How to Use the Chainlit MCP in OpenAI Agents SDK

Q: Can OpenAI Agents SDK read user ratings from Chainlit?

Yes, the agent exposes the listfeedbacks tool automatically. It queries absolute user review ratings for conversational accuracy and value across your deployments.

Q: What happens if a Chainlit thread is too large for OpenAI Agents SDK?

The agent pulls the exact payload using getthread, which maps node topologies. Built-in guardrails in the SDK validate the payload size before attempting to process massive interaction histories.

Connect Chainlit to OpenAI Agents SDK and let your production agents audit chat threads and analyze LLM observability metrics natively.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Chainlit MCP to OpenAI Agents SDK

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

GDPR Free for Subscribers

Setup Chainlit with OpenAI Agents SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Audit Chainlit projects with this MCP Server

Production agents need context on past user interactions. Your OpenAI agent calls `list_projects` to find active Chainlit Cloud deployments, then maps the environment. Once it identifies a target space, it pulls conversational histories using `list_threads`. Handoffs between specialized agents run smoother when they share exact interaction boundaries. The agent grabs the specific payload using `get_thread` before passing control to a specialized audit agent. You get full tracing in the OpenAI dashboard for every API call.

Trace raw model steps and node topologies

Guardrails matter when analyzing programmatic interactions. Using `list_steps`, your agent unpacks the exact prompts and generations that occurred inside a single thread. It sees the raw data before deciding if a safety constraint was violated. This MCP integration gives your system direct access to node topologies. Instead of guessing why a conversation derailed, the agent reads the exact payload. Every step gets evaluated against your predefined safety rules before execution continues.

Pull user feedback and resource consumption

Deployed products require hard metrics on conversational accuracy. Your agent hits `list_feedbacks` to aggregate absolute user review ratings across all deployments. It filters for low-value interactions and flags them for human review. Tracking infrastructure costs happens in the same workflow. The agent runs `get_stats` to pull traffic boundaries and resource consumption for native projects. You keep your LLM observability tight without writing custom API wrappers.

Setup guide

Set up Chainlit MCP in OpenAI Agents SDK

Prerequisites

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

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Chainlit tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Chainlit tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Chainlit tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Chainlit Agent",
            instructions="You have access to Chainlit tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Chainlit now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Chainlit MCP in OpenAI Agents SDK

Install openai-agents via pip. Create an MCPServerStreamableHttp instance with your Vinkius endpoint URL. Pass it as mcp_servers=[server] to your Agent constructor and set cacheToolsList=True for better performance.

Yes, the agent exposes the list_feedbacks tool automatically. It queries absolute user review ratings for conversational accuracy and value across your deployments.

The agent pulls the exact payload using get_thread, which maps node topologies. Built-in guardrails in the SDK validate the payload size before attempting to process massive interaction histories.

Tools are auto-discovered by the agent on startup. It runs list_projects to find globally configured Chainlit Cloud projects managing independent app tracking spaces.

Vinkius runs the server in an ephemeral V8 Isolate Sandbox. When your agent accesses raw prompts and generations via list_steps, the connection uses a zero-trust architecture. Tokens are securely managed and never stored permanently.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript