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Cognee

by Cognee

Open-source knowledge graph memory layer for LLM applications — extracting entities and relationships from APAC documents and conversations into a queryable knowledge graph that enables multi-hop reasoning and contextual memory beyond simple vector similarity retrieval.

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AIMenta verdict
Decent fit
4/5

"Knowledge graph memory for LLMs — APAC AI teams use Cognee to build knowledge graphs from documents and conversations that enable LLMs to reason over entity relationships, enabling APAC RAG pipelines to answer multi-hop questions that vector search cannot."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Knowledge graph extraction: APAC entity and relationship extraction from documents
  • Multi-hop reasoning: APAC entity relationship traversal beyond vector similarity
  • Graph + vector: combined APAC knowledge graph and vector RAG retrieval
  • Neo4j/TiDB: APAC graph database backend for production deployments
  • LangChain/LlamaIndex: APAC pipeline integration alongside existing RAG
  • Open-source: Apache 2.0 for APAC self-hosted data sovereignty deployment
When to reach for it

Best for

  • APAC AI teams building knowledge bases with complex entity relationships where vector RAG fails on multi-hop questions — particularly APAC regulatory compliance, financial research, and enterprise knowledge management applications where entity relationships are as important as semantic similarity.
Don't get burned

Limitations to know

  • ! Knowledge graph quality depends on LLM extraction accuracy — APAC entity errors propagate
  • ! Graph indexing adds processing time and cost to APAC document ingestion pipeline
  • ! Smaller APAC community than vector RAG tools — fewer examples and integrations
Context

About Cognee

Cognee is an open-source knowledge graph memory layer for LLM applications — automatically extracting entities, facts, and relationships from APAC documents and conversations into a structured knowledge graph that LLMs can traverse for multi-hop reasoning questions. APAC AI teams find that standard vector RAG answers simple factual questions well but fails on complex questions requiring entity relationship traversal ('What MAS requirements apply to AI systems used in credit decisions at APAC banks with >$10B assets?').

Cognee's knowledge extraction pipeline processes APAC text documents and conversations, identifies named entities (organizations, regulations, people, products, dates), extracts relationships between entities, and stores the resulting knowledge graph in a graph database (Neo4j, NetworkX, or TiDB). For APAC regulatory compliance knowledge bases, Cognee builds relationship graphs between regulations, affected entities, compliance requirements, and penalties — enabling relationship-traversal queries that vector search cannot perform.

Cognee's LLM integration sends graph traversal results alongside vector-retrieved context to the LLM — for a query about APAC regulatory relationships, Cognee retrieves both similar document chunks (vector RAG) and traversed graph paths (knowledge graph RAG) to provide richer, more accurate APAC context. APAC teams with complex domain knowledge that has significant entity relationships (regulatory maps, product catalogs, organizational hierarchies) benefit most from Cognee's graph augmentation.

Cognee integrates with LangChain, LlamaIndex, and the Cognee Python SDK — APAC teams add knowledge graph extraction to existing RAG pipelines as an additional indexing step alongside vector embedding. Cognee Cloud provides managed knowledge graph infrastructure; Cognee open-source runs on-premise with APAC data sovereignty for regulated APAC industries.

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