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.

Mem0 MCP Server for LangChain 4 tools — connect in under 2 minutes

Name: Mem0
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 4 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Mem0 through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "mem0": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Mem0, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Mem0 MCP Server

Connect your AI agent to Mem0 — the industry-standard memory layer that enables agents to remember, learn, and personalize across conversations.

LangChain's ecosystem of 500+ components combines seamlessly with Mem0 through native MCP adapters. Connect 4 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Add Memories — Store facts, preferences, and context from conversations. Mem0 AI automatically extracts key information and structures it as searchable memories
Semantic Search — Find relevant memories using natural language queries. Ask 'What does the user prefer?' and get ranked results by relevance
List Memories — View all stored memories for a user to build comprehensive profiles and understand accumulated context
Delete Memories — Remove outdated or incorrect memories to keep the knowledge base clean

The Mem0 MCP Server exposes 4 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Mem0 to LangChain via MCP

Follow these steps to integrate the Mem0 MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 4 tools from Mem0 via MCP

Why Use LangChain with the Mem0 MCP Server

LangChain provides unique advantages when paired with Mem0 through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Mem0 MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Mem0 queries for multi-turn workflows

Mem0 + LangChain Use Cases

Practical scenarios where LangChain combined with the Mem0 MCP Server delivers measurable value.

RAG with live data: combine Mem0 tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Mem0, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Mem0 tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Mem0 tool call, measure latency, and optimize your agent's performance

Mem0 MCP Tools for LangChain (4)

These 4 tools become available when you connect Mem0 to LangChain via MCP:

add_memory

The system automatically extracts structured facts from the provided content and stores them as searchable, persistent memories associated with the given user ID. Store a new memory for a user. The AI extracts key facts and preferences from the content and stores them as persistent memories

delete_memory

Use with caution — this action cannot be undone. Delete a specific memory by its ID

get_memories

Useful for reviewing what the agent knows about a user or for building a user profile. List all stored memories for a specific user

search_memories

Returns results ranked by relevance score, enabling the agent to recall past preferences, facts, and context. Semantically search stored memories for a specific user. Returns the most relevant memories matching your query

Example Prompts for Mem0 in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Mem0 immediately.

"Remember that I prefer dark mode, use VS Code, and my favorite language is TypeScript."

"What do you remember about my coding preferences?"

"Show me all the memories you have stored for my user profile."

Troubleshooting Mem0 MCP Server with LangChain

Common issues when connecting Mem0 to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Mem0 + LangChain FAQ

Common questions about integrating Mem0 MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.