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

Q: Can New Relic AI (LLM Observability) help OpenAI Agents SDK debug costs?

Yes. The queryllmcosts tool gives you raw data on token consumption. Your agent can query this to decide if it should switch to a cheaper model mid-flow.

Q: Does New Relic AI (LLM Observability) track OpenAI Agents SDK latency?

It does. Use queryllmlatency to retrieve p95 timings. Your agent can use this data to adjust its timeout thresholds dynamically.

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Connect New Relic AI (LLM Observability) MCP to OpenAI Agents SDK

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

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Track LLM spend and speed in OpenAI Agents SDK

Feed your agent precise financial and performance data. Use `query_llm_costs` to monitor token usage and `query_llm_latency` to keep your p95 response times within budget. Your agent now knows exactly when a model run exceeds its financial threshold. It stops runaway processes before they hit your billing statement.

Audit production errors for OpenAI Agents SDK

Stop guessing why your agents fail. Trigger `query_llm_errors` to pull specific stack traces and fault logs from your telemetry stream. This gives your agent the ability to self-diagnose issues during runtime. It flags problematic prompts before they break your user experience.

Log custom agent states via OpenAI Agents SDK

Push internal agent events directly to your monitoring stack. Call `post_custom_event` to insert `CustomAITelemetry` rows into your existing records. This maps your agent's decision-making process to your broader application health. You get a clear view of how your agents behave under load.

Setup guide

Set up New Relic 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 New Relic AI (LLM Observability) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives New Relic 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 New Relic 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="New Relic AI (LLM Observability) Agent",
            instructions="You have access to New Relic 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 New Relic AI (LLM Observability) MCP in OpenAI Agents SDK

You connect the server by passing it to your Agent constructor. Use the `MCPServerStreamableHttp` class with your endpoint, then include it in the `mcp_servers` list.

Yes. The `query_llm_costs` tool gives you raw data on token consumption. Your agent can query this to decide if it should switch to a cheaper model mid-flow.

It does. Use `query_llm_latency` to retrieve p95 timings. Your agent can use this data to adjust its timeout thresholds dynamically.

Vinkius handles the auth layer. You provide one endpoint token, and the server manages the secure tunnel between your agent and your telemetry data.

It accesses your LLM telemetry, including cost, latency, error logs, and feedback events. It does not touch your raw private user PII or proprietary databases.

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