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

Chain together experiment data and model logs using the Neptune.ai (ML Experiment Tracking) MCP Server inside your LangChain pipelines.

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

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

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Sequence experiment data directly into chains

Feed the output of `search_runs` straight into your next agent step. You avoid manual data handling by letting the agent chain the results of one query into the parameters for the next. This keeps your reasoning pipelines moving without human intervention. The agent identifies which runs match your criteria and pulls the exact metrics needed for the chain.

Automate metric analysis with LangSmith

Use `get_attributes` to pull run parameters and verify model performance in real time. LangSmith traces every tool call so you can see exactly what data was pulled and why. Debugging becomes a matter of checking the trace logs. You verify exactly what the model saw before it made a decision based on your experiment history.

Multi-server aggregation for complex workflows

Connect the Neptune.ai (ML Experiment Tracking) server alongside your databases or vector stores. Your agent pulls experiment history and compares it against production logs in a single pass. This architecture lets you build sophisticated logic that spans multiple data sources. The agent handles the switching between your experiment records and external data.

Setup guide

Set up Neptune.ai (ML Experiment Tracking) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Neptune.ai (ML Experiment Tracking) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "neptuneai-ml-experiment-tracking-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Neptune.ai (ML Experiment Tracking) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Neptune.ai (ML Experiment Tracking) MCP in LangChain

Yes. You connect the server to your client and pass the tools to your agent. It will fetch run data and include it in your conversational context.

The agent treats the tools as callable functions. It decides when to trigger a lookup based on your prompt and returns the results to the chain.

Yes. Every tool call is logged in your tracing environment. You can see the exact input and output for every request made to the server.

The MCP server is stateless. Use a session client if you need to keep context alive between different prompts or agent turns.

The server uses a single endpoint token for authentication. Your experiment metadata stays within the Vinkius sandbox and is only accessed when your agent explicitly calls a tool.

Use it with your favorite AI tools

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