LangSmith MCP Server
Provides read-only access to LangSmith traces that contain LangGraph auto-instrumented configuration, including graph_id, thread_id, and model information, enabling inspection of LangGraph runs.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@LangSmith MCP Serverlist recent runs from project 'my-app'"
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
FastMCP-based MCP server exposing LangSmith read-only tools for querying existing traces (runs, children, URLs) without extra instrumentation.
Tools
ls_list_runs(project_name, is_root=True, limit=50, select?)— List recent runs with filteringls_read_run(run_id, hydrate_children=False, child_limit=100)— Read single run with optional childrenls_get_run_url(run_id)— Generate shareable LangSmith URLsls_list_children(parent_run_id, limit=100)— List child spans for parent run
Related MCP server: langfuse-mcp-java
Requirements
Python 3.10+
LANGSMITH_API_KEYin your environmentOptional:
LANGSMITH_ENDPOINTfor on-prem deployments
Quick Start
# Clone and setup
git clone <repo-url>
cd langsmith-mcp-server
# Install dependencies (uv manages environment automatically)
uv sync
export LANGSMITH_API_KEY="lsv2_pt_..."
# Run smoke test
uv run python tests/smoke_test.py
# Run unit tests
uv run pytest tests/test_server.py -vRun as MCP Server
Local Development
Recommended (using fastmcp.json for configuration):
# FastMCP auto-detects fastmcp.json in current directory
fastmcp run
# Or via uv
uv run fastmcp run fastmcp.jsonAlternative (direct Python):
uv run python src/server.pyFastMCP Cloud Deployment
Deploy to FastMCP Cloud for free hosting:
Push to GitHub:
don-aie-cohort8/langsmith-mcp-serverSign in to https://fastmcp.cloud
Create new project:
Repository:
don-aie-cohort8/langsmith-mcp-serverBranch:
mainEntrypoint:
src/server.py:app
Add environment variable:
LANGSMITH_API_KEYDeploy!
Auto-deploys on every push to main.
MCP Client Configuration
Local Development
Add to your MCP client config (Claude Desktop, Claude Code, etc.):
{
"mcpServers": {
"langsmith-local": {
"command": "uv",
"args": [
"run",
"--with", "fastmcp",
"fastmcp",
"run",
"/absolute/path/to/langsmith-mcp-server/src/server.py:app"
]
}
}
}Note: Replace /absolute/path/to/langsmith-mcp-server with your actual project path.
Why this pattern?
Uses
uv run --with fastmcpper official FastMCP recommendationsCreates isolated environment with clean dependency management
Avoids dependency on global
fastmcpinstallationRuntime dependencies pulled from
pyproject.tomlvia editable installNo need to list all dependencies in MCP config
FastMCP Cloud
For cloud deployment:
{
"mcpServers": {
"langsmith-cloud": {
"url": "https://langsmith-mcp-server.fastmcp.app/mcp"
}
}
}Project Structure
Following FastMCP and Python best practices:
langsmith-mcp-server/
├── src/
│ └── server.py # MCP server implementation (~190 lines)
├── tests/
│ ├── smoke_test.py # Startup validation
│ ├── test_server.py # Unit tests
│ ├── test_integration.py # Integration tests
│ └── README.md # Testing documentation
├── scripts/
│ ├── integration_demo.py # Demo script for testing tools
│ └── claude-agent-sdk-testing/ # Claude Agent SDK integration
├── docs/
│ ├── PRODUCTION_READINESS.md # Production deployment guide
│ ├── MCP_FIX_REPORT.md # Historical fix documentation
│ ├── SERIALIZATION_FIX.md # Pydantic compatibility fixes
│ └── TESTING_REPORT.md # Testing results
├── notebooks/ # Jupyter notebooks for exploration
├── fastmcp.json # Deployment configuration
├── pyproject.toml # Package metadata and dependencies
└── README.md # This fileDependency Management
This project uses a dual-file approach for dependencies:
pyproject.toml: Defines all Python dependencies (runtime + dev)
fastmcp.json: Deployment configuration that references pyproject.toml via
"editable": ["."]
When you run uv sync, dependencies are installed from pyproject.toml. FastMCP automatically loads them via the editable install.
Usage Tips
Use
select=["id","name","error","extra"]for minimal payloadsLangGraph auto-instrumented config appears under
run.extra(e.g.,graph_id,thread_id,research_model)All tools are read-only by design (no create/update/delete operations)
References
Client:
Server:
This server cannot be installed
Maintenance
Resources
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