How to Use the LangGraph Cloud (Stateful AI Agents) MCP in AutoGen

Q: How do I configure LangGraph Cloud (Stateful AI Agents) for AutoGen?

You use the mcpservertools function from the autogen-ext package to connect the MCP Server. The McpToolAdapter automatically converts the schemas so your AssistantAgent can call them natively.

Q: How do the agents know what automated jobs are running?

They call the listcrons tool. This returns the active scheduled tasks, allowing your squad to account for automated executions in their planning.

Q: Does AutoGen track the history of LangGraph Cloud (Stateful AI Agents) executions?

A dedicated agent can pull the execution history using listruns. It reviews the past actions on a thread to inform future decisions.

Give your AutoGen debating squads direct control over remote stateful agents.

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Connect LangGraph Cloud (Stateful AI Agents) MCP to AutoGen

Create your Vinkius account to connect LangGraph Cloud (Stateful AI Agents) to AutoGen 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 LangGraph Cloud (Stateful AI Agents) with AutoGen

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Remote Execution Coordination

AutoGen squads negotiate decisions before acting. Once they reach consensus, they use this MCP integration to trigger `create_run` and send the finalized input payload to a specific thread. The `list_assistants` tool tells your squad exactly which graph configurations are available on the backend. Polling execution status keeps your agents informed. A monitoring agent calls `get_run` to check the remote process. If the task completes, the squad analyzes the output and decides on the next move without writing any custom API wrappers.

Supervising AutoGen State via MCP

Your security and performance agents need to audit remote activity. The `list_threads` tool exposes every active conversation happening on the infrastructure. They pick a target and use `get_thread_state` to pull the exact messages array. This MCP Server turns opaque backend processes into transparent data structures. Your debating agents read the graph variables, argue about the results, and formulate a response. You build systems where local agents govern remote ones based on hard evidence.

Intervention and Scheduling

When a remote graph goes off the rails, your squad takes action. One agent decides to kill the process using `cancel_run`. Another agent follows up with `update_thread_state` to inject a corrected state graph and restart the workflow through the MCP connection. Automated tasks require oversight too. Your agents check `list_crons` to see what scheduled jobs are active. They cross-reference those jobs with `list_runs` to ensure the automated runs align with the squad's overall objectives.

Setup guide

Set up LangGraph Cloud (Stateful AI Agents) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes LangGraph Cloud (Stateful AI Agents) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="LangGraph Cloud (Stateful AI Agents)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent LangGraph Cloud (Stateful AI Agents) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="LangGraph Cloud (Stateful AI Agents)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent LangGraph Cloud (Stateful AI Agents) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LangGraph Cloud. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about LangGraph Cloud (Stateful AI Agents) MCP in AutoGen

You use the mcp_server_tools function from the autogen-ext package to connect the MCP Server. The McpToolAdapter automatically converts the schemas so your AssistantAgent can call them natively.

Your agents debate the necessary changes and agree on a new payload. They then execute `update_thread_state` to manually override the remote graph variables.

They call the `list_crons` tool. This returns the active scheduled tasks, allowing your squad to account for automated executions in their planning.

A dedicated agent can pull the execution history using `list_runs`. It reviews the past actions on a thread to inform future decisions.

The tool accesses conversation thread states and run payloads. Vinkius routes these requests through ephemeral, stateless sandboxes. No persistent storage exists at the routing layer, guaranteeing your proprietary agent debates remain private.

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