Vector Databases · topoteretes

cognee

Cognee is an open-source Python platform that gives AI agents persistent long-term memory by building and querying a self-hosted knowledge graph. It ingests data in multiple formats, combines vector embeddings with graph reasoning, and enables agents to recall and act on information across sessions.

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Source: GitHub — github.com/topoteretes/cognee

27.3k

GitHub stars

2.5k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	topoteretes/cognee
Owner	topoteretes
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	27.3k
Forks	2.5k
Open issues	630
Latest release	v1.2.2.dev4 (2026-07-07)
Last updated	2026-07-07
Source	https://github.com/topoteretes/cognee

What cognee is

Cognee provides unified data ingestion, knowledge graph construction with ontology grounding, dual vector/graph search with auto-routing, and session memory caching. Built in Python with support for multiple LLM providers, vector databases, and graph backends (Neo4j, PostgreSQL/PGVector). Includes async APIs, CLI tools, and Docker deployment.

Quickstart

Get the cognee source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/topoteretes/cognee.git2cd cognee3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Enterprise Knowledge Management

Consolidate domain knowledge from multiple data sources into a single queryable graph, enabling agents to reason across corporate documents, policies, and historical context with persistent memory.

Multi-Turn Agent Applications

Deploy long-lived agents that learn from interaction history, maintain user/tenant isolation, and leverage cross-agent knowledge sharing while staying grounded in structured relationships.

RAG Systems with Graph Reasoning

Go beyond vector-only retrieval by combining semantic search with relationship traversal, enabling complex multi-hop reasoning and improved context quality for agentic decision-making.

Implementation considerations

Choose backing storage (Neo4j vs PostgreSQL/PGVector) early; migration between graph backends is not documented.
Plan LLM provider configuration upfront (OpenAI, others) and budget for embedding/synthesis API costs during ingestion and recall.
Session memory caching syncs to persistent graph asynchronously; design error handling and eventual consistency expectations into agent logic.
Ontology generation relies on LLM inference; document quality and schema evolution depend on prompt engineering and feedback loops.
Scale testing and custom vector/graph indexes are not documented; run POCs with production-scale data volumes to validate performance.

When to avoid it — and what to weigh

Fully Managed Cloud-Native Requirement — Cognee emphasizes self-hosted deployment. If your team requires SaaS with zero ops burden, the self-hosting requirement (even via Docker) adds operational overhead.
Real-Time Performance at Massive Scale (Billions of Nodes) — No benchmark data provided on query latency or throughput at very large scales. Graph performance characteristics and indexing strategies are not documented.
Strict Air-Gap or Offline-Only Environments — Requires external LLM API calls for memory synthesis and ontology grounding. No clear offline fallback or local model integration strategy is documented.
Highly Regulated Compliance (SOC 2, FedRAMP) — No security audit, compliance certification, or data residency guarantees are documented. Audit trails mentioned but not detailed; requires thorough review before regulated use.

License & commercial use

Apache License 2.0 (Apache-2.0). Permissive OSI license permitting commercial use, modification, and distribution with no warranty or liability.

Apache 2.0 is a permissive OSI license that explicitly permits commercial use, including bundling into proprietary products, subject to attribution and license disclosure. No additional commercial license, SaaS restrictions, or per-seat fees are stated in the provided data. Verify derivative work and bundling terms with legal counsel if distributing as part of a commercial offering.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Audit trails and OTEL collector integration are mentioned but not detailed. No security audit, penetration test results, or CVE disclosure process documented. Multi-tenant isolation claims require review. LLM API keys and data stored in self-hosted graph require encryption at rest and in transit; no hardening guidance provided. Session memory and persistent graph separation may introduce side-channel risks if not carefully configured. Recommend threat modeling before production use in sensitive environments.

Alternatives to consider

LlamaIndex (formerly GPT Index)

Mature Python library for RAG with similar ingestion, indexing, and retrieval; tighter LLM integration but less graph-native reasoning; larger ecosystem but less agent memory focus.

Neo4j with LangChain

Combine Neo4j directly with LangChain agents for graph-native workflows; more flexibility but requires manual orchestration; no built-in ontology generation or session memory.

Weaviate or Pinecone (Vector-Only RAG)

Simpler, fully managed vector search; sufficient for keyword + semantic retrieval but no graph reasoning; lower operational overhead but limited for complex agent memory.

Software development agency

Build on cognee with DEV.co software developers

Start with the Colab quickstart or install via pip. Self-hosted, Apache 2.0 licensed, active community on Discord and Reddit.

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cognee FAQ

Can I use Cognee with proprietary LLMs (Claude, Gemini, Grok)?

Yes, the documentation references configurable LLM providers beyond OpenAI. Exact list and setup steps require consulting the LLM Provider Documentation link in the README.

Is Cognee suitable for production use today?

Possibly, but with caution. Active development (latest tag is 'dev'), high issue count, and no stated production SLA or enterprise support imply early-stage maturity. Recommended for pilot deployments or where rapid iteration is acceptable.

What is the minimum infrastructure to run Cognee?

Python 3.10+ locally, or Docker + backing store (Neo4j or PostgreSQL). No minimum hardware specs are documented; POC with your expected data volume to validate performance.

How is multi-tenancy handled?

Mentioned as a design goal (user/tenant isolation, per-tenant session memory) but implementation details are not documented. Verify isolation boundaries before multi-tenant deployment.

Custom software development services

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If cognee is part of your vector databases roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Build Agent Memory?

Start with the Colab quickstart or install via pip. Self-hosted, Apache 2.0 licensed, active community on Discord and Reddit.

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