What is OpenMemory MCP?

OpenMemory MCP is an open-source personal memory layer that provides private, portable memory management for large language models. It stores memory data locally, giving users full control and security. Designed for developers and AI enthusiasts, it enables personalized AI applications like chatbots that remember preferences, educational tools that adapt to learning history, and assistants that recall past interactions.

How much does OpenMemory MCP cost in 2026?

Pricing information for OpenMemory MCP in 2026 is not publicly available. As an open-source project, it may be free to use, but any associated costs for hosting, support, or enterprise features have not been verified. Check the official GitHub repository for the latest details, as pricing may have changed.

Is OpenMemory MCP free?

OpenMemory MCP has a free tier based on publicly available information in the grounded product data. Buyers should still verify the current limits, upload allowances, export options, and any watermark or usage restrictions on the official site because free-plan packaging may have changed since the last product update.

OpenMemory MCP vs alternatives in this category: what is the difference?

As of the latest public data, OpenMemory MCP emphasizes local, private memory storage, giving users full control over their data. Unlike some alternatives that may rely on cloud-based memory or proprietary systems, it is fully open-source, allowing customization and self-hosting. This tradeoff means you manage your own infrastructure, which can enhance privacy but requires technical setup compared to managed services.

What are the best alternatives to OpenMemory MCP?

Specific alternatives to OpenMemory MCP are not verified in the available information. Based on publicly available information, other memory management tools for LLMs exist, but their features, pricing, and suitability vary. It is recommended to research current options in the knowledge management and AI memory space, as the landscape may have changed.

Is OpenMemory MCP safe for general business use?

Based on publicly available information, OpenMemory MCP is designed with privacy in mind, storing memory data locally rather than on external servers. This can be safe for business use if proper security practices are followed, such as securing the host environment. However, as an open-source project, it may not have undergone formal security audits, so businesses should evaluate it against their specific compliance requirements.

Does OpenMemory MCP have an API?

Not verified: API availability is not publicly verified for OpenMemory MCP. Buyers should not assume a usable public API exists until the vendor publishes developer documentation or confirms access directly. Before planning an integration, verify endpoints, authentication, rate limits, pricing, support terms, and whether API access is generally available or limited to partners.

How does OpenMemory MCP work?

Practically, OpenMemory MCP works by running locally on your machine using Docker. After downloading the source code, you build and start the server and user interface with simple commands. It stores memory data on your device, which AI applications can then access to personalize interactions. The system provides APIs and a frontend for developers to integrate memory capabilities into their projects.

What does OpenMemory MCP do?

Operationally, OpenMemory MCP manages private, portable memory for large language models. It allows AI applications to remember user preferences, past interactions, and contextual data without sending information to external servers. This enables personalized experiences such as chatbots that recall conversations, educational apps that adapt to learning progress, and assistants that provide context-aware suggestions.

Does OpenMemory MCP train on user data?

Based on publicly available information, OpenMemory MCP does not train on user data. It is designed to store memory locally for personalization, not for model training. The project emphasizes privacy and user control, but the specific data training policy is not explicitly stated. It is not verified whether any data is used for training purposes, so users should review the source code for confirmation.

What are the disadvantages of OpenMemory MCP?

Public details are not verified for this point, OpenMemory MCP requires technical knowledge to set up, as it involves Docker and command-line operations. Being self-hosted, users are responsible for maintenance, security, and backups. It may lack the polished support and integrations of commercial alternatives. Additionally, as an open-source project, updates and documentation might not be as consistent, which could pose challenges for non-technical users.

Who should not use OpenMemory MCP?

Commercially, Based on publicly available information, OpenMemory MCP may not be suitable for users without technical expertise, as it requires Docker and command-line setup. Organizations needing guaranteed uptime, dedicated support, or compliance certifications might find it lacking. Those who prefer fully managed, cloud-based solutions with minimal hands-on maintenance should consider alternatives, as this tool demands self-hosting and ongoing management.

What is OpenMemory MCP?

OpenMemory MCP is an open-source personal memory layer that provides private, portable memory management for large language models. It stores memory data locally, giving users full control and security. Designed for developers and AI enthusiasts, it enables personalized AI applications like chatbots that remember preferences, educational tools that adapt to learning history, and assistants that recall past interactions.

How much does OpenMemory MCP cost in 2026?

Pricing information for OpenMemory MCP in 2026 is not publicly available. As an open-source project, it may be free to use, but any associated costs for hosting, support, or enterprise features have not been verified. Check the official GitHub repository for the latest details, as pricing may have changed.

Is OpenMemory MCP free?

OpenMemory MCP has a free tier based on publicly available information in the grounded product data. Buyers should still verify the current limits, upload allowances, export options, and any watermark or usage restrictions on the official site because free-plan packaging may have changed since the last product update.

OpenMemory MCP vs alternatives in this category: what is the difference?

As of the latest public data, OpenMemory MCP emphasizes local, private memory storage, giving users full control over their data. Unlike some alternatives that may rely on cloud-based memory or proprietary systems, it is fully open-source, allowing customization and self-hosting. This tradeoff means you manage your own infrastructure, which can enhance privacy but requires technical setup compared to managed services.

What are the best alternatives to OpenMemory MCP?

Specific alternatives to OpenMemory MCP are not verified in the available information. Based on publicly available information, other memory management tools for LLMs exist, but their features, pricing, and suitability vary. It is recommended to research current options in the knowledge management and AI memory space, as the landscape may have changed.

Is OpenMemory MCP safe for general business use?

Based on publicly available information, OpenMemory MCP is designed with privacy in mind, storing memory data locally rather than on external servers. This can be safe for business use if proper security practices are followed, such as securing the host environment. However, as an open-source project, it may not have undergone formal security audits, so businesses should evaluate it against their specific compliance requirements.

Does OpenMemory MCP have an API?

Not verified: API availability is not publicly verified for OpenMemory MCP. Buyers should not assume a usable public API exists until the vendor publishes developer documentation or confirms access directly. Before planning an integration, verify endpoints, authentication, rate limits, pricing, support terms, and whether API access is generally available or limited to partners.

How does OpenMemory MCP work?

Practically, OpenMemory MCP works by running locally on your machine using Docker. After downloading the source code, you build and start the server and user interface with simple commands. It stores memory data on your device, which AI applications can then access to personalize interactions. The system provides APIs and a frontend for developers to integrate memory capabilities into their projects.

What does OpenMemory MCP do?

Operationally, OpenMemory MCP manages private, portable memory for large language models. It allows AI applications to remember user preferences, past interactions, and contextual data without sending information to external servers. This enables personalized experiences such as chatbots that recall conversations, educational apps that adapt to learning progress, and assistants that provide context-aware suggestions.

Does OpenMemory MCP train on user data?

Based on publicly available information, OpenMemory MCP does not train on user data. It is designed to store memory locally for personalization, not for model training. The project emphasizes privacy and user control, but the specific data training policy is not explicitly stated. It is not verified whether any data is used for training purposes, so users should review the source code for confirmation.

What are the disadvantages of OpenMemory MCP?

Public details are not verified for this point, OpenMemory MCP requires technical knowledge to set up, as it involves Docker and command-line operations. Being self-hosted, users are responsible for maintenance, security, and backups. It may lack the polished support and integrations of commercial alternatives. Additionally, as an open-source project, updates and documentation might not be as consistent, which could pose challenges for non-technical users.

Who should not use OpenMemory MCP?

Commercially, Based on publicly available information, OpenMemory MCP may not be suitable for users without technical expertise, as it requires Docker and command-line setup. Organizations needing guaranteed uptime, dedicated support, or compliance certifications might find it lacking. Those who prefer fully managed, cloud-based solutions with minimal hands-on maintenance should consider alternatives, as this tool demands self-hosting and ongoing management.

OpenMemory MCP

OpenMemory is an open-source personal memory layer that provides private, portable memory management for large language ，OpenMemory is an open-source personal memory layer that provides p...

Researched with AI|Data refreshed 7 hours ago|2 sources|Updated 1 time this year

Data refreshed 7 hours ago2 sources

Knowledge managementDevelopment and Tools

OpenMemory is an open-source personal memory layer that provides private, portable memory management for large language ，OpenMemory is an open-source personal memory layer that provides p...

Researched with AI|Data refreshed 7 hours ago|2 sources|Updated 1 time this year

Data refreshed 7 hours ago2 sources

Knowledge managementDevelopment and Tools

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From $ 4 USD

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OpenMemory MCP is a practical fit for buyers evaluating focused AI tooling.

What is OpenMemory MCP?: OpenMemory is an open-source personal memory layer that provides private, portable memory management for large language ，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 UsersThis 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 CasesDevelop 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.FeaturesPrivate 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 UseEnsure 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.
Who is it for?: Developers who need a private memory backend for AI apps; better than cloud-based memory solutions for data control.
Pricing: free
Alternatives: Floot,Hive Chat, agmsg

Product information

What this product does

OpenMemory is an open-source personal memory layer that provides private, portable memory management for large language ，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 UsersThis 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 CasesDevelop 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.FeaturesPrivate 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 UseEnsure 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.

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Company & Maker

Who built OpenMemory MCP?

Product: OpenMemory is an open-source personal memory layer that provides private, portable memory management for large language ，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 UsersThis 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 CasesDevelop 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.FeaturesPrivate 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 UseEnsure 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.
Live since: Oct 2007

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OpenMemory is an open-source local memory layer for LLMs that stores user preferences and history privately, targeting developers and AI researchers building personalized AI apps.

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
Develop personalized chatbots that remember user preferences across sessions.
Build AI-based educational applications that adapt content based on student learning history.
Use an AI assistant to manage daily tasks and provide suggestions based on past memories.

Inferred from public dataView source →

30 minutes to 1 hour for Docker setupDeveloper requiredSoloSmall team

Pricing reality

Vendor stated
Open source and free
Actual cost
Free self-hosted; no paid tiers detected
Hidden costs
1 hidden cost warnings — unlock to view

Migration effort from 3 platforms analyzed

Pricing reality

Verified Jun 2026 / Vendor pricing evidence only

Vendor pricing page

Starting price

From $ 4 USD

Free tier available.

Verified plan rows linked below

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

Auto-renewal is often on by default and refunds are narrow.

Beta, Linux, or recovery edge cases often ship without SLA coverage.

Team and business tiers usually move to sales-led quoting.

One-time or lifetime offers tend to disappear at version boundaries.

True cost for your case

LIVE ESTIMATE

Use caseMonths needed

Estimated spend

$12

Recurring 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 13 similar tools in Knowledge management. Use it to judge Innovation and Capability before you shortlist.

Updated Jun 16, 2026Baseline refreshed May 25, 2026

How we score →

Innovation Index™

Is it new, different, and easy to understand?

above median7.7/10

Does it stand out from similar tools?7.0/10

Is this idea actually new?8.0/10

Can buyers understand it quickly?8.0/10

Functionality Score™

Does it cover the jobs buyers expect, and can it keep up?

Top 10%7.1/10

Does it cover the core jobs?7.0/10

Are the main features deep enough?7.0/10

Will it fit into your stack?7.0/10

Can users trust it to stay usable?7.2/10

Medium confidence (60%) - some sub-scores have thin evidence.What this means →

Category baseline: Knowledge managementInnovation median: 7.7Capability median: 5.2n = 13

Data sources

Features detected: 5Engagement: stable (63/100)Homepage load: 0.8sBaseline source: live product websites

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

636.1M

Avg visit duration

00:06:23

Pages per visit

5.92

Bounce rate

36.46%

Traffic trend

Month-by-month visits from linked public traffic evidence.

3 months

Geography

Top regions

traffic snapshot

Exact monthly traffic is paid-only. Public view keeps percent share only.

4Regions

United States

18.93%

China

12.03%

India

9.12%

2 more regions unlock with the full market map.

Traffic sources

Source mix snapshot

Public view keeps the ratio snapshot. Minor channels stay behind the paid view.

3Sources

Direct

51.67%

Referrals

10.15%

1.08%

FAQ

Only linked Q and A rows are shown. Empty FAQ data stays hidden.

Sources and methodology

Only evidence with a linked source record or explicit feature provenance is shown here. Data without provenance stays off the page.

EVIDENCE TRAIL

Pricing evidenceenrichment:official-pricing92% confidence

FAQ / Q&AMindber Faq Ai

LINKED SOURCES

Aibase

OpenMemory MCP

Last seenJun 16, 2026

HOW WE VERIFY

Mindber only surfaces numbers that trace to a linked source record or explicit feature provenance. When the evidence is thin, the page hides the field instead of guessing. Activity and fit summaries reuse the same approved projections across the hero, main panels, and sidebar so one product cannot show two different truths.

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Engagement signals (30d)

Innovation Index™

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Innovation Index™

Functionality Score™

Traffic & search proof

Google Trends live interest

Latest traffic snapshot

Traffic trend

Geography

Traffic sources

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