Can I see the accuracy metrics for a specific ML run through my agent?

Yes. Use the `get_attributes` tool with your Project ID and Run ID. Your agent will retrieve the detailed telemetry logged during that execution, including accuracy, loss, and any custom attributes defined in your code.

How do I check which model versions are currently stable in my registry?

The `list_models` tool retrieves all packaged ML models within a project. Your agent will expose the promoted model versions, helping you distinguish between experimental runs and stable candidates ready for deployment.

Can my agent search through hundreds of past ML experimentation runs?

Absolutely. Use the `search_runs` tool with your Project ID. Your agent will query Neptune's tracking server to identify historical experiment state checkpoints, making it easy to locate specific training results across your entire research timeline.

Neptune.ai (ML Experiment Tracking) MCP Server for LangChain 6 tools — connect in under 2 minutes

Name: Neptune.ai (ML Experiment Tracking)
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Neptune.ai (ML Experiment Tracking) through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "neptuneai-ml-experiment-tracking": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Neptune.ai (ML Experiment Tracking), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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 Neptune.ai (ML Experiment Tracking) MCP Server

Connect your Neptune.ai account to any AI agent and take full control of your machine learning experimentation, model versioning, and training telemetry through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Neptune.ai (ML Experiment Tracking) through native MCP adapters. Connect 6 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Experiment Orchestration — List all managed ML projects and retrieve detailed metadata configurations tracking active runs and workspace boundaries directly from your agent
Run Audit & Search — Discover specific training runs or historical experiment state checkpoints mapping deep ML parameter sets and performance bounds securely
Attribute Inspection — Extract detailed telemetry capturing the exact variables, accuracy metrics, and loss curves logged during specific execution checkpoints natively
Model Registry Management — List and retrieve trained tracking models promoted and logged explicitly, isolating stable versions from ephemeral experimentation runs
Organizational Visibility — Enumerate accessible workspaces and projects to understand your ML research footprint and documentation distribution natively
Credential Audit — Verify specific user identifies and availability details bound inherently against your active service account token securely
Metadata Retrieval — Deep-dive into specific Project or Run IDs to retrieve precise JSON representations and chronological experimentation insights instantly

The Neptune.ai (ML Experiment Tracking) MCP Server exposes 6 tools through the Vinkius. Connect it to LangChain 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 Neptune.ai (ML Experiment Tracking) to LangChain via MCP

Follow these steps to integrate the Neptune.ai (ML Experiment Tracking) MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 6 tools from Neptune.ai (ML Experiment Tracking) via MCP

Why Use LangChain with the Neptune.ai (ML Experiment Tracking) MCP Server

LangChain provides unique advantages when paired with Neptune.ai (ML Experiment Tracking) through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Neptune.ai (ML Experiment Tracking) MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Neptune.ai (ML Experiment Tracking) queries for multi-turn workflows

Neptune.ai (ML Experiment Tracking) + LangChain Use Cases

Practical scenarios where LangChain combined with the Neptune.ai (ML Experiment Tracking) MCP Server delivers measurable value.

RAG with live data: combine Neptune.ai (ML Experiment Tracking) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Neptune.ai (ML Experiment Tracking), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Neptune.ai (ML Experiment Tracking) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Neptune.ai (ML Experiment Tracking) tool call, measure latency, and optimize your agent's performance

Neptune.ai (ML Experiment Tracking) MCP Tools for LangChain (6)

These 6 tools become available when you connect Neptune.ai (ML Experiment Tracking) to LangChain via MCP:

get_attributes

Get parameters mapped within an experiment runtime bounds

get_project

Get specific details for a targeted Neptune ML project

get_user

Get specific user credentials and availability details

list_models

List trained tracking models packaged natively within a project

list_projects

List accessible Neptune workspaces and projects

search_runs

Search explicitly tracked ML experimentation runs inside a project

Example Prompts for Neptune.ai (ML Experiment Tracking) in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Neptune.ai (ML Experiment Tracking) immediately.

"List all training runs for the 'Customer-Churn' project"

"Show me the metrics for run ID 'churn-exp-123'"

"List all registered models in project 'Fraud-Detection'"

Troubleshooting Neptune.ai (ML Experiment Tracking) MCP Server with LangChain

Common issues when connecting Neptune.ai (ML Experiment Tracking) to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Neptune.ai (ML Experiment Tracking) + LangChain FAQ

Common questions about integrating Neptune.ai (ML Experiment Tracking) MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.