LangSmith MCP Server for LangChain 3 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect LangSmith through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"langsmith": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using LangSmith, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About LangSmith MCP Server
Connect your AI agent to LangSmith — the observability platform from the LangChain team that gives you complete visibility into your LLM applications.
LangChain's ecosystem of 500+ components combines seamlessly with LangSmith through native MCP adapters. Connect 3 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- List Projects — View all tracing projects with aggregate metrics: total runs, median latency, feedback scores, and creation dates
- List Runs — Browse recent traces in any project. See run names, types (LLM, chain, tool), status (success/error), token usage, and timing
- Run Details — Deep-dive into any specific run to see its full execution trace, inputs, outputs, and associated feedback
The LangSmith MCP Server exposes 3 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect LangSmith to LangChain via MCP
Follow these steps to integrate the LangSmith MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 3 tools from LangSmith via MCP
Why Use LangChain with the LangSmith MCP Server
LangChain provides unique advantages when paired with LangSmith through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine LangSmith MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across LangSmith queries for multi-turn workflows
LangSmith + LangChain Use Cases
Practical scenarios where LangChain combined with the LangSmith MCP Server delivers measurable value.
RAG with live data: combine LangSmith tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query LangSmith, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain LangSmith tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every LangSmith tool call, measure latency, and optimize your agent's performance
LangSmith MCP Tools for LangChain (3)
These 3 tools become available when you connect LangSmith to LangChain via MCP:
langsmith_get_run
Useful for debugging specific LLM calls or agent actions. Get detailed information about a specific run/trace by its ID
langsmith_list_projects
Each project groups related traces together and shows aggregate metrics like total runs, median latency, and feedback counts. List all tracing projects in your LangSmith account with run counts, latency stats, and feedback metrics
langsmith_list_runs
Each run represents a single LLM call, chain execution, or agent action. Shows status (success/error), latency, and token consumption. List recent traces/runs in a specific LangSmith project. Shows run names, types, status, token usage, and timing
Example Prompts for LangSmith in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with LangSmith immediately.
"List all my LangSmith projects and show their metrics."
"Show me the last 5 runs in my production-agent project."
"Get details on the failed run a0b1c2."
Troubleshooting LangSmith MCP Server with LangChain
Common issues when connecting LangSmith to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLangSmith + LangChain FAQ
Common questions about integrating LangSmith MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect LangSmith with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect LangSmith to LangChain
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.