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graphrag-ts citation provenance

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Source-tiered retraction and citation judgments for DOIs; abstains when evidence is thin.

Source code Documentation

Install

Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).

Remote (streamable-http)
Claude Code 
claude mcp add --transport http graphrag-ts-citation-provenance 'https://mcp.ashesborn.cloud/mcp' --header 'Authorization: <Optional. Without a key you get the public read tier: integrity_paper_status, retraction_propagation and integrity_evidence_audit (evidence paths omitted), 20 requests/minute per IP. A key unlocks the full tool set.>'
JSON configuration (Claude Desktop, Cursor, VS Code) 
{
    "mcpServers": {
        "graphrag-ts-citation-provenance": {
            "type": "http",
            "url": "https://mcp.ashesborn.cloud/mcp",
            "headers": {
                "Authorization": "<Optional. Without a key you get the public read tier: integrity_paper_status, retraction_propagation and integrity_evidence_audit (evidence paths omitted), 20 requests/minute per IP. A key unlocks the full tool set.>"
            }
        }
    }
}

Documentation

From the project's README on GitHub, refreshed weekly. Links open on GitHub.

Contents
  1. graphrag-ts
  2. Overview
  3. Quick start
  4. Benchmark
  5. Documentation
  6. Contributing
  7. License

graphrag-ts

TypeScript GraphRAG for Markdown corpora. Build a knowledge graph, detect communities, and answer with evidence-backed retrieval.

Benchmarks / 基准报告 English report · 中文报告

Overview

graphrag-ts is a TypeScript GraphRAG reference implementation for Markdown corpora. It builds a knowledge graph in PostgreSQL and answers questions with hybrid retrieval and source evidence, without requiring a Python runtime.

  • Markdown ingestion, entity/relationship extraction, and community detection
  • Full and incremental builds, with unchanged community summaries reused
  • Hybrid vector, keyword, and graph retrieval, plus corpus-wide Map-Reduce queries
  • MCP server with isolated workspaces and a local browser showcase

See the architecture guide for the pipeline and module details.

Quick start

MCP with Docker Compose

The Compose stack starts PostgreSQL/pgvector and the MCP service. You must provide reachable chat and embedding endpoints; see the MCP guide for configuration, client setup, and workspace behavior.

cp .env.example .env
# Configure the database password and model endpoints in .env.
docker compose up -d --build
curl http://127.0.0.1:3000/healthz

Connect an MCP client to http://127.0.0.1:3000/mcp. For the optional local browser showcase, configure the models and run docker compose --profile showcase up -d --build --wait, then open http://127.0.0.1:8080. The showcase is a local demo, not an authentication layer; do not expose it to untrusted users.

Use the library
pnpm install
pnpm run db:generate
cp .env.example .env
# Configure DATABASE_URL and RAG_* model settings; see the guides below.
pnpm run db:push
bun run examples/demo.ts

The library requires Bun, PostgreSQL with pgvector, and compatible chat and embedding endpoints. See the English guide or 中文指南 for environment setup, API examples, adapters, and tuning.

Benchmark

The reported in-corpus run reached 10/12 strict hits and 95.8% combined lexical recall on 12 questions from five indexed chapters. This is a small project benchmark, not human semantic grading or a cross-project comparison. The reports include methodology, per-query results, and limitations.

The English and Chinese reports are linked at the top of this page. To run the three-question Docker smoke benchmark, use pnpm benchmark:docker:quick with the model endpoints configured and loaded. See the MCP guide for prerequisites and the report for benchmark scope.

Documentation

Contributing

Contributions are welcome through issues and pull requests. The project is organized around small, testable modules, so the most useful changes usually fit into one of these areas:

  • graph construction quality
  • retrieval quality and ranking
  • namespace isolation
  • model-loading robustness
  • documentation and examples

License

MIT

Frequently asked questions

What is graphrag-ts citation provenance?

Source-tiered retraction and citation judgments for DOIs; abstains when evidence is thin.

How do I install graphrag-ts citation provenance?

It runs as a remote server. In Claude Code: claude mcp add --transport http graphrag-ts-citation-provenance 'https://mcp.ashesborn.cloud/mcp' --header 'Authorization: <Optional. Without a key you get the public read tier: integrity_paper_status, retraction_propagation and integrity_evidence_audit (evidence paths omitted), 20 requests/minute per IP. A key unlocks the full tool set.>'. For other clients, paste the JSON configuration from this page into their MCP settings.

Does graphrag-ts citation provenance need an API key?

Yes. It expects Authorization, which you set in its configuration.

Is graphrag-ts citation provenance open source?

Yes. Its code is on GitHub under the MIT license.

Who maintains graphrag-ts citation provenance?

It is published by sadofriod. AI.tn lists it but does not maintain or endorse it.

From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.