screenpipe

One API for AI agent memory: ingest, extract, store, retrieve.

A cloud-native platform that gives AI agents semantic memory and operational context through a single API. It ingests documents, audio, video, and web pages, extracts entities and relationships, and handles storage and retrieval so you don't assemble the RAG pipeline yourself. Integrates with frontier models from OpenAI, Anthropic, and Google for extraction.

On-device AI memory and copilot that recalls your work context.

Pieces is a developer-focused AI tool that automatically captures your work context — code snippets, docs, chats, and links — across your apps and surfaces it through a copilot. Its Long-Term Memory engine records a rolling window of activity at the OS level, enabling time-based questions about what you were doing. It runs on-device and air-gapped from the cloud by default, with optional cloud LLMs, and plugs into desktop, VS Code, JetBrains, and the browser.

Open-source memory for AI agents.

An open-source semantic memory layer for AI agents. Cognee ingests documents, relational data, and system context, then runs an Extract-Cognify-Load pipeline that uses an LLM to build a knowledge graph with embeddings and relationships. Agents query it for durable, cross-session context that captures how concepts connect. Self-host the Python SDK for free, or use the managed cloud tiers.

Continual learning memory for stateful agents. Better context, fewer tokens.

A memory and user-personalization layer for AI agents that keeps reasoning about each user across sessions, so apps get richer context without stuffing whole histories into the prompt. It models peers and sessions, runs background inference to derive durable facts, and answers natural-language questions about a user at query time. Available as a managed API or self-hosted FastAPI server, with Python and TypeScript SDKs.

User profile-based long-term memory for LLM applications.

Memobase gives AI apps persistent, per-user memory by distilling conversations into a structured user profile and a chronological event timeline, rather than storing raw transcript embeddings. It exposes Python, Node, and Go SDKs plus a REST API and an MCP server, and is open source (Apache-2.0) with a managed cloud option. Built on FastAPI, Postgres, and Redis for low-latency retrieval.

Temporal knowledge-graph memory for AI agents.

Memory layer that gives agents long-term context by building a temporal knowledge graph from chat history and business data, tracking how facts evolve over time. It's powered by Graphiti, Zep's Apache-2.0 open-source temporal graph engine, with Zep Cloud offering a managed, credit-based service on top. Used to keep agent context relevant as conversations and data grow.

Memory API that gives any AI agent long-term recall.

Supermemory is a memory and context engine for AI apps. It ingests documents, chat histories, and connector data (Drive, Gmail, Notion), turns them into a searchable store, and serves relevant context back to agents over a single API. It works with any model and ships an MCP server alongside official SDKs.

Long-term memory layer for AI agents. Self-hostable.

Persistent memory store + retrieval pipeline for agent applications. Handles per-user/per-session/per-agent scope cleanly; pairs with OpenAI, Anthropic, and local models.

Stateful agents with structured memory. Successor to MemGPT.

Open-source framework for building stateful agents — memory blocks, context-window management, tool-use primitives baked in. Useful as a reference architecture for long-running agents.