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

Q: How do I run a custom fine-tuning run using Gradient AI (LLM API & Finetuning) in LangChain?

You upload your training data using uploadfile and trigger finetunemodel inside a LangGraph node. The LangChain agent tracks the training state and outputs the new Gradient AI model ID for subsequent chain steps.

Q: Can LangChain agents manage RAG collections on Gradient AI (LLM API & Finetuning)?

Yes. Your LangChain agent calls createragcollection to initialize the vector space, then uses generateembeddings to populate it. LangChain handles the orchestration while Gradient AI manages the storage.

Q: What is the best way to handle document extraction with Gradient AI (LLM API & Finetuning) and LangChain?

Use the extractpdf tool within a LangChain document loading chain. The extracted text is returned directly to your LangChain agent, which can then summarize it or load it into a vector store.

Connect Gradient AI's model tuning and embedding tools directly to your LangChain pipelines for multi-step execution.

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

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

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Fine-Tuning Pipelines in LangChain

The `fine_tune_model` tool initiates training runs on Gradient AI directly from your LangChain execution paths using this MCP Server. You pass training datasets uploaded via `upload_file` and kick off custom model runs without leaving your python environment. This setup lets your LangChain agent evaluate training performance after each Gradient AI run. If validation metrics hit your target, the chain automatically swaps the active model ID using `get_model` for the next steps in your pipeline.

Automated PDF Parsing and RAG Setup

Document parsing gets simple when the `extract_pdf` tool pulls structured text from raw files to feed your MCP retrieval pipelines. Your LangChain agent handles the document ingestion, extracts clean text with `extract_pdf`, and prepares it for embedding generation. Once extracted, the agent calls `create_rag_collection` to spin up a vector space on Gradient AI. It then routes the chunked text directly into the Gradient AI collection, making live data searchable for your LangChain chains.

Dynamic Model Completion and Sentiment Analysis

Running prompts against custom weights is exactly what the `complete_model` tool handles on Gradient AI. LangChain chains pass intermediate outputs to this tool to generate context-aware completions. To branch your LangChain logic, the agent calls `analyze_sentiment` on the Gradient AI completion. Negative sentiment results trigger a LangChain correction chain, while positive results proceed to the final output node.

Setup guide

Set up Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) 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({
    "gradient-ai-llm-api-finetuning-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 Gradient AI (LLM API & Finetuning) transactions"
    })
    print(result["messages"][-1].content)

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

You upload your training data using `upload_file` and trigger `fine_tune_model` inside a LangGraph node. The LangChain agent tracks the training state and outputs the new Gradient AI model ID for subsequent chain steps.

Yes. Your LangChain agent calls `create_rag_collection` to initialize the vector space, then uses `generate_embeddings` to populate it. LangChain handles the orchestration while Gradient AI manages the storage.

Use the `extract_pdf` tool within a LangChain document loading chain. The extracted text is returned directly to your LangChain agent, which can then summarize it or load it into a vector store.

Connect your LangChain agent to LangSmith to monitor latency and payload sizes for Gradient AI calls. Every tool invocation, from embedding generation to model completion, shows up as a distinct span in your trace.

Your training files and PDFs are uploaded securely to Gradient AI's isolated environment. Vinkius runs the MCP server in a zero-trust sandbox, meaning your proprietary prompts and datasets are never exposed to external networks or cached between sessions.

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