Chainlit Connector for AI agents.
6 live capabilities
Audit chat threads and track LLM observability metrics in real time.
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Why people use Chainlit
Chainlit for debugging production AI chat telemetry
This Connector brings that data straight to your chat. You just tell your agent to find the bad thread and show the steps. It pulls the data, formats it, and lets you see the root cause in seconds.
What Vinkius changes
You get a direct window into your production chat telemetry without leaving your chat interface.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Diagnosing a hallucination
A user reports a weird answer.
- Real-world use case 02
Sentiment Analysis
A product manager asks the agent to find all negative feedback from list_feedbacks and summarize the top three complaints.
- Real-world use case 03
Traffic Monitoring
An engineer wants to know how many users hit the bot today.
Complete set · 6capabilities
The complete Chainlit capability set.
These are the exact actions your AI can choose when you ask it to work with Chainlit.
01—03
3 capabilities in this set.
Part of 6 available through Chainlit.
- 01 Capability
List projects
See all your globally configured Chainlit Cloud projects and tracking spaces. This helps you manage multiple app environments in one place.
- 02 Capability
List threads
Find specific user interaction boundaries inside a deployed project. Use this to quickly locate relevant conversations for debugging.
- 03 Capability
Get thread
Get the exact payload and node topology for a specific conversation. It shows the full structure of a user interaction.
04—06
3 capabilities in this set.
Part of 6 available through Chainlit.
- 04 Capability
List steps
See the raw prompts and generations used within a single thread. This reveals the internal logic jumps of your AI agent.
- 05 Capability
List feedbacks
Pull user ratings and textual reviews regarding conversational accuracy. Use this to gather qualitative data on your bot's performance.
- 06 Capability
Get stats
Fetch analytics on traffic boundaries and resource consumption for your projects. It provides a clear picture of your app's usage.
Set up in minutes
One URL. Then ask Chainlit to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Chainlit from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Chainlit, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Chainlit for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Chainlit URL.
- Step 03
Save and start
Save the connection and enable Chainlit in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"chainlit": {
"url": "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Chainlit
Open Agent mode in chat and ask: "Using Chainlit, help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"chainlit": {
"url": "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Chainlit
Ask Copilot: "Using Chainlit, help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"chainlit": {
"url": "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Chainlit
Open Cascade and ask: "Using Chainlit, help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"chainlit": {
"url": "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Chainlit
Ask Cline: "Using Chainlit, help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add chainlit --transport http "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Chainlit
Ask Claude: "Using Chainlit, show me...". 6 tools are ready
Where the request belongs
Work Chainlit can move forward.
This is for the engineers and product owners who are tired of manual log diving. It's for the people responsible for making sure an AI app actually works for real humans.
AI Developer
Diagnosing why a production model failed by checking the exact parameter stack and logic jumps on a Tuesday afternoon.
Product Manager
Summarizing user sentiment and identifying the worst-performing chat interactions to prioritize the next sprint.
QA Specialist
Auditing hundreds of hours of conversations to check for tone and compliance issues without reading every single log.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsLangSmith
Observability and evaluation platform for LLM applications. monitor traces, debug agent runs, and track performance metrics across your AI stack.
Aporia
Monitor AI models and validate LLM interactions with guardrails directly from your AI agent to ensure safety and observability.
Langfuse (LLM Tracing & Evals)
Monitor LLM apps via Langfuse. track traces, manage prompt templates, and audit evaluation scores.
Datadog AI (LLM Observability)
Monitor LLM performance via Datadog. track token usage, audit prompts, and monitor AI model metrics directly from any AI agent.
LangSmith (LLM Observability & Hub)
Monitor LLM apps via LangSmith. track traces, audit prompt templates, and manage evaluation datasets.
Portkey
AI gateway observability: monitor logs, costs, and manage LLM configurations via agents.
Bring your own AI
Change the model, client or framework. Keep Chainlit connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Chainlit.
The practical details behind the request, access and result.
Can Chainlit MCP help me find why my bot is hallucinating?
Yes. It allows your agent to pull the exact logic steps and prompts used for a specific conversation. You can see exactly where the model went off track.
How does Chainlit MCP show me user ratings?
It pulls the thumbs up and thumbs down signals directly from your project. Your agent can then summarize these ratings to show you what's working and what isn't.
Can I use Chainlit MCP to see my token usage?
Yes. You can ask your agent to pull analytics on resource consumption and traffic boundaries for any of your projects.
Does Chainlit MCP work with my existing Chainlit Cloud account?
It does. You just need to provide your Chainlit Cloud URL and Project API Key to connect your data to your AI client.
Can I use Chainlit MCP to audit my bot's compliance?
Absolutely. You can have your agent pull recent threads to check for tone, relevance, and compliance across hundreds of hours of chat logs.
How many projects can I see with Chainlit MCP?
You can see all the globally configured projects in your Chainlit Cloud account, allowing you to manage multiple apps from one place.
Will the AI agent be able to monitor the user interactions and evaluate chat history?
Yes! The agent can dive into the list_threads and get_thread endpoints to retrieve comprehensive interaction logs from your deployed Chainlit apps. You can essentially command the agent to read past AI chats, summarize usage, or identify edge cases in the user input.
Can it track the individual thought steps and LLM prompt tokens consumed?
Absolutely. Using the list_steps capability, your agent analyzes the programmatic trace—including specific LLM calls, function blocks, or retrieval events. Thus, identifying hallucinations or latency issues is as easy as typing a prompt.
Is it possible to extract and analyze human feedback scores instantly?
Yes. The integration provides native capabilities via list_feedbacks to retrieve the explicit thumbs up, down, and textual comments your users left on specific messages, streamlining QA.
One connection away
Give your agent a direct line to Chainlit.
Connect Chainlit once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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