How to Use the New Relic AI (LLM Observability) MCP in AutoGen

Q: How do I register the New Relic AI (LLM Observability) tools with AutoGen?

You use the autogen-extmcp library to connect to the Vinkius MCP Server. The McpToolAdapter automatically translates the tools into schemas that your AssistantAgent can understand.

Q: Can AutoGen agents use New Relic AI (LLM Observability) to check system health?

Yes, agents can run listapmapps to verify if the underlying application servers are healthy. If the APM status is degraded, the agents can pause their work to avoid sending traffic to a broken system.

Q: How does New Relic AI (LLM Observability) help AutoGen agents manage alert policies?

The listalertpolicies tool lets your coordinator agent inspect active alerts. If a critical alert is active, the agent can notify the user or take corrective action.

Q: Can my agents run custom telemetry queries in AutoGen?

Yes, customnrql allows your agents to execute read-only queries against your New Relic data. They can use this to fetch historical performance trends during their debates.

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Connect New Relic AI (LLM Observability) MCP to AutoGen

Create your Vinkius account to connect New Relic AI (LLM Observability) to AutoGen 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 AutoGen

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Resolve Agent Debates with Cost Data

`query_llm_costs` provides the exact financial impact of your multi-agent conversations. During a debate, a budget-enforcer agent can call this tool to halt a conversation if the token spend exceeds a predefined limit. This prevents runaway agent loops from draining your wallet. One agent proposes a complex multi-step plan, while the budget agent checks the live cost data and demands a cheaper alternative if the projection is too high.

Track AutoGen Latency via MCP Server

`query_llm_latency` monitors the response times of each agent's model calls. This tool lets a coordinator agent detect when a specific specialist agent is slowing down the entire conversation. Instead of waiting for a timeout, the coordinator agent checks the latency metrics. If an agent takes longer than 2 seconds to respond, the system routes the task to a faster, lighter agent.

Log Agent Conversations as Custom Events

`post_custom_event` writes custom event records directly to your New Relic account. This tool allows your AutoGen manager to log the outcome of agent debates, tracking which agents reached consensus and how many turns it took. You get a clean audit trail of your multi-agent system's internal thoughts. The telemetry is stored alongside your standard application metrics, making it easy to debug complex agent interactions.

Setup guide

Set up New Relic AI (LLM Observability) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes New Relic AI (LLM Observability) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="New Relic AI (LLM Observability)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent New Relic AI (LLM Observability) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="New Relic AI (LLM Observability)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent New Relic AI (LLM Observability) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by New Relic AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about New Relic AI (LLM Observability) MCP in AutoGen

You use the `autogen-ext[mcp]` library to connect to the Vinkius MCP Server. The `McpToolAdapter` automatically translates the tools into schemas that your AssistantAgent can understand.

Yes, agents can run `list_apm_apps` to verify if the underlying application servers are healthy. If the APM status is degraded, the agents can pause their work to avoid sending traffic to a broken system.

The `list_alert_policies` tool lets your coordinator agent inspect active alerts. If a critical alert is active, the agent can notify the user or take corrective action.

Yes, `custom_nrql` allows your agents to execute read-only queries against your New Relic data. They can use this to fetch historical performance trends during their debates.

Yes, Vinkius handles authentication securely with a single endpoint token. Your New Relic API credentials never leave the zero-trust V8 isolate, ensuring your token usage and latency telemetry remain strictly private.

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