
MemMachine
MemMachine is an open-source memory layer for AI agents. It stores working, episodic and profile memory that persists across sessions, agents and language models, and connects through REST, Python, TypeScript or MCP.
What is MemMachine?
MemMachine is an open-source memory layer for AI agents, released under the Apache 2.0 licence. It gives AI-powered applications the ability to learn, store and recall data and preferences from earlier sessions, so that an assistant carries context forward instead of starting cold each time. The system distinguishes three kinds of memory. Working memory holds short-term context, episodic memory retains long-term conversational history, and profile memory accumulates durable facts about the user. Together they build an evolving profile that persists across sessions, across separate agents and across different language models. Architecturally, MemMachine sits in three layers. Agents reach it through an API layer that exposes a REST API, a Python SDK, a TypeScript SDK and a Model Context Protocol server. The core then routes each interaction into the appropriate memory type. Persistence is split by nature: episodic memory goes to a graph database such as Neo4j, while profile memory is stored in SQL. The project is deliberately model-agnostic. It works with OpenAI, Anthropic, Amazon Bedrock, Ollama and any other provider, and can serve several models at once, including specialised models running in a private cloud or an on-premises data centre. That is the argument its authors make against memory built into a single frontier lab's service: such memory stays captive and does not travel to other models. MemMachine was incubated by MemVerge, Inc. and is now presented as community-driven, with MemVerge still assigning engineers to it. The GitHub repository shows 3,400 stars and 203 forks. Ten integrations are documented, among them LangChain, LangGraph, CrewAI, LlamaIndex, n8n, Dify, Claude Code and the GPT Store. Beyond self-hosting, a managed MemMachine Platform console offers usage dashboards, API-call metrics, API key management and memory governance. Sample agents ship as code recipes for CRM, healthcare navigation, personal finance and content writing. An Enterprise edition with dedicated support is announced but not yet available. Deployment is equally flexible: locally, in Docker, in a private VPC or on-premises, which lets an organisation keep full control of the data its agents remember.
What it does
- Store and recall user data and preferences from past sessions
- Build an evolving user profile shared across several agents
- Keep conversational context across long-running, multi-step tasks
- Serve memory to several language models at once, without vendor lock-in
- Expose memory to agents through REST, Python, TypeScript or MCP
- Persist episodic memory in a graph database and profile memory in SQL
- Run the whole memory layer locally, in Docker, in a private cloud or on-premises
When to use MemMachine / When not to
A quick filter to help you decide if MemMachine is the right fit.
When to use MemMachine
- Developers building AI agents, assistants or autonomous workflows that must remember earlier sessions
- Engineering teams running several language models at once and wanting memory that is not locked to one vendor
- Organisations with strict data-control requirements, able to deploy in a private VPC or on-premises
- Researchers experimenting with agent architectures and cognitive models
- Product teams building domain assistants for CRM, healthcare navigation, personal finance or content work
When not to use MemMachine
- End users looking for a ready-to-use chatbot: this is a component, not a finished application
- Non-technical buyers, since every integration happens in code
- Teams unwilling or unable to operate a graph database and a SQL store
- Buyers who need a published price list before starting a conversation
- Organisations requiring a contractual support channel by email or a formal SLA today
How to use MemMachine
A typical end-to-end flow, from setup to results.
- Decide between self-hosting the open-source edition and using the hosted MemMachine Platform
- For self-hosting, install with pip or clone the source from the GitHub repository
- Provision the two data stores: a graph database such as Neo4j for episodic memory and SQL for profile memory
- Work through the quickstart guide in the documentation to get a first instance running
- Choose an access path: REST API, Python SDK, TypeScript SDK or MCP server
- For MCP clients, launch memmachine-mcp-stdio for Claude Desktop or memmachine-mcp-http for web clients
- Wire your agent framework in using one of the documented integrations, such as LangChain, LangGraph, CrewAI, LlamaIndex, n8n or Dify
- For the hosted route, create an account on the Platform console and generate an API key
- Monitor usage, API call volume and stored memories from the Platform dashboard
- Import an existing history if needed with the documented ChatGPT2MemMachine tool, and raise questions on Discord or GitHub Issues
Pros & Cons
Pros
- Apache 2.0 licence: the code can be audited and deployed without a commercial agreement
- Full data control is achievable through private cloud or on-premises deployment
- No model lock-in: several language models can be served at the same time
- Four access paths rather than one, covering REST, Python, TypeScript and MCP
- Ten documented integrations with mainstream agent frameworks
- Verifiable public traction: 3,400 GitHub stars, 203 forks and three third-party products built on it
- A Data Processing Agreement is available on request
Cons
- No public pricing for the hosted service: every quote goes through a negotiated Order
- The Enterprise edition and its dedicated support are announced but not yet available
- Operating a graph database and a SQL store adds real infrastructure cost
- No support email: help is limited to Discord and GitHub Issues
- The privacy policy announces an EEA representative but never names one, and no subprocessor list is published
- Nothing is stated either way about whether customer data is used to train AI models
- A young project: the domain dates from August 2025 and the first web archive from September 2025
Pricing & Plans
A permanent free option exists: the open-source edition is available under the Apache 2.0 licence at no cost and may be self-hosted without limitation of duration. No paid price point is published. Fees for the hosted MemMachine Platform are set out in a commercial Order, payable in advance on a monthly or annual basis and settled in United States dollars, but no rate is disclosed on the site, in the documentation index or in the console. An Enterprise edition is announced as forthcoming, also without published pricing.
- Open Source Version — free
- Apache 2.0 licence
- self-hosted locally
- in Docker
- in a private cloud or on-premises
- outside the commercial terms of service
- MemMachine Playground — hosted instance of the open-source edition
- MemVerge reserves the right to cap memory
- usage or capacity and to suspend it at its sole discretion
- MemMachine Platform — managed cloud service with usage dashboard
- API-call metrics and API key management
- priced per commercial Order
- no public rate
- Enterprise — additional features and dedicated support
- announced as coming soon and not available as of 11 August 2026
Data, GDPR & hosting
A consolidated view of how MemMachine handles your data.
GDPR overview
The privacy policy, last updated on 20 May 2026, addresses the GDPR explicitly and at length. A dedicated section for users in the EEA and the UK sets out the legal bases relied on and enumerates nine rights: information, access, rectification, erasure, restriction, portability, objection, protections around automated decision-making and profiling, and the right to complain to a supervisory authority. Requests are answered within one month, free of charge unless manifestly unfounded, repetitive or excessive, and are exercised at privacy@memverge.com. International transfers are covered by standard contractual clauses and the international data transfer agreement or addendum, available on request. A Data Processing Agreement can be requested. One gap is worth noting: the policy says an EEA representative may be contacted "as explained below", but no representative is ever named and no contact details appear anywhere in the document.
Who owns the data?
MemVerge's terms treat customer data as the customer's. "Customer Data" covers everything submitted, uploaded, transmitted, stored or indexed through the Services, and on termination MemVerge undertakes to destroy it, subject to applicable law. Where MemVerge processes personal information on behalf of a customer, it acts as processor or service provider, and the customer's own privacy policy governs that processing rather than MemVerge's. MemVerge states that it does not sell or share personal information with third parties for their promotional or direct marketing purposes. Running the open-source edition in a private cloud or on-premises keeps the data entirely within the customer's own infrastructure.
Reuse rights
The open-source edition is licensed under Apache 2.0, so the code may be used, modified and redistributed without asking permission, subject to the licence's attribution and notice requirements. The hosted Services are different: the licence granted is non-exclusive, non-transferable and limited to the customer's personal or internal business use, with no right to sublicense. Customers may not resell, lease, distribute or create derivative works of the Services, reverse engineer them, or use them to build competing functionality or train competing AI models. Data the customer puts in remains theirs to use. On the vendor side, MemVerge collects user content submitted to the Services, including prompts and uploaded files, images and audio, and uses usage data to administer and improve the platform; it also shares aggregated, non-personal information with third parties for business and marketing purposes. Nothing in the privacy policy or the terms states whether customer data is or is not used to train AI models.
Data retention & training
Hosting summary
For the hosted service, the jurisdiction is the United States and only the United States. MemVerge states that it is located there and that the personal information it collects is stored on servers located there. Users are asked to consent explicitly to their data being processed and stored in the United States, with the policy acknowledging that protection may fall short of what other regions, and the European Union in particular, require. No alternative region and no data-residency option are offered. For transfers, MemVerge says it may enter into standard contractual clauses and an international data transfer agreement or addendum, and that further details are available on request. The self-hosted route changes the picture completely: because the open-source edition runs locally, in Docker, in a private cloud or in an on-premises data centre, hosting is wherever the customer puts it, and MemVerge sees none of it. One detail worth separating out: the marketing site itself resolves to a GitHub Pages address behind a Fastly anycast front end, which describes the website's hosting and not the service's.
Where MemMachine works
Country-level availability.
Not available in
Things to keep in mind
Risks and trade-offs to weigh before adopting MemMachine.
- The hosted service commits you before you see a price: terms renew automatically, require thirty days' notice and give the customer no right to terminate for convenience
- Fees are non-refundable and late payment carries a 1.5% monthly finance charge
- Disputes go to binding JAMS arbitration in Milpitas or San Jose, California, with a class-action waiver
- Data for the hosted service leaves your jurisdiction: storage is exclusively in the United States and you consent to that transfer explicitly
- Compliance gaps to weigh before storing personal data: no named EEA representative, no subprocessor list, and no published position on model training
- Delegating memory to an external layer concentrates a detailed, durable profile of your users in one place; the more an assistant remembers, the more damaging a breach or a misconfiguration becomes
- The free Playground can be capped or withdrawn at MemVerge's sole discretion, so it should not carry anything you depend on
Setup & Integrations
Technical difficulty
Developer-level. There is no consumer installer: MemMachine is integrated in code. Self-hosting means installing via pip or from source, then configuring and operating two data stores, a graph database for episodic memory and SQL for profiles. Docker and cloud deployments are documented, and a quickstart guide exists. Ten ready-made framework integrations and a two-command MCP server cut the wiring work considerably. The hosted Platform is far lighter: create an account, generate an API key, and no infrastructure to run. Expect a comfortable afternoon for a developer, longer if the databases must be provisioned and operated properly.
Deployment
Integrations
Supported languages
Behind MemMachine
Fundraising
Resources
All the official URLs gathered for verification and reference.
Frequently asked questions
What exactly is MemMachine?
Is it for developers or for end users?
Which licence does it use, and where is the code?
Does it work with more than one AI model?
How much does it cost?
How do I connect an agent to it?
Where is data stored?
How do I get support?
Who is behind the project?
Is there a minimum age?
Should you pick MemMachine?
MemMachine occupies a specific slot in the AI stack: it is infrastructure, not a finished product. Nothing here is aimed at an end user; the deliverable is a memory layer that developers wire into agents they are already building. Its strongest argument is independence. Because the core is Apache 2.0 and can run locally, in Docker, in a private cloud or on-premises, an organisation can keep every stored memory inside its own perimeter. Because it is model-agnostic, that memory follows the user across OpenAI, Anthropic, Bedrock or a self-hosted model rather than staying locked inside one vendor's assistant. For teams that already run several models, this is the practical reason to look at it. The reservations are mostly about what the site does not say. The hosted Platform carries no published price: the terms send you to a negotiated Order, with automatic renewal, a thirty-day notice period and no right to terminate for convenience. The promised Enterprise edition is not available yet. There is no support email at all, only Discord and GitHub Issues. The privacy policy announces an EEA representative and then never names one, publishes no subprocessor list, and says nothing either way about whether customer data is used to train models. Data for the hosted service sits exclusively in the United States. Maturity is a fair question too: the domain was registered in August 2025 and first archived a month later. Against that, the traction is real, with 3,400 GitHub stars, 203 forks and three third-party products already built on it. The tool suits engineering teams comfortable operating a graph database and a SQL store, and wanting memory they own. Anyone expecting a ready-made assistant, a published price list or a formal support channel should look elsewhere for now.
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