How to Use the Datadog AI (LLM Observability) MCP in OpenAI Agents SDK

Q: How do I link Datadog AI (LLM Observability) to my OpenAI Agents SDK project?

You instantiate the MCPServerStreamableHttp class and pass it into your agent constructor. The SDK auto-discovers every tool, letting you call them immediately in your async code.

Track token usage and audit model prompts directly inside your OpenAI Agents SDK production pipeline.

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Connect Datadog AI (LLM Observability) MCP to OpenAI Agents SDK

Create your Vinkius account to connect Datadog AI (LLM Observability) to OpenAI Agents SDK 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 Datadog AI (LLM Observability) with OpenAI Agents SDK

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Monitor token metrics in OpenAI Agents SDK

Call `query_metrics` to pull live data on `datadog.llm_observability.tokens` without leaving your Python environment. You get immediate visibility into usage patterns for every agent interaction. This MCP Server handles the data bridge so your agents stay within budget. It keeps the feedback loop tight by surfacing consumption stats right when you need them.

Audit spans within OpenAI Agents SDK

Use `search_llm_spans` to provision a JSON payload that maps your customer bindings. It extracts the raw input and output data from your model calls for easier debugging. Your production agent system benefits from this visibility by pinpointing exactly where prompts drift. It makes the diagnostic process repeatable and grounded in hard evidence.

Manage observability events for OpenAI Agents SDK

Invoke `create_event` to inspect deep internal arrays and mitigate math errors in your agent logic. You can see how specific plan sequences perform under load. This tool ensures your agent state stays consistent during complex task execution. It removes the guesswork by exposing how your system handles specific operational constraints.

Setup guide

Set up Datadog AI (LLM Observability) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Datadog AI (LLM Observability) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Datadog AI (LLM Observability) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Datadog AI (LLM Observability) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Datadog AI (LLM Observability) Agent",
            instructions="You have access to Datadog AI (LLM Observability) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Datadog AI (LLM Observability) MCP in OpenAI Agents SDK

You instantiate the `MCPServerStreamableHttp` class and pass it into your agent constructor. The SDK auto-discovers every tool, letting you call them immediately in your async code.

Yes, by querying the token metrics tool. You can set up triggers to alert your dev team when usage patterns shift unexpectedly.

The server provides the data needed to inform your guardrail logic. You validate agent actions against the trace data provided by the spans tool before committing to a final output.

Traces reside in your configured Datadog environment. This server simply acts as a conduit for your agent to read and write that data.

The server only transmits what you explicitly request via the tool calls. Your sensitive prompt text is never cached by the infrastructure, maintaining a secure link between your agent and the platform.

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