recall

One memory for all your coding agents.

Your coding agents forget everything between sessions, and none of them knows what you told the others. recall reads the chat history that Claude Code, Codex and Cursor already keep on your machine, and gives every agent two things:

• Search across every chat. "Find the Cursor chat where we planned the auth refactor" works in any agent, whichever tool the chat happened in.
• Memory. A small set of plain markdown notes, rewritten nightly: how you like to work, what each project needs, what was decided, what went wrong, and what is still open. Each note links to the chats it came from.

It runs on your machine. You choose the model that writes the memories: a local one that keeps everything private, your ChatGPT or Claude subscription, or your own API key or server.

you   › what did we decide about retries in the payments service?
agent › Searching your past chats… In "Payment webhook retries" (Codex, 2 Oct) you settled
        on exponential backoff capped at 5 attempts, and a dead-letter queue for the rest.
        Memory also notes: "Never retry card captures; they are not idempotent."

Contents

Install · Quick start · Choosing a model · How agents use it · How it works · Memory notes · Commands · FAQ · Contributing

Install

Requirements: macOS or Linux, Python 3.11+, and at least one of Claude Code, Codex or Cursor.

uv tool install git+https://github.com/alirng/recall
# or: pipx install git+https://github.com/alirng/recall

A PyPI release (recall-agents) is coming; the command is recall either way.

Quick start

recall setup

Setup asks a few questions and does the rest:

1. Agents      finds Claude Code, Codex and Cursor; import from all of them or some
2. Model       picks who writes your memories, then tests it with a live call
3. Index       reads your chat history into a local search index
4. Connect     adds the MCP tools, the /recall skill and a Claude Code session hook
5. Schedule    updates memory nightly (launchd on macOS, systemd on Linux)
6. First run   starts in the background, newest chats first

Then ask any agent about an earlier conversation, or just start working: Claude Code opens each session with your preferences and the current project's notes already loaded.

Choosing a model

recall distils chats with a language model. Pick the one that fits your privacy and budget:

Change it any time with recall model. The choice is saved in ~/.agents/recall/config.toml:

[model]
provider = "local"                     # local | chatgpt | claude | openai | anthropic | openrouter | custom
name = "qwen3:4b"
server = "ollama"                      # local only
base_url = "http://localhost:11434"    # local and custom
api_key = "keychain"                   # env:NAME, keychain or file; never the key itself

Whichever you pick, every call is validated against a strict JSON schema, retried with backoff, cached, logged, and capped by time and call budgets. recall's own calls never show up in your chat history, and the subscription CLIs run with your MCP servers, hooks and plugins switched off.

For a local model, smaller is kinder to your laptop: a 4B model suits 16 GB of memory.

How agents use it

• Automatic: the session-start hook loads your working preferences and the current project's notes, capped at 6,000 characters.
• On demand: six read-only MCP tools: search_chats, read_chat, recent_chats, memory_search, read_note and project_memory.
• Explicit: type /recall what did we decide about auth in any agent with skills.

Agents treat notes and transcripts as background, not instructions: the current request and the code win when they disagree.

How it works

 Claude Code ─┐                                    ┌─ dream (nightly) ──────────────────────────┐
 Codex ───────┼─ parse & clean ─ redact ─ SQLite ──┤  digest → extract → consolidate → notes     │
 Cursor ──────┘                          (FTS5)    │          (batched)   (per project)  (git)   │
                                                   └────────────────────────────────────────────┘
               ↓ search_chats, read_chat                      ↓ MEMORY.md indexes
          MCP server · /recall skill · CLI            session-start hook · memory_search

1. Index. Each agent's history is parsed into your turns and the agent's turns. Tool output and text the tools inject (system reminders, plan-mode boilerplate) are dropped, and secrets are redacted before anything is stored. Headless runs and subagents stay searchable but are never mined for memory. Clones and worktrees of one repo share a project, keyed on the git remote.
2. Digest. Each chat shrinks to your words plus the start and end of each agent reply, which is where agents say what they will do and what they did.
3. Extract. The model proposes candidate memories (preference, fact, decision, lesson, open), each with a supporting quote. A quote that can't be found in the chat demotes the candidate to low confidence.
4. Consolidate. Per project, oldest first, candidates are folded into notes: create, update, supersede (newer facts win) or discard. Every operation is validated before a file changes. Open threads expire after 14 days.
5. Commit. The MEMORY.md indexes are rebuilt, and the run becomes one git commit in ~/.agents/memory.

Each step resumes where the last run stopped, so a crash or a spent budget only delays the rest until the next night.

Memory notes

~/.agents/memory/
  MEMORY.md                      how you work, and your projects
  global/<note>.md               cross-project preferences
  projects/<repo>/MEMORY.md      what an agent loads when it starts in that repo
  projects/<repo>/<note>.md      facts, decisions, lessons, open threads
  archive/                       superseded and expired notes

A note is plain markdown with a little YAML:

---
id: run-migrations-through-make-3f2a
type: lesson
scope: project
confidence: high
last_seen: '2026-09-30'
sources: [codex:0192f…, claude:7c41e…]
---
Run database migrations with `make migrate`, never by hand: the Makefile also seeds
the audit tables. Why: two hand-run migrations left staging without audit rows.

The files are the source of truth. Edit them, and recall keeps your edits; delete one, and it is never learned again. The folder is a git repository, so git log shows what each night changed and git revert undoes it. It also opens as an Obsidian vault, though nothing requires Obsidian. Why markdown rather than a vector or graph store: docs/research/agent-memory-prior-art.md.

Commands

Environment: RECALL_HOME moves the index, config and logs (default ~/.agents/recall); RECALL_MEMORY moves the notes (default ~/.agents/memory).

Uninstall

recall uninstall && recall schedule disable
uv tool uninstall recall-agents
rm -rf ~/.agents/recall          # the index, config and logs
# ~/.agents/memory holds your notes; keep it or delete it

FAQ

Does recall send my chats anywhere? Only to the model you choose, and only digests of chats, after secrets are redacted. With a local model nothing leaves your machine. recall has no telemetry. See SECURITY.md.

What does it cost? With a local model, nothing but electricity. With a subscription it uses your plan's allowance; the nightly run is capped (80 model calls by default), and a first backfill of a few thousand chats spreads across several nights. With an API key, a small model such as Claude Haiku costs cents per night for typical use.

How is this different from Claude Code's or Codex's own memory? Each tool's memory only knows that tool's chats and lives in its own format. recall spans all of them, keeps memory as plain files you own, and links every note to its source chats.

Can I use it with only one agent? Yes. Search and memory work with any one of Claude Code, Codex or Cursor.

Windows? Not yet. The chat parsers are portable, but file locking, the nightly scheduler and key storage need Windows equivalents. Contributions welcome.

Will memory pick up something wrong? Sometimes. Every note carries its sources and a confidence level, newer chats supersede older notes, and you can edit or delete any note. Treat memory as a well-informed colleague's notes, not as ground truth.

Quality

There are no unit tests by design. recall is checked with evals that drive the real pipeline, plus an end-to-end smoke test in CI:

recall eval search                 # hand-written and auto-generated "find that chat" queries
recall eval memory --fresh         # extraction against hand-labelled chats; average several runs

On one developer's 2,700 chats, search finds the intended chat first 80–100% of the time and in the top five 97–100% of the time. Memory extraction quality depends on the model. Small hosted models find roughly half to three quarters of hand-labelled memories, with very little junk. Single runs vary by ±12 points, so compare averages.

Contributing

Contributions are welcome, especially new agent sources (opencode, Gemini CLI, Copilot CLI, Goose and more are mapped out in docs/research/agent-storage-and-integration.md) and new model providers. See CONTRIBUTING.md to get set up, and SECURITY.md to report a vulnerability privately. Please never include real chat content in issues or pull requests.

Roadmap: more agents and chat-export imports, a Codex session-start hook, a "remember this" write path from any agent, usefulness evals that replay tasks with and without memory, and a Claude Code plugin package.

License

MIT