Agent infrastructure

Long-term memory layer that lets AI assistants and agents keep user context across sessions.

Agent memory system that helps agents learn over time through retain, recall and reflect operations.

Open-source AI memory platform that turns documents, code and conversations into a knowledge graph for agents.

Memory and context engine for AI that extracts facts, keeps user profiles and feeds context back to agents.

Open-source Python framework for building temporal knowledge graphs that AI agents can query.

Platform for stateful agents that learn from experience and keep their memory across sessions.

Memory built from what agents do, not only what they say, so later runs can avoid repeated failures.

Memory operating system for LLMs and AI agents that stores, retrieves and manages long-term memory.

Open-source memory for AI agents that reasons over conversations to model each user and agent.

Open-source, Markdown-based knowledge graph memory that AI clients use over MCP.

Memory system for agents that captures, structures and retrieves memories, with a Docker backend and Python client.

Open-source long-term memory layer for AI agents with episodic and profile memory.

Library that lets LangGraph agents extract, consolidate and search their own long-term memory.

Single-file memory layer for AI agents with persistent, versioned and portable memory and no database.

Managed memory layer from Redis that lets AI agents recall facts and preferences across sessions.

Context and memory layer that syncs project decisions and rules selectively across coding agents and teammates.

Context layer that unifies business data, documents and conversations into governed context for agents.

Fully managed AWS service that gives agents short-term memory within a session and long-term memory across sessions.

Managed Google Cloud feature that generates, consolidates and stores long-term memories for agents.