How to Use the LangSmith (LLM Observability & Hub) MCP in LangChain

Q: Does this MCP server support multiple projects in LangChain?

It does. You can use listprojects to identify the correct project scope for your current agent run.

Chain your LLM observability into LangChain pipelines to track every decision your agent makes in real-time.

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Connect LangSmith (LLM Observability & Hub) MCP to LangChain

Create your Vinkius account to connect LangSmith (LLM Observability & Hub) to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup LangSmith (LLM Observability & Hub) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Trace agent reasoning with LangChain

Pipe trace data directly into your LangChain chains to see exactly how your agent behaves during execution. When you use `get_run`, you pull the raw telemetry for specific steps to debug logic gaps instantly. You can also use `list_runs` to filter through historical execution logs. This keeps your multi-step chains transparent and easier to audit when performance dips.

Manage prompt templates in LangChain

Sync your version-controlled prompts from the Hub directly into your agent's reasoning loop. Calling `list_prompts` lets you verify which template version is currently active in your chain. This ensures your agents always pull the latest instructions without manual updates. It keeps your prompt engineering and chain logic tightly coupled.

Evaluate agent performance

Assess your agent's factual accuracy by pulling existing test sets into your workflow. Use `list_datasets` to grab evaluation benchmarks that your chains can run against. Then, use `list_annotation_queues` to see where human feedback is needed. This closes the loop between agent output and production-grade validation.

Setup guide

Set up LangSmith (LLM Observability & Hub) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes LangSmith (LLM Observability & Hub) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "langsmith-llm-observability-hub-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent LangSmith (LLM Observability & Hub) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about LangSmith (LLM Observability & Hub) MCP in LangChain

Install the necessary adapters and initialize the MCP client in your pipeline. Once the connection is live, you can pass tools like `get_run` directly into your agent's tool set.

Yes, you can pull prompt templates into your agent logic. Use `list_prompts` to check available versions before injecting them into your chain's prompt template.

The server acts as a bridge for your metadata. It touches trace IDs, prompt content, and input-output pairs, which are processed according to your local security configurations.

It does. You can use `list_projects` to identify the correct project scope for your current agent run.

Data remains within the scope of your configured LangSmith backend. The server simply provides the interface for your agent to query those logs.

Use it with your favorite AI tools

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