How to Use the HyperDX (Open Source Observability) MCP in LangChain

Q: Can LangChain agents automatically resolve issues found in HyperDX (Open Source Observability)?

Yes. By chaining listlogs to diagnostic logic and using createalert to tune thresholds, your agents can perform active incident management.

Q: Does HyperDX (Open Source Observability) allow LangChain agents to modify alert rules?

It does. You can use the createalert and deletealert tools to manage your monitoring configuration programmatically within your agent's execution flow.

Chain your observability data directly into LangChain pipelines for automated incident response.

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Connect HyperDX (Open Source Observability) MCP to LangChain

Create your Vinkius account to connect HyperDX (Open Source Observability) 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 HyperDX (Open Source Observability) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automated log analysis in LangChain

Pipe raw telemetry into your chains to diagnose production issues without leaving your IDE. Use `list_logs` to fetch error bursts and let your agent parse the stack traces immediately. Your agent inspects the output of `list_logs` to trigger downstream logic. This keeps your diagnostic loop tight and removes the need for manual copy-pasting between browser tabs.

Dynamic alert management in LangChain

Build agents that react to system spikes by modifying your monitoring surface. Use `create_alert` to establish temporary thresholds during high-traffic deployments and `list_alerts` to verify your new rules. This MCP Server lets LangChain agents take direct control of your alert lifecycle. Once your load test ends, use `delete_alert` to clean up the environment and prevent alert fatigue.

Real-time dashboard telemetry

Pull live metrics into your reasoning pipelines using `get_dashboard` and `list_dashboards`. Your agent sees exactly what the dashboard reports, ensuring every decision is based on current system health. This gives your agent the context it needs to correlate events with dashboard trends. It turns static observability data into an active input for your decision-making chains.

Setup guide

Set up HyperDX (Open Source Observability) 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 HyperDX (Open Source Observability) 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({
    "hyperdx-open-source-observability-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 HyperDX (Open Source Observability) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about HyperDX (Open Source Observability) MCP in LangChain

Connect the server via the LangChain MCP adapter and call the `list_logs` tool. You can pass specific query strings to filter by service or log level directly in your chain.

Yes. By chaining `list_logs` to diagnostic logic and using `create_alert` to tune thresholds, your agents can perform active incident management.

It does. You can use the `create_alert` and `delete_alert` tools to manage your monitoring configuration programmatically within your agent's execution flow.

Use LangSmith tracing. Every call to these tools is captured as a distinct step, showing you the exact inputs and outputs for every observability query.

The server only touches the specific telemetry logs and alert configurations you grant access to via your API key. It operates as a scoped proxy to your data.

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