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Vinkius

LangSmith Connector for AI agents.

3 live capabilities

Monitor LLM production traces and performance metrics in real-time.

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AI Agent

Why people use LangSmith

LangSmith for Debugging LLM Production Traces

With this Connector, your agent does that heavy lifting for you. You just ask it to find the error, and it pulls the trace details directly into your chat. You get the specific input, output, and error message without ever leaving your workspace.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get instant access to your LLM production data without leaving your chat interface.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Debugging a production failure

    An engineer notices a chatbot is acting weird.

  2. Real-world use case 02

    Cost auditing

    A manager wants to know the budget impact.

  3. Real-world use case 03

    Latency checks

    A dev wants to see if a new prompt is slower.

Complete set · 3capabilities

The complete LangSmith capability set.

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

Capability set01 / 01

01—03

3 capabilities in this set.

Part of 3 available through LangSmith.

  1. 01 Capability

    Langsmith get run

    Pulls detailed info about a specific trace ID to help you figure out why a certain call failed. It shows the full execution path and inputs.

  2. 02 Capability

    Langsmith list projects

    Shows all your tracing projects along with high-level stats like total runs and median latency. Use it to get a project overview.

  3. 03 Capability

    Langsmith list runs

    Lists recent traces in a project so you can quickly see success rates and token usage. It helps you spot errors in your recent history.

Set up in minutes

One URL. Then ask LangSmith to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LangSmith from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_JBueIzhraYvzf2S9aR0UP9Q6bjldfIBgmwxTvRHt/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it LangSmith, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable LangSmith for the conversation.

Where the request belongs

Work LangSmith can move forward.

Built around the request

AI engineers who are tired of manual dashboard digging and ML teams needing to see real-time performance metrics to catch regressions before they hit production.

01

AI Engineer

Debugging production errors in real-time by pulling specific trace IDs into the chat to see where a chain broke.

02

ML Researcher

Comparing model outputs and latency stats across different projects to see which prompts actually perform better.

03

DevOps Engineer

Monitoring for latency spikes or sudden jumps in token costs to keep the AI budget in check.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
LangSmith (LLM Observability & Hub) logo
01 6 capabilities

LangSmith (LLM Observability & Hub)

Monitor LLM apps via LangSmith. track traces, audit prompt templates, and manage evaluation datasets.

View Connector
Langfuse (LLM Tracing & Evals) logo
02 10 capabilities

Langfuse (LLM Tracing & Evals)

Monitor LLM apps via Langfuse. track traces, manage prompt templates, and audit evaluation scores.

View Connector
Datadog AI (LLM Observability) logo
03 10 capabilities

Datadog AI (LLM Observability)

Monitor LLM performance via Datadog. track token usage, audit prompts, and monitor AI model metrics directly from any AI agent.

View Connector
Helicone (LLM Observability) logo
04 10 capabilities

Helicone (LLM Observability)

Monitor LLM usage via Helicone. track requests, analyze costs, measure latency, and manage prompts.

View Connector
Portkey logo
05 10 capabilities

Portkey

AI gateway observability: monitor logs, costs, and manage LLM configurations via agents.

View Connector
Aporia logo
06 7 capabilities

Aporia

Monitor AI models and validate LLM interactions with guardrails directly from your AI agent to ensure safety and observability.

View Connector

Bring your own AI

Change the model, client or framework. Keep LangSmith connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
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  • TypingMind
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  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about LangSmith.

The practical details behind the request, access and result.

What can I do with LangSmith MCP?

You can use it to monitor your LLM applications in real-time. It allows your agent to pull project metrics, browse recent traces, and get deep details on specific failed runs.

How do I see my LLM costs with LangSmith MCP?

The Connector can pull recent runs which include token consumption data. You can ask your agent to summarize these counts to see how much your project is costing you.

Can I debug failed agent actions using LangSmith MCP?

Yes, you can pull the full execution trace for a specific run ID. This shows you the exact inputs and outputs of every step in the chain, making it easy to find where things went wrong.

Does LangSmith MCP show project health?

It does by providing aggregate metrics for all your tracing projects. You can quickly see things like median latency and total run counts to gauge overall health.

Is LangSmith MCP for production monitoring?

Exactly. It is designed to give you visibility into live LLM applications so you can catch errors, monitor latency, and track costs as they happen.

How do I get my LangSmith API key?

You can find your API key in your LangSmith account settings. Once you have it, you just need to add it to your Connector configuration to get started.

What is LangSmith and why do I need it?

LangSmith is the 'Datadog for LLM applications'. Without observability, AI agents in production are black boxes. you can't see what they're doing, why they fail, or how much they cost. LangSmith traces every LLM call, chain execution, and capability use, giving you complete visibility into inputs, outputs, latency, token usage, and error rates.

Does LangSmith work only with LangChain?

No! While LangSmith is built by the LangChain team and has native LangChain/LangGraph integration, it works with any LLM application. You can trace OpenAI, Anthropic, or any LLM provider directly using the REST API. It also integrates with CrewAI, AutoGen, and other frameworks.

How much does LangSmith cost?

LangSmith offers a generous free tier with 5,000 traces per month. no credit card required. The Developer plan is $39/month with 50,000 traces. Enterprise plans include SSO, RBAC, dedicated support, and unlimited traces with volume discounts.

One connection away

Give your agent a direct line to LangSmith.

Connect LangSmith once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available