How to Use the Neptune.ai (ML Experiment Tracking) MCP in AutoGen

Let your agents debate experiment results using the Neptune.ai (ML Experiment Tracking) MCP Server in AutoGen.

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Connect Neptune.ai (ML Experiment Tracking) MCP to AutoGen

Create your Vinkius account to connect Neptune.ai (ML Experiment Tracking) 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 Neptune.ai (ML Experiment Tracking) with AutoGen

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Perplexity

Debate model performance between agents

One agent calls `search_runs` while another challenges the findings. They negotiate which run performed best based on the retrieved metrics. This process surfaces issues that a single agent might miss. The conversation between agents ensures that your experiment analysis is vetted before you get a final report.

Automated cross-referencing of run attributes

Trigger `get_attributes` to feed data into a security agent and a performance agent. They compare the results and decide if the model meets your internal standards. This removes the need for manual review. Your agents reach a consensus on whether to proceed with a model deployment based on the data they pull.

Sync project status across the agent swarm

Use `list_projects` to keep every agent updated on the current workspace status. They coordinate their tasks based on which projects are active. This keeps your multi-agent system aligned. Each agent knows exactly where to look for data because they share the project context provided by the server.

Setup guide

Set up Neptune.ai (ML Experiment Tracking) 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 Neptune.ai (ML Experiment Tracking) 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="Neptune.ai (ML Experiment Tracking)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Neptune.ai (ML Experiment Tracking) 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="Neptune.ai (ML Experiment Tracking)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Neptune.ai (ML Experiment Tracking) 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 Neptune.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 Neptune.ai (ML Experiment Tracking) MCP in AutoGen

Agents share the tool output in their conversation history. They critique each other's interpretation of the metrics until they reach a conclusion.

Yes. You assign the server tools to the agents who need them. Others can request information from them during the debate process.

You use the MCP adapter to convert the tools into a format AutoGen understands. It treats the server as an extension of the agent's capabilities.

If a tool call fails, the agents discuss the error. One agent might suggest retrying the search while another checks the project ID for typos.

Your data stays behind the Vinkius firewall. The MCP server acts as a gateway that only provides access to the agents you have explicitly configured.

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