How to Use the Mem AI (Knowledge Workspace) MCP in CrewAI

Q: How do I manage collections in Mem AI (Knowledge Workspace) with CrewAI?

Give your librarian agent the listcollections and addmemtocollection tools. It will sort your notes based on the strategy you give it.

Deploy a specialized agent crew to manage your Mem AI (Knowledge Workspace) content automatically.

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Connect Mem AI (Knowledge Workspace) MCP to CrewAI

Create your Vinkius account to connect Mem AI (Knowledge Workspace) 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 Mem AI (Knowledge Workspace) with CrewAI

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Role-based note management with CrewAI

Assign a researcher agent to use `search_mems` and a librarian agent to use `add_mem_to_collection`. Your crew collaborates to keep your knowledge structured without you typing a word. This works because agents share context. Your research agent finds the info, and the librarian agent ensures it ends up in the right place.

Automated knowledge capture

Set up an agent to watch your incoming data and run `mem_it` whenever it finds something worth keeping. It turns raw input into indexed knowledge immediately. This is about reducing friction. Your agents handle the indexing so your workspace stays accurate and searchable as you generate more content.

Large-scale workspace maintenance

Use a monitor agent to audit your notes with `list_mems` and flag anything that needs a refresh. It keeps your workspace from becoming a graveyard of stale info. When the agent finds old content, it can use `update_mem` to append new context or `delete_mem` to purge junk. Your crew keeps the workspace clean while you focus on high-level tasks.

Setup guide

Set up Mem AI (Knowledge Workspace) 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 Mem AI (Knowledge Workspace) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Mem AI (Knowledge Workspace) 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="Mem AI (Knowledge Workspace) Analyst",
        goal="Access and analyze Mem AI (Knowledge Workspace) data via MCP.",
        backstory="Expert analyst with direct Mem AI (Knowledge Workspace) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Mem AI (Knowledge Workspace) 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 Mem.ai. 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 Mem AI (Knowledge Workspace) MCP in CrewAI

Pass the server URL to your agent configuration. Your crew will then have full access to the toolset to manage your notes.

Absolutely. Your agents can search your notes for existing info before they go out to find new data, preventing duplicate work.

Give your librarian agent the `list_collections` and `add_mem_to_collection` tools. It will sort your notes based on the strategy you give it.

It uses standard transports. As long as your agent has access to the endpoint, it will execute tasks reliably.

You control access at the tool level. Only the agents you explicitly authorize can access your note bodies or metadata, ensuring your private workspace remains private.

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