How to Use the Cognee MCP in CrewAI

Deploy a research crew that learns. Pair Cognee with CrewAI to give your agents a shared knowledge graph.

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Connect Cognee MCP to CrewAI

Create your Vinkius account to connect Cognee to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cognee with CrewAI

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Perplexity

Shared memory for CrewAI agents

Agents use `cognee_add_data` to dump research findings into a common knowledge base. This stops agents from repeating work others have already completed. It gives your crew a collective brain. Every agent benefits from the discoveries made by its peers during the research phase.

Graph-aware analysis in CrewAI

Have your analysts run `cognee_cognify` to structure the gathered intel. The agents now work with a graph instead of just flat text files. This makes your agents significantly faster at connecting dots. They can identify the relationship between two entities discovered by different agents in the crew.

Autonomous discovery with Cognee

Agents can use `cognee_search` to find answers to complex prompts. It allows your crew to operate autonomously without needing constant human input. `cognee_get_insights` lets them report back on the state of the graph. Your agents become project managers that understand the full context of the mission.

Setup guide

Set up Cognee MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Cognee tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Cognee Analyst",
    goal="Access and analyze Cognee data via MCP.",
    backstory="Expert analyst with direct Cognee access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Cognee transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Cognee Analyst",
        goal="Access and analyze Cognee data via MCP.",
        backstory="Expert analyst with direct Cognee access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Cognee transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cognee. 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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Common questions about Cognee MCP in CrewAI

In your agent definition, list the server tools in the mcps parameter. You can filter the tools so only the research agent gets access to the write functions.

Yes, the server handles concurrent requests. Your agents can query the graph at the same time without locking issues.

Every agent in the crew points to the same server instance. This ensures they are all contributing to and learning from the same knowledge graph.

Check the tool call logs in your agent output. Each call to the server is recorded, showing you exactly what the crew is searching for.

We enforce strict access controls on the graph. Only your authorized agents can read or write to the knowledge store, ensuring your research data remains private to your specific crew.

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