How to Use the Chainlit MCP in CrewAI

Q: Can I build a CrewAI team to monitor all my Chainlit projects?

Absolutely. You can create a 'Manager' agent that uses listprojects, then dynamically assigns 'Monitor' agents to each project returned. It's a great way to scale your observation without writing complex code.

Q: How does a CrewAI agent get access to specific Chainlit tools like getthread?

You pass the MCP server URL from Vinkius into your Agent's mcps parameter. CrewAI automatically discovers all available tools, like getthread. You can also use toolfilter if you want an agent to only have access to a subset of tools.

Deploy a crew of AI agents to monitor, analyze, and report on your Chainlit applications using CrewAI.

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Connect Chainlit MCP to CrewAI

Create your Vinkius account to connect Chainlit 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 Chainlit with CrewAI

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Deploy an Autonomous Monitoring Crew

This isn't about one agent. It's about a team. With CrewAI, you can assign roles. A 'Watcher' agent runs `list_threads` every few minutes. When it spots a new conversation, it passes the ID to an 'Analyst' agent. The Analyst agent then takes over. It uses `get_thread` and `list_steps` to dig into the conversation, checking for errors or low-quality model responses. If it finds an issue, it can pass the findings to a 'Notifier' agent for escalation. The whole process is autonomous.

Assemble a Quality Assurance Team

Use this MCP server to build a dedicated QA crew for your Chainlit apps. The 'Auditor' agent's only job is to run `list_feedbacks` and look for 1-star or 2-star reviews. It doesn't do anything else. When the Auditor finds a bad review, it hands the thread ID off to the 'Investigator' agent. The Investigator uses `get_thread` to pull the full conversation and assembles a detailed report. CrewAI manages the state and communication between them, so the workflow is clean and reliable.

Run Cross-Project Analysis with a Crew

Some tasks need a bigger team. A 'Planner' agent can start by using `list_projects` to get a list of every application you're running on Chainlit Cloud. Then, it can spawn a 'Worker' agent for each project. Each Worker agent is tasked with one thing: call `get_stats` for its assigned project. As they finish, they report back to a final 'Aggregator' agent. This agent's job is to collect all the individual stats and compile a single, fleet-wide health report. This is how you manage observability at scale.

Setup guide

Set up Chainlit 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 Chainlit tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Chainlit 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 Chainlit. 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 Chainlit MCP in CrewAI

Absolutely. You can create a 'Manager' agent that uses `list_projects`, then dynamically assigns 'Monitor' agents to each project returned. It's a great way to scale your observation without writing complex code.

You pass the MCP server URL from Vinkius into your Agent's `mcps` parameter. CrewAI automatically discovers all available tools, like `get_thread`. You can also use `tool_filter` if you want an agent to only have access to a subset of tools.

The most direct way is to add your unique Vinkius URL to the `mcps` list when defining your Agent in CrewAI. For more complex setups, you can use `MCPServerHTTP` to get more control over things like transport protocols.

CrewAI's process model helps manage this. If an agent in a sequential crew fails, the task stops. You can build error handling into your Task definitions to manage this, or have a 'Supervisor' agent that monitors the crew's health and can restart failed tasks.

Your data is kept secure. The connection between your CrewAI agents and the MCP server is isolated within a V8 sandbox on our end. Your agents can access and process Chainlit thread and feedback data, but that data is only visible during the active session and is never stored by Vinkius.

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