Provena

Provena

Enables tracking and querying the provenance of AI-generated code, showing which sources influenced each line of code.

Category
访问服务器

README

Provena

CI License: MIT

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
→ ← 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 — model knowledge to verify by hand. That honesty is the point.

📄 Paper: PAPER.md — Hook-Mediated, Span-Level Provenance for Code Written by LLM Agents. Method, benchmarks, and results (0% false attribution across all configs; held-out F1 90.9–94.7%, ceiling 95.7–100%).

How it works

Claude Code session
  Read / WebFetch / Grep ─┐ PostToolUse hook
  your prompts ───────────┤   → captured as `source`
  Write / Edit ───────────┘   → captured as `artifact`
                              ↓
                   .provena/provenance.db  (SQLite)
                              ↑
       provena_* MCP tools (query · cite · audit)

Capture needs no cooperation from the model — hooks sit in the tool path. Storage is local SQLite (Node's built-in node:sqlite), so nothing leaves your machine.

Requirements

  • Node ≥ 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 ✅ attribution-accuracy spike (spike/) — 100% top-1 retrieval, ungrounded cleanly separated
  • Phase 1 ✅ capture + store + MCP query
  • Phase 2 ✅ embedding attribution engine + LLM judge + audit report + eval harness. Embedding-only F1 94.7%, 0% false-attribution.
  • Phase 3 ✅ 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.md iter 3–4). CI gate (provena gate) shipped. Paper in PAPER.md.
  • Next ⬜ 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)

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选