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GraphRAG SDK

The most accurate Graph RAG framework. Built on FalkorDB.

GraphRAG SDK builds knowledge graphs from documents and answers questions over them using graph-based retrieval-augmented generation. Every pipeline step is a swappable strategy behind an abstract interface.

Key Highlights

  • #1 on GraphRAG-Bench Novel — 63.73 ACC on 2,010 questions (benchmark)
  • Simple API -- ingest() + completion() with sensible defaults
  • 100+ LLM providers via LiteLLM (OpenAI, Azure, Anthropic, Cohere, Ollama, and more)
  • Fully modular -- swap chunking, extraction, resolution, retrieval, and reranking strategies
  • Production-ready -- async-first, connection pooling, circuit breaker, batched writes
  • Full provenance -- every answer traces back to its source document and chunk

Quick Start

pip install graphrag-sdk[litellm]
docker run -p 6379:6379 falkordb/falkordb

import asyncio
from graphrag_sdk import GraphRAG, ConnectionConfig, LiteLLM, LiteLLMEmbedder

async def main():
    async with GraphRAG(
        connection=ConnectionConfig(host="localhost", graph_name="my_graph"),
        llm=LiteLLM(model="openai/gpt-4o"),
        embedder=LiteLLMEmbedder(model="openai/text-embedding-3-small"),
    ) as rag:
        await rag.ingest("my_document.pdf")
        await rag.finalize()
        answer = await rag.completion("What is the main topic?")
        print(answer.answer)

asyncio.run(main())

Next Steps