Cognee
Memory control plane for AI Agents in 6 lines of code
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What is Cognee?
Cognee is an open-source AI memory engine that transforms raw data into intelligent, searchable memory by combining vector search with graph databases. It creates a unified memory layer using knowledge graphs and vector search to give AI systems true understanding of data, enabling agents to recall context across sessions. The platform captures documents, tickets, transcripts, product data, and research papers, turning them into cited memory that agents can inspect and reuse.
Key features include three complementary storage systems: a relational store tracking documents and provenance, a vector store holding embeddings for semantic similarity, and a graph store capturing entities and relationships in a knowledge graph. Cognee supports over 28 data sources including Snowflake, Postgres, PDFs, Slack, and REST APIs. It offers main operations like remember (store memory), recall (query memory), improve (enrich memory), and forget (remove memory). The platform integrates with Claude Code, LangGraph, OpenClaw, MCP-compatible clients, Cursor, and custom runtimes through first-party integrations and an MCP server.
Cognee is designed for solo developers, agent hackers, data/platform teams, and product engineers building vertical agents. It supports custom ontologies, custom data models, and permissions control. The tool is part of Berkeley Xcelerator, has 17.5k GitHub Stars, and performs 5M+ SDK runs per month. Companies like Bayer, Knowunity, SlideSpeak, and Dynamo use it in production for intelligent agentic research systems and personalized customer support.
Cognee pricing
Pricing model: Freemium
Free tier: Build and run memory workflows with tasks and pipelines, auto-generate knowledge structures, integrated evaluations, 28+ data sources supported, community support. Developer plan: $35/month including 1,000 documents or 1 GB of data, 1 user, fully hosted on AWS/GCP/Azure, comprehensive API endpoints, automated scaling and parallel processing, automatic updates, 10,000 API calls included. Cloud (Team) plan: $200/month including 2,500 documents or 2 GB of data, 10 users, multi-tenant architecture, ability to group memories per user and domain, dedicated Slack channel, 10,000 API calls included. Top-up packs available: +1,000 docs (~1 GB) for $35, +3,000 docs (~3 GB) for $100, +15,000 docs (~15 GB) for $750. On-Prem (Enterprise): Custom pricing with on-prem or private cloud deployment, security/data isolation/optimal latency, dedicated architecture review, Premium Support Plan/SLA, access to AI FDE Engineers, roadmap prioritization.
Cognee pros
- Open source with Apache 2.0 license for full transparency and control
- Combines graph and vector storage for both semantic search and structural reasoning
- Supports 28+ data sources including Snowflake, Postgres, PDFs, Slack, REST APIs
- 17.5k GitHub Stars and 5M+ SDK runs/month proving active adoption
- Works with Claude Code, LangGraph, Cursor, MCP, OpenClaw out of the box
- Provides graph visualization tools to inspect and debug knowledge graphs
- Includes built-in evaluation benchmarks for pipeline accuracy testing
- Custom ontologies support for domain-specific knowledge grounding
- Node Sets tagging system for organizing and filtering knowledge bases
- Memify function enriches graphs with deeper semantic connections
- Feedback system enables continuous learning from user interactions
- Can run locally with lightweight defaults or scale to Cognee Cloud
- MCP server lets compatible agents read/write memory without custom glue
- Permission control and recall tuning stay with the memory layer
- Provenance tracking shows where data came from and how it's linked
- Part of Berkeley Xcelerator accelerator program
- Async/await architecture enables non-blocking I/O for better performance
Cognee cons
- Technical integration demand requires advanced engineering expertise for modular pipelines
- Community maturity constraints as newer open-source solution with limited documentation
- Operational complexity in scaling with RDF ontologies and distributed systems
- Requires understanding of graph databases and vector stores for optimal use
- Asynchronous programming model has steeper learning curve for Python beginners
- Enterprise adoption may face limited third-party integrations compared to mature tools
- Managing custom schema evolution introduces governance overhead as data grows
- Local deployment requires manual setup of .cognee_system folder or virtual environment
Frequently asked questions about Cognee
What is cognee?
Cognee is an open-source AI memory engine that turns data into a queryable knowledge graph for agents and LLM applications. It captures context, transforms it into graph memory, and lets every agent recall it across sessions. You can start local with open source and scale on Cognee Cloud.
What do you mean by memory?
At cognee, memory includes user interactions, preferences, context, and any incremental learnings. This is layered in addition to the initial knowledge base, which is the predefined or preloaded information your agent starts with.
How does cognee handle large datasets in knowledge graphs?
Cognee uses scalable and efficient graph technologies designed to manage large volumes of data without significant performance overhead. Graph databases are optimized to represent relationships efficiently, enabling rapid querying and retrieval even with extensive datasets.
Where is my data stored after running cognee locally?
Data is stored in the .cognee_system folder or your virtual environment. You can check these locations to locate your data when running cognee locally.
Can I verify if the knowledge graph accurately represents my data?
Yes, cognee provides tools and visualizations that allow you to inspect, verify, and debug your generated knowledge graph. This ensures transparency and accuracy, letting you validate entities and relationships quickly.
How is cognee different from a vector database?
A vector database retrieves similar chunks based on similarity. Cognee combines vectors with graph relationships, generated data models, and memory operations so agents can retrieve connected context and reason across sources, not just find nearest chunks.
Is cognee open source?
Yes. The cognee SDK is open source on GitHub under Apache 2.0 license, and teams can also use hosted Cognee Cloud or enterprise deployment options. The framework, vector and graph database adapters, and default data pipeline are all open source.
What storage backends does cognee support?
Cognee supports common document and data formats, and can work with graph and vector storage backends such as Kuzu, NetworkX, Neo4j, FalkorDB, LanceDB, Qdrant, Milvus, Redis, and related infrastructure.
Can I trust cognee with critical or core applications?
Yes. Cognee is designed for stability, transparency, and control, especially important for products central to your business. The tools give you the ability to extensively inspect and control your knowledge graph, reducing risks and enhancing reliability for production-grade applications.
How do I get started with cognee?
Start with the open-source package by running pip install cognee. Connect an agent through MCP or the SDK to give your agents memory in minutes. Move to Cognee Cloud when you need managed scale. The quickstart guide shows how to add content, process it with cognify, and search the knowledge graph.