“agent memory”

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

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

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

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

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

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

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

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

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

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

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

Open source control plane for AI and agent traffic, powered by Envoy, with one OpenAI-compatible API.

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

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

CPU and GPU machines and sandboxes for agents, billed by the millisecond, with VMs that pause with memory intact.

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

Headless browser built from scratch in Zig for AI agents and automation, not based on Chromium.

Durable runtime that runs AI agents and their tool calls in WebAssembly VMs that resume after crashes.

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

Perpetual sandboxes that idle to standby and resume in about 25 milliseconds, with shared agent storage.