OpenMemory

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OpenMemory is an AI Agents & Automation tool that provides a local persistent memory store for LLM applications. It offers real long-term memory for AI agents, distinguishing itself from RAG and vector databases.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is OpenMemory?

OpenMemory is a cognitive memory engine designed to provide real long-term memory for LLMs and AI agents, moving beyond traditional RAG pipelines and vector databases. It is self-hosted and local-first, supporting both Python and Node.js SDKs, with options for SQLite or Postgres databases. Key features include multi-sector memory (episodic, semantic, procedural, emotional, reflective), temporal reasoning, decay and reinforcement mechanisms, and explainable traces. OpenMemory integrates with popular frameworks like LangChain, CrewAI, AutoGen, and Streamlit, and offers connectors for ingesting data from sources like GitHub, Notion, and Google Drive. It can run as an SDK within an application or as a central backend server with a dashboard and MCP server for multi-user environments.

Best used for

Ideal for developers and data scientists who need to equip AI agents with persistent, intelligent memory, manage complex temporal knowledge, and integrate memory capabilities into LLM applications. Especially valuable for building agents, copilots, journaling systems, and coding assistants that require more than just vector-based retrieval.

Common actions

manage AI memory

build AI agents

integrate LLM memory

store persistent data

enable temporal reasoning

face swappinggithub copilotautomated workflowopen-sourceworkflowslow-code/no-codecollaboration"AI Agents"deepfake

Capabilities

Key features

Real long-term memory
Self-hosted local-first
Python + Node SDKs
Multi-sector memory
Temporal knowledge graph
Explainable recall traces
LangChain, CrewAI integration

Target Audience

developerdata scientiststartup founder

Integrations

langchaincrewaiautogenstreamlitopenaigithubnotiongoogle-drive+ 3 more

Pricing & Plans

Open Source

Free

FAQs

How does OpenMemory differ from traditional RAG or vector databases?

OpenMemory provides a cognitive memory engine with multi-sector memory (episodic, semantic, procedural, emotional, reflective) and temporal reasoning, unlike RAG or vector databases that primarily rely on embeddings and similarity retrieval. It understands context, recency, importance, and links between memories, offering a more sophisticated memory system for AI agents.

What deployment options are available for OpenMemory?

OpenMemory can be deployed in several ways: as a Python or Node.js SDK running locally with SQLite or external databases, or as a central backend server. The backend server provides an HTTP API, a dashboard, and an MCP server for multi-user environments, supporting Docker deployment.

Which LLM frameworks and data sources does OpenMemory integrate with?

OpenMemory integrates with popular LLM frameworks such as LangChain, CrewAI, AutoGen, and Streamlit. For data ingestion, it offers connectors for GitHub, Notion, Google Drive, Google Sheets, Google Slides, OneDrive, and a web crawler, allowing direct import of external data into memory.

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Subcategories trending in AI Agents & Automation

Chatbots & Conversational AI General-Purpose Agents Workflow Agents Personal Assistants RAG & Document AI Voice Agents

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