LangSmith (LLM Observability & Hub) MCP Server for LangChain 6 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect LangSmith (LLM Observability & Hub) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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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-llm-observability-hub": {
"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 (LLM Observability & Hub), 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 (LLM Observability & Hub) MCP Server
Connect your LangSmith account to any AI agent and take full control of your LLM observability, tracing, and prompt management through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with LangSmith (LLM Observability & Hub) through native MCP adapters. Connect 6 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
- Trace Orchestration — List active tracing projects and retrieve detailed execution logs for specific LLM invocation runs directly from your agent
- Performance Telemetry — Extract precise metrics including token consumption, prompt latency, and exact error strings from your AI pipelines
- Prompt Hub Access — Navigate and retrieve managed prompt templates, variable definitions, and version histories hosted in the LangChain Hub
- Evaluation Datasets — Enumerate curated 'golden' datasets used for automated evaluation of prompt logic or few-shot injection models
- Human-in-the-Loop Audit — Monitor active annotation queues where human reviewers assess the alignment, accuracy, and safety of generated LLM traces
- Agentic Step Analysis — Deep-dive into multi-turn agentic workflows to understand nested tool calls and internal reasoning paths securely
The LangSmith (LLM Observability & Hub) MCP Server exposes 6 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 (LLM Observability & Hub) to LangChain via MCP
Follow these steps to integrate the LangSmith (LLM Observability & Hub) 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 6 tools from LangSmith (LLM Observability & Hub) via MCP
Why Use LangChain with the LangSmith (LLM Observability & Hub) MCP Server
LangChain provides unique advantages when paired with LangSmith (LLM Observability & Hub) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine LangSmith (LLM Observability & Hub) 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 (LLM Observability & Hub) queries for multi-turn workflows
LangSmith (LLM Observability & Hub) + LangChain Use Cases
Practical scenarios where LangChain combined with the LangSmith (LLM Observability & Hub) MCP Server delivers measurable value.
RAG with live data: combine LangSmith (LLM Observability & Hub) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query LangSmith (LLM Observability & Hub), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain LangSmith (LLM Observability & Hub) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every LangSmith (LLM Observability & Hub) tool call, measure latency, and optimize your agent's performance
LangSmith (LLM Observability & Hub) MCP Tools for LangChain (6)
These 6 tools become available when you connect LangSmith (LLM Observability & Hub) to LangChain via MCP:
get_run
Get precise telemetry for a single LLM invocation run
list_annotation_queues
List active human-in-the-loop annotation queues
list_datasets
List all evaluation and fine-tuning datasets mapped in LangSmith
list_projects
Maps out the boundaries of distinct AI pipelines currently monitored by LangSmith. List all active LangSmith tracing projects/sessions
list_prompts
Extract prompt templates hosted in the LangChain Hub
list_runs
Isolates the raw interactions containing prompts sent to and responses received from the AI models. List explicit LLM invocation runs within a specific project
Example Prompts for LangSmith (LLM Observability & Hub) in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with LangSmith (LLM Observability & Hub) immediately.
"List all active tracing projects in LangSmith"
"Show me the telemetry for the last run in the 'Production-Bot-V2' project"
"List all prompts hosted in our Hub repository"
Troubleshooting LangSmith (LLM Observability & Hub) MCP Server with LangChain
Common issues when connecting LangSmith (LLM Observability & Hub) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLangSmith (LLM Observability & Hub) + LangChain FAQ
Common questions about integrating LangSmith (LLM Observability & Hub) 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 (LLM Observability & Hub) 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 (LLM Observability & Hub) to LangChain
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.