Provena
Enables tracking and querying the provenance of AI-generated code, showing which sources influenced each line of code.
README
Provena
Track where every line of AI-generated code came from.
When Claude (or any agent) writes code, Provena records what it saw (files read, pages fetched, your instructions) and what it produced, then lets you ask:
provena_why src/auth.ts:42
โ โ docs/oauth-spec.md [file] via declared (conf 1.00)
evidence: "access tokens expire after 15 minutes"
Lines with no backing source are flagged ungrounded โ model knowledge to verify by hand. That honesty is the point.
๐ Paper: PAPER.md โ Hook-Mediated, Span-Level Provenance for Code Written by LLM Agents. Method, benchmarks, and results (0% false attribution across all configs; held-out F1 90.9โ94.7%, ceiling 95.7โ100%).
How it works
Claude Code session
Read / WebFetch / Grep โโ PostToolUse hook
your prompts โโโโโโโโโโโโค โ captured as `source`
Write / Edit โโโโโโโโโโโโ โ captured as `artifact`
โ
.provena/provenance.db (SQLite)
โ
provena_* MCP tools (query ยท cite ยท audit)
Capture needs no cooperation from the model โ hooks sit in the tool path.
Storage is local SQLite (Node's built-in node:sqlite), so nothing leaves your machine.
Requirements
- Node โฅ 23.6 (runs TypeScript directly; uses built-in
node:sqlite)
Setup
npm install # installs @modelcontextprotocol/sdk + zod
node src/cli.ts init # wires hooks into .claude/settings.json + registers MCP in .mcp.json
Then restart Claude Code in this project so it loads the hooks and the provena MCP server.
CLI
| command | what it does |
|---|---|
provena init |
configure Claude Code hooks + register the MCP server |
provena status |
counts of captured sources / artifact versions / links |
provena sources |
list captured sources |
provena audit <file> |
attribute a generated file and print its coverage report |
provena gate <file...> [--max-ungrounded <pct>] |
CI gate: exit non-zero if a file's ungrounded ratio exceeds the budget |
provena export <file> [--out f] |
write a signed (ed25519) provenance attestation |
provena verify <attestation> |
verify a signed attestation is authentic and unaltered |
provena reset |
wipe the local provenance graph |
MCP tools
| tool | purpose |
|---|---|
provena_status |
how much provenance has been captured |
provena_sources |
list sources the model saw |
provena_cite |
declare that a line range derives from a source |
provena_why |
explain where a given line came from |
LLM judge (optional)
Embedding attribution decides the confident cases on its own. For the borderline band, an LLM judge reads the candidate sources and decides derivation. Set one key:
export GEMINI_API_KEY=... # uses gemini-2.0-flash
# or
export ANTHROPIC_API_KEY=... # uses claude-haiku-4-5
# optional overrides:
export PROVENA_JUDGE_MODEL=... # pick a specific model
export PROVENA_JUDGE_PROVIDER=gemini # force a provider if both keys are set
Without a key, borderline spans are honestly reported as uncertain rather than guessed.
Status
- Phase 0 โ
attribution-accuracy spike (
spike/) โ 100% top-1 retrieval, ungrounded cleanly separated - Phase 1 โ capture + store + MCP query
- Phase 2 โ embedding attribution engine + LLM judge + audit report + eval harness. Embedding-only F1 94.7%, 0% false-attribution.
- Phase 3 โ
live judge (gemini-2.5-flash-lite) reaches F1 100% on Benchmark A;
multi-language held-out Benchmark B (TS/Python/Go): embedding-only test F1 90.9%,
0% false-attribution, oracle ceiling 95.7%, live 90.9% (
eval/RESULTS.mditer 3โ4). CI gate (provena gate) shipped. Paper in PAPER.md. - Next โฌ larger naturally-occurring corpus, judge-model sweep, signed regulatory export.
Try the evaluation
node src/eval.ts # labeled benchmark: precision / recall / F1 / false-attribution
node test-judge.ts # judge wiring unit test (mocked transport)
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