ai-baton
Portable, auditable, file-first handoff protocol for AI assistants.
Lets you move between AI tools — Claude Code, Codex CLI, Cursor, or
anything else that reads/writes files — on the same long-running project
without re-explaining context every time. State lives in plain Markdown +
YAML files in your own repo: a memory/ for durable facts and decisions,
a status/CURRENT_STATUS.md for what's happening right now, an
append-only evidence/ trail, handover/ snapshots, and archive/ for
superseded plans. No server, no vector DB, no vendor plugin required —
every change is just a git diff.
Requires local filesystem access — this works with tools that run on your
machine or have been given access to a folder (Claude Code, Codex CLI,
Cursor, Windsurf, Claude Desktop with a filesystem connector, etc.). Plain
web ChatGPT or web Claude.ai chat, without file access, can't read
PROTOCOL.md at all, regardless of Agent Skills support.
Not the first system aiming at cross-tool AI memory — Mem0, OpenMemory,
and Letta solve overlapping problems with a vector store and/or an agent
runtime. This makes the opposite trade-off: zero infrastructure and
git-native auditability, at the cost of semantic search and automatic
extraction. See docs/comparison.md.
Status
Pre-alpha. pip install ai-baton-tool (PyPI distribution name differs
from the ai-baton command — an existing unrelated package blocked that
name). Working: the spec (SPEC.md), the init / validate / status /
list / skill install CLI, a default workspace convention
(~/ai-baton-workspace/<project-name>/, discoverable across tools/sessions
via ai-baton list), a full worked example (examples/demo-project/),
and an Agent Skills skill — ai-baton skill install puts it where Claude Code and Codex CLI look for it, triggered
live in Claude Code but not yet in Codex or Cursor. validate also flags
well-known credential formats (AWS/GitHub/Slack keys, private key blocks)
as a heuristic safety net, not a full secrets scanner. 28 tests pass
locally. Not built: semantic search (by design) and any automated
measurement of handoff effectiveness (methodology sketched in
docs/metrics.md, nothing wired up).
Quick orientation
docs/quickstart.md— install and try it.SPEC.md— the protocol.docs/comparison.md— vs. Mem0 / OpenMemory / Letta / Letta Code.docs/metrics.md— how we'd measure handoff quality.examples/demo-project/— worked example..agents/skills/ai-baton/SKILL.md— install once, an AI tool follows the protocol without being reminded.
License
MIT — see LICENSE.
1 maintainer
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