How to Use the Gradient AI (LLM API & Finetuning) MCP in AutoGen

Q: How do AutoGen agents coordinate fine-tuning on Gradient AI (LLM API & Finetuning)?

One AutoGen agent uploads training data using uploadfile and calls createmodel. A second AutoGen agent runs validation tests using completemodel to verify the new Gradient AI model's performance.

Q: Can I run audio transcription workflows with Gradient AI (LLM API & Finetuning) and AutoGen?

Yes. An AutoGen agent initiates the job with createtranscription, and a monitoring agent polls gettranscription until the text is ready for downstream analysis.

Q: How do AutoGen agents manage RAG collections using Gradient AI (LLM API & Finetuning)?

The database AutoGen agent calls createragcollection to set up the workspace. Other AutoGen agents then populate it and run semantic queries to resolve debates with grounded Gradient AI facts.

Q: How do AutoGen agents handle model lifecycle management?

AutoGen agents query listmodels to see what is available, and can decommission old experiments by calling deletemodel once a consensus is reached on the MCP backend.

Run multi-agent debates over model fine-tuning and transcription analysis using AutoGen.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Gradient AI (LLM API & Finetuning) MCP to AutoGen

Create your Vinkius account to connect Gradient AI (LLM API & Finetuning) 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.

GDPR Free for Subscribers

Setup Gradient AI (LLM API & Finetuning) with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Collaborative Model Fine-Tuning

The `create_model` tool instantiates a new fine-tuned Gradient AI model instance based on a foundational base. In an AutoGen setup powered by this MCP Server, a developer agent initiates this Gradient AI call while an analyst agent monitors the parameters. Once the Gradient AI model is ready, an AutoGen tester agent runs validation prompts. If the outputs fall short, the AutoGen agents negotiate a new training run using `fine_tune_model` with adjusted Gradient AI datasets.

Multi-Agent Audio Transcription in AutoGen

Asynchronous audio processing begins when the `create_transcription` tool starts a job on Gradient AI via the MCP interface. An AutoGen ingestion agent triggers the job, while a coordinator agent polls Gradient AI for completion. Once `get_transcription` returns the text, an AutoGen supervisor agent routes the Gradient AI output to a writer agent. The AutoGen writer agent then calls `personalize_document` to adapt the Gradient AI transcript.

Consensus-Driven Document Summarization

Condensing massive documents is what the `summarize_document` tool does on Gradient AI. An AutoGen summarizer agent runs this tool, and an AutoGen critic agent reviews the output for accuracy. If the AutoGen critic finds missing details, it asks the summarizer to call `extract_entity` to pull specific facts from Gradient AI. The AutoGen agents iterate until they agree on a final, verified Gradient AI summary.

Setup guide

Set up Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) 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="Gradient AI (LLM API & Finetuning)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Gradient AI (LLM API & Finetuning) data")
print(result.messages[-1].content)

Get your connection token →

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="Gradient AI (LLM API & Finetuning)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Gradient AI (LLM API & Finetuning) 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 Gradient 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Gradient AI (LLM API & Finetuning) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Gradient AI (LLM API & Finetuning) MCP in AutoGen

One AutoGen agent uploads training data using `upload_file` and calls `create_model`. A second AutoGen agent runs validation tests using `complete_model` to verify the new Gradient AI model's performance.

Yes. An AutoGen agent initiates the job with `create_transcription`, and a monitoring agent polls `get_transcription` until the text is ready for downstream analysis.

The database AutoGen agent calls `create_rag_collection` to set up the workspace. Other AutoGen agents then populate it and run semantic queries to resolve debates with grounded Gradient AI facts.

AutoGen agents query `list_models` to see what is available, and can decommission old experiments by calling `delete_model` once a consensus is reached on the MCP backend.

Yes. Audio files and text documents are transmitted directly to Gradient AI via secure API calls. The Vinkius runtime ensures the MCP server environment is completely isolated, preventing cross-tenant data leaks.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript