🦜🛠️ LangSmith MCP Server

LangSmith MCP Hero

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

LangSmith MCP Hero

📋 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