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Memory Systems

Cognee

Open-source memory and data infrastructure for AI applications that need reliable context.

Visit official site Open repository
Apache-2.0 License
Open sourceSelf-hostedAPI
Cognee Apache-2.0 License cognee.ai verified 2026-04-19
About

Cognee overview

Cognee is an open-source memory and data layer for AI applications, focused on turning data into structured, retrievable context for agents and LLM systems.

✦

Data-to-memory pipeline

Cognee focuses on transforming input data into usable memory and context.

AI apps fail when the context layer is improvised.
✦

Open-source context infrastructure

The project gives builders a repository and docs for evaluation.

Context infrastructure needs inspectability and deployment control.
✦

Useful for agent apps

Structured context can support agents that need to retrieve and reason over data.

Agents need grounded context to avoid acting on stale or missing information.
Use cases

When to use Cognee

Knowledge ingestion

Prepare documents, records, or project data for AI workflows.

Agent memory layer

Use it as a context layer that agents can retrieve from during tasks.

Internal AI apps

Build assistants that answer with company or project-specific context.

Compare

How it compares

Cognee fits context engineering vs plain document loaders

A document loader moves data; Cognee is closer to an infrastructure layer for preparing and retrieving AI context.

FAQ

Questions

What should I check before using Cognee?

Test Cognee with repeated sessions. Add facts, update them, ask for recall, inspect retrieval behavior, and verify deletion or scoping controls before storing sensitive user or project memory.

Is Cognee open source?

Cognee is listed with Apache-2.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.

Who should evaluate Cognee?

Cognee is most worth evaluating for builders creating AI apps over messy data sources.

Tags

Capabilities

memoryragcontext retrievalstate managementopen sourceself hostedself hosted aipersonal memory
Decision brief

Should you use Cognee?

JSON
Best for
  • Builders creating AI apps over messy data sources
  • Teams that need memory plus retrieval infrastructure
  • Developers comparing RAG, graph, and context-engineering approaches
Not for
  • Users who want a fully managed consumer product with no setup work
  • Teams that cannot review the linked source, license, and operational requirements before adoption
Trust and freshness
  • Verified 2026-04-19
  • License: Apache-2.0
  • Repo: topoteretes/cognee
  • Open-source signal
Deployment

self hosted, cloud

Permission surface

memory

Decision signals

Self-hostable, API

Agent packet

Structured decision data for Cognee

This packet is the compact machine-readable view agents should use before following source links or taking action.

Full JSON Agent packet Markdown brief
Capabilities

memory, rag, context retrieval, state management

Constraints

open source, self hosted

Deployment

self hosted, cloud

Permission surface

memory

Recommended workflows

Memory or RAG workflow

Overview

What Cognee does

What it is

Cognee is an open memory-system resource to evaluate by what it stores, how recall works, how memory is scoped, and whether users or teams can inspect, correct, export, or delete durable context.

Why it matters

Cognee matters because teams quickly outgrow raw prompts and scattered documents. AI applications need a way to prepare, connect, and retrieve context in a structured way.

How to evaluate it

Test Cognee with repeated sessions. Add facts, update them, ask for recall, inspect retrieval behavior, and verify deletion or scoping controls before storing sensitive user or project memory.

Facts

Known metadata and operating surface

These fields are separated from editorial interpretation so agents can reason over facts and missing checks.

Resource type memory system
Category Memory Systems
Maturity active
Difficulty Unknown
License Apache-2.0
Pricing open source
Verified 2026-04-19
Source confidence high
Risk level moderate
Fit matrix

Where Cognee fits in an agent stack

strong

Memory or RAG workflow

Cognee has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning.

  • Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Coding agent workflow

Cognee has at least one signal for coding agent workflow, but should be checked against a real task before adoption.

  • Run a small repository change and inspect the diff, tests, and rollback path.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Evaluation and observability

Cognee has at least one signal for evaluation and observability, but should be checked against a real task before adoption.

  • Add one repeatable test case and confirm results can run again in review or CI.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Local or private AI stack

Cognee has at least one signal for local or private ai stack, but should be checked against a real task before adoption.

  • Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak

Browser automation

Cognee is not primarily positioned for browser automation in the current metadata.

  • Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak

Connector or protocol layer

Cognee is not primarily positioned for connector or protocol layer in the current metadata.

  • Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs

What an agent should inspect

Likely inputs

  • Repositories, files, issues, terminal output, and test results
  • Documents, user facts, entities, context, or retrieval queries
  • Official setup instructions and a small real workflow

Likely outputs

  • Diffs, commits, explanations, test results, or review notes
  • Retrieved context, memory updates, graph relations, or citations
  • Scores, traces, regression results, dashboards, or failure cases
  • A decision on whether this resource fits the target workflow
Evidence

Sources, claims, and missing checks

Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.

GitHub github

Repository source for code, license, issues, releases, and implementation details.

 Homepage homepage

Official or project-controlled source for this resource profile.

 Docs docs

Documentation source for setup, API shape, and operational behavior.
verified

Cognee is listed as open source.

License metadata: Apache-2.0
verified

Cognee has a recorded GitHub repository: topoteretes/cognee.

Resource facts and GitHub source link.
inferred

Cognee supports these recorded deployment modes: self hosted, cloud.

OpenAgent decision signal metadata.
inferred

Cognee is tagged with memory, rag, context retrieval, state management capabilities.

OpenAgent capability taxonomy.
Missing checks
  • Repository freshness has not been recorded.
Next action

How to start evaluating Cognee

Inspect repository

Check license, recent activity, issues, examples, and security-sensitive code paths.

Open source

Open Homepage

Start from the official source before adopting third-party instructions.

Open source

Read setup docs

Use docs as the source of truth for installation and supported interfaces.

Open source

Install Cognee

Check the docs for storage, graph, and provider options.

pip install cognee
Compare

Alternatives and nearby resources

Use related resources to compare category fit, license, deployment model, and first-workflow behavior.

Mnemo Cortex

Open-source memory coprocessor for AI agents with persistent recall, semantic search, and crash-safe capture.

Memory Systems OpenLore

Persistent architectural memory for AI coding agents using queryable codebase knowledge graphs and MCP tools.

Memory Systems AgentMemory

Persistent memory for AI coding agents, with benchmarks and local-first workflows.

Memory Systems
FAQ

Common questions about Cognee

What should I check before using Cognee?

Test Cognee with repeated sessions. Add facts, update them, ask for recall, inspect retrieval behavior, and verify deletion or scoping controls before storing sensitive user or project memory.

Is Cognee open source?

Cognee is listed with Apache-2.0 based on the official source links in this profile. Re-check the repository, model card, or docs before production use.

Who should evaluate Cognee?

Cognee is most worth evaluating for builders creating AI apps over messy data sources.