How to Use the Flowise MCP in LlamaIndex

Q: Can LlamaIndex search through Flowise chatflow configurations?

Yes, by calling listchatflows you can pull configuration metadata into your index. This allows the agent to search and retrieve specific flow details when needed.

Index your Flowise API data into LlamaIndex for grounded, searchable AI knowledge bases.

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Connect Flowise MCP to LlamaIndex

Create your Vinkius account to connect Flowise to LlamaIndex 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 Flowise with LlamaIndex

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Index Flowise data with LlamaIndex

Turn your chatflow outputs into searchable vector data. By using `predict` within your retrieval pipeline, you ground your agent's answers in actual API results. This prevents the agent from hallucinating about your workflows. It uses the real-time data returned by the server to construct accurate, context-aware responses.

Query history in LlamaIndex

Call `get_history` to ingest past chat sessions into your knowledge base. Your agent can then perform semantic searches across previous interactions. It transforms static logs into a living index. You can ask your agent about past performance or specific user queries, and it finds the exact execution record.

Manage Flowise flows in LlamaIndex

Use `list_chatflows` and `list_agentflows` to map your entire library of workflows into a queryable format. The agent knows exactly which flow to call to get the data you need. Everything becomes discoverable. You stop guessing which endpoint to use and let the agent navigate your Flowise setup through its own indexed knowledge.

Setup guide

Set up Flowise MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Flowise MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Flowise tools.",
)
response = await agent.run("List recent Flowise data")

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Common questions about Flowise MCP in LlamaIndex

Install the MCP tool spec, initialize the client with your Vinkius endpoint, and convert the tools to a format LlamaIndex understands using the tool spec library.

Yes, by calling `list_chatflows` you can pull configuration metadata into your index. This allows the agent to search and retrieve specific flow details when needed.

By providing live API data directly to the index, your RAG pipeline avoids stale info. The agent uses the `predict` tool to fetch fresh results before generating an answer.

The server accesses chatflow configurations, execution history, and credential metadata. This data is used solely to power your agent's knowledge retrieval.

We enforce strict credential isolation. The server uses an ephemeral sandbox for all operations, ensuring your API keys and workflow logs remain contained.

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