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OpenMemory

Free#25 of 34 in AI Agent Memory Tools

OpenMemory: A modular memory engine for LLM applications, with sector-specific embeddings and retrieval. Ranked #25 of 34 in AI Agent Memory Tools by our editors (5.7/10); pricing: Open source; best for developers needing sectorized, protected memory.

5.7/10Editor score
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At a glance

  • Editor score
    5.7 / 10
  • Pricing
    Open source
  • Best for
    Developers needing sectorized, protected memory
  • Free plan
    No
  • Paid from
    None
  • Semantic memory
    Yes
  • Facts checked
    13 Oct 2026

  • Where it wins

    • Separates episodic, semantic, procedural, emotional, and reflective memory
    • Combines associative graph linking with composite similarity retrieval
    • Offers REST, LangGraph, and MCP interfaces plus optional encryption and PII scrubbing

  • Where it doesn't

    • Requires a self-hosted deployment or use of its API
    • Community is the listed support channel
    • No published commercial pricing or hosted subscription plan

Our verdict on OpenMemory

OpenMemory gives developers a modular memory engine for LLM applications, agents, assistants, and copilots. It stores persistent structured and semantic memories such as user data, preferences, and prior interactions. Its sector-based design is suited to teams that want to keep different kinds of context distinct, while its API and self-hosted options suit projects that need control over deployment. It is less suited to buyers looking for a conventional hosted subscription or vendor-led support model.

The core differentiator is its Hierarchical Memory Decomposition architecture. It uses separate embeddings for episodic, semantic, procedural, emotional, and reflective memory, then links memories through single-waypoint associative graph connections. Retrieval combines similarity with activation spreading. This approach offers more structure than a single undifferentiated memory store, but teams should consider whether they need these distinct memory types and retrieval behavior; simpler memory requirements may not call for this architecture.

Integration and deployment options cover common agent workflows: OpenMemory provides REST APIs for adding, querying, listing, and deleting memories, LangGraph endpoints with namespace scoping, and a built-in HTTP and stdio Model Context Protocol server. Listed integrations also include Ollama, OpenAI, and Google Gemini. It can run locally or through Docker, and optional controls include encryption, PII scrubbing, tenant isolation, and erasure APIs. The project is open source and supports self-hosted deployment, with cloud also listed as a deployment option; the repository does not publish commercial pricing or a hosted subscription plan. Community is the listed support channel, so teams needing a defined commercial support arrangement should consider alternatives.

Your product? Our editors can test OpenMemory in depth and publish a review, labelled as paid · from $99/mo →

OpenMemory pricing

Plans Open sourceFree Free to use — no paid tier required for the core job.
See plans on github.com

OpenMemory fact sheet

Free planNo
Paid fromNone
Semantic memoryYes
Episodic memoryYes
User/profile memoryYes
Graph-based memoryYes
Deployment optionsBoth
SDK languages2
Retention policyConfigurable
DeploymentCloud, Self-hosted
SupportCommunity
Built forSolo, Small business, Mid-market, Enterprise (editorial estimate)
Integrations5 integrations: LangGraph, Model Context Protocol (MCP), Ollama, OpenAI, Google Gemini
PricingOpen source
Websitegithub.com
Facts checked13 Oct 2026

OpenMemory integrations

OpenMemory lists 5 integrations on its own site.

  • LangGraph
  • Model Context Protocol (MCP)
  • Ollama
  • OpenAI
  • Google Gemini

Alternatives to OpenMemory

See all OpenMemory alternatives →

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Featured on iTechGuides

Featured on iTechGuides — OpenMemory 5.7/10

OpenMemory is listed in our AI Agent Memory Tools directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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