LangSmith MCP Server

The LangSmith MCP Server is a Model Context Protocol (MCP) server that integrates with LangSmith. It lets MCP-compatible clients (for example, AI coding assistants) read conversation history, prompts, runs and traces, datasets, experiments, and billing usage from your LangSmith workspace.

Example use cases

Conversation history: “Fetch the history of my conversation from thread ‘thread-123’ in project ‘my-chatbot’”
Prompt management: “Get all public prompts” or “Pull the template for the ‘legal-case-summarizer’ prompt”
Traces and runs: “Fetch the latest 10 root runs from project ‘alpha’” or “Get all runs for a trace by UUID”
Datasets: “List datasets of type chat” or “Read examples from dataset ‘customer-support-qa’”
Experiments: “List experiments for dataset ‘my-eval-set’ with latency and cost metrics”
Billing: “Get billing usage for September 2025”

Use the server in code or Agent Builder

To connect and use remote MCP servers (including this one) in your Python application, see MCP (Model Context Protocol).
To connect and use this server in Agent Builder, see Remote MCP servers.

Quickstart (hosted)

A hosted version of the LangSmith MCP Server is available over HTTP, so you can connect without running the server yourself.

URL: https://langsmith-mcp-server.onrender.com/mcp
Authentication: Send your LangSmith API key in the LANGSMITH-API-KEY header.

The hosted instance is for LangSmith Cloud. For a self-hosted LangSmith instance, run the server yourself and point it at your endpoint (see Docker deployment).

Example (Cursor mcp.json):

{
  "mcpServers": {
    "LangSmith MCP (Hosted)": {
      "url": "https://langsmith-mcp-server.onrender.com/mcp",
      "headers": {
        "LANGSMITH-API-KEY": "lsv2_pt_your_api_key_here"
      }
    }
  }
}

Optional headers: LANGSMITH-WORKSPACE-ID, LANGSMITH-ENDPOINT (same as in Environment variables).

Available tools

The server exposes these tool groups for LangSmith.

Conversation and threads

Tool	Description
`get_thread_history`	Get message history for a conversation thread. Uses character-based pagination: pass `page_number` (1-based) and use the returned `total_pages` to request more pages. Optional: `max_chars_per_page`, `preview_chars`.

Prompt management

Tool	Description
`list_prompts`	List prompts with optional filtering by visibility (public/private) and limit.
`get_prompt_by_name`	Get a single prompt by exact name (details and template).
`push_prompt`	Documentation-only: how to create and push prompts to LangSmith.

Traces and runs

Tool	Description
`fetch_runs`	Fetch runs (traces, tools, chains, etc.) from one or more projects. Supports filters (`run_type`, `error`, `is_root`), FQL (`filter`, `trace_filter`, `tree_filter`), and ordering. When `trace_id` is set, results are character-based paginated; otherwise one batch up to `limit`. Always pass `limit` and `page_number`.
`list_projects`	List projects with optional filtering by name, dataset, and detail level.

Datasets and examples

Tool	Description
`list_datasets`	List datasets with filtering by ID, type, name, or metadata.
`list_examples`	List examples from a dataset by dataset ID/name or example IDs; supports filter, metadata, splits, and optional `as_of` version.
`read_dataset`	Read one dataset by ID or name.
`read_example`	Read one example by ID, with optional `as_of` version.
`create_dataset`	Documentation-only: how to create datasets.
`update_examples`	Documentation-only: how to update dataset examples.

Experiments and evaluations

Tool	Description
`list_experiments`	List experiment (reference) projects for a dataset. Requires `reference_dataset_id` or `reference_dataset_name`. Returns metrics (latency, cost, feedback).
`run_experiment`	Documentation-only: how to run experiments and evaluations.

Billing

Tool	Description
`get_billing_usage`	Get organization billing usage (e.g. trace counts) for a date range. Optional workspace filter.

Pagination (character-based)

Tools that return large payloads use character-budget pagination so responses stay within a size limit:

Used by: get_thread_history and fetch_runs (when trace_id is set).
Parameters: Send page_number (1-based) on each request. Optional: max_chars_per_page (default 25000, max 30000), preview_chars (truncate long strings with ”… (+N chars)”).
Response: Includes page_number, total_pages, and the page payload. Request more by calling again with page_number = 2, then 3, up to total_pages.
Benefits: Pages are built by character count, not item count; no cursor or server-side state—just page numbers.

Installation (run locally)

If you prefer to run the server locally (or use a self-hosted LangSmith endpoint), install it and configure your MCP client.

Prerequisites

Install uv (Python package installer):

curl -LsSf https://astral.sh/uv/install.sh | sh

Install the package:

uv run pip install --upgrade langsmith-mcp-server

MCP client configuration

Add the server to your MCP client config. Use the path from which uvx for the command value. PyPI / uvx:

{
  "mcpServers": {
    "LangSmith API MCP Server": {
      "command": "/path/to/uvx",
      "args": ["langsmith-mcp-server"],
      "env": {
        "LANGSMITH_API_KEY": "your_langsmith_api_key",
        "LANGSMITH_WORKSPACE_ID": "your_workspace_id",
        "LANGSMITH_ENDPOINT": "https://api.smith.langchain.com"
      }
    }
  }
}

From source (clone langsmith-mcp-server first):

{
  "mcpServers": {
    "LangSmith API MCP Server": {
      "command": "/path/to/uv",
      "args": [
        "--directory",
        "/path/to/langsmith-mcp-server",
        "run",
        "langsmith_mcp_server/server.py"
      ],
      "env": {
        "LANGSMITH_API_KEY": "your_langsmith_api_key",
        "LANGSMITH_WORKSPACE_ID": "your_workspace_id",
        "LANGSMITH_ENDPOINT": "https://api.smith.langchain.com"
      }
    }
  }
}

Replace /path/to/uv, /path/to/uvx, and /path/to/langsmith-mcp-server with your actual paths.

Docker deployment (HTTP-streamable)

You can run the server as an HTTP service with Docker so clients connect via the HTTP-streamable protocol.

Build and run:

docker build -t langsmith-mcp-server .
docker run -p 8000:8000 langsmith-mcp-server

Use the langsmith-mcp-server repository for the Dockerfile and context.

Connect your MCP client to http://localhost:8000/mcp with the LANGSMITH-API-KEY header (and optional LANGSMITH-WORKSPACE-ID, LANGSMITH-ENDPOINT).
Health check (no auth):
curl http://localhost:8000/health

For full Docker and HTTP-streamable details, see the LangSmith MCP Server repository.

Deployment overview

Use the hosted MCP server to connect to LangSmith Cloud (smith.langchain.com or eu.smith.langchain.com). Run the server locally (Installation (run locally)) to connect to Cloud or self-hosted LangSmith (via LANGSMITH_ENDPOINT). If you use self-hosted LangSmith, you can instead run the server via the Docker image in your VPC so it can reach your self-hosted instance.

Environment variables

Variable	Required	Description
`LANGSMITH_API_KEY`	Yes	Your LangSmith API key for authentication.
`LANGSMITH_WORKSPACE_ID`	No	Workspace ID when your API key has access to multiple workspaces.
`LANGSMITH_ENDPOINT`	No	API endpoint URL (for self-hosted or custom regions). Default: `https://api.smith.langchain.com`.

For the hosted server, use the same names as headers: LANGSMITH-API-KEY, LANGSMITH-WORKSPACE-ID, LANGSMITH-ENDPOINT.

TypeScript implementation

A community-maintained TypeScript/Node.js port of the official Python server is available. To run it: LANGSMITH_API_KEY=your-key npx langsmith-mcp-server. Source and package: GitHub · npm. Maintained by amitrechavia.

Edit this page on GitHub or file an issue.

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Tracing setup

Configuration & troubleshooting

Viewing & managing traces

Automations

Feedback & evaluation

Monitoring & alerting

Data type reference

Example use cases

Quickstart (hosted)

Available tools

Conversation and threads

Prompt management

Traces and runs

Datasets and examples

Experiments and evaluations

Billing

Installation (run locally)

Prerequisites

MCP client configuration

Docker deployment (HTTP-streamable)

Deployment overview

Environment variables

TypeScript implementation

​Example use cases

​Quickstart (hosted)

​Available tools

​Conversation and threads

​Prompt management

​Traces and runs

​Datasets and examples

​Experiments and evaluations

​Billing

​Pagination (character-based)

​Installation (run locally)

​Prerequisites

​MCP client configuration

​Docker deployment (HTTP-streamable)

​Deployment overview

​Environment variables

​TypeScript implementation

Example use cases

Quickstart (hosted)

Available tools

Conversation and threads

Prompt management

Traces and runs

Datasets and examples

Experiments and evaluations

Billing

Pagination (character-based)

Installation (run locally)

Prerequisites

MCP client configuration

Docker deployment (HTTP-streamable)

Deployment overview

Environment variables

TypeScript implementation