Shared semantic memory for Claude Code, Codex, and Cursor.
Find an old decision by meaning. Open the raw evidence. Continue the work.

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Your coding agents remember the current chat. Your work lives across months of chats — resumed sessions, parallel subscriptions, worktrees, different agents.

Session Recall turns that history into one local-first index and serves it back through five focused MCP tools. A fresh session can recover what Codex worked out yesterday and what Claude Code rejected three months ago — with links back to the actual turns, tool output, and reasoning. Not a summary file someone maintains by hand: the original conversation stays the source of truth.

you: we were fixing the auth token conflict between the two services — where did we land?

agent: (recall_search → expand_around) Both services shared one OAuth account, and the provider rotates refresh tokens per account, so each refresh invalidated the other's copy. You rejected the shared-credentials-directory patch as too coupled, and settled on a keeper service owning the session. The spec was never written — that was the next step.

What you get

	Capability	What it changes
One memory	Claude Code, Codex, and Cursor feed the same index	Switch agents without resetting the project story
Semantic retrieval	Search by meaning, not only exact words	Recover decisions you can describe but cannot quote
Deep navigation	Open raw turns: tool calls, outputs, reasoning	Verify the answer instead of trusting a summary
Honest degradation	A semantic outage is reported explicitly	A literal-only fallback never poses as semantic search
Local by default	Bundled ONNX embeddings and local SQLite	Start without a key, a server, or an account
Scoped recall	Filter by repo, source, or local calendar dates	Keep unrelated projects out of the answer
Team answers	Ask a colleague's local memory, owner-approved	Share hard-won context without exposing raw sessions

Where it pays off

• Session onboarding. A fresh session starts already in context — whether you juggle several subscriptions, hop between agents, or return to a task you "discussed at some point".
• Bugs and regressions. Before fixing anything, the agent asks the history: was this bug seen before? how was it fixed? why did we believe it was fixed? A recurrence stops looking like a fresh bug — and the fix turns from a patch into a dig into the component.
• Procedures. Explain a workflow once — how to read a trace, how to break down token spend per task — and any later session replays it without being walked through again.
• Cause and effect. Say "let's change this decision", and the agent looks up the moment it was made: "we picked X for compatibility with Y — before changing anything, make sure Y survives."

Five tools, one workflow

The interface stays deliberately small:

MCP tool	Use it when
recall_search(query)	You remember the idea, not the wording
expand_around(session_id, uuid)	You found an anchor and need the surrounding evidence
step(session_id, uuid, direction)	You need the adjacent raw turn without another search
grep(pattern)	You know an exact error, symbol, path, or identifier
recent_sessions()	You want the freshest work — and the index freshness

flowchart LR
    Q["describe the old problem"] --> S["recall_search"]
    X["exact error / symbol / path"] --> G["grep"]
    S --> A["anchor: session + turn"]
    G --> A
    A --> E["expand_around"]
    E <--> T["step next / prev"]
    E --> V["grounded answer + raw evidence"]
    R["what is current?"] --> RS["recent_sessions"]

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Every discovery tool accepts an optional source (claude | codex | cursor), a scope_cwd to narrow results to the current repo (worktrees collapse to the repo root), and local calendar dates (on_date, or start_date / end_date, plus an IANA timezone). Ranked anchors carry provenance and a human-readable timestamp. grep scans all indexed transcripts on demand — including under-the-hood turns (tool output, thinking) that never became search chunks. On-demand only: no proactive context injection into every prompt.

See a complete call

{
  "query": "why did refresh tokens conflict?",
  "scope_cwd": "/work/keeper",
  "source": "codex",
  "start_date": "2026-05-01",
  "end_date": "2026-06-30",
  "timezone": "Europe/Moscow"
}

recall_search answers {"anchors": [...], "degraded": null | "reason"}. When degraded is set, the embedding provider was unreachable and only literal matching ran — the agent can say so instead of mistaking a lexical miss for an empty history.

Quick start

Two pieces: a Python CLI (which also ships the MCP server) and a plugin that wires it into your agent. Budget about two minutes plus the first index run.

1. Install the CLI and build the index

pipx install git+https://github.com/AbsoluteMode/session-recall
session-recall setup   # one question (interaction language), then the first index

No key required: with nothing configured, indexing runs on a bundled CPU model, downloaded once and picked by your interaction language. The first run walks your whole history — minutes for months of transcripts, seconds after that. Scripted installs: session-recall setup --lang en --yes.

$ session-recall index
indexed 2175 chunks from changed transcripts

your history: 1053 sessions spanning 168 days, 40,037 searchable fragments
  Claude Code 372 · Codex 680 · Cursor 1
  busiest: sidekey, trend_detection, glitch

Hosted Voyage embeddings rank noticeably better than the bundled model; to use them, export VOYAGE_API_KEY before indexing — see Embedding providers.

2. Connect your agents

pipx puts session-recall and session-recall-mcp on ~/.local/bin — exactly where the plugin manifests look for them.

Claude Code

/plugin marketplace add AbsoluteMode/session-recall
/plugin install session-recall

Then start a new session — MCP servers, skills, and the SessionStart hook load at session start, not on install. Prefer to let the agent finish the job? Say set up session-recall (or run /session-recall:setup): it asks the onboarding questions in chat, runs the commands itself, and ends with a health check and a real search over your history.

Codex

The repository ships a native .codex-plugin/plugin.json — ready to drop into a local repo or your personal marketplace; see the local plugin installation guide. Codex also asks you to review newly installed hooks once via /hooks.

Cursor

Requires Cursor 2.5+ (plugins were introduced there). Add the repository as a marketplace:

cursor-agent plugin marketplace add https://github.com/AbsoluteMode/session-recall.git

Then type /add-plugin session-recall in Cursor Agent and approve the local stdio MCP server once, so the tools can start. For plugin development, launch cursor-agent --plugin-dir /absolute/path/to/session-recall instead of installing a cached copy.

Cursor is auto-detected at its normal macOS/Linux data path and does not need to be running. Portable or custom profile? Point at the database directly with SESSION_RECALL_CURSOR_DB=/path/to/User/globalStorage/state.vscdb.

3. Check it works

session-recall search "something you actually discussed last week"

Hits with a score mean semantic search is live. In the agent, claude mcp list should show session-recall ✔ Connected, and asking about past work should trigger recall_search. Nothing else to configure: each plugin ships its host's startup hook and re-indexes in the background, so the shared index keeps up with all three histories on its own.

How it works

flowchart TB
    subgraph Sources["local history sources"]
      CC["Claude Code JSONL"]
      CX["Codex JSONL"]
      CU["Cursor SQLite"]
    end

    CC --> I["incremental indexer"]
    CX --> I
    CU -->|"consistent WAL snapshot"| I
    I --> V["conversation surface → embeddings"]
    I --> R["raw trace, kept local"]
    V --> DB["SQLite · sqlite-vec KNN · FTS5"]
    R --> DB
    DB --> MCP["five on-demand MCP tools"]
    MCP --> A["Claude Code · Codex · Cursor · any MCP client"]

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Only the conversation "surface" is embedded — user prompts and assistant text replies. Tool calls, results, reasoning, and other trace data are never sent to an embedding provider but stay reachable on demand via expand_around, step, and grep. Claude sidechains and spawned-subagent sessions are intentionally skipped: under-the-hood tooling, not the conversation.

Cursor is read from its SQLite store with the online backup API, so a live WAL database is captured consistently without blocking the editor. Its bubbles are normalized into durable, content-addressed JSONL snapshots under the data directory — deep navigation keeps working after Cursor closes, upgrades, or is uninstalled.

Indexing is incremental and cheap on live transcripts: they are append-only, so unchanged chunks are matched by content hash and their vectors reused — only new turns hit the embedding provider. Moving a Codex rollout into the archive also reuses its vectors. Each file indexes in its own transaction; a failing file is logged and retried next run, never aborting the rest.

CLI cheat sheet

# Refresh every history, or one source
session-recall index
session-recall index --source cursor

# Semantic search — unified by default, scopable to a repo
session-recall search "why did we choose the keeper service?"
session-recall search "deployment work" --source codex --scope /work/keeper

# Local calendar dates, any IANA timezone (defaults to this computer's)
session-recall recent --date 2026-07-14
session-recall search "deployment work" \
  --start-date 2026-07-14 --end-date 2026-07-16 \
  --timezone Asia/Yekaterinburg

# Exact raw scan — no embedding call, caps at 100 matches by default
session-recall grep "invalid_grant" --limit 100

# Housekeeping
session-recall prune    # drop rows for transcripts deleted from disk
session-recall health   # the whole chain, verdict GREEN/AMBER/RED

search, recent, grep, and prune all take --source claude|codex|cursor; omit it for the unified history. Date filters are inclusive and either boundary may be omitted.

Embedding providers

Nothing is locked to one vendor. SESSION_RECALL_EMBED=<preset> sets endpoint, model, dimension, and reranker together, because those four are not independent choices:

Preset	Runs	Model	Dim	Reranker
builtin-en	bundled, free	bge-small-en-v1.5	384	—
builtin-zh	bundled, free	bge-small-zh-v1.5	512	—
builtin-multi	bundled, free	paraphrase-multilingual-MiniLM-L12-v2	384	—
ollama	local, free	nomic-embed-text	768	—
lmstudio	local, free	nomic-embed-text-v1.5	768	—
voyage	hosted, needs a key	voyage-4-large	1024	rerank-2.5
openai	hosted, needs a key	text-embedding-3-large	1024	—

With no preset set, Session Recall picks Voyage when VOYAGE_API_KEY is present, then probes for a local server already listening, and otherwise runs the bundled ONNX model — out of the box always works. The bundled flavor follows the interaction language you chose at onboarding (SESSION_RECALL_LANG=en|zh|…: a small English or Chinese specialist, multilingual otherwise). First use downloads the model once into the data dir (70–240 MB), CPU inference from then on. Ranking is noticeably coarser than hosted Voyage — a starting point, not the ceiling. Local presets ship no reranker, so ranking is KNN + FTS only.

Free and local, start to finish:

ollama pull nomic-embed-text
export SESSION_RECALL_EMBED=ollama
session-recall index

Your own endpoint — any server speaking /v1/embeddings (llama.cpp, vLLM, a company gateway). Individual variables always beat the preset, so mix freely:

export SESSION_RECALL_EMBED_PROVIDER=openai-compatible
export SESSION_RECALL_EMBED_BASE_URL=https://embeddings.internal/v1
export SESSION_RECALL_EMBED_MODEL=your-model
export SESSION_RECALL_EMBED_DIM=1024

A different embedder needs its own index. Vector tables are fixed-width, so changing the model or dimension means rebuilding: delete ~/.local/share/session-recall/index.db and re-run index. Session Recall fingerprints the embedding space of every indexed file and refuses to mix spaces — semantic search shuts off with an explicit message instead of returning misleading rankings.

Model licensing notes

nomic-embed-text is the local default because it is Apache-2.0 and installs in one command. Stronger small models exist — jina-embeddings-v5-text-nano scores far higher for its size — but they are CC BY-NC, which anyone indexing work history would be violating without ever being told. If your use is genuinely non-commercial, point the variables above at one. If you work in more than English, qwen3-embedding:0.6b (Apache-2.0) handles multilingual history far better than nomic.

Keeping the index fresh

If you installed a plugin, this is already handled: the bundled SessionStart hook runs session-recall index in the background on every session start, and incremental indexing keeps it cheap.

Registered the MCP server by hand? Add the hook yourself

In ~/.claude/settings.json:

"hooks": {
  "SessionStart": [
    { "hooks": [ {
      "type": "command",
      "command": "sr=/abs/path/.venv/bin/session-recall; pgrep -f \"$sr index\" >/dev/null 2>&1 || (VOYAGE_API_KEY=... \"$sr\" index >/tmp/sr-index.log 2>&1 &)"
    } ] }
  ]
}

The pgrep guard prevents overlapping runs; ( … & ) detaches so session start doesn't wait. Keep the host-level hook synchronous — the shell already backgrounds the indexer, and Codex ignores Claude's async extension. A launchd/cron timer works too.

Team mode — ask a colleague's history

The same recall, across machines: pair with a colleague once, and your agent can ask their agent about their past work.

you → a colleague's agent: when you hit the local-launch problem with X — how did you solve it?

their agent (after the colleague approves the answer): pin the config to …, then …, and the problem does not come back.

What used to be a Slack thread and a half-remembered explanation becomes one question and one grounded answer. You never see the colleague's raw history — only the answer they approved.

Privacy here is mechanics, not policy:

• questions and answers travel as end-to-end encrypted envelopes; the relay stores blind blobs it cannot read;
• answers are built by an isolated read-only worker, scoped to the projects that contact was explicitly granted (share allow);
• every candidate answer passes a secret scanner and then explicit owner approval (Telegram bot, or share approve locally) before it leaves the machine;
• a contact can be paused any time (share pause), a peer revoked (share revoke).

Searching a peer's index needs no embedding setup on your side: the query travels as text, and the owner's worker embeds it with their own provider against their own index.

Pick a transport (once, before pairing)

A fresh install has no transport and never talks to a server you didn't choose. The relay is blind — everything it carries is sealed and signed on the clients — so which one to use is coordination between peers, not a matter of trust.

Shared folder — zero infrastructure. Two accounts on one machine, or any folder both peers sync (Syncthing, Dropbox, an NFS mount):

export SESSION_RECALL_SHARE_TRANSPORT_DIR=~/Sync/sr-share   # both peers, same folder

Your relay on the LAN. One machine runs it, everyone points at it. Envelopes are end-to-end encrypted regardless, but this is plain HTTP — keep it to a network you trust:

session-recall share relay --port 8787 --host 0.0.0.0       # on the relay machine
export SESSION_RECALL_RELAY_URL=http://192.168.1.20:8787    # on every peer

Your relay on the internet. The relay binds localhost on purpose and expects a TLS terminator in front (Caddy is the two-line option):

session-recall share relay --port 8787    # binds 127.0.0.1

relay.example.com {
    reverse_proxy 127.0.0.1:8787
}

Then on every peer: export SESSION_RECALL_RELAY_URL=https://relay.example.com. The relay stores only sealed blobs, and a mailbox is emptied on fetch. SESSION_RECALL_RELAY_URL=none keeps an install network-silent on purpose. Put the export in your shell profile so agents and timers see it too.

Pairing and asking

Pairing is a one-time ceremony with a short SAS check, then asking is one command:

session-recall share init            # once per device, both sides
session-recall share invite          # you: prints a one-time code
session-recall share join <code>     # colleague: accepts it
session-recall share complete        # you: finish the handshake
session-recall share trust <name>    # both: confirm the SAS matched, name the peer
session-recall share allow <name> <project>
session-recall share notify          # owner side: worker + approval loop

session-recall share ask <name> "how did you fix the local X launch?"
session-recall share fetch           # collect the answers

Meta docs — the project's memory, written down

Raw recall answers what was said. Meta docs answers what agents actually ask mid-task: was this bug fixed before? how do I perform this action? why was it decided this way? A daily job hands each session's dialogue — user messages and final answers, never the tool noise — to a distiller agent that maintains Markdown entries in a Git repository you choose:

• <project>/bugs/ — bugs that were actually fixed: how each was recognized, diagnosed, fixed, and proven fixed;
• <project>/actions/ — procedures, step by step, written so an agent asked again can follow the entry alone;
• <project>/decisions/ — contested choices: what was decided, why that way, what was rejected;
• USER/ — a global map of where your information lives and how to find it (lookup commands and storage locations — never the stored values themselves).

session-recall metadocs init ~/meta-docs --from-today   # memory starts now
session-recall metadocs run                             # one pass now
session-recall metadocs enable   # daily job: launchd (macOS) / systemd user timer (Linux)
session-recall metadocs status
session-recall metadocs index-history --days 30         # opt-in: distill the past, once

The distiller's whole world is four MCP verbs — search / create / edit / delete — and the load-bearing rules are server mechanics, not prompt requests: create is refused until the agent has searched (dedup is mandatory), entries are scanned for secrets before a byte reaches disk, and delete demands a reason. Runs are incremental, and each changed project gets its own local commit — review is a diff, undo is a revert, and sharing the memory with a team is just pushing the repo somewhere private. Nothing is pushed unless you opt into --push; the engine and model come from config only (init --engine claude-cli|codex --model …) — nothing is picked silently.

Privacy is a hard invariant

This is a public repository. Only code goes in it. Runtime data lives under ~/.local/share/session-recall/, outside the repo tree — it physically cannot be committed.

Stays on your machine	Leaves only when you choose it
Original Claude Code and Codex transcripts	Conversation surface text → your configured hosted embedder
Cursor's SQLite store and its normalized snapshots	An explicitly approved team-mode answer
Tool calls, outputs, reasoning — the whole raw trace	Nothing, on the bundled/local embedding path
The SQLite index and stored vectors

• API keys are environment variables only; .gitignore blocks .env.
• Tests use synthetic fixtures, never a real slice of a session.
• The bundled provider keeps the entire indexing path on-device. If you choose a hosted provider, pick one you trust with your transcript surface text.

Troubleshooting

Start here — it checks the whole chain and exits non-zero when something is actually broken, so it also works from a timer:

$ session-recall health
[ok  ] Freshness  2 minutes behind
[warn] Embedder   responded in 5828 ms
                  → slow provider will make indexing crawl
[ok  ] Vector space  builtin/BAAI/bge-small-en-v1.5/384
[ok  ] Corpus     1054 sessions (claude 373, codex 680, cursor 1)
[ok  ] Sources    claude, codex, cursor present

verdict: AMBER (voyage/voyage-4-large, index at ~/.local/share/session-recall/index.db)

Freshness compares the newest transcript on disk against the newest turn in the index, so an indexer that runs on every session and fails every time still shows as behind — exactly the failure that is otherwise invisible.

Symptom	Cause / next step
recall_search answers with degraded set	The embedding provider is unreachable — only literal matching ran. Results are real, but a miss proves nothing.
degraded says "embedder changed"	The index was built in a different embedding space. Run session-recall index to re-embed; semantic ranking stays off until then, on purpose.
Indexer logs HTTP code 403 with an HTML body	Not your key: a WAF is blocking your IP (common on VPN and datacenter exits). The same 403 appears with no key at all. Route egress elsewhere or switch provider.
Missing dependencies for SOCKS support	A SOCKS proxy is set in the environment but PySocks is not installed in that venv.
recent_sessions shows an old timestamp	The indexer has not succeeded recently. Run session-recall index by hand and read the output.
Cursor lives in a custom profile	Set SESSION_RECALL_CURSOR_DB=/path/to/User/globalStorage/state.vscdb.

Development

git clone https://github.com/AbsoluteMode/session-recall.git
cd session-recall
python -m venv .venv
.venv/bin/pip install -e ".[dev]"
.venv/bin/pytest -q

To register the MCP server by hand instead of using the plugin:

claude mcp add session-recall --scope user -- /absolute/path/.venv/bin/session-recall-mcp

Engineering rationale and invariants live in docs/decisions/. Start with:

• Unified Claude Code + Codex index
• Cursor as a durable raw source
• Project-scoped recall
• P2P sharing security gate
• Meta docs living project memory

Roadmap

• Hosted/team index — one shared index for a team instead of per-machine copies. The honest open question: whoever searches must embed the query, so a shared vector space implies a shared embedding path.
• Per-contact approval bypass — skip per-answer approval for peers you fully trust; today every answer is approved explicitly.
• More histories — other agents' transcripts beyond Claude Code, Codex, and Cursor.

Contributing

Issues, documentation improvements, host adapters, and translations are welcome. Keep fixtures synthetic and never commit real transcripts, indexes, embeddings, or credentials.

Stop rebuilding context. Continue it.

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