How to Use the AgentOps (Agent Telemetry and Monitoring) MCP in LangChain

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Connect AgentOps (Agent Telemetry and Monitoring) MCP to LangChain

Create your Vinkius account to connect AgentOps (Agent Telemetry and Monitoring) 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 AgentOps (Agent Telemetry and Monitoring) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Inspect LangChain agent execution metrics

Pull raw performance data directly into your LangChain workflow using `get_project`. You see exactly how your chains perform as they move through complex logic. This keeps your reasoning pipelines tight. You spot bottlenecks before they impact your end users.

Analyze LangChain trace history

Feed specific run data into your agent loop by calling `get_trace`. It provides the context needed to debug why a specific step failed in your sequence. Your chains become predictable. You stop guessing why a tool call failed and start fixing the logic.

Monitor tool invocation in LangChain

Query `get_span` to see the granular details of every tool call made by your agent. It exposes the inputs and outputs that define your agent's decision-making process. This visibility matters. You verify that your agents aren't just guessing, but actually executing the right steps.

Setup guide

Set up AgentOps (Agent Telemetry and Monitoring) 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 AgentOps (Agent Telemetry and Monitoring) 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({
    "agentops-agent-telemetry-and-monitoring-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 AgentOps (Agent Telemetry and Monitoring) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about AgentOps (Agent Telemetry and Monitoring) MCP in LangChain

Connect the server to your LangChain environment using the MCP adapter. Your agent then gains the ability to query its own telemetry data during runtime.

Yes, use the trace metrics tools to identify slow calls. You can pinpoint exactly which part of your chain is dragging down performance.

It logs the spans you send to the server. You control what data is transmitted, keeping your LangChain configuration private.

It works natively with LangGraph by treating telemetry calls as standard tool nodes. You add them to your graph just like any other integration.

The Vinkius platform manages your authentication tokens securely. Your LangChain code never handles raw credentials directly.

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