/install.md

• docs/quickstart.md

• docs/architecture.md

• docs/adapter-convergence.md

• docs/distribution.md

• docs/publish-smoke-checklist.md

• docs/release-process.md

• docs/prerelease-checklist.md

Status

Current implemented areas:

• local/global brain split
• merged retrieval
• candidate/rule/history lifecycle
• dashboard UI
• global promotion and sync
• npm wrapper + source checkout helper

Release note

If you are validating a release, use:

python scripts/release/publish_smoke_check.py --json
python scripts/release/published_runtime_smoke.py --project-root /path/to/repo --json --skip-commands

Or from a source checkout:

./bin/publish-smoke.sh --json

Then follow docs/publish-smoke-checklist.md.