How to Use the Axiom MCP in LangChain

Chain Axiom observability data into your LangChain pipelines for real-time analysis.

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Connect Axiom MCP to LangChain

Create your Vinkius account to connect Axiom 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 Axiom with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Run APL queries in LangChain

Feed live log data directly into your agent logic. Use `run_query` to pull insights from your datasets before deciding on the next step in your chain. Your agent parses the APL output to trigger follow-up actions. It keeps your decision-making grounded in raw system metrics.

Manage monitoring with LangChain

Programmatically define your alerting thresholds without leaving your code. Call `create_monitor` to set up new checks based on specific log patterns you track. Keep your infrastructure state in sync with your automation. Use `update_monitor` and `delete_monitor` to adjust or prune alerts as your system architecture evolves.

Automate Axiom dataset maintenance

Stop manual cleanup of your log storage. Use `create_dataset` and `delete_dataset` to manage your Axiom resources dynamically as part of your LangChain workflow. This keeps your data organized during high-volume events. It ensures your agent only interacts with relevant, current datasets.

Setup guide

Set up Axiom 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 Axiom 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({
    "axiom-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 Axiom transactions"
    })
    print(result["messages"][-1].content)

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Why Choose Vinkius

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Common questions about Axiom MCP in LangChain

Yes. You can use the Axiom MCP Server to query your logs directly within a LangChain agent. This allows the agent to identify anomalies and provide diagnostic summaries.

Absolutely. The server exposes every Axiom function as a standard tool for your agents. You just pass the tool list into your agent constructor.

Connect the server to LangSmith to see exactly what data returns from your queries. Each tool execution shows up as a distinct step in your trace.

Your tokens stay local to the client environment. The MCP server handles the handshake, keeping your API keys out of your application code.

We enforce strict transport security for every log event. Your raw observability data is scoped to your specific session, ensuring that only your authorized agents can read or modify your datasets.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript