Can I check the budget and rate limits for a specific proxy key?

Yes. Use the `get_key_info` tool with the specific Key ID. Your agent will retrieve the exact rate limits, budget constraints, and current RPM usage associated with that token.

How do I see the model fallback paths configured in my proxy?

The `get_model_info` tool allows your agent to extract the global model directory. You'll see the exact fallback chains (e.g., if OpenAI fails, use Anthropic) and the physical endpoints assigned to each model name.

Can my agent create a new team to track specific division costs?

Absolutely. Use the `create_team` tool and provide a JSON payload defining the team name and optional budget limits. Your agent will provision the new team identity in LiteLLM, allowing for precise organizational cost tracking.

LiteLLM (LLM Proxy & Spend Tracking) MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes

Name: LiteLLM (LLM Proxy & Spend Tracking)
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 10 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect LiteLLM (LLM Proxy & Spend Tracking) through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="LiteLLM (LLM Proxy & Spend Tracking) Assistant",
            instructions=(
                "You help users interact with LiteLLM (LLM Proxy & Spend Tracking). "
                "You have access to 10 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from LiteLLM (LLM Proxy & Spend Tracking)"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About LiteLLM (LLM Proxy & Spend Tracking) MCP Server

Connect your LiteLLM Proxy instance to any AI agent and take full control of your LLM infrastructure, load balancing, and spend management through natural conversation.

The OpenAI Agents SDK auto-discovers all 10 tools from LiteLLM (LLM Proxy & Spend Tracking) through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries LiteLLM (LLM Proxy & Spend Tracking), another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Key Orchestration — Generate and manage proxy API keys to isolate distinct microservices or teams, including precise budget and rate limit constraints directly from your agent
Model Routing Intelligence — Get detailed info on fallback paths (e.g., OpenAI -> Anthropic -> Groq) and verify exact routing endpoints assigned to your models
Real-time Spend Audit — Track total USD consumed by specific end-users or teams and monitor budget ceilings to ensure cost-effective AI deployments
Dynamic Model Control — Inject fresh routing endpoints (e.g., new AWS Bedrock or Azure OpenAI deployments) into your proxy runtime with zero downtime
Team & Organizational Isolation — Create and manage team profiles to track exact cost limits and operational boundaries per organizational division
Infrastructure Security — Instantly vaporize malicious or leaked keys and remove broken LLM deployments to prevent downstream 500 errors dynamically

The LiteLLM (LLM Proxy & Spend Tracking) MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect LiteLLM (LLM Proxy & Spend Tracking) to OpenAI Agents SDK via MCP

Follow these steps to integrate the LiteLLM (LLM Proxy & Spend Tracking) MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 10 tools from LiteLLM (LLM Proxy & Spend Tracking)

Why Use OpenAI Agents SDK with the LiteLLM (LLM Proxy & Spend Tracking) MCP Server

OpenAI Agents SDK provides unique advantages when paired with LiteLLM (LLM Proxy & Spend Tracking) through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

LiteLLM (LLM Proxy & Spend Tracking) + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the LiteLLM (LLM Proxy & Spend Tracking) MCP Server delivers measurable value.

Automated workflows: build agents that query LiteLLM (LLM Proxy & Spend Tracking), process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries LiteLLM (LLM Proxy & Spend Tracking), another analyzes results, a third generates reports

Data enrichment pipelines: stream data through LiteLLM (LLM Proxy & Spend Tracking) tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query LiteLLM (LLM Proxy & Spend Tracking) to resolve tickets, look up records, and update statuses without human intervention

LiteLLM (LLM Proxy & Spend Tracking) MCP Tools for OpenAI Agents SDK (10)

These 10 tools become available when you connect LiteLLM (LLM Proxy & Spend Tracking) to OpenAI Agents SDK via MCP:

create_model

Inject completely fresh routing endpoints (ex: new Bedrock Llama 4 endpoints)

create_team

Generate pristine organizational isolation tracking exact cost limits per division

create_user

Insert specific End-User identities bridging Vinkius with Proxy logs

delete_key

Delete an existing LLM proxy key entirely

delete_model

Delete explicitly routed LLM deployments preventing 500s dynamically

generate_key

Generate a new proxy API key isolating distinct microservices or teams

get_key_info

Get configuration and budget bounds for a specific LiteLLM API Key

get_model_info

Get array endpoints tracing exact Fallback paths like OpenAI -> Anthropic

get_team_info

Get internal logic bounds matching multiple routing users via Team UUID

get_user_info

Return precise End-User abstractions tracking total USD consumed natively

Example Prompts for LiteLLM (LLM Proxy & Spend Tracking) in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with LiteLLM (LLM Proxy & Spend Tracking) immediately.

"List all active model fallback paths in LiteLLM"

"Generate a new API key for the 'Customer-Service' team with a $50 monthly budget"

"How much has user 'alex_dev' spent on LLM tokens today?"

Troubleshooting LiteLLM (LLM Proxy & Spend Tracking) MCP Server with OpenAI Agents SDK

Common issues when connecting LiteLLM (LLM Proxy & Spend Tracking) to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

LiteLLM (LLM Proxy & Spend Tracking) + OpenAI Agents SDK FAQ

Common questions about integrating LiteLLM (LLM Proxy & Spend Tracking) MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.