GraphRAG that survives real deployments, not just demos

What it does

GraphRAG-SDK ingests documents into a FalkorDB knowledge graph, uses an LLM to extract entities and relationships, and answers questions by traversing the graph rather than relying solely on vector similarity. It wraps ingestion, deduplication, embedding, and retrieval into an async SDK meant for multi-tenant deployments where source documents change. Every answer carries provenance via MENTIONS edges that point back to the original chunks.

The interesting bit

The framework treats incremental updates as a first-class concern: you can add, modify, or delete individual documents and batch them for CI-driven re-syncs without rebuilding the entire graph. It uses an idempotent rollforward cutover with a __pending__ document state to stay crash-safe during updates, which is exactly the kind of unglamorous plumbing most RAG toolkits ignore.

Key highlights

• Claims top spot on the public GraphRAG-Bench leaderboard (Novel and Medical datasets) using gpt-4o-mini, with per-category breakdowns and reproduction instructions published in the repo.
• Multi-tenant isolation via per-tenant graph names, combining vector search, full-text search, relationship expansion, and local cosine reranking at retrieval time.
• Optional strict schema enforcement for entities and relations, or free-form extraction if you prefer to let the LLM infer structure.
• LiteLLM integration avoids locking you into a single provider for embeddings or generation.
• The README is upfront that finalize() runs cross-document deduplication across the full entity table, making it O(graph size); batching changes and calling it once per run keeps costs predictable.

Caveats

• Concurrent document updates default to a single worker because orphan-cleanup correctness depends on a pipeline-ordering invariant; raising concurrency is only safe if concurrent updates never touch the same entity.
• Text-to-Cypher retrieval is marked experimental.
• Competitor benchmark numbers are taken from the published GraphRAG-Bench leaderboard, not from an independent head-to-head run.

Verdict

Teams already running FalkorDB or shipping RAG into production—especially those needing incremental ingestion, source citations, and tenant isolation—should look here. If you just need a quick vector-only RAG prototype, this is overkill.