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 AutoGen 10 tools — connect in under 2 minutes

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

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add LangGraph Cloud (Stateful AI Agents) as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.

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

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="langgraph_cloud_stateful_ai_agents_agent",
            tools=tools,
            system_message=(
                "You help users with LangGraph Cloud (Stateful AI Agents). "
                "10 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

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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.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use LangGraph Cloud (Stateful AI Agents) tools. Connect 10 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

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 AutoGen 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 AutoGen via MCP

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

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 10 tools from LangGraph Cloud (Stateful AI Agents) automatically

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

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

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use LangGraph Cloud (Stateful AI Agents) tools to solve complex tasks

Role-based architecture lets you assign LangGraph Cloud (Stateful AI Agents) tool access to specific agents — a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive LangGraph Cloud (Stateful AI Agents) tool calls

Code execution sandbox: AutoGen agents can write and run code that processes LangGraph Cloud (Stateful AI Agents) tool responses in an isolated environment

LangGraph Cloud (Stateful AI Agents) + AutoGen Use Cases

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

Collaborative analysis: one agent queries LangGraph Cloud (Stateful AI Agents) while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from LangGraph Cloud (Stateful AI Agents), a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using LangGraph Cloud (Stateful AI Agents) data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process LangGraph Cloud (Stateful AI Agents) responses in a sandboxed execution environment

LangGraph Cloud (Stateful AI Agents) MCP Tools for AutoGen (10)

These 10 tools become available when you connect LangGraph Cloud (Stateful AI Agents) to AutoGen 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 AutoGen

Ready-to-use prompts you can give your AutoGen 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 AutoGen

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

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

LangGraph Cloud (Stateful AI Agents) + AutoGen FAQ

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

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call LangGraph Cloud (Stateful AI Agents) tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.