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 LangChain 10 tools — connect in under 2 minutes

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

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect LangGraph Cloud (Stateful AI Agents) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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 langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "langgraph-cloud-stateful-ai-agents": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using LangGraph Cloud (Stateful AI Agents), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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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.

LangChain's ecosystem of 500+ components combines seamlessly with LangGraph Cloud (Stateful AI Agents) through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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 LangChain 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 LangChain via MCP

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

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

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

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

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

The largest ecosystem of integrations, chains, and agents — combine LangGraph Cloud (Stateful AI Agents) MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across LangGraph Cloud (Stateful AI Agents) queries for multi-turn workflows

LangGraph Cloud (Stateful AI Agents) + LangChain Use Cases

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

RAG with live data: combine LangGraph Cloud (Stateful AI Agents) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query LangGraph Cloud (Stateful AI Agents), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain LangGraph Cloud (Stateful AI Agents) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every LangGraph Cloud (Stateful AI Agents) tool call, measure latency, and optimize your agent's performance

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

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

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

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

LangGraph Cloud (Stateful AI Agents) + LangChain FAQ

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

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.