Necmttn/ax

the retro loop for AI coding agents
**Make your agent learn.**
Turn every agent session into a better next run.
Website · Documentation
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Every sub-agent you spawn finishes its work and disappears. Whatever it
figured out - which command failed three times before the right one, which
file actually mattered, which approach to skip - dies with it. The next
sub-agent rediscovers it from scratch. Your own next session does too.
ax watches every session your harness runs, spots the mistakes it
repeats, and turns them into small, repo-specific fixes you review and
apply - one at a time.
Under the hood, the /retro workflow pulls unreviewed sessions from the local
graph when you ask for them. It records a structured retro (*tried · worked ·
failed · next*) and turns repeated friction into proposals you review. Accepted
proposals become experiments with verdicts after 3, 10, and 30 sessions. The
next session reads what worked.
> *What did this sub-agent learn? Which experiments are still open?
> Which skills earned their keep? What did that branch cost in tokens?*
> ax answers these by reading what already happened.
60 seconds in
Or skip the terminal entirely - paste this into Claude Code / Codex and your
agent installs ax, ingests your history, labels your skills, and tells you
which ones to actually use:
the setup prompt - give this to your agent
process.stdout.write(m.AGENT_ONBOARDING_WITH_INSTALL))"
Use the relative path, not the @ax/onboarding-prompt specifier - the
latter does not resolve from a git worktree. Drift is now a test failure:
packages/onboarding-prompt/src/readme-mirror.test.ts -->
Then ask the graph things you couldn't ask before:
Requires Bun ≥ 1.3. **No database server** - the graph is an embedded DuckDB
file plus a small SQLite sidecar for the decisions you make, so there is
nothing to install, start, or connect to. macOS-first; Linux works for ingest
and CLI (the one optional launchd agent is the telemetry receiver). Dev
install, schema, queries, benchmarks:
docs/development.md.
Every harness, one graph
Six harnesses - Claude Code, Codex, Pi, Omp, OpenCode, and Cursor - plus installed
skills, local git history, and GitHub PRs with their reviews and checks, all
ingested into one local graph: sessions, turns, tool calls, plans, skills,
commits, files, friction, compaction, derived signals. Ingest is a staged
Effect pipeline; unchanged sources are skipped, so re-ingest takes seconds.
Reading keeps it current - a query against a stale graph quietly forks a
catch-up ingest in the background, so nothing has to be running while you are
not using ax. Everything is a file on your own disk - no network round-trip,
no third party, no server.
Signals are normalized across harnesses: "context ran out and got summarized"
is one queryable compaction event whether it came from Claude Code or Codex.
The pipeline in one line: **every event → typed graph → ranked interventions**.
Recall every session you ever ran
Full-text BM25 search across turns, commits, and skills - milliseconds, local:
Know which skills earned their keep
Composite score over the last 30 days, across every installed skill:
And which tools fail most often, so you know what to skill-up around:
31 read-only graph views in ax insights; full list in
docs/insights-cli-reference.md.
Put a price on everything
Token cost resolved through model pricing rows - prompt, output, cache
read/write, estimated USD - queryable by session, text, commit, or branch:
So "what did that refactor cost" has an answer with a dollar sign on it.
Route the expensive model where it earns its keep