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 CrewAI 4 tools — connect in under 2 minutes

Name: Mem0
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 4 Tools Framework

Connect your CrewAI agents to Mem0 through the Vinkius — pass the Edge URL in the `mcps` parameter and every Mem0 tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Mem0 Specialist",
    goal="Help users interact with Mem0 effectively",
    backstory=(
        "You are an expert at leveraging Mem0 tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Mem0 "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 4 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Mem0 becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Mem0 tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 4 tools from Mem0

Why Use CrewAI with the Mem0 MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Mem0 through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Mem0 + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Mem0 for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Mem0, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Mem0 tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Mem0 against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Mem0 MCP Tools for CrewAI (4)

These 4 tools become available when you connect Mem0 to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Mem0 + CrewAI FAQ

Common questions about integrating Mem0 MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.