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 AutoGen 10 tools — connect in under 2 minutes

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

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add LiteLLM (LLM Proxy & Spend Tracking) as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

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 autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="litellm_llm_proxy_spend_tracking_agent",
            tools=tools,
            system_message=(
                "You help users with LiteLLM (LLM Proxy & Spend Tracking). "
                "10 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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High SecurityEnterprise-grade

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DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use LiteLLM (LLM Proxy & Spend Tracking) tools. Connect 10 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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 AutoGen 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 AutoGen via MCP

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

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 10 tools from LiteLLM (LLM Proxy & Spend Tracking) automatically

Why Use AutoGen with the LiteLLM (LLM Proxy & Spend Tracking) MCP Server

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

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use LiteLLM (LLM Proxy & Spend Tracking) tools to solve complex tasks

Role-based architecture lets you assign LiteLLM (LLM Proxy & Spend Tracking) tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive LiteLLM (LLM Proxy & Spend Tracking) tool calls

Code execution sandbox: AutoGen agents can write and run code that processes LiteLLM (LLM Proxy & Spend Tracking) tool responses in an isolated environment

LiteLLM (LLM Proxy & Spend Tracking) + AutoGen Use Cases

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

Collaborative analysis: one agent queries LiteLLM (LLM Proxy & Spend Tracking) while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from LiteLLM (LLM Proxy & Spend Tracking), a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using LiteLLM (LLM Proxy & Spend Tracking) data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process LiteLLM (LLM Proxy & Spend Tracking) responses in a sandboxed execution environment

LiteLLM (LLM Proxy & Spend Tracking) MCP Tools for AutoGen (10)

These 10 tools become available when you connect LiteLLM (LLM Proxy & Spend Tracking) to AutoGen 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 AutoGen

Ready-to-use prompts you can give your AutoGen 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 AutoGen

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

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

LiteLLM (LLM Proxy & Spend Tracking) + AutoGen FAQ

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

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call LiteLLM (LLM Proxy & Spend Tracking) tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.