How to Use the VectorShift (AI Workflow & RAG Automation) MCP in LangChain

Build Multi-Step Reasoning Chains with VectorShift (AI Workflow & RAG Automation) for LangChain.

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Connect VectorShift (AI Workflow & RAG Automation) MCP to LangChain

Create your Vinkius account to connect VectorShift (AI Workflow & RAG Automation) 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 VectorShift (AI Workflow & RAG Automation) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Orchestrate complex workflows.

You can build multi-step pipelines where the agent decides exactly what to do. Start by running a workflow using `run_pipeline`, and if that fails, you'll try a different path. It’s about linking tools together. After fetching a chatbot status with `get_chatbot`, your chain might decide it needs to execute a transformation via `run_transformation` first.

Manage knowledge sources.

Need to ground the agent's decisions in real data? You can index files or URLs into a knowledge base using `index_knowledge_base`. Then, your chain queries that data with `query_knowledge_base` for answers. Forget guessing. If the agent needs context about what chatbots are available, it just calls `list_chatbots`, getting immediate input to guide its next step.

Control and deploy bots.

Want your agent to test a new conversational flow? You can create one with `create_chatbot`. When the time comes, you send it a message using `run_chatbot` to get an immediate response. The system also lets you clean up. If the chat session is done, use `terminate_chatbot`. For deeper control, you've got tools like `upload_chatbot_files`.

Setup guide

Set up VectorShift (AI Workflow & RAG Automation) 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 VectorShift (AI Workflow & RAG Automation) 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({
    "vectorshift-ai-workflow-rag-automation-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 VectorShift (AI Workflow & RAG Automation) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about VectorShift (AI Workflow & RAG Automation) MCP in LangChain

The MCP Server makes every tool call a piece of the chain. When your LangChain agent calls `get_knowledge_base`, that output instantly becomes usable input for another step, like running a transformation via `run_transformation`.

You're dealing with knowledge base documents. You add content using `index_knowledge_base`, and your agent then uses those indexed files to answer questions via `query_knowledge_base`.

Absolutely. You can list, create, and delete pipelines using functions like `list_pipelines`, `create_pipeline`, or `delete_pipeline`. This lets your agent dynamically manage its own workflow paths.

Yes. If you need to remove outdated context, the MCP Server provides `delete_knowledge_base_documents` so you can specifically target and delete records by ID from a knowledge base.

This server deals primarily with structured configuration data and unstructured documents. You're managing the content you feed into the knowledge base, so always verify access controls on those uploaded files.

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