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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Chainlit with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Empower your AI agents to audit chat threads, analyze model steps, and track LLM observability metrics securely.

Included with plan

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Complete set · 6 capabilities

The complete Chainlit capability set.

These are the exact actions your AI can choose when you ask it to work with Chainlit.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Chainlit.

  1. 01

    List steps

    List raw programmatic interaction steps explicitly defining prompts and generations inside a single thread

  2. 02

    Get stats

    Retrieve explicit analytics statistics representing traffic boundaries and resource consumptions over native projects

  3. 03

    Get thread

    Retrieve the exact payload for a specific conversational thread locating exact node topologies

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Chainlit.

  1. 04

    List feedbacks

    List absolute user review feedbacks rating explicitly conversational accuracy and value across deployments

  2. 05

    List projects

    List explicit globally configured Chainlit Cloud projects managing independent app tracking spaces

  3. 06

    List threads

    List conversational threads identifying user interaction boundaries inside a specific deployed project

Observed, not estimated

848ms average. Fast in production.

Chainlit is checked daily against the live service.

Daily averagePeak 1030ms
Aug 20Today
Fastest day
672ms
Slowest day
1030ms
14-day trend
Slowing+16%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Chainlit, so you can see the experience inside your AI.

It does not authenticate your account with Chainlit. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Chainlit Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Chainlit capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "chainlit-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

FAQ

Questions Chainlit owners ask.

  • 01

    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.

  • 02

    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.

  • 03

    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.

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