Index and recall coding decisions across all AI agents

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 · 69.6% on LoCoMo · millisecond lookups over 5 GB 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.

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

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 regression floor, 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

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 200x fewer tokens, and about 60x
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 1,551 sessions and 143k messages - 5.2 GB across nine
harnesses:

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:
~0.4 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.