Deja Vu README

The one memory your coding agents share, built from the history already on your disk.

Your agent is about to re-debug something you fixed in March — in a different agent. deja indexes the sessions Claude Code, Codex, Cursor and every other agent on this machine already wrote to disk, and hands the right one back in whichever agent asks.

_{Nobody searched anything — the agent called deja itself. Every line is quoted from two real sessions.}

Every memory tool starts empty and records forward. deja starts full.

And nobody has to ask for it: recall arrives at session start, on every prompt, before a file is edited or a command runs, and after one fails. Keys and tokens are stripped as the index is built, so what reaches the model is safe to send.

85.3% hit@1 on LongMemEval-S · 69.7% on LoCoMo · millisecond lookups over gigabytes of history
_{Both harnesses ship in this repo and run on the public datasets in minutes · check the numbers yourself}

English | 中文

Docs · Benchmarks · How it compares

Install

curl -fsSL https://raw.githubusercontent.com/vshulcz/deja-vu/main/install.sh | sh
deja install --auto

Ten seconds to install, about ten to index, and it is useful. The second command wires MCP recall into every agent it finds, turns on session-start recall where the agent supports it, and builds the first index so the next session does not pay for it.

Start a new agent session and ask it something you worked on months ago:

have we dealt with jwt refresh rotation before? check your memory

It does not have to be asked, either — with auto-recall the agent already knows what you solved in that project when the session opens.

claude plugin marketplace add vshulcz/deja-vu && claude plugin install deja-vu@deja-vu

scoop install deja-vu

What you get

Solve it in Codex. Claude remembers. Twenty-five coding agents write every conversation to local files, and deja turns those files into one memory layer all of them read.

Your own work, wrapped

deja stats --card draws it in the terminal; give it a filename and it writes an SVG for a profile README. To post it anywhere else, turn it into a PNG — that page converts it in your own browser.

The full feature reference lives in the docs.

Privacy

Indexing and search are local. The network is used only by deja update, deja sync ssh, and the version check in deja doctor.

Credentials are redacted at index time: AWS keys, api_key= and token= assignments, bearer tokens and raw JWTs, PEM private key blocks, provider tokens, scheme://user:pass@host URLs, and high-entropy values for shapes no pattern knows. The value becomes [redacted:<kind>] and the surrounding text stays searchable. deja share and deja sync export re-apply redaction on the way out.

deja forget removes sessions from a rebuilt index and writes tombstones, so a later deja index cannot restore them from the source history. --unforget lifts a tombstone. Project exclusions are one pattern per line in ~/.config/deja/exclude.

The security model documents data flows, redaction limits, trust assumptions and release verification.

CLI

$ deja "jwt refresh token"
[claude] api        · Jul 8 · 8f31c0a9 — 2 matches
  login started failing after refresh token rotation; jwt kid mismatch in tests
  fixed by reloading jwks cache after rotateKey and adding a clock-skew test
[codex]  web        · Jul 1 · b77d91e2 — 1 match
  refresh token cookie needed SameSite=Lax in local callback flow

Ask your history

Full reference: commands and JSON output.

MCP tools

The server exposes one tool, deja, with a mode. deja install wires it in, so this is only needed to configure an agent by hand. The six older tool names (recall, recall_context, blame, fix, how, remember) still answer for anything already wired to them.

Supported harnesses

With auto-recall installed, Claude Code and Codex hand deja the transcript as a compaction starts, and it keeps what the summary is about to drop: the task, the conclusions, the files, the commands with what each one did, and what was left open. The next hook for the same session and workspace gives it back once, inside a 4 KB budget, with a line saying whether the repository moved since. deja stats counts the tool calls before the first edit after a compaction, which is the number this is measured against. See automatic compaction recovery for what is read, what is stored and where the limits are.

aider · Amp · Antigravity · Claude Code · Cline · Codex CLI · Copilot CLI · VS Code Copilot Chat · Cursor · DeepSeek Harness · Gemini CLI · Goose · Grok Build · Hermes · Kimi Code · omp (Oh My Pi) · OpenClaw · opencode · Continue · Crush · pi · prime-agent (PrimeIntellect) · Qwen Code · Roo Code · Zed.

Custom store locations go through DEJA_*_ROOT variables, and each agent's own relocation variable is honored too. The session format registry documents the observed paths, record schemas and role mapping per harness, with synthetic fixtures keeping those descriptions checked against the parsers.

Harnesses with a package of their own

deja install --auto wires all six of these like every other harness, and that stays the shortest path. They also have a package in their own ecosystem, for people who install extensions there rather than from a CLI:

Either path is enough on its own, and having both is not a problem: the opencode, dsh, Kimi, Grok and Codex packages read what deja install wrote and contribute only what is missing, and in Zed both halves use one server id, so there is nothing to have twice whichever order you install in.

Each uses the deja you already have; the copy it bundles is only the fallback.

The same search is also a skill, for any agent that loads a SKILL.md:

npx skills add https://github.com/vshulcz/deja-vu --skill deja-search   # skills CLI: Claude Code, Cursor, Goose, Copilot…
openclaw skills install @vshulcz/deja-search                            # ClawHub
hermes skills install vshulcz/deja-vu/skills/deja-search                # Hermes

The skill drives the deja binary from the install step above; it does not bundle one.

Semantic recall (optional)

Point deja embed at a local Ollama, LM Studio or OpenAI-compatible endpoint with DEJA_EMBED_URL and rephrased queries still hit. Without a reachable runtime, lexical search and MCP recall continue unchanged. OpenAI Platform works with its standard key:

export OPENAI_API_KEY='sk-...'
export DEJA_EMBED_URL='https://api.openai.com/v1/embeddings'
export DEJA_EMBED_MODEL='text-embedding-3-small'
deja embed

With no DEJA_EMBED_URL set, deja probes localhost:11434 and localhost:1234, so a machine already running Ollama or LM Studio is picked up without being asked. DEJA_EMBED_OFF=1, or DEJA_EMBED_URL=off, turns that probe off — any other configured DEJA_EMBED_URL still wins.

For another authenticated OpenAI-compatible endpoint, set DEJA_EMBED_KEY explicitly:

export DEJA_EMBED_URL='https://example.com/v1/embeddings'
export DEJA_EMBED_MODEL='embedding-model'
export DEJA_EMBED_KEY='...'
deja embed

DEJA_EMBED_KEY takes precedence. OPENAI_API_KEY is used automatically only for an HTTPS api.openai.com URL; it is never implicitly sent to local or third-party endpoints.

Proof

deja bench recall     # ranking floor: 100 queries, half Russian, CI fails if recall drops
deja bench context    # 30 seeded task chains plus five negative controls
deja bench block      # does the answer survive into what deja hands over
deja bench prompt     # what the per-prompt hook fires on, and what it fires on wrongly

bench block asks the question the other three cannot: with the right session in hand, does the block carry what that session settled. Eight sessions discuss each subject and one of them settles it, in the middle of its own transcript rather than at the end — so the baseline arm, the newest turns of the top hit, scores zero and an arm above zero had to choose.

The context experiment compares deja-recall against full-history, naive grep and cold context. With the default seed:

Same fact coverage as grepping the raw logs for about 250x fewer tokens, and about 70x fewer than replaying the matched sessions in full, while injecting nothing on the chains where no prior fact is relevant. The corpus generator and the relevance labels are ordinary reviewed Go. Audit what "relevant" means before trusting any figure, ours included.

Measured on a real store of 2,419 sessions and 179k messages, 1.9 GB of transcripts:

The index is incremental. When a session file grows, only that file is re-read.

How it works

Local inverted index in ~/.cache/deja: parse the JSONL and SQLite stores, redact credentials, write records.bin plus token buckets, and track per-file state in manifest.gob so repeat runs only ingest what changed. The MCP server, stats, share and sync all read that one index. Details in docs/ARCHITECTURE.md.

FAQ

Does anything leave my machine? No, unless you ask it to. See the data flows.

What about secrets already in my logs? They stay in the original harness files, which are your agent's data. Known shapes — AWS keys, api_key=/token= assignments, bearer tokens and bare JWTs, PEM blocks, provider tokens, high-entropy values — are stripped as the index is built, so they do not reach digests, shares or sync exports. Pattern matching is not secret detection: a shape it does not know can pass through. See the security model.

Will it slow my agent down? A recall is a lexical lookup against a local index: ~0.7 ms median, and nothing waits on a model. A hook adds the process start and a freshness check over your stores on top of that — tens of milliseconds on a store of a few gigabytes.

Do I have to change how I work? No. The agent calls recall itself, and with auto-recall it already knows the project's prior decisions when the session opens.

How is this different from the other memory tools?

engram is the strongest of the record-forward tools and worth your time if that model fits you; it still starts empty and knows only what an agent chose to save. The full comparison covers eleven of them.

Where is Claude Code session history stored, and can I search it? Under ~/.claude/projects, one JSONL file per session; Codex keeps ~/.codex/sessions, Cursor a SQLite state.vscdb. deja search reads them all in place, deja last lists the recent sessions of every agent, and deja view opens the whole history as one local page. Paths for each agent: where sessions are stored.

My Claude Code session history disappeared. Is it gone? Claude Code deletes transcripts older than 30 days (cleanupPeriodDays in ~/.claude/settings.json), and claude --resume lists only what is left. A session deja indexed before the cleanup stays searchable after the file is gone. Details: session files on disk.

What about Windows? Builds exist and CI runs the suite there. macOS and Linux are the battle-tested paths. Field reports welcome in #9.

How do I wipe everything?

deja uninstall --all
rm -rf ~/.cache/deja

Guides

Written for the situation rather than the feature:

• Does your agent remember previous conversations? — what each agent keeps between sessions, and what it drops
• Session files on disk — how big ~/.claude/projects gets, and what deleting it costs
• The agent lost the context you had — after a crash, a clear, or a session that came back empty
• The context window is full — what compaction keeps, measured, and what to do instead
• Resuming yesterday's session — find it across every agent, reopen it in the one that owns it
• The agent repeats a mistake you already fixed
• Finding the session where you solved it
• Why agents forget between sessions · Where each agent keeps its history
• What compaction drops · Switching agents · Auditing an agent · Exporting a conversation · Across machines · What memory costs

Per harness: opencode · DeepSeek Harness · Kimi Code · Zed · Grok Build · Gemini CLI · Qwen Code · OpenClaw · Goose · Cline · pi and omp · Hermes

Try it on your own history

curl -fsSL https://raw.githubusercontent.com/vshulcz/deja-vu/main/install.sh | sh
deja install --auto

Ten seconds to install, about ten to index. The next session your agent opens, it already knows what you solved in that project — including everything from before you installed this.

Contributing

make build test lint, then CONTRIBUTING.md. Adding a harness starts in the parser registry. Priorities and non-goals are in ROADMAP.md. Good first issues are labeled.

License

MIT © Vladislav Shulcz