Which integrations are available for this server?

Provides seamless integration with the LangSmith observability platform, enabling language models to fetch conversation history, manage prompts, retrieve traces and runs, work with datasets and examples, and access experiment and evaluation data from LangSmith projects.

How do I use LangSmith MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LangSmith MCP Server fetch the history of my conversation from thread 'thread-123' in project 'my-chatbot'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

🦜🛠️ LangSmith MCP Server

LangSmith MCP Hero

License: MIT Python 3.10

A production-ready Model Context Protocol (MCP) server that provides seamless integration with the LangSmith observability platform. This server enables language models to fetch conversation history and prompts from LangSmith.

📋 Overview

The LangSmith MCP Server bridges the gap between language models and the LangSmith platform, enabling advanced capabilities for conversation tracking, prompt management, and analytics integration.

🚀 Example Use Cases

The server enables powerful capabilities including:

💬 Conversation History: "Fetch the history of my conversation with the AI assistant from thread 'thread-123' in project 'my-chatbot'"
📚 Prompt Management: "Get all public prompts in my workspace"
🔍 Smart Search: "Find private prompts containing the word 'joke'"
📝 Template Access: "Pull the template for the 'legal-case-summarizer' prompt"
🔧 Configuration: "Get the system message from a specific prompt template"

🛠️ Available Tools

The LangSmith MCP Server provides the following tools for integration with LangSmith:

📚 Prompt Management

Tool Name	Description
`list_prompts`	Fetch prompts from LangSmith with optional filtering. Filter by visibility (public/private) and limit results.
`get_prompt_by_name`	Get a specific prompt by its exact name, returning the prompt details and template.
`push_prompt`	Documentation tool for understanding how to create and push prompts to LangSmith (documentation-only).

🔍 Traces & Runs

Tool Name	Description
`fetch_runs`	Fetch LangSmith runs (traces, tools, chains, etc.) from one or more projects using flexible filters, query language expressions, and trace-level constraints.
`list_projects`	List LangSmith projects with optional filtering and detail level control. Can return simplified or full project information.
`list_experiments`	List LangSmith experiment projects (reference projects) with mandatory dataset filtering. Returns experiment information with key metrics (latency, cost, feedback stats).

📊 Datasets & Examples

Tool Name	Description
`list_datasets`	Fetch LangSmith datasets with filtering options by ID, type, name, or metadata.
`list_examples`	Fetch examples from a LangSmith dataset with advanced filtering options.
`read_dataset`	Read a specific dataset from LangSmith using dataset ID or name.
`read_example`	Read a specific example from LangSmith using the example ID and optional version information.
`create_dataset`	Documentation tool for understanding how to create datasets in LangSmith (documentation-only).
`update_examples`	Documentation tool for understanding how to update dataset examples in LangSmith (documentation-only).

🧪 Experiments & Evaluations

Tool Name	Description
`run_experiment`	Documentation tool for understanding how to run experiments and evaluations in LangSmith (documentation-only).

🛠️ Installation Options

📝 General Prerequisites

Install uv (a fast Python package installer and resolver):
curl -LsSf https://astral.sh/uv/install.sh | sh
Clone this repository and navigate to the project directory:
git clone https://github.com/langchain-ai/langsmith-mcp-server.git cd langsmith-mcp-server

🔌 MCP Client Integration

Once you have the LangSmith MCP Server, you can integrate it with various MCP-compatible clients. You have two installation options:

📦 From PyPI

Install the package:
uv run pip install --upgrade langsmith-mcp-server
Add to your client MCP config:
{ "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

Add the following configuration to your MCP client settings:

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

Replace the following placeholders:

/path/to/uv: The absolute path to your uv installation (e.g., /Users/username/.local/bin/uv). You can find it running which uv.
/path/to/langsmith-mcp-server: The absolute path to your langsmith-mcp project directory
your_langsmith_api_key: Your LangSmith API key (required)
your_workspace_id: Your LangSmith workspace ID (optional, for API keys scoped to multiple workspaces)
https://api.smith.langchain.com: The LangSmith API endpoint (optional, defaults to the standard endpoint)

Example configuration:

{ "mcpServers": { "LangSmith API MCP Server": { "command": "/Users/mperini/.local/bin/uvx", "args": [ "langsmith-mcp-server" ], "env": { "LANGSMITH_API_KEY": "lsv2_pt_1234", "LANGSMITH_WORKSPACE_ID": "your_workspace_id", "LANGSMITH_ENDPOINT": "https://api.smith.langchain.com" } } } }

Copy this configuration in Cursor > MCP Settings.

LangSmith Cursor Integration

🔧 Environment Variables

The LangSmith MCP Server supports the following environment variables:

Variable	Required	Description	Example
`LANGSMITH_API_KEY`	✅ Yes	Your LangSmith API key for authentication	`lsv2_pt_1234567890`
`LANGSMITH_WORKSPACE_ID`	❌ No	Workspace ID for API keys scoped to multiple workspaces	`your_workspace_id`
`LANGSMITH_ENDPOINT`	❌ No	Custom API endpoint URL (for self-hosted or EU region)	`https://api.smith.langchain.com`

Notes:

Only LANGSMITH_API_KEY is required for basic functionality
LANGSMITH_WORKSPACE_ID is useful when your API key has access to multiple workspaces
LANGSMITH_ENDPOINT allows you to use custom endpoints for self-hosted LangSmith installations or the EU region

🐳 Docker Deployment (HTTP-Streamable)

The LangSmith MCP Server can be deployed as an HTTP server using Docker, enabling remote access via the HTTP-streamable protocol.

Building the Docker Image

docker build -t langsmith-mcp-server .

Running with Docker

docker run -p 8000:8000 langsmith-mcp-server

The API key is provided via the LANGSMITH-API-KEY header when connecting, so no environment variables are required for HTTP-streamable protocol.

Connecting with HTTP-Streamable Protocol

Once the Docker container is running, you can connect to it using the HTTP-streamable transport. The server accepts authentication via headers:

Required header:

LANGSMITH-API-KEY: Your LangSmith API key

Optional headers:

LANGSMITH-WORKSPACE-ID: Workspace ID for API keys scoped to multiple workspaces
LANGSMITH-ENDPOINT: Custom endpoint URL (for self-hosted or EU region)

Example client configuration:

from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client headers = { "LANGSMITH-API-KEY": "lsv2_pt_your_api_key_here", # Optional: # "LANGSMITH-WORKSPACE-ID": "your_workspace_id", # "LANGSMITH-ENDPOINT": "https://api.smith.langchain.com", } async with streamablehttp_client("http://localhost:8000/mcp", headers=headers) as (read, write, _): async with ClientSession(read, write) as session: await session.initialize() # Use the session to call tools, list prompts, etc.

Cursor Integration

To add the LangSmith MCP Server to Cursor using HTTP-streamable protocol, add the following to your mcp.json configuration file:

{ "mcpServers": { "HTTP-Streamable LangSmith MCP Server": { "url": "http://localhost:8000/mcp", "headers": { "LANGSMITH-API-KEY": "lsv2_pt_your_api_key_here" } } } }

Optional headers:

{ "mcpServers": { "HTTP-Streamable LangSmith MCP Server": { "url": "http://localhost:8000/mcp", "headers": { "LANGSMITH-API-KEY": "lsv2_pt_your_api_key_here", "LANGSMITH-WORKSPACE-ID": "your_workspace_id", "LANGSMITH-ENDPOINT": "https://api.smith.langchain.com" } } } }

Make sure the server is running before connecting Cursor to it.

Health Check

The server provides a health check endpoint:

curl http://localhost:8000/health

This endpoint does not require authentication and returns "LangSmith MCP server is running" when the server is healthy.

🧪 Development and Contributing 🤝

If you want to develop or contribute to the LangSmith MCP Server, follow these steps:

Create a virtual environment and install dependencies:
uv sync
To include test dependencies:
uv sync --group test
View available MCP commands:
uvx langsmith-mcp-server
For development, run the MCP inspector:
uv run mcp dev langsmith_mcp_server/server.py
- This will start the MCP inspector on a network port
- Install any required libraries when prompted
- The MCP inspector will be available in your browser
- Set the LANGSMITH_API_KEY environment variable in the inspector
- Connect to the server
- Navigate to the "Tools" tab to see all available tools
Before submitting your changes, run the linting and formatting checks:
make lint make format

📄 License

This project is distributed under the MIT License. For detailed terms and conditions, please refer to the LICENSE file.

Made with ❤️ by the LangChain Team

LangSmith MCP Server