RAG Frameworks · FalkorDB

GraphRAG-SDK

GraphRAG-SDK is a Python framework for building retrieval-augmented generation (RAG) systems using knowledge graphs stored in FalkorDB. It automates entity extraction, relationship mapping, and graph construction from raw documents, then retrieves cited answers by traversing the graph rather than matching vectors alone.

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Source: GitHub — github.com/FalkorDB/GraphRAG-SDK

963

GitHub stars

134

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	FalkorDB/GraphRAG-SDK
Owner	FalkorDB
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	963
Forks	134
Open issues	26
Latest release	v1.3.0 (2026-06-04)
Last updated	2026-07-05
Source	https://github.com/FalkorDB/GraphRAG-SDK

What GraphRAG-SDK is

A modular GraphRAG system built on FalkorDB (Redis-based graph database) that ingests documents via LLM-powered entity/relationship extraction, deduplicates entities cross-document, embeds nodes, and serves completions via graph traversal + LLM generation. Supports incremental document updates with orphan cleanup and per-tenant isolation via named graphs.

Quickstart

Get the GraphRAG-SDK source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/FalkorDB/GraphRAG-SDK.git2cd GraphRAG-SDK3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-document knowledge synthesis

Connect facts across dozens or hundreds of documents; retrieve answers that traverse relationships discovered during ingestion. Benchmark data shows 63.73 acc on multi-doc novel QA vs. 48.35 for vector RAG.

CI/CD-integrated document management

Incrementally sync added/modified/deleted files without full graph rebuild. SHA-256 short-circuits no-op updates; orphan cleanup ensures consistency. Suitable for versioned docs (wikis, API specs, policy databases).

Multi-tenant SaaS knowledge bases

Per-tenant graph isolation via named graphs. Single FalkorDB instance scales to multiple customers with strong logical separation.

Implementation considerations

FalkorDB must be deployed and accessible (Docker/cloud); includes web UI on port 3000. Requires network configuration for production.
LLM and embedder model selection directly impacts accuracy and cost. README recommends gpt-4o-mini; larger models will increase latency and token spend.
Ingestion sanitizes control characters in PDFs to avoid Cypher parse errors; may lose fidelity on binary/special content. Test with your document corpus.
Finalize step (deduplication + embedding backfill) is O(graph size); batch all changes before calling finalize once, not per-file, to avoid quadratic cost.
Schema is optional but improves extraction coherence. Define EntityType and RelationType upfront if domain ontology is important.

When to avoid it — and what to weigh

Real-time streaming ingestion at extreme scale — Ingestion pipeline runs sequentially per document; designed for batch/async workflows, not sub-second latency on continuous streams.
Zero external LLM dependencies required — Entity/relationship extraction and embedding rely on external LLM/embedder services (OpenAI, Anthropic, etc.). No purely local model integration shown in docs.
Highly specialized domain ontologies with strict enforcement — Schema is optional; extraction is LLM-driven and may hallucinate outside defined types. Requires careful prompt tuning and validation if strict ontology compliance is critical.
Existing graph already built elsewhere — SDK assumes raw text ingestion; no bulk import from existing RDF/Property Graph shown in data provided.

License & commercial use

Apache License 2.0 (OSI-approved permissive license). Permits commercial use, modification, distribution, and private use with attribution and patent termination clauses.

Apache-2.0 is a standard permissive OSI license with clear commercial use rights. No proprietary components or dual-licensing evident in data provided. Suitable for commercial products. FalkorDB itself (required dependency) license not stated in this data; requires independent review.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Data stored in FalkorDB (Redis-based) at rest and in transit; encryption/TLS config not detailed in provided data. LLM API keys passed to external services (OpenAI, Anthropic, etc.); SDK does not handle key rotation or audit logging shown. Document ingestion may contain PII; no built-in PII detection/redaction visible. Graph queries via Cypher; injection risk depends on LLM-generated query safety (not detailed). Requires review of FalkorDB hardening, network isolation, and API key management before production.

Alternatives to consider

Microsoft GraphRAG (ms-graphrag)

Larger community adoption, works with any graph backend (Kuzu, Neo4j, etc.). Scored 48.05 acc on benchmarks vs. FalkorDB's 69.73. More loosely coupled; less opinionated retrieval strategy.

LightRAG

Lightweight alternative, 53.84 acc on benchmarks. Simpler setup, lower operational overhead. Trade-off: less accuracy on multi-doc retrieval.

Neo4j with LangChain RAG

Mature graph database (16+ years), larger ecosystem, stronger HA/replication story. Requires manual entity extraction and schema; not turnkey. Benchmark data not available for direct comparison.

Software development agency

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GraphRAG-SDK FAQ

Does GraphRAG-SDK work without an external LLM?

No. Entity extraction, relationship discovery, and completion generation all require an LLM service (OpenAI, Anthropic, Ollama via litellm). No purely local model pipeline shown in docs.

How is incremental document updates different from re-ingesting everything?

Updates use content hash to skip no-op (touch-only) changes, orphan-cleanup to preserve shared entities, and atomic cutover to ensure crash safety. Finalize is O(graph size), not O(change size), so batch all changes before finalizing once.

Can I use my own graph backend instead of FalkorDB?

Unknown. SDK is tightly coupled to FalkorDB; no adapter pattern or alternative backend integration documented in provided data. Requires review of source code.

What is the cost model for running this in production?

Primary costs: LLM API calls (entity extraction, embedding, completion), FalkorDB storage/compute, and network egress. Benchmark uses gpt-4o-mini; larger models (gpt-4o) will increase token spend. No per-query cost estimates provided in docs.

Custom software development services

Adopting GraphRAG-SDK is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate rag frameworks software in production.

Build accurate, cited answers from your documents

Start with GraphRAG-SDK: 5-minute setup, benchmark-leading accuracy, incremental updates for CI/CD workflows.

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