chat-memory-sync

Turn your AI coding assistants' chat history into a unified, searchable memory bank.

chat-memory-sync exports conversations from every AI coding harness you use into one project-organized Markdown repo, syncs it across all your machines via git, and gives agents a recall command to look up relevant past chats — so an assistant working on a feature can learn from what you discussed about it in a different tool weeks ago.

• Many harnesses → one place: Cursor, Cursor Agent CLI, Claude Code, OpenCode, Pi, Codex, Gemini CLI. (Antigravity is stubbed — opaque local store.)
• Project-organized so memory is scoped to the repo you're in (worktrees/sub-apps merged into the parent repo).
• Multi-machine via a client dimension (mac / pc / cloud) — each machine syncs independently; no machine ever clobbers another's chats.
• Private by design: the tooling is open source; your chats live in a separate private data repo you own.
• Incremental & idempotent: only changed chats are rewritten; updates to old chats are picked up; deleted chats are pruned.

How it's split

The tool reads ~/.config/chat-memory/config.json for your data repo's location. Machine-local state (search index, client name, logs) also lives under ~/.config/chat-memory/ — never inside the data repo.

Install

Get the chat-memory CLI (pure Python, no dependencies):

# Homebrew
brew install insideaayush/tap/chat-memory-sync

# pipx (recommended for pip users)
pipx install chat-memory-sync

# pip
pip install --user chat-memory-sync

# from source
git clone https://github.com/insideaayush/chat-memory-sync.git
pip install ./chat-memory-sync

Then set it up on this machine:

chat-memory install \
    --data-remote git@github.com:<you>/chat-memory.git \
    --client "macbook-air" \
    --time 21:00

That one command: writes config, clones (or inits) your private data repo, runs the first sync, registers a daily background job for your OS, and ensures the recall command is available.

Commands

chat-memory install   # config + data repo + daily job + recall
chat-memory sync      # pull -> export -> commit -> push (what the job runs)
chat-memory export    # export only (no git)
chat-memory recall …  # search past chats (also the standalone `recall` command)
chat-memory serve     # browse & search your chats in a local web UI
chat-memory doctor    # config, detected harnesses, chat counts

Only needs git + python3. Re-running chat-memory install is safe.

No private data repo yet? Create an empty private repo on your git host first, then pass its URL as --data-remote. Or omit --data-remote to keep it local-only.

Releases are automated (PyPI + Homebrew) — see RELEASING.md.

Finding relevant chats — recall

cd ~/code/my-project
recall "auth token refresh"        # ranked (BM25) hits for THIS repo, with snippets
recall "rate limiting" --all       # every project
recall "flaky integration test" --harness cursor --since 2026-09 -k 10
recall "..." --full                # just file paths (pipe-friendly)

Auto-scopes to the project you're in (via CWD), builds a local SQLite full-text index that rebuilds when chats change. The index is machine-local (~/.config/chat-memory/ index.db), never synced.

For agents: run recall "<topic>" from the repo you're working in, read the top hits, then open those .md files for full context. Add a line to your project's AGENTS.md/CLAUDE.md so assistants do this automatically.

Browse in a UI

chat-memory serve          # opens http://127.0.0.1:8787
chat-memory serve --port 9000 --no-open

A lightweight local web UI (pure stdlib, no deps, binds to localhost only): a sidebar of projects, instant BM25 search across everything, and a reader that renders each chat. Backed by the same index as recall.

Multi-machine

Every machine runs the same job independently. Each run:

1. git pull --rebase the data repo (other machines' chats)
2. export this machine's chats (tagged with its client id)
3. prune only this client's deleted chats
4. rebuild indexes from the union, git commit, git push

Set a machine's name with --client (default: hostname), or CHAT_MEMORY_CLIENT=cloud-vm.

Harness support

Legend: ✅ supported · 🧪 partial · 🔜 stub/planned · ❓ wanted (help us!)

Can't track every tool in the world — adapters are community-driven. Adding one is usually a single function; see CONTRIBUTING.md and the ROADMAP.

How it works

Adding a harness

Adapters are community-driven — adding one is usually a single generator function in sync.py. See CONTRIBUTING.md for the full contract and the support table above.

Config reference

~/.config/chat-memory/config.json:

{ "data_dir": "/Users/you/chat-memory", "client": "macbook-air" }

Env overrides: CHAT_MEMORY_DATA_DIR, CHAT_MEMORY_CLIENT, CHAT_MEMORY_HOME.

Give your agents memory

Any assistant with shell access can query all your past chats via recall. Add a short snippet to your project's AGENTS.md / CLAUDE.md / GEMINI.md so assistants do it automatically — copy-paste blocks per harness are in docs/agent-integration.md.

Contributing & community

• CONTRIBUTING.md — add a harness adapter (the main ask) or fix a bug.
• ROADMAP.md — planned work; pick anything up.
• CODE_OF_CONDUCT.md.
• Not a coder? A harness request with storage paths + a redacted sample is hugely useful.

License

MIT — see LICENSE. Contributions accepted under the same license.

The tooling is open source; your chats stay in your own private data repo. This project reads local chat stores and writes Markdown — review the code before running it.