How to Use the Langflow (Visual Multi-agent Orchestrator) MCP in Google ADK

Q: How does Google ADK handle file transfers to Langflow (Visual Multi-agent Orchestrator)?

The agent uses listfilesv2 and getfilev2 to manage workspace documents. This lets Google ADK move files between Google Cloud and your visual pipelines.

Q: How does Google ADK monitor performance in Langflow (Visual Multi-agent Orchestrator)?

Your agent queries getmonitortraces and getlogs to retrieve execution metrics. Google ADK then parses these logs to track latency and component failures.

Q: Can I restrict which tools my Gemini agent can access?

Yes. Use the toolnames filter in your McpToolset configuration to expose only specific tools like runflow or getproject.

Run visual Langflow graphs and query execution histories using Gemini's long-context capabilities on Google ADK with this MCP Server.

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Connect Langflow (Visual Multi-agent Orchestrator) MCP to Google ADK

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Setup Langflow (Visual Multi-agent Orchestrator) with Google ADK

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Pass visual flow data to Google ADK context

The `list_flows` tool allows Gemini models to inspect your entire workspace of visual graphs. This MCP Server connects your Google ADK environment directly to Langflow, letting your agent select the correct pipeline for any given user request. Once selected, the agent executes the graph using `run_flow`. Because Gemini supports massive context windows, you can feed entire flow structures and historical traces back into the model for deep reasoning.

Analyze execution traces inside Google ADK

The `get_monitor_traces` tool retrieves detailed execution logs and span trees from your visual pipelines. Your Google ADK agent can ingest these spans to diagnose bottlenecks in your Langflow components. Combine this with `get_monitor_transactions` to let Gemini audit component-level interactions. This setup allows your agent to cross-reference Google Cloud data with visual execution metrics to verify pipeline health.

Manage Langflow files with this MCP Server

The `list_files_v2` tool exposes user files directly to your Gemini agent. Your Google ADK client can use `get_file_v2` to download files or `delete_file_v2` to clean up workspace storage. This direct file access allows your agent to pass documents from Google Cloud Storage into your visual pipelines. The agent coordinates files and runs workflows using `run_workflow` in a single execution loop.

Setup guide

Set up Langflow (Visual Multi-agent Orchestrator) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Langflow (Visual Multi-agent Orchestrator) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Langflow (Visual Multi-agent Orchestrator)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Langflow (Visual Multi-agent Orchestrator) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Langflow (Visual Multi-agent Orchestrator) MCP in Google ADK

Yes. Your Google ADK agent pulls data from BigQuery, then passes that payload directly to the `run_flow` tool to execute your visual graph.

The agent uses `list_files_v2` and `get_file_v2` to manage workspace documents. This lets Google ADK move files between Google Cloud and your visual pipelines.

Your agent queries `get_monitor_traces` and `get_logs` to retrieve execution metrics. Google ADK then parses these logs to track latency and component failures.

Yes. Use the tool_names filter in your McpToolset configuration to expose only specific tools like `run_flow` or `get_project`.

All API requests, visual flow structures, and execution histories are processed in an isolated, ephemeral V8 sandbox. No data is stored on Vinkius servers, maintaining strict isolation for your enterprise workflows.

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