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superseded plans.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.
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 /
skill install CLI, 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. 19 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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