Can I manually approve an agent's step using this server?

Yes. Use the `update_thread_state` tool to perform manual node state overrides. This is the standard way to implement human-in-the-loop (HITL) patterns, allowing you to modify or approve graph variables directly mid-execution.

How do I see the current memory of a conversation thread?

The `get_thread_state` tool retrieves the exact execution state of a thread, including all cyclical node variables and structured outputs stored in the cloud checkpoints. This gives your agent full visibility into the conversation history.

Can my agent trigger a new run on an existing thread?

Absolutely. Use the `create_run` tool and provide the Thread ID, Assistant ID, and your new input payload. Your agent will fire the graph dynamically, allowing for multi-turn engagements within the same stateful boundary.

LangGraph Cloud (Stateful AI Agents) MCP Server for Google ADK 10 tools — connect in under 2 minutes

Name: LangGraph Cloud (Stateful AI Agents)
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add LangGraph Cloud (Stateful AI Agents) as an MCP tool provider through the Vinkius and your ADK agents can call every tool with full schema introspection.

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

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token — get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="langgraph_cloud_stateful_ai_agents_agent",
    instruction=(
        "You help users interact with LangGraph Cloud (Stateful AI Agents) "
        "using 10 available tools."
    ),
    tools=[mcp_tools],
)

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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 LangGraph Cloud (Stateful AI Agents) MCP Server

Connect your LangGraph Cloud account to any AI agent and take full control of your stateful multi-turn agents and complex graph-based AI workflows through natural conversation.

Google ADK natively supports LangGraph Cloud (Stateful AI Agents) as an MCP tool provider — declare the Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 10 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

What you can do

Assistant Orchestration — List deployed assistants representing compiled LangGraph applications ready to process stateful workloads directly from your agent
Thread Management — Create and monitor conversation threads to maintain long-term memory buffers explicitly managed by cloud checkpoints
State Inspection & Override — Retrieve the exact execution state of a thread and perform manual node overrides for human-in-the-loop approvals or mid-execution adjustments
Run Control — Trigger fresh graph executions with specific input payloads and monitor or cancel asynchronous runs to manage system resources
Cron Automation Audit — List scheduled background jobs configured to autonomously trigger LangGraph execution runs periodically
History Tracking — Extract historical run steps indicating explicit graph invocations and internal reasoning paths within a stateful thread

The LangGraph Cloud (Stateful AI Agents) MCP Server exposes 10 tools through the Vinkius. Connect it to Google ADK 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 LangGraph Cloud (Stateful AI Agents) to Google ADK via MCP

Follow these steps to integrate the LangGraph Cloud (Stateful AI Agents) MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 10 tools from LangGraph Cloud (Stateful AI Agents) via MCP

Why Use Google ADK with the LangGraph Cloud (Stateful AI Agents) MCP Server

Google ADK provides unique advantages when paired with LangGraph Cloud (Stateful AI Agents) through the Model Context Protocol.

Google ADK natively supports MCP tool servers — declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with LangGraph Cloud (Stateful AI Agents)

Production-ready features like session management, evaluation, and deployment come built-in — not bolted on

Seamless integration with Google Cloud services means you can combine LangGraph Cloud (Stateful AI Agents) tools with BigQuery, Vertex AI, and Cloud Functions

LangGraph Cloud (Stateful AI Agents) + Google ADK Use Cases

Practical scenarios where Google ADK combined with the LangGraph Cloud (Stateful AI Agents) MCP Server delivers measurable value.

Enterprise data agents: ADK agents query LangGraph Cloud (Stateful AI Agents) and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine LangGraph Cloud (Stateful AI Agents) tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query LangGraph Cloud (Stateful AI Agents) regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including LangGraph Cloud (Stateful AI Agents)

LangGraph Cloud (Stateful AI Agents) MCP Tools for Google ADK (10)

These 10 tools become available when you connect LangGraph Cloud (Stateful AI Agents) to Google ADK via MCP:

cancel_run

Interrupt and cancel an ongoing graph execution run

create_run

Execute an assistant run on a specific thread with an input payload

create_thread

Create a new LangGraph thread to hold conversational state

get_run

Get complete details and status of a specific language graph run

get_thread_state

g., current messages array or structured outputs) generated by the LangGraph application. Retrieve the exact state graph and variables for a specific thread

list_assistants

List LangGraph deployed assistants (graph configurations)

list_crons

List active scheduled cron jobs automating agent runs

list_runs

List execution runs assigned to a specific thread

list_threads

List active LangGraph conversation threads

update_thread_state

Manually override or update a thread state graph

Example Prompts for LangGraph Cloud (Stateful AI Agents) in Google ADK

Ready-to-use prompts you can give your Google ADK agent to start working with LangGraph Cloud (Stateful AI Agents) immediately.

"List all deployed assistants in my LangGraph Cloud account"

"Show me the current state for thread ID 'abc-123-xyz'"

"List all active scheduled crons in my account"

Troubleshooting LangGraph Cloud (Stateful AI Agents) MCP Server with Google ADK

Common issues when connecting LangGraph Cloud (Stateful AI Agents) to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

LangGraph Cloud (Stateful AI Agents) + Google ADK FAQ

Common questions about integrating LangGraph Cloud (Stateful AI Agents) MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.