OpenMemory MCP
A drop-in memory infrastructure layer that gives AI agents and apps persistent, context-aware memory across sessions.
A drop-in memory infrastructure layer that gives AI agents and apps persistent, context-aware memory across sessions.
Starting price
From $ 4 USD
Best for
Knowledge management
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OpenMemory MCP is a practical fit for buyers evaluating Knowledge management, Development and Tools, and #AI tools.
- What is OpenMemory MCP?
- A drop-in memory infrastructure layer that gives AI agents and apps persistent, context-aware memory across sessions.
- Who is it for?
- AI application developers who need to add persistent memory to agents without building a custom pipeline; better than rolling your own vector store because it ships a drop-in MCP server and local storage.
- Pricing
- free · View pricing
Product information
What this product does
OpenMemory is an open-source personal memory layer that provides private, portable memory management for large language models (LLMs). It ensures users have full control over their data, maintaining its security when building AI applications. This project supports Docker, Python, and Node.js, making it suitable for developers seeking personalized AI experiences. OpenMemory is particularly suited for users who wish to use AI without revealing personal information Target Users: This product is suitable for developers, AI researchers, and ordinary users interested in personalized AI experiences. Through local memory management, users can securely build and use AI applications without worrying about privacy leaks. Use Cases: Develop personalized chatbots that remember user preferences.Build AI-based educational applications that adjust content based on students' learning history.Use an AI assistant to manage daily tasks and provide suggestions based on past memories. Features: Private Data Management: Users' memory data is stored locally, ensuring security and privacy.Personalized Experience: By storing personalized memories, AI applications can better adapt to user needs.Open Source Project: Users and developers can freely view, modify, and extend the code.User-Friendly Interfaces: Provides simple APIs and frontend interfaces for easy developer integration.Community-Driven: Encourages user feedback and contributions to continuously improve and expand features. How to Use: Ensure that Docker and Docker Compose are installed on your device.Download the source code for the OpenMemory project.Run the command 'make build' in the terminal to build the server and user interface.Run the command 'make up' to start the OpenMemory MCP server and UI.Visit http://localhost:8765 to view the API documentation, or http://localhost:3000 to use the user interface.
Sourced from mem0.ai
Works With
Platforms
- iOS
Problem Solved
What problem does OpenMemory MCP solve?
It's for developers building AI agents and apps who currently lose context between sessions, forcing them to re-feed redundant history and pay higher token costs. Today they either rebuild memory pipelines themselves or accept agents that forget user preferences, past interactions, and domain facts. Mem0 replaces that with a drop-in SDK that adds, extracts, and retrieves memories automatically without pipeline changes. A developer shipping a support agent can have it recall a user's prior tickets and preferences on the next session instead of starting from zero.
Company & Maker
Who built OpenMemory MCP?
- Company
- Mem0 (mem0.ai)
- Product
- A drop-in memory infrastructure layer that gives AI agents and apps persistent, context-aware memory across sessions.
- HQ
- San Francisco
- Founded
- 2024
- Team size
- 11-50
- Live since
- Oct 2007
- Founders
- Taranjeet Singh - CEO & Co-founder
- Deshraj Yadav - CTO & Co-founder
Some facts above are based on publicly available information and may have changed.
“Every agentic application needs memory, just as every application needs a database. We're building the default memory layer for AI agents - making LLM memory accessible and reliable for every developer.”
Researched with AI · Data refreshed 1 hour ago · How we score
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OpenMemory MCP is an open-source, self-hosted memory layer that gives LLM applications persistent, user-controlled memory via an MCP server and local storage. It is used primarily by developers and AI engineers who want to add recall of user preferences, past interactions, and domain facts without sending memory data to a third-party service.
Who should use
Buyer-fit is derived from approved personas and use-case evidence. Search-volume noise stays labeled separately.
Best-fit workflows
- Develop personalized chatbots that remember user preferences
- Build AI-based educational applications that adjust content based on students' learning history
- Use an AI assistant to manage daily tasks and provide suggestions based on past memories
- Adds persistent recall to a support chatbot so it can reference a user's prior tickets and stated preferences on the next session.
- Stores and retrieves student learning history for an AI tutor that adjusts content based on past performance.
- Maintains a local memory store for a personal AI assistant that remembers tasks, preferences, and past decisions across sessions.
Pricing reality
Vendor stated
Open-source project; no stated license fee on the public page.
Actual cost
Self-hosting requires your own compute, storage, and maintenance; any paid tier or support is not clearly documented in the provided signals.
Hidden costs
3 hidden cost warnings — unlock to view
Pricing reality
Verified Oct 2026 / Vendor pricing evidence only
Starting price
From $ 4 USD
Free tier available.
Mindber only shows named plan cards when they come from linked pricing evidence. If the tier data is incomplete, this page falls back to the verified starting price instead of fabricating plan names or $0 tiers.
Git Large File Storage
$5 per month for 50 GB bandwidth and 50 GB of storage.
$5/mo
$ 4 USD per user/month
$ 4 USD
$ 4 USD per user/month for the first 12 months*
$ 4 USD
$ 21 USD per user/month
$ 21 USD
What pricing pages often hide
True cost for your case
LIVE ESTIMATEEstimated spend
$12Recurring billing is usually the real cost driver, not the first-month sticker price.
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Engagement signals (30d)
Based on 6 linked public signals.
Mindber Activity Score™
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HOW THIS TOOL SCORES
Scores compare this tool with 17 similar tools in Knowledge management. Use it to judge Innovation and Capability before you shortlist.
Innovation Index™
Is it new, different, and easy to understand?
Functionality Score™
Does it cover the jobs buyers expect, and can it keep up?
Low confidence (40%) - some sub-scores have thin evidence.What this means →
Data sources
Traffic & search proof
Public view keeps the ratio story. Paid view unlocks exact monthly visits, full region coverage, and CPC-level keyword intelligence.
Latest traffic snapshot
Monthly visits
645.5M
Avg visit duration
00:06:27
Pages per visit
5.69
Bounce rate
36.44%
Traffic trend
Month-by-month visits from linked public traffic evidence.
Geography
Top regions
Exact monthly traffic is paid-only. Public view keeps percent share only.
Traffic sources
Source mix snapshot
Public view keeps the ratio snapshot. Minor channels stay behind the paid view.
FAQ
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Social mentions
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Sources and methodology
Only evidence with a linked source record or explicit feature provenance is shown here. Data without provenance stays off the page.
COMMUNITY CREDITS
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