Comparison · GraphRAG
Seedthink vs Microsoft GraphRAG
Microsoft's GraphRAG made knowledge-graph RAG a mainstream technique. Seedthink takes the same idea and makes it a growing product — a tiny distilled LLM tied to a verified graph that never stops learning.
The short version
Microsoft GraphRAG is a library. You install it, point it at a corpus, and get a graph-augmented retrieval index you can query. It's excellent at what it does — and it stops there. When your data changes, you re-index.
Seedthink is an intelligence that grows. Each Seed pairs a tiny distilled LLM with a persistent, verified knowledge graph. Ingestion, verification, and reasoning are one continuous loop, not a build step. The graph gets more accurate the more it's used.
Side by side
| Dimension | Microsoft GraphRAG | Seedthink |
|---|---|---|
| What it is | An open-source Python library from Microsoft Research for building a knowledge graph from a document corpus and querying it with an LLM. | A hosted intelligence platform. Every Seed is a tiny distilled LLM paired with its own verified knowledge graph that ingests, learns, and compounds continuously. |
| Setup | You bring the infra: Python environment, vector store, model keys, indexing scripts. Every project rebuilds the same plumbing. | Create a Seed, point it at URLs or documents, and it starts extracting, resolving, and verifying facts immediately. No infra to run. |
| Extraction | LLM-driven entity and relationship extraction over chunks. Output is a static index — good, but re-running requires re-indexing the corpus. | Same triple-extraction backbone, plus canonical entity resolution and typed edges — feeding a living graph that grows on every new source, prompt, and correction. |
| Verification | No built-in verification loop. Facts are extracted, indexed, and trusted; contradictions surface only through downstream QA. | Every fact is contradiction-checked, cross-verified by a second model, and pinned to its provenance before it enters retrieval. Failed facts become surfaced gaps, not silent noise. |
| Retrieval | Community summaries + local/global search over the pre-built index. Powerful for one-shot QA over a fixed corpus. | Vector seed nodes plus graph traversal, with the tiny distilled LLM answering from the verified neighborhood — auditable, cited, and grounded in the Seed's own facts. |
| Freshness | You re-run indexing to refresh. Between runs, the graph is a snapshot with a rising error rate. | Facts re-verify on a rolling schedule. Contradicted facts retire; deltas feed back into extraction. The graph is a moving target, not a static one. |
| Ownership | You own the code and the artifacts you build. You also own the maintenance. | You own the Seed. Publish it, license it, or keep it private. Seedthink handles the lifecycle — you focus on the knowledge. |
| Best for | Research teams comfortable with Python who want a customisable library over a fixed corpus. | Anyone — from a solo builder to a team — who wants a domain intelligence that keeps learning without operating the pipeline themselves. |
When to pick which
Reach for Microsoft GraphRAG when you have engineering capacity, a fixed corpus, and want full control over the code path. It's a great foundation to prototype graph-RAG ideas.
Reach for Seedthink when you want a domain intelligence that ships — one that verifies its own facts, grows with every prompt, and can be published, licensed, or embedded without you standing up the pipeline. The graph isn't the deliverable; the growing Seed is.