Cognee

Most AI agents still operate with the memory of a goldfish. You spin up an agent in Claude Code or Cursor, walk it through your project's architectural decisions, close the terminal, and the next day it starts from complete scratch. Standard vector databases do not fix this—they simply pull snippets based on cosine similarity, failing to register how concepts connect or the fact that an earlier decision was superseded.

Cognee tackles agent amnesia by treating memory as an interconnected system rather than a flat pile of text chunks. Created by Berlin-based Topoteretes UG, it is an open-source memory engine that integrates vector retrieval, knowledge graphs, and relational storage into one persistence layer. When you feed it documentation, repos, or chat logs, Cognee does not just chop them into pieces; it maps entities, extracts ontologies, and maintains relationships across sessions.

The entire memory lifecycle is controlled through four core operations:

• `remember`: Ingests files, repositories, or raw text, detects entities, builds knowledge graph connections, and updates vector embeddings.
• `recall`: Pulls relevant context using 14 distinct retrieval modes, ranging from keyword matching and standard chunks to graph Chain-of-Thought traversal and temporal timeline lookups.
• `improve`: Runs self-improvement cycles to reinforce frequently accessed facts, prune outdated nodes, and recalibrate edge weights based on feedback.
• `forget`: Cleanly removes individual data points, specific datasets, or an entire workspace.

While workspaces like Jitera embed a continuous knowledge graph directly into a full-cycle software development and multi-agent environment, Cognee functions as an independent, modular memory layer that connects to any setup. It provides SDKs in Python and high-performance Rust, a native Model Context Protocol (MCP) server, and plug-and-play integrations for Cursor, Claude Code, Codex, OpenClaw, Gemini CLI, Cline, VS Code, and Slack. It also slots directly into agent frameworks like LangGraph, CrewAI, n8n, and Dify, pulling live context from GitHub, Google Drive, and Notion.

Under the hood, the engine is open source under the Apache 2.0 license, allowing teams to run it self-hosted, on-premise, or in air-gapped environments without external network calls. For those who prefer a managed setup, Cognee Cloud offers hosted multi-tenant workspaces backed by an open-weight model from OpenAI.

On the flip side, keep in mind a few operational constraints: the standard workspace interface limits you to one external data connection per workspace, idle cloud instances scale to zero and take up to a minute to wake up, and full PostgreSQL graph unification remains a demo feature in the community edition compared to enterprise deployments.