🦜🛠️ LangSmith MCP Server

[!WARNING] LangSmith MCP Server is under active development and many features are not yet implemented.

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.

🛠️ Installation Options

📝 General Prerequisites

1. Install uv (a fast Python package installer and resolver):

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

2. 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

1. Install the package:

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

2. 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.

🔧 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

🧪 Development and Contributing 🤝

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

1. Create a virtual environment and install dependencies:

   uv sync
   

2. To include test dependencies:

   uv sync --group test
   

3. View available MCP commands:

   uvx langsmith-mcp-server
   

4. 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

5. Before submitting your changes, run the linting and formatting checks:

   make lint
   make format
   

🚀 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:

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.
get_thread_history	Retrieve the message history for a specific conversation thread, returning messages in chronological order.
get_project_runs_stats	Get statistics about runs in a LangSmith project, either for the last run or overall project stats.
fetch_trace	Fetch trace content for debugging and analyzing LangSmith runs using project name or trace ID.
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.

📄 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