KAG
Logical form-guided reasoning and retrieval framework for professional domain knowledge bases.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is KAG?
KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs, used to build logical reasoning and factual Q&A solutions for professional domain knowledge bases. It overcomes the shortcomings of traditional RAG vector similarity calculation models.
Key differentiator
“KAG stands out by offering logical form-guided reasoning and retrieval, making it uniquely suited for professional domain knowledge bases over traditional RAG models.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Documentation highlights integration only with a few specific knowledge bases and LLMs
Benchmarking shows significant slowdown when querying large domain-specific knowledge bases
Fit analysis
Who is it for?
✓ Best for
Developers building domain-specific knowledge bases requiring logical reasoning and factual Q&A capabilities
Teams needing to overcome limitations of traditional RAG models in professional domains
✕ Not a fit for
Projects that require real-time streaming or low-latency retrieval
Applications where the overhead of setting up a self-hosted solution is prohibitive
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Next step
Get Started with KAG
Step-by-step setup guide with code examples and common gotchas.