cognee Review: Open-Source Knowledge-Graph Memory for AI Agents

What Is cognee?

• Product type: a self-hosted Python library and pipeline framework, not a standalone downloadable app or a fully managed SaaS by default
• Creator: Topoteretes UG (haftungsbeschränkt), a Berlin-registered company; managing director Vasilije Markovic
• Repository: github.com/topoteretes/cognee
• License: Apache-2.0, confirmed via the repository's license file
• Locality: hybrid — the knowledge-graph engine, vector store, and any local databases run on your own infrastructure, but text sent for entity extraction, enrichment, and embedding is by default routed to whichever external LLM/embedding provider you configure; cognee also documents support for routing that processing through a local Ollama model instead
• Scale: over 30,700 GitHub stars, and cognee's own site cites more than 5 million SDK runs per month as of this review
• Current version: v1.6.1, released 2026-09-24 (confirmed via cognee's GitHub releases)

cognee's Project History

• cognee positions itself around an Extract-Cognify-Load–style pipeline: ingest data, build a knowledge graph and enrichment layer from it ("cognify"), then make that graph queryable — the current SDK exposes this as add(), cognify(), and search(), with newer remember()/recall() wrapper functions layered on top for simpler use
• cognee reports over 5 million SDK runs per month and named production users including Bayer (research memory), Knowunity, SlideSpeak, and the University of Wyoming, per cognee's own site
• Cognee Cloud, a separate managed offering, was introduced as an optional path for teams that want the same memory layer without self-hosting the engine

What Can You Do With cognee?

• Knowledge graph construction — cognee extracts entities, relationships, and structure from text and code and builds a graph out of it, rather than only storing flat embedding vectors
• Multi-format ingestion — documents, source code, and conversation history can all be fed into the same pipeline
• Automatic retrieval routing — cognee's search layer selects a retrieval strategy (graph traversal, vector similarity, or a hybrid) based on the query, instead of requiring you to hand-pick one
• Session memory with background sync — conversational context can be held in session memory and synchronized into the permanent graph in the background
• Graph visualization and custom ontologies — you can inspect the built graph visually and define your own ontology so extraction follows your domain's specific entity and relationship types
• Hybrid locality with local-model support — cognee's own default flow sends data to a configured external LLM/embedding provider for processing, but it documents support for pointing that processing at a local Ollama instance instead, keeping the whole pipeline on your own hardware
• Agent and IDE integrations — documented integrations include Claude Code (as a memory plugin), Cursor, Cline, LangGraph, CrewAI, OpenClaw, and Model Context Protocol (MCP) support for connecting cognee as a tool to MCP-compatible clients
• Multiple vector store backends — LanceDB is the default, with built-in support for PGVector, Turso/libSQL, Neptune Analytics, and ChromaDB, plus community adapters for Qdrant, Redis, FalkorDB, Pinecone, Turbopuffer, Milvus, and Weaviate, per cognee's vector store documentation

Usage Examples: Three Ways to Use cognee

• Build memory from a piece of text and query it: run pip install cognee, then call cognee.remember(text) to ingest and cognee.recall(query_text=...) to query — cognee's own documentation describes remember() as running ingestion, chunking, entity extraction, and graph building in one step, and recall() as auto-routing the query to the best retrieval strategy
• Use the lower-level ECL-style pipeline directly for more control: call cognee.add(data) to ingest raw text or files, cognee.cognify() to run entity/relationship extraction and build the knowledge graph, then cognee.search(query_text=...) to query it — useful when you want to inspect or customize what happens at each pipeline stage rather than using the single-call remember()/recall() wrappers
• Point cognee at a local Ollama model instead of a cloud LLM/embedding provider: cognee's configuration supports setting the LLM and embedding provider to a local Ollama endpoint, so the entity-extraction and embedding steps run on your own hardware instead of calling out to an external API — check cognee's own configuration documentation for the current environment-variable names before wiring this into a project, since provider configuration details can change between releases

import asyncio
import cognee

async def main():
    text = "cognee turns documents into AI memory."
    await cognee.remember(text)
    results = await cognee.recall(query_text="What does cognee do?")
    for result in results:
        print(result.text)

asyncio.run(main())

Install cognee

cognee Pricing: Is cognee Really Free?

• Open-source engine: free, Apache-2.0, no usage limits imposed by cognee itself
• Your own costs still include whatever your configured external LLM/embedding provider charges for the API calls cognee makes during cognify()/remember() — or nothing extra beyond your own hardware if you route that processing through a local Ollama model
• Cognee Cloud: a separate, optional managed offering; cognee's own site directs interested teams to "move to Cognee Cloud when you need managed scale" without listing exact tier pricing on the main page
• No account or sign-up is required to install and use the open-source library

cognee vs. Langchain-Chatchat

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Who Should Use cognee?

Competitors and Alternatives

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Common Mistakes When Evaluating cognee

Frequently Asked Questions

Sources

Related Reading

• Local LLM Software Directory 2026 — the full catalog of local AI tools this article belongs to, including cognee's own directory entry.
• Chroma Review — a self-hostable vector database often paired with retrieval and memory layers like cognee.
• LangChain Review — a general-purpose LLM application framework that can be combined with cognee's memory layer.
• LlamaIndex Review — a data-indexing framework for LLM applications, for comparison against cognee's knowledge-graph approach.