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Cognee

Category: AI Infrastructure

An open-source AI memory engine that builds dynamic, self-improving knowledge graphs and persistent memory layers for AI agents, combining graph and vector databases in a unified platform. Cognee was founded in 2024. The company is led by Vasilije Markovic. Based in Berlin, Germany. Team size: 11-50. Total funding raised: $9.09M. Latest round: Seed ($7.5M, Feb 2026). Key investors include ["Pebblebed","42CAP","Vermilion Ventures","Angel investors from Google DeepMind, n8n, and Snowplow"].

Founded
2024
Headquarters
Berlin, Germany
Team size
11-50
Total funding
$9.09M

Value proposition

Simplifies AI memory infrastructure by providing a unified platform that combines graph and vector databases with automatic ontology generation, self-improving knowledge graphs, and millisecond response times—replacing custom knowledge graph and vector store implementations with production-ready memory in 6 lines of code.

Products and solutions

["Open-source AI Memory Engine (ECL Framework)","Cloud Platform (Managed Service)","Enterprise On-Premise Solution","Model Context Protocol (MCP) Integration","Cognee UI (Local & Cloud Notebooks with Graph Explorer)","Rust Engine for On-Device Memory","Memify Post-Processing Pipeline","Auto-Optimization with User Feedback","38+ Data Source Connectors","Claude Code Plugin","OpenClaw Plugin","Claude Agent SDK Integration","OpenAI Agents SDK Integration","Google ADK Integration"]

Unique value

Pioneered ECL (Extract, Cognify, Load) pipeline architecture combining graph databases for relationships with vector stores for semantic search. Features self-improving knowledge graphs that learn from user feedback, poly-store architecture supporting multiple databases (Neo4j, Memgraph, FalkorDB, Redis, Qdrant, etc.), and achieved 87% accuracy on HotpotQA benchmark with human labeling. Open-source with 7,000+ GitHub stars and production deployment across 70+ companies.

Target customer

AI/ML developers, data engineers, and enterprises building AI agents, copilots, and applications requiring persistent memory, context awareness, and knowledge retrieval capabilities

Industries served

["Artificial Intelligence & Machine Learning","Enterprise Software","Financial Services & Banking","Healthcare & Medical Research","Knowledge Management","Data Analytics & Business Intelligence","Regulated Industries","Software Development"]

Technology advantage

Production-ready hybrid graph-vector memory system with 0.93 human-like correctness score and millisecond response times. Plugin-based architecture supports multiple LLM providers and embedding engines without vendor lock-in. GDPR-compliant with encryption at rest and in transit, supports air-gapped enterprise deployment. Multi-tenant architecture with automatic ontology generation (commercial tier) and user database isolation. Automatic scaling and distributed graphs handle production workloads from gigabytes to terabytes.

How they differentiate

Hybrid graph-vector architecture with proprietary ECL (Extract, Cognify, Load) pipeline combining self-improving knowledge graphs with automatic ontology generation. Production-ready platform serving 70+ companies with 87% accuracy on HotpotQA benchmark and 0.93 human-like correctness score. Open-source approach (7,000+ GitHub stars) with enterprise-grade features including GDPR compliance, multi-tenant architecture, and support for 30+ database integrations (Neo4j, Memgraph, FalkorDB, Qdrant, Redis).

Main competitors

["Mem0","Graphiti (Zep)","LightRAG"]

Key partnerships

["Memgraph (Official Graph Database Partner)","Neo4j (Graph Database Integration)","Redis (Vector Store Partner)","FalkorDB, Kuzu, LanceDB, Qdrant, Weaviate (Database Integrations)","LangGraph, CrewAI, LangChain, LlamaIndex (Framework Integrations)","Model Context Protocol (OpenAI MCP, Anthropic MCP)","AWS, GCP, Azure (Cloud Infrastructure)","Pebblebed, 42CAP, Vermilion Ventures (Investors)","Production Customers: Bayer, Tier 1 US Bank, University of Wyoming, dltHub, Dilbloom, Knowunity, Dynamo, Luccid, DeepMetis"]

Notable customers

["Bayer","University of Wyoming","Tier 1 US Bank","dltHub","Dilbloom"]

Major milestones

["Founded in 2024 by Vasilije Markovic and Boris Arzentar in Berlin","Pre-seed round of €1.5M ($1.58M) raised in November 2024","Production deployment across 70+ companies globally","Seed round of $7.5M led by Pebblebed in February 2026","Opened San Francisco office to expand North American operations","Established official partnership with Memgraph as graph database partner"]

Growth metrics

70+ companies in production, 17,000+ GitHub stars, 5.3M+ SDK runs in past 30 days, 80+ contributors, 87% accuracy on HotpotQA benchmark, 0.93 human-like correctness score

Market positioning

Mid-market enterprise AI infrastructure platform positioned between pure open-source solutions and fully managed SaaS, targeting AI/ML developers and enterprises building persistent memory systems for AI agents and copilots

Geographic focus

Europe (Germany HQ) and North America (San Francisco office), serving global enterprise customers including Bayer, University of Wyoming, Tier 1 US Bank, and 70+ companies across multiple continents

Patents and IP

No registered patents disclosed. Competitive strategy relies on open-source community adoption (7,000+ GitHub stars), proprietary enterprise features (auto-ontology generation, premium support), and first-mover advantage in AI memory infrastructure.

About Vasilije Markovic

Over a decade in big data engineering at Berlin-based unicorns including Team Lead at Taxfix (first Data Product Manager, built data tools, shaped data warehouse, delivered customer segmentation), Machine Learning Engineer at Zalando (deployed ML applications for production systems), and Big Data Systems Supervisor at Omio (supervised real-time systems handling large-scale data processing). Background in Economics and Clinical Psychology studies. Transitioned to AI infrastructure to solve fundamental memory persistence challenges in AI agents.

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