AI Agent for Cognee MCP
Integrate Cognee MCP with FlowHunt to run Cognee’s advanced memory engine as a Model Context Protocol server. Instantly enable your AI agents and developer tools with powerful, context-rich memory, seamless code and data ingestion, and real-time search capabilities. Serve memory over HTTP, SSE, or stdio, and manage knowledge graphs for your agents in any environment, from terminal to IDE.
Serve AI Memory Over Any Protocol
Cognee MCP empowers you to run a robust memory engine for agents across multiple environments. Expose memory functionality via HTTP, SSE (Server Sent Events), or stdio for seamless integration with web apps, IDEs, or custom clients. Benefit from persistent, context-rich memory to supercharge your AI workflows and agent interactions.
- Multi-Transport Support.
- Choose HTTP for web deployments, SSE for real-time streaming, or stdio for classic CLI integration.
- Contextual AI Memory.
- Empower agents with persistent, searchable memory graphs for context-aware decision making.
- Easy Deployment.
- Run as a local server, in Docker, or from your IDE/terminal for maximum flexibility.
- Integrated Logging.
- All actions are logged to rotating files and mirrored to the console for robust traceability.
Automate Data & Code Ingestion for Agents
Automatically turn files, markdown, and code repositories into structured, queryable knowledge graphs using tools like cognify and codify. Feed your AI agents with rich context from source files, documentation, and custom rule sets—all ingested in the background and accessible via simple API calls.
- Local File Ingestion.
- Feed markdown, source code, and rule sets directly from disk for rapid bootstrapping.
- Background Pipelines.
- Long-running cognify and codify jobs run asynchronously—track progress with status endpoints.
- Developer Rules Bootstrap.
- One command indexes rule sets and agent docs into structured memory nodes.
Advanced Data Management & Search
Leverage powerful tools to manage, search, and prune your agent’s memory. Perform complex queries, list or delete datasets, and reset memory for new projects. With detailed status tracking and flexible deletion modes, maintain total control over your AI’s knowledge base.
- Flexible Search Tools.
- Query memory with support for graph completion, RAG, code search, and insights extraction.
- Prune & Reset.
- Wipe memory clean with a single call to start fresh for new projects or experiments.
- List & Delete Data.
- Enumerate datasets, review items, and perform soft or hard deletions as needed.
MCP INTEGRATION
Available Cognee MCP Integration Tools
The following tools are available as part of the Cognee MCP integration:
- search
Query memory using various modes such as GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, and INSIGHTS.
- cognify
Transform your data into a structured knowledge graph and store it in memory for advanced querying.
- codify
Analyze a code repository, build a code graph, and store it in memory for code-centric operations.
- list_data
List all datasets and their data items, supporting detailed views for management or deletion.
- delete
Delete specific data from a dataset, with support for both soft and hard deletion modes.
- prune
Reset Cognee by wiping all memory and data for a fresh start.
- cognify_status
Track the progress and status of background cognify jobs and pipelines.
- codify_status
Monitor the progress and status of ongoing codify jobs and code analysis pipelines.
Supercharge Your AI Agents with Cognee Memory
Experience seamless memory management and knowledge graph building for your AI agents with Cognee. Book a demo or start your free trial to see how easy it is to integrate advanced memory features into your projects.
What is Cognee
Cognee is an open-source AI memory engine designed to enhance AI infrastructure by offering an advanced, production-grade data layer for AI agents and applications. The company specializes in simplifying AI data management by allowing users to ingest, structure, and store both structured and unstructured data. Cognee’s robust platform enables the creation of custom ontologies and reasoners for domain-aware applications, and supports seamless interaction with a broad array of data types—ranging from documents and images to audio files and databases. Built by a team with over a decade of experience in scalable systems, Cognee is trusted by AI researchers and developers worldwide for its high accuracy, flexibility, and ability to connect with leading vector and graph databases, as well as popular LLMs and frameworks. Cognee’s active open-source community and modern SDKs make it an ideal choice for building intelligent, context-aware AI agents.
Capabilities
What we can do with Cognee
With Cognee, users can efficiently structure, store, and interact with vast volumes of data, making it possible to build powerful, context-aware AI agents and applications. Cognee empowers developers to create custom knowledge graphs, leverage vector and graph databases, and achieve high relevancy in AI-generated answers. It streamlines the ingestion and embedding of data from over 30 file types, integrates with a variety of LLMs, and supports advanced reasoning and memory layers for agents.
- Build custom knowledge graphs
- Easily create, manage, and query complex, dynamically evolving knowledge graphs tailored to your domain.
- Seamless data ingestion
- Ingest and structure unstructured or structured data from PDFs, DOCX, images, audio, and more.
- Advanced AI memory and reasoning
- Implement multi-layered memory and domain-aware reasoning for more accurate AI responses.
- Integrate with any stack
- Connect to leading vector (Qdrant, Milvus, Redis) and graph (Neo4j, NetworkX) databases, plus LLMs like OpenAI and Gemini.
- SDKs and interfaces
- Use Python SDK, MCP, and other integrations for streamlined development.
- Community-driven open source
- Leverage and contribute to an active, innovative open-source community.
How AI Agents Benefit from Cognee
AI agents using Cognee gain access to a highly accurate, flexible memory layer that enables context-aware reasoning and improves the quality of generated answers. By leveraging Cognee’s custom ontologies, multi-type data ingestion, and advanced integration with vector and graph databases, agents can maintain nuanced, persistent knowledge across tasks and domains, resulting in more intelligent, scalable, and reliable AI systems.