KAG vs graphrag
Knowledge-grounded Q&A and graph-powered search compared
KAG and graphrag are Python projects that use knowledge graphs and language models to answer questions from documents or structured data. KAG focuses on domain-specific reasoning across graph facts and source text, while graphrag provides a TigerGraph-based application with document ingestion, retrieval, and chat interfaces.
KAG: Build Knowledge-Grounded Reasoning and Q&A Systems
KAG is a Python framework for building domain-specific question-answering systems that combine knowledge graphs, source text, and LLMs. It targets factual and multi-hop reasoning where vector similarity alone may be insufficient.
graphrag: Build Graph-Powered AI Search and Chat
TigerGraph GraphRAG builds a knowledge graph from documents and combines graph queries with vector retrieval to answer natural-language questions. It suits teams already using TigerGraph that want a self-hosted, configurable RAG application.
| KAG | graphrag | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | AGPL-3.0 |
| Stars | 9.1k | 23 |
| Forks | 722 | 23 |
| Last analyzed | Oct 3, 2026 | Oct 4, 2026 |
Key differences
- KAG combines graph knowledge with source text and supports logical-form-guided, multi-hop reasoning; graphrag combines TigerGraph queries and graph traversal with vector retrieval.
- KAG depends on the OpenSPG engine and supporting services, while graphrag supports TigerGraph as its graph and vector database.
- KAG is a framework for teams to configure knowledge sources, schemas, and workflows; graphrag includes a web chat interface, backend APIs, and an admin UI.
- KAG uses the Apache-2.0 license, while graphrag uses AGPL-3.0, which teams should assess against their deployment and distribution plans.
- KAG describes knowledge construction and question-solving components, with kag-model planned for gradual release; graphrag documents Classic and Agentic chat modes, including Planned and Reactive styles.
- KAG lists 9.1k stars and 722 forks, while graphrag lists 23 stars and 23 forks.
Choose KAG if you…
- need multi-hop factual reasoning that combines graph facts, source text, and calculations.
- want to configure a domain-specific knowledge base using schema-free or schema-constrained construction.
- prefer an Apache-2.0-licensed Python framework built around OpenSPG.
Choose graphrag if you…
- already use TigerGraph and want graph and vector retrieval in a document Q&A application.
- want a web chat interface, APIs, and an admin UI for ingestion and graph workflows.
- want to explore Classic or Agentic chat modes and can operate TigerGraph and LLM services.
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.