How to Use the Chainlit MCP in LangChain

Q: How do I connect the Chainlit MCP server to LangChain?

Install langchain-mcp-adapters and langgraph. Pass your endpoint to MultiServerMCPClient and call gettools() to hand the functions to your agent.

Q: Can LangChain agents analyze specific Chainlit threads?

Yes. Your agent calls listthreads to find the interaction boundaries, then uses getthread to inspect the exact payload.

Connect the Chainlit MCP Server directly into your LangChain agents to audit LLM interactions and trace multi-step reasoning.

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Connect Chainlit MCP to LangChain

Create your Vinkius account to connect Chainlit to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Audit multi-step reasoning with LangChain MCP Server

The `list_steps` tool pulls the exact programmatic prompts and generations from your conversational threads. Your ReAct agent grabs this raw interaction data to evaluate how previous models handled specific inputs. You chain this output directly into an analysis node. The agent decides which steps need review based on token consumption or latency, passing the flagged data down your pipeline for further processing.

Track project metrics and resource consumption

Calling the `get_stats` tool fetches explicit traffic boundaries and resource usage from your configured spaces. Your agent queries this data to monitor load across different deployments without manual dashboard checks. Pairing this with `list_projects` lets your LangChain pipeline loop through all active applications automatically. You get a clear, programmatic view of where your tokens go entirely within your script.

Evaluate conversational accuracy and user feedback

Running the `list_feedbacks` tool retrieves absolute user review ratings across your deployments. Your agent reads these explicit accuracy scores to flag problematic interactions immediately. When a bad rating hits, the chain triggers `get_thread` to pull the exact payload and node topology. The agent then analyzes the failure path and logs the findings straight to your LangSmith trace.

Setup guide

Set up Chainlit MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Chainlit tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "chainlit-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Chainlit transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Chainlit MCP in LangChain

Install `langchain-mcp-adapters` and `langgraph`. Pass your endpoint to `MultiServerMCPClient` and call `get_tools()` to hand the functions to your agent.

Yes. Your agent calls `list_threads` to find the interaction boundaries, then uses `get_thread` to inspect the exact payload.

It turns observability into an active pipeline component. Instead of just looking at dashboards, your agent programmatically reacts to bad feedback or high token usage.

You can combine these tools with database or vector store APIs in the same chain. The client handles the routing automatically.

The V8 Isolate Sandbox ensures your prompt generations and user feedback ratings stay strictly within the ephemeral session. Your token grants access only for the duration of the run, leaving zero residual access to your raw programmatic steps.

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