GraphRAG is live · free · last verified 26 Oct 2026 · viability monitor
GraphRAG
Open-source Microsoft Research RAG that builds a knowledge graph and community summaries from your documents, then queries them in four modes.
GraphRAG is the reference implementation of graph-based RAG and still the most serious open-source option for multi-hop synthesis over private corpora, and self-hosting keeps your documents on-prem. The pipeline is transparent: TextUnits, entity/relationship/claim extraction, Leiden communities, bottom-up summaries, and four distinct query modes you can audit individually. The costs are real and documented by Microsoft itself — indexing runs LLM calls over every TextUnit, quality out of the box is explicitly not the best possible (the project tells you to tune prompts), and every minor version bump requires graphrag init --force with a migration notebook for major versions. If you want a
Verdict generatedVerified 4d ago · liveness 65/100 · cite: rightaichoice.com/tools/graphrag
- Research teams doing deep cross-document analysis
- Enterprises with large private policy, legal or research corpora
- Data scientists who need transparent, graph-based reasoning
- Developers building custom RAG pipelines on private data
- Teams needing low-latency, real-time question answering
- Simple FAQ bots where top-k vector search is faster and cheaper
- Organizations without Python or prompt-engineering capacity
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Skip GraphRAG if your questions are simple factual lookups that top-k vector search already answers, or if you have no one to own prompt tuning and manual version migrations.
Indexing runs an LLM call over every TextUnit in your corpus, so the bill scales with document count and length rather than with query volume
GraphRAG is an MIT-licensed open-source project, so there is no per-seat or per-query subscription — your real spend is the LLM API calls consumed during indexing plus the infrastructure you run it on. That math favors teams with a large, stable corpus they re-index infrequently and penalizes teams re-indexing frequently, where a hosted managed-RAG service may cost less in engineering time even with a subscription attached.
In short
GraphRAG — Open-source Microsoft Research RAG that builds a knowledge graph and community summaries from your documents, then queries them in four modes. Best for Research teams doing deep cross-document analysis, Enterprises with large private policy, legal or research corpora, Data scientists who need transparent, graph-based reasoning. Free to use.
No head-to-head comparison for GraphRAG yet — the alternatives below are the closest matches we list.
What is GraphRAG?
GraphRAG — Open-source Microsoft Research RAG that builds a knowledge graph and community summaries from your documents, then queries them in four modes. Best for Research teams doing deep cross-document analysis, Enterprises with large private policy, legal or research corpora, Data scientists who need transparent, graph-based reasoning. Free to use.
GraphRAG is an open-source project from Microsoft Research that replaces naive vector-similarity retrieval with a knowledge graph built from your own corpus. The indexing pipeline slices input documents into TextUnits, uses an LLM to extract entities, relationships and key claims, then hierarchically clusters the resulting graph with the Leiden algorithm and writes bottom-up summaries for each community of entities. At query time those structures are handed to the model as context across four modes: Global Search for corpus-wide themes, Local Search for entity-centric questions, DRIFT Search which adds community context to a local fan-out, and Basic Search as a plain top-k vector fallback. It ships as a Python library (3.10-3.12) with a CLI (graphrag init, index, query) and YAML configuration, uses uv for dependency management and poethepoet for build scripts including index, query and test runners. The documented example indexes with GPT-4 Turbo. It is self-hosted and MIT-licensed, aimed at developers, researchers and enterprises with large private corpora who need auditable, cross-document reasoning and on-premise data control rather than a hosted chat product.
Key features
- Knowledge graph extraction from raw text corpora
- Entity, relationship and key claim extraction via LLM
- Hierarchical community clustering using Leiden
- Bottom-up community summary generation
- Global Search for corpus-level thematic questions
- Local Search for entity-centric fan-out queries
- DRIFT Search blending local and community context
- Basic Search top-k vector fallback
- TextUnit slicing with fine-grained output references
- Prompt auto-tuning and manual tuning guides
- CLI: graphrag init, index, query
- YAML-based configuration
- Python 3.10-3.12 library with uv package management
- Versioning with init --force and migration notebooks
- Graph visualization guide
Behind the verdict
GraphRAG exists because baseline RAG fails at two specific things, and Microsoft Research says so explicitly on the project page: connecting dots that require traversing disparate pieces of information through shared attributes, and holistically understanding summarized semantic concepts across a large collection or even a single long document. If your questions are of that shape, GraphRAG's architecture is the right one. The indexing stage turns your corpus into TextUnits, extracts entities, relationships and key claims, clusters them with Leiden into hierarchical communities sized by entity degree and colored by community, and then summarizes each community from the bottom up. At query time you choose a mode rather than getting one opaque answer: Global Search reasons over community summaries for corpus-level themes, Local Search fans out from a named entity to its neighbors and associated concepts, DRIFT Search does the local fan-out with community context added, and Basic Search falls back to standard top-k vector search when that is genuinely the better tool. Strengths: the structure is inspectable at every stage, the CLI and YAML configuration make it scriptable, the project ships a documented prompt auto-tuning and manual tuning path, a visualization guide, and an Operation Dulce worked example. It runs on your infrastructure, which matters when the corpus is proprietary research, business documents or communications the model has never seen. Weaknesses and the honest part: the project states outright that running GraphRAG on your data out of the box may not yield the best possible results and strongly recommends fine-tuning prompts first. Extraction quality depends on those prompts and on how clean your text is. Versioning is manual — always run graphrag init --root [path] --force between minor bumps, and use the migration notebook between major bumps if you want to avoid re-indexing, knowing it overwrites your configuration and prompts, so back them up. The development guide assumes Python 3.10-3.12, uv and poethepoet, and the build requirements include LLVM for some installs, which is a real barrier for teams without Python engineering capacity. Where it fits: research teams, legal and policy groups, data scientists who need transparent graph-based reasoning, and organizations that need on-premise governance over large private corpora. Where it doesn't: latency-sensitive question answering, simple FAQ lookup where vector search is faster and cheaper, and small datasets where the graph machinery does not pay for itself.
Models under the hood
as of 2026-10-23
Limitations
- Microsoft states on the project page that using GraphRAG with your data out of the box may not yield the best possible results, and strongly recommends fine-tuning prompts using the Prompt Tuning Guide before you trust the output.
- Versioning is manual: you must always run graphrag init --root [path] --force between minor version bumps to get the latest config format, and run the supplied migration notebook between major bumps if you want to avoid re-indexing prior datasets — and that migration overwrites your configuration and prompts, so they need to be backed up.
- The development guide requires Python 3.10-3.12 with uv and poethepoet, and some installs hit LLVM configuration issues.
- It is a self-hosted research project, so you own indexing compute, model costs and operational upkeep; there is no managed service.
as of 2026-09-29
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Is GraphRAG still active?
GraphRAG is live · free · last verified 26 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is GraphRAG? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about GraphRAG?
What people actually say about GraphRAG — is it worth it?
Our community sentiment research written Jul 25, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about GraphRAG— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 26 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified GraphRAG 20 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed use cases, description, our verdict, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing tiers, pricing fit, setup time, summary, who it suits
- — changed SSL certificate
- — changed setup time, features, switching out, hidden costs, who should skip it, pricing fit, switching in, SSL certificate
- — changed pricing page
- — changed pricing tiers, our verdict
Showing the 5 most recent of 20 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does GraphRAG cost in 2026?
GraphRAG: Free to use. Pricing last verified 26 Oct 2026.
Plans compared
Every published GraphRAG tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Open Source | Free | — | Developers, researchers and enterprises with a large private corpus who can self-host and own prompt tuning and indexing computeAdds: Starting tier: MIT-licensed self-hosted library with the full indexing pipeline and all four query modes at no license cost |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where GraphRAG's pricing actually pencils out — and where peers do it cheaper.
GraphRAG is an MIT-licensed open-source project, so there is no per-seat or per-query subscription — your real spend is the LLM API calls consumed during indexing plus the infrastructure you run it on. That math favors teams with a large, stable corpus they re-index infrequently and penalizes teams re-indexing frequently, where a hosted managed-RAG service may cost less in engineering time even with a subscription attached.
Has GraphRAG's pricing changed?
No plan price for GraphRAG has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick GraphRAG?
Pick GraphRAG if you are Research teams doing deep cross-document analysis, Enterprises with large private policy, legal or research corpora, Data scientists who need transparent, graph-based reasoning, Developers building custom RAG pipelines on private data.
Look elsewhere if you are Teams needing low-latency, real-time question answering, Simple FAQ bots where top-k vector search is faster and cheaper, Organizations without Python or prompt-engineering capacity, Small corpora where the graph pipeline is overkill.
Skip it if Skip GraphRAG if your questions are simple factual lookups that top-k vector search already answers, or if you have no one to own prompt tuning and manual version migrations.
GeneratedReal-world workflow fit
Concrete scenarios for the personas GraphRAG actually fits — and what changes day-one when you adopt it.
Slice the corpus into TextUnits with graphrag init and index, extract entities, relationships and claims, let Leiden cluster them into communities, generate bottom-up summaries, then run Global Search when the question is thematic and Local Search when it starts from a named entity.
Outcome: A queryable knowledge graph with community summaries that answers cross-document synthesis questions baseline vector search gets wrong, with fine-grained references back to the original TextUnits.
Self-host the pipeline so the documents never leave your infrastructure, run DRIFT Search to reason about a specific entity with surrounding community context, and fall back to Basic Search for straightforward lookups.
Outcome: Corpus-level questions answered with an auditable path through entities, relationships and community summaries rather than an opaque similarity match.
Install the Python package (3.10-3.12) with uv sync --all-packages, drive indexing and queries through the poe scripts or CLI, tune extraction prompts with the auto-tuning guide, and wire the graph outputs into your own application.
Outcome: A reproducible, configuration-driven RAG backend with four query modes you can route between, instead of a black-box retrieval call.
Use cases
- Answer holistic thematic questions across a large document collection where no single passage holds the answer
- Trace relationships between stakeholders, organizations and concepts across a corpus of meeting transcripts
- Cross-document research synthesis over a body of academic papers
- Connect patient data spread across records to reason about entity relationships
- Summarize recurring outcome patterns across a legal case document set
- Query a large email archive for e-discovery relationship mapping
- Build an inspectable entity graph over an internal technical knowledge base
Setup time & first value
How long it actually takes to get something useful out of GraphRAG — broken out by persona, not the marketing-page minute.
Developers: an afternoon to install the Python package with uv, run graphrag init and get a small corpus indexed. Data scientists and researchers: a day or more to first useful answers, because prompt tuning is recommended before quality is acceptable. Enterprise teams without Python capacity: budget weeks, since someone must own prompt tuning, re-indexing and manual version migrations.
Switching to or from GraphRAG
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a baseline vector-only RAG pipeline: keep your chunking ideas but map them onto TextUnits, then add the entity/relationship extraction and Leiden community stages
- →From a hosted managed RAG service: replicate the corpus locally, run graphrag init, and accept that indexing is now your compute and API cost
- ↗To a managed RAG service: export the source documents rather than the graph, since the community summaries are project-specific
- ↗To a lightweight vector store: reuse your TextUnit chunks directly as the embedding corpus and drop the graph stages
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “GraphRAG”, and we withheld 6: 6 could not be judged, because “GraphRAG” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about GraphRAG.
Official links
What are the best GraphRAG alternatives?
The closest alternatives to GraphRAG we list are OpenCrab, Lance and RAGFlow. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| OpenCrab | Freemium | Pricing not published | — |
| Lance | Free | Free | — |
| RAGFlow | Freemium | Pricing not published | — |
Alternatives to GraphRAG
View all
OpenCrab
OpenCrab turns your documents into versioned ontology knowledge packs that Claude, ChatGPT, Cursor and Codex query through one MCP URL.
Tools that pair well with GraphRAG
Common stack mates teams adopt alongside GraphRAG, with the specific reason each pairing earns its keep.
OpenCrab
OpenCrab turns your documents into versioned ontology knowledge packs that Claude, ChatGPT, Cursor and Codex query through one MCP URL.
Lance
Lance is an open lakehouse format for multimodal AI, pairing a file format, table format, and catalog spec with hybrid vector and full-text search on object
RAGFlow
Open-source RAG engine with built-in ETL, hybrid search and visual AI agent orchestration
Frequently asked questions
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