topoteretes/cognee
Cognee
Cognee is an agentic memory management platform designed to provide autonomous agents with long-term semantic recall and structured knowledge. It functions as a framework for building persistent memory systems that connect large language models to graph-based knowledge and vector storage, enabling agents to maintain context across complex tasks and multiple sessions.
The platform distinguishes itself through a hybrid approach that combines semantic similarity search with structural graph traversal, allowing for context-aware information retrieval. It features a modular architecture that orchestrates data ingestion, enrichment, and graph construction through reproducible pipelines. To support collaborative or enterprise environments, the system enforces multi-tenant data governance, ensuring strict logical isolation between user datasets and access permissions.
Beyond its core memory capabilities, the project provides a comprehensive suite of tools for managing the data lifecycle, including schema configuration, storage backend abstraction, and system monitoring. It supports the integration of diverse relational, vector, and graph databases, allowing for flexible deployment across various infrastructure requirements. The system also includes built-in observability features, such as graph visualization and retrieval quality benchmarking, to assist in debugging and performance optimization.
Features
- Agent Memory Stores - Provides long-term semantic recall and structured memory for autonomous agents across complex tasks.
- Agentic Context Management - Provides a platform for long-term semantic recall and context-aware retrieval for autonomous agents.
- Knowledge Graphs - Connects persistent knowledge graphs to AI assistants to provide long-term context and semantic understanding.
- Hybrid Vector-Graph Databases - Combines semantic vector similarity with structural graph traversal for context-aware information retrieval.
- Long-term Memory Stores - Stores information as interconnected nodes and relationships to enable long-term semantic recall for autonomous agents.
- Context-Aware Retrieval - Bridges vector stores and knowledge graphs to provide context-aware information retrieval for AI assistants.
- Contextual Retrieval - Queries knowledge using natural language to select optimal retrieval strategies across graph memory and caches.
- Memory Persistence - Maintains consistent agent understanding by storing and recalling information across multiple sessions.
- Data Ingestion APIs - Provides interfaces for importing raw data into structured knowledge bases for agentic memory.
- Knowledge Graph Construction Tools - Transforms unstructured data into interconnected, queryable knowledge representations for improved semantic understanding.
- Multi-Tenant Data Management - Enforces logical separation between user datasets and access permissions for secure multi-tenant collaboration.
- LLM Provider Integrations - Connects multiple language model providers through a unified interface for automated memory pipeline processing.
- AI and LLM Tools - AI memory layer for knowledge graphs and vector search.
- GraphRAG Frameworks - Memory engine converting data into knowledge graphs for AI agents.
- Memory Management - Memory framework for agents with minimal configuration.
- Knowledge and Memory - Manage GraphRAG memory with custom data ingestion and processing.
- Data Ingestion - Ingests text and structured data into permanent graph or session-based storage for agent retrieval.
- Data Pipeline Orchestration - Executes modular, reproducible workflows for data ingestion and graph construction with distributed processing support.
- Graph Querying - Combines vector similarity and graph traversal to answer complex questions from multiple datasets.
- Search and Indexing - Executes context-aware queries across knowledge graphs using configurable search modes.
- Multi-Tenancy Access Controls - Enforces data isolation and hierarchical access boundaries between users and datasets.
- Knowledge Graph Extraction - Automates the extraction of entities and relationships from unstructured data to enrich persistent knowledge graphs.
- Event-Driven Data Pipelines - Orchestrates reproducible data enrichment and graph construction workflows triggered by system events.
- Data Lifecycle Management - Manages data lifecycle through schema migrations and removal of obsolete resources to maintain memory integrity.
- Retrieval Benchmarks - Evaluates retrieval accuracy and relevance to ensure high-quality context for agents.
- Data Ingestion Pipelines - Automates the extraction and transformation of unstructured data into structured knowledge representations.
- Graph Visualizers - Displays interactive node-link diagrams to allow exploration of entities and relationships within the data graph.
- Storage Backend Configurators - Configures diverse relational, vector, and graph database backends for persistent memory storage.
- Ontology Configurations - Defines structural rules and schemas to ensure consistent data organization within knowledge graphs.
- Pluggable Storage Drivers - Abstracts database interactions to support diverse relational, vector, and graph storage backends.
- API Request Authentication - Secures knowledge base access using API keys and bearer token validation for authorized requests.
- Shared Knowledge Graph Memory - Supports shared knowledge graph access for collaborative environments with session-level isolation.
- Monitoring and Observability - Captures telemetry and execution data to monitor memory performance in production environments.
