Open-source framework that builds knowledge graphs from your data so AI systems can reason over connected information rather than isolated text chunks.
Builds a knowledge graph from your data that AI can reason over — your AI understands relationships between concepts, not just keywords.
Cognee is an open-source framework for building knowledge graphs and memory systems for AI applications. It positions itself as the cognitive layer between raw data and LLM applications — processing unstructured content into structured, interconnected knowledge that agents can reason over.
Cognee's pipeline takes documents, conversations, or any text input and processes them through configurable steps: chunking, entity extraction, relationship identification, and graph construction. The output is a knowledge graph stored in a graph database (Neo4j by default) alongside vector embeddings for semantic search. This dual-representation — graph structure for relational queries and vectors for semantic similarity — gives you more flexible retrieval than either approach alone.
The framework uses a pipeline-based architecture where processing steps are composable. You can customize chunking strategies, entity extraction models, relationship types, and storage backends. This modularity is a strength for teams that need control over how knowledge is structured, but it also means more configuration than turn-key solutions.
Cognee supports multiple data source types: documents (PDF, markdown, text), web pages, API responses, and conversation transcripts. The processing pipeline normalizes these into a unified knowledge representation. For RAG applications, this means your retrieval can combine graph traversal ('find all entities related to X') with vector search ('find chunks semantically similar to the query').
The project includes a managed cloud platform alongside the open-source library. The cloud version adds a dashboard for exploring the knowledge graph, monitoring pipeline health, and managing data sources.
Cognee's limitations are typical of knowledge graph tools: building a good knowledge graph requires domain-specific configuration. The default entity extraction works well for general text but may need customization for specialized domains. Graph construction quality varies with input quality — garbage in, garbage out applies strongly. The project is also relatively young compared to established graph solutions, with documentation and examples still catching up to the codebase.
For teams building RAG systems that need structured knowledge beyond flat vector retrieval — applications with complex entity relationships, multi-hop reasoning requirements, or domain-specific knowledge structures — Cognee provides a solid open-source foundation.
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Cognee brings knowledge graph construction to AI memory, using graph databases (Neo4j) to store relationships between entities rather than just vector similarity. This graph-based approach excels at complex reasoning over interconnected information. The pipeline for extracting and structuring knowledge from documents is well-designed. Still an early-stage project with a small community, limited documentation, and fewer production deployments. Best for teams that need relationship-aware memory and are comfortable with emerging tools.
Processes unstructured text through configurable extraction pipelines to build knowledge graphs with entities, relationships, and properties. Supports named entity recognition, relation extraction, and coreference resolution.
Use Case:
Building a knowledge graph from a company's internal documentation that connects products, features, teams, and customer segments for an internal search agent.
Modular pipeline architecture where chunking, extraction, embedding, and storage steps can be configured, extended, or replaced independently. Custom steps can be added for domain-specific processing.
Use Case:
Creating a legal document processing pipeline with custom entity extractors for legal terms, case citations, and regulatory references.
Combines graph traversal queries with vector similarity search for retrieval. Supports queries that navigate entity relationships and then rank results by semantic relevance.
Use Case:
Answering 'What projects is John working on that are related to machine learning?' by traversing John's project relationships and filtering by semantic similarity to ML.
Ingests documents (PDF, markdown, HTML), web pages, API responses, and conversation transcripts. Each source type has configurable preprocessing before entering the knowledge extraction pipeline.
Use Case:
Building a unified knowledge graph from a company's Confluence wiki, Slack conversations, and customer support tickets.
Stores knowledge graphs in Neo4j with support for other graph backends. Full Cypher query support for complex graph traversals and pattern matching beyond what vector search can provide.
Use Case:
Running graph queries to find all customers who are connected to a specific product through support tickets, feature requests, and sales conversations.
Cloud platform provides visual knowledge graph exploration, pipeline monitoring, data source management, and usage analytics. Visualize entities and relationships in an interactive graph interface.
Use Case:
Exploring how a new batch of processed documents connects to existing knowledge, identifying missing relationships or unexpected entity clusters.
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View Pricing Options →RAG applications requiring multi-hop reasoning and structured entity relationships
Enterprise knowledge management systems processing diverse document types
Legal document analysis where case citations and regulatory relationships matter
Medical knowledge systems connecting symptoms, treatments, and research
Financial applications tracking complex entity relationships and compliance requirements
Cognee works with these platforms and services:
We believe in transparent reviews. Here's what Cognee doesn't handle well:
Vector-only RAG retrieves chunks by semantic similarity. Cognee adds structured relationships between entities, enabling multi-hop reasoning and relational queries. If your questions require understanding connections between concepts (not just finding similar text), Cognee adds meaningful capability.
For basic use, no — Cognee handles graph construction and provides high-level retrieval functions. For advanced queries and customization, Neo4j knowledge helps. You can start without graph expertise and learn as you need more complex queries.
Cognee supports incremental processing where updated documents are reprocessed and the graph is updated. However, managing knowledge graph consistency across updates requires attention — deleted information in source documents doesn't automatically remove graph nodes.
The open-source library is usable in production with proper testing for your domain. The managed cloud platform adds operational features. For critical applications, thoroughly test extraction quality with your specific data types and configure custom extraction rules as needed.
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