Mem0 MCP. Keep your AI agent from forgetting everything.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Mem0 gives your AI agent persistent memory. Store, search, and recall facts, preferences, and context across conversations using an industry-standard memory layer.
Your agent remembers things—user habits, project details, past decisions—even after the chat window closes.
What your AI agents can do
Add memory
The system extracts facts and stores them as searchable memories for a user ID.
Delete memory
Deletes one specific memory record by its unique ID. Use this only when absolutely sure the data is wrong.
Get memories
Lists all stored memories for a user, helping you build a complete historical profile.
The add_memory tool pulls structured facts and preferences from text, saving them as persistent, searchable user memories.
Use search_memories to find the most relevant stored facts or user preferences matching a natural language query.
The get_memories tool pulls a list of every single memory record currently saved for a specific user ID.
Call delete_memory to permanently remove outdated or incorrect facts from the user's knowledge base.
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Supported MCP Clients
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Mem0 MCP Server: 4 Tools for Context Retention
Use these tools to manage all aspects of your agent's knowledge base—storing new facts, searching old context, listing profiles, and deleting bad data.
019d75d2add memory
The system extracts facts and stores them as searchable memories for a user ID.
019d75d2delete memory
Deletes one specific memory record by its unique ID. Use this only when absolutely sure the data is wrong.
019d75d2get memories
Lists all stored memories for a user, helping you build a complete historical profile.
019d75d2search memories
Searches saved memories using natural language and returns the most relevant facts, ranked by score.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Mem0, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Your AI agent needs a memory, and Mem0 provides it. It's an industry-standard layer that lets your client remember things—user preferences, project details, past decisions—even after the chat window closes. You don't want your system forgetting context just 'cause the conversation ends; you need facts to stick around.
When you connect your agent using Mem0, it gains persistent memory capabilities. It handles structured data extraction and makes those facts searchable for a specific user ID. This means your agent isn't just talking in the moment; it's drawing on everything it’s learned about the user over time. You're building an actual profile of the person interacting with your system.
add_memory
The add_memory tool pulls structured facts and specific preferences right out of text, saving them as searchable memories tied to a user ID. When you use this, the system doesn't just store a chunk of text; it extracts core information—like 'the client prefers blue widgets' or 'Project Alpha is due next month'—and saves it in a persistent format.
This ensures that when your agent needs to reference something foundational about the user, that fact is right there and structured for quick retrieval.
search_memories
If you need your agent to recall specific details without knowing exactly where they were stored, use search_memories. You can query it using natural language—you just ask a question like, 'What was the budget cap for Q3?' and the tool handles the rest. It searches all saved memories against your query and returns only the most relevant facts.
These results are ranked by score, so you always get the top hits first, letting you see exactly what aligns with your current conversational need.
get_memories
To build a complete historical picture of a user—a full profile of everything the agent knows about them—you call get_memories. This tool lists every single memory record currently saved for that specific user ID. It gives you visibility into the entire knowledge base, letting you see all past facts and preferences that have accumulated over time.
You can use this list to verify if certain critical data points are present in the system’s memory layer.
delete_memory
When things get messy or inaccurate, you gotta clean house. The delete_memory tool lets you permanently remove outdated or incorrect facts from the knowledge base. You pass it a unique memory ID and that record vanishes completely. This is something to use with caution—you're making a permanent deletion of data—but it gives you necessary control over maintaining data integrity, ensuring your agent only relies on accurate information.
Overall, Mem0 makes sure your AI client doesn't forget crucial details. It allows your agent to constantly remember user habits, project constraints, and past decisions across multiple sessions. You get a reliable memory layer that keeps the context alive, making interactions feel natural and consistent.
How Mem0 MCP Works
- 1 First, subscribe your AI agent client to the Mem0 MCP Server and enter an API key.
- 2 Your agent uses
add_memorywhenever a new fact or preference is discussed in a chat session. - 3 Later, when context is needed, the agent calls
search_memoriesto pull relevant facts into the current conversation.
The bottom line is: your AI client can now keep track of user history and preferences indefinitely, regardless of how many times the chat restarts.
Who Is Mem0 MCP For?
This is for developers building agents that need to feel smart. If your chatbot forgets what the user said two weeks ago, you're using it wrong. You need persistent memory management when creating anything beyond a simple Q&A bot.
Build agents that track user preferences (like IDE choice or favorite language) and recall past decisions without having to ask the user again.
Integrate persistent memory into features so users feel understood, making the product seem smarter than a standard chatbot.
Design conversational flows that feel truly personal by recalling context from previous, unrelated sessions using search_memories.
What Changes When You Connect
- Persistence: Use
add_memoryto ensure the agent keeps facts, even after sessions end. It stops the need for users to repeat themselves every time they talk to the bot. - Contextual Recall: The
search_memoriestool lets your agent find relevant past information (like 'user prefers dark mode') instead of guessing or asking vague follow-up questions. - Full User Profiling: Call
get_memoriesto pull every piece of data associated with a user. This builds the deep profile necessary for advanced, tailored experiences. - Data Integrity: Need to fix bad info? Use
delete_memory. It gives you explicit control over cleaning up outdated or incorrect memories, keeping your knowledge base clean. - Reliability: By separating memory storage from active context, your agent's understanding stays reliable. You don't lose critical data just because the chat window refreshed.
Real-World Use Cases
The Onboarding Flow Failure
A new client talks to the support bot about their account setup, mentioning they use a specific payment gateway and have an enterprise license. If the agent doesn't remember this, it asks for the info again next month. Solution: Use add_memory immediately after the conversation ends. The agent stores 'Gateway X used' and 'License Type Enterprise,' so when the user returns, the bot already knows those details.
Troubleshooting an Error
A developer reports a bug but vaguely mentions 'the build pipeline.' If the system relies on current chat context, it might miss that the developer previously stored their preferred branch name. Solution: Run search_memories with 'build pipeline' to pull up the specific details (like the correct Git branch) that were saved last week.
Building a Recommendation Engine
You want an agent to suggest features, but it needs to know what the user already owns. Without memory, suggestions are generic. Solution: Use get_memories to pull all stored facts (e.g., 'Owns feature A,' 'Needs integration with tool B'). The agent uses this full context list before making a recommendation.
Data Cleanup After Pivot
The business changes direction, and the bot starts referencing old product lines or deprecated features. This clutters the memory base and confuses users. Solution: Systematically use delete_memory to wipe out memories related to the old product line, keeping the knowledge base focused only on current offerings.
The Tradeoffs
Relying on Chat History
The agent can't remember what the user said three days ago because it only reads the last 50 messages in the chat history. It treats every session as brand new.
→
Don't rely on short-term context windows. When a key fact is mentioned, use add_memory to save it permanently. Then, when needed, call search_memories instead of assuming the information is still in the chat log.
Searching by Keywords Only
A user asks 'What did we talk about regarding billing?' but only mentions vague terms. A simple keyword search might miss related, contextually similar memories.
→
Use search_memories. Because it uses semantic search, you can ask open-ended questions (like 'What were the payment requirements?') and the tool finds memories based on meaning, not just keywords.
Ignoring Data Bloat
Over time, the agent accumulates thousands of irrelevant or outdated facts. The memory base gets too big, slowing down all retrieval calls.
→
Periodically run get_memories to audit the data. If you find old records that are no longer relevant, use delete_memory to keep the knowledge base fast and clean.
When It Fits, When It Doesn't
Use Mem0 if your AI agent needs to remember things permanently across sessions. This is critical for any application where context matters more than a single chat thread—think customer support, personal assistants, or complex developer tools.
Don't use this if the interaction is strictly transactional and stateless (e.g., 'What time is it?'). For those simple tasks, the memory layer adds unnecessary overhead. If your only goal is to manage data structure in a database, you don't need Mem0; just use standard CRUD operations. But if the intelligence of remembering is what matters, then Mem0 solves that.
Remember: add_memory writes the data. search_memories reads the data. Always pair them up.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mem0. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 4 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
The pain point isn't forgetting; it's having to re-explain yourself.
Today, if you build an agent that talks to a user over several days, the moment they start a new chat session, everything resets. The bot doesn't know their preferred tools, their company name, or the fact that they only work during specific hours. You have to hardcode these details or prompt the user every time.
With Mem0 MCP Server, the agent remembers it. Use `add_memory` to save facts like 'user works M-F' and 'prefers Jira.' When the conversation restarts, calling `search_memories` instantly feeds the correct context back into the chat stream. The user feels understood.
Mem0 MCP Server: Never lose track of a single fact.
Manual memory management means building separate databases, handling complex indexing yourself, and writing custom logic every time context needs to be retrieved. It's slow and brittle.
Now you connect Mem0. Your agent calls the tools—`search_memories`, `get_memories`—and gets structured data back instantly. The memory layer handles all the complicated database stuff for you.
Common Questions About Mem0 MCP
How does add_memory work with my user ID? +
When you call add_memory, you must provide a unique User ID. This tells Mem0 exactly which profile the new fact belongs to, ensuring data stays separated and organized for retrieval.
Is search_memories better than get_memories? +
Yes. get_memories returns everything stored for a user—a massive list. You usually want to use search_memories, which filters the results and ranks them by relevance score, giving you only what matters right now.
Can I delete all memories at once using delete_memory? +
No. The delete_memory tool requires a specific memory ID for deletion. You must first use get_memories to list the IDs you want to remove.
Does Mem0 handle different types of data (e.g., preferences vs facts)? +
The system pulls out key information automatically, structuring it as a searchable memory record regardless of whether it's a preference ('dark mode') or a hard fact ('user is based in Berlin').
What happens if I use add_memory with unstructured or ambiguous text? +
The system attempts to automatically extract structured facts. If the input is too vague, it stores a less granular memory and may return an extraction warning. Always review the stored output for accuracy.
If I run many queries with search_memories, are there rate limits? +
Yes, Vinkius implements rate limiting to ensure stability. If you exceed the limit, your agent will receive a specific HTTP error code. Implement exponential backoff in your client logic.
Can I filter results when using get_memories? +
You can filter retrieved memories by creation date range or memory type (e.g., 'preference' vs 'fact'). This allows you to narrow down the scope of your user profile review.
Are my memories encrypted when I use delete_memory? +
The data is secured both in transit and at rest using industry-standard encryption protocols. Deletion requests immediately flag the memory ID for irreversible removal from the active database.
Is Mem0 free to use? +
Yes! Mem0 offers a free Hobby tier with 10,000 memories and 1,000 search calls per month — no credit card required. Paid plans start at $19/month for higher limits. An open-source version (Apache 2.0) is also available for self-hosting.
How does Mem0 extract and store memories? +
When you send content to Mem0, its AI automatically extracts key facts and structured information. For example, if you send 'I prefer Python over JavaScript and work best in the morning', Mem0 creates two separate memories: one about language preference and one about work schedule. These are stored in a hybrid architecture (key-value + vector + graph) for fast semantic retrieval.
Can I organize memories by user or agent? +
Yes! Every memory operation supports scoping by user_id, agent_id, or run_id. This means you can maintain separate memory banks for different users, different agents, or even different conversation runs — keeping context perfectly isolated.
Multi-server workflows that include Mem0 MCP
Get a Daily AI Intelligence Briefing via MCP
You read 30 tabs every morning trying to stay current on AI news , your agent reads them all in 90 seconds, remembers what you care about from previous sessions, and delivers a personalized daily briefing that skips what you already know
MCP Servers That Remember Every Meeting
You had a critical decision in a meeting 3 weeks ago but nobody remembers the exact reasoning , Deepgram transcribes every meeting, Mem0 stores decisions with persistent memory, and Sheets tracks all commitments
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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