Connected memory engine
Combines vector search, graph relationships, and relational storage into one memory layer for connected retrieval.
Cognee is an open-source memory platform for AI agents and applications. It connects data across sources, builds connected memory using vector, graph, and relational storage, and makes that context available through SDK, cloud, or self-hosted deployments.
Cognee is an open-source memory platform for AI agents and applications. It ingests information from sources such as Slack, GitHub, Linear, documents, warehouses, and APIs, then organizes it into connected, queryable memory. The platform combines vector search, knowledge graphs, and relational storage so agents can retrieve relationships and context across sources rather than only similar text fragments.
Cognee can be used through its SDK, Cognee Cloud, or a self-hosted deployment. It integrates with agent tools and frameworks including Claude Code, Cursor, Codex, LangGraph, n8n, CrewAI, the OpenAI Agents SDK, Google ADK, and MCP-compatible clients. Cloud provides multi-tenant workspaces and per-session memory, while self-hosting supports teams that need more control over infrastructure and data boundaries.
Combines vector search, graph relationships, and relational storage into one memory layer for connected retrieval.
Brings together sources such as Slack, Notion, Google Drive, GitHub, Confluence, Jira, Dropbox, Amazon S3, and Salesforce, with additional data and API connections supported through the platform.
Builds knowledge graphs with automatically generated data models, while allowing teams to define custom ontologies and domain-specific schemas.
Provides an SDK and MCP server, with integrations for Claude Code, Cursor, Codex, LangGraph, n8n, CrewAI, the OpenAI Agents SDK, Google ADK, and other compatible clients.
Runs locally, on a team’s own infrastructure, or through Cognee Cloud. Enterprise options include bring-your-own-cloud deployment, SLAs, dedicated support, provenance on answers, and personalization per user and agent.
Give coding and task-oriented agents context that carries across sessions, so they can recall prior work instead of starting from an empty context each time.
Connect team documents, tickets, conversations, repositories, and other sources so employees can search a shared, connected knowledge layer.
Build retrieval systems that use both semantic similarity and relationships between entities for document intelligence, research, and knowledge-base applications.
Enrich support agents with connected customer or product information and maintain per-user or per-session memory in multi-tenant applications.
Define custom ontologies and deploy memory in a private or bring-your-own-cloud environment when agents must follow organization-specific rules or data boundaries.
Cognee is an open-source memory platform for AI agents. It turns source data into connected, queryable memory using vector search, knowledge graphs, and relational storage.
It ingests data, generates a knowledge graph and data model, and stores the result across vector, graph, and relational layers. Teams can use automatically generated models or define custom models and ontologies.
Yes. Cognee can run locally or on a team’s own infrastructure. The site also describes air-gapped and bring-your-own-cloud deployment options for teams with stricter infrastructure or data-boundary requirements.
Cognee supports Claude Code, Cursor, Codex, LangGraph, n8n, CrewAI, the OpenAI Agents SDK, Google ADK, and MCP-compatible clients. Data connectors include Slack, Notion, Google Drive, GitHub, Confluence, Jira, Dropbox, Amazon S3, and Salesforce, among others.
Cognee Cloud has a free tier with one workspace and 1M included tokens, followed by usage-based Standard pricing at $1 per 1M tokens; additional workspaces cost $5 per month each. The open-source engine can be self-hosted for free, while Enterprise is offered as a bring-your-own-cloud engagement with support and service-level options.
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