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LangSmith MCP Server for Claude

Connect Claude to LangSmith — retrieve conversation threads and traces, fetch and push prompts, browse evaluation datasets and experiments, and access billing usage — with the official LangSmith Model Context Protocol server from LangChain.

HarnessClaude CodeCodexCursorClaude Desktop
Review first review before installing

Open the source and read safety notes before installing.

Safety notes

  • The server runs locally via `uvx` and connects to the LangSmith cloud API using your API key.
  • The server provides read access to your traces, prompts, datasets, and experiments; it can also push prompts (write).
  • Trace data may contain sensitive LLM inputs and outputs from your production systems — treat retrieved traces as confidential.

Privacy notes

  • Conversation threads, prompt content, run inputs/outputs, and dataset examples are surfaced in Claude's context.
  • LANGSMITH_API_KEY is a secret — store it in your environment or MCP client config, never in repositories.

Prerequisites

  • A LangSmith API key from smith.langchain.com (free tier available).
  • Python package manager `uv` installed (for `uvx`).
  • An MCP client such as Claude Code or Claude Desktop.

Schema details

Install type
cli
Troubleshooting
No
Source repository stats
Scope
Source repo
Collection metadata
Estimated setup
5 minutes
Difficulty
beginner
Tool listing metadata
Full copyable content
{
  "mcpServers": {
    "langsmith": {
      "command": "uvx",
      "args": ["langsmith-mcp-server"],
      "env": {
        "LANGSMITH_API_KEY": "your-langsmith-api-key"
      }
    }
  }
}

About this resource

Overview

The LangSmith MCP Server is the official Model Context Protocol server from LangChain for LangSmith. It gives Claude direct access to your LangSmith workspace — retrieving LLM run traces with FQL filtering, fetching and pushing prompts, browsing evaluation datasets and experiments, and accessing billing usage — through natural language. It runs locally via uvx and is MIT-licensed.

Key capabilities

  • Conversation threads — retrieve message history from LangSmith conversation threads with pagination.
  • Prompts — list, fetch, and push prompts to and from your LangSmith prompt hub.
  • Traces & runs — fetch LangSmith runs (traces, chains, tool calls) with FQL filter support.
  • Datasets & examples — list and read evaluation datasets and their examples.
  • Experiments — list experiment projects with metrics and evaluation results.
  • Billing usage — retrieve organization-level billing usage data.

How it compares

Server LLM traces Prompt management Dataset access Experiment metrics Auth
LangSmith MCP Yes Yes (read+write) Yes Yes API key
Weights & Biases MCP Yes Limited Yes Yes API key
MLflow MCP Yes Limited Yes Yes Token
Arize MCP Yes No Limited Limited API key

LangSmith's MCP is notable for combining trace retrieval with bidirectional prompt management — you can read production traces and push improved prompts back to the hub in the same session.

Installation

Claude Code

claude mcp add langsmith \
  -e LANGSMITH_API_KEY=your-key \
  -- uvx langsmith-mcp-server

Claude Desktop

{
  "mcpServers": {
    "langsmith": {
      "command": "uvx",
      "args": ["langsmith-mcp-server"],
      "env": {
        "LANGSMITH_API_KEY": "your-langsmith-api-key"
      }
    }
  }
}

Multi-workspace

{
  "env": {
    "LANGSMITH_API_KEY": "your-key",
    "LANGSMITH_WORKSPACE_ID": "your-workspace-id"
  }
}

Custom endpoint (self-hosted LangSmith)

Set LANGSMITH_ENDPOINT to your self-hosted LangSmith URL.

Requirements

  • A LangSmith API key (free tier available at smith.langchain.com).
  • uv installed (pip install uv or brew install uv).
  • An MCP client (Claude Code or Claude Desktop).

Security

  • The server only reads (and optionally writes prompts to) your LangSmith workspace.
  • Treat LANGSMITH_API_KEY as a secret.
  • Trace data may include sensitive LLM inputs and outputs from your applications — use this in trusted environments only.

Source Verification Notes

Verified on 2026-06-18:

  • The official repository github.com/langchain-ai/langsmith-mcp-server (MIT) documents the uvx langsmith-mcp-server install command, LANGSMITH_API_KEY and LANGSMITH_WORKSPACE_ID env vars, stdio transport, and the six capability areas (threads, prompts, runs, datasets, experiments, billing).
  • LangChain's LangSmith MCP documentation at docs.langchain.com/langsmith/langsmith-mcp-server confirms the server features and setup instructions.
  • Claude Code's MCP documentation describes the -e env var injection pattern used above.

Source citations

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How it compares

LangSmith MCP Server for Claude side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

FieldLangSmith MCP Server for Claude

Connect Claude to LangSmith — retrieve conversation threads and traces, fetch and push prompts, browse evaluation datasets and experiments, and access billing usage — with the official LangSmith Model Context Protocol server from LangChain.

Open dossier
Arize Phoenix MCP Server for Claude

Inspect LLM traces and spans, manage prompts, explore datasets, and review evaluation experiments from Claude — with the official Arize Phoenix MCP server, built into the open-source Phoenix AI observability platform.

Open dossier
Opik MCP Server for Claude

Debug, evaluate, and monitor LLM applications from Claude — read traces and spans, score outputs, save prompts, run evaluation experiments, and query project metrics — with the official Opik MCP server by Comet.

Open dossier
OpenTelemetry MCP Server for Claude

Analyze distributed traces and LLM observability data from Claude — search traces and spans, find errors, list services, analyze LLM token usage, identify slow LLM operations, and discover AI model usage patterns — with the OpenTelemetry MCP server supporting Jaeger, Grafana Tempo, and Traceloop backends.

Open dossier
Trust
Install riskReview firstReview firstReview firstReview first
Notes Safety Privacy Safety Privacy Safety Privacy Safety Privacy
Categorymcpmcpmcpmcp
Sourcesource-backedsource-backedsource-backedsource-backed
AuthorLangChainArize AICometTraceloop
Added2026-06-182026-06-182026-06-182026-06-18
Platforms
Claude CodeCodexCursorClaude Desktop
Claude CodeClaude Desktop
Claude CodeClaude Desktop
Claude CodeClaude Desktop
Source repo
Safety notesThe server runs locally via `uvx` and connects to the LangSmith cloud API using your API key. The server provides read access to your traces, prompts, datasets, and experiments; it can also push prompts (write). Trace data may contain sensitive LLM inputs and outputs from your production systems — treat retrieved traces as confidential.The `write` and upsert tools can create or modify prompts and add dataset examples — review Claude's proposed changes before confirming. For self-hosted deployments, ensure your Phoenix instance is accessible from the machine running the MCP server.The `write` tool can create traces, spans, scores, and prompts in your Opik workspace — review any write operations before confirming. The `run_experiment` tool executes evaluation experiments end-to-end, which may incur LLM API costs depending on your experiment configuration.All tools are read-only — the server queries trace data but does not modify your application or tracing backend. Trace data may contain sensitive information (request parameters, user IDs, SQL queries) — ensure Claude has appropriate access to this data.
Privacy notesConversation threads, prompt content, run inputs/outputs, and dataset examples are surfaced in Claude's context. LANGSMITH_API_KEY is a secret — store it in your environment or MCP client config, never in repositories.LLM traces (including prompts and completions), evaluation annotations, session data, datasets, and experiment results from your Phoenix workspace are surfaced in Claude's context. Your `PHOENIX_API_KEY` is passed as an environment variable — store it in a secrets manager rather than plaintext shell config.LLM traces (including prompts and completions), evaluation scores, experiment results, and prompt library contents from your Opik workspace are surfaced in Claude's context. Your `OPIK_API_KEY` is passed as an environment variable — store it in a secrets manager rather than plaintext shell config.Distributed trace content including service names, operation names, error messages, HTTP parameters, and LLM prompt/response metadata may be surfaced in Claude's context. For Traceloop backend, `BACKEND_API_KEY` is required and grants access to your Traceloop organization's trace data.
Prerequisites
  • A LangSmith API key from smith.langchain.com (free tier available).
  • Python package manager `uv` installed (for `uvx`).
  • An MCP client such as Claude Code or Claude Desktop.
  • An Arize Phoenix account (cloud at app.phoenix.arize.com) or self-hosted Phoenix instance.
  • A Phoenix API key from your account settings.
  • Node.js 18+ for `npx`.
  • An MCP client such as Claude Code or Claude Desktop.
  • An Opik account (cloud at comet.com or self-hosted) and an API key from your workspace settings.
  • Python with `uv` installed: `pip install uv` or `brew install uv`.
  • An MCP client such as Claude Code or Claude Desktop.
  • An OpenTelemetry-compatible tracing backend: Jaeger (local), Grafana Tempo, or Traceloop Cloud.
  • Services instrumented with OpenTelemetry sending traces to your backend.
  • Python 3.11+ with `uvx` available.
  • An MCP client such as Claude Code or Claude Desktop.
Install
claude mcp add langsmith -e LANGSMITH_API_KEY=your-key -- uvx langsmith-mcp-server
claude mcp add phoenix -e PHOENIX_HOST=https://app.phoenix.arize.com -e PHOENIX_API_KEY=your-key -- npx -y @arizeai/phoenix-mcp@latest
claude mcp add opik -e OPIK_API_KEY=your-api-key -- uvx opik-mcp
claude mcp add opentelemetry -e BACKEND_TYPE=jaeger -e BACKEND_URL=http://localhost:16686 -- uvx opentelemetry-mcp
Config
{
  "mcpServers": {
    "langsmith": {
      "command": "uvx",
      "args": ["langsmith-mcp-server"],
      "env": {
        "LANGSMITH_API_KEY": "your-langsmith-api-key"
      }
    }
  }
}
{
  "mcpServers": {
    "phoenix": {
      "command": "npx",
      "args": ["-y", "@arizeai/phoenix-mcp@latest"],
      "env": {
        "PHOENIX_HOST": "https://app.phoenix.arize.com",
        "PHOENIX_API_KEY": "your-api-key"
      }
    }
  }
}
{
  "mcpServers": {
    "opik": {
      "command": "uvx",
      "args": ["opik-mcp"],
      "env": {
        "OPIK_API_KEY": "your-api-key",
        "OPIK_WORKSPACE": "default"
      }
    }
  }
}
{
  "mcpServers": {
    "opentelemetry": {
      "command": "uvx",
      "args": ["opentelemetry-mcp"],
      "env": {
        "BACKEND_TYPE": "jaeger",
        "BACKEND_URL": "http://localhost:16686",
        "MAX_TRACES_PER_QUERY": "100",
        "BACKEND_TIMEOUT": "30"
      }
    }
  }
}
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