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

Q: Can LlamaIndex search the conversation history of LangGraph Cloud (Stateful AI Agents)?

Your agent uses listthreads to find active conversations. It then pulls the data with getthreadstate and indexes the text into your vector database for semantic search.

Q: How do I find out which assistants are deployed?

Execute the listassistants tool. It returns the available graph configurations so your RAG application knows exactly what remote capabilities exist.

Q: Does LangGraph Cloud (Stateful AI Agents) let LlamaIndex interrupt long tasks?

You interrupt active tasks by calling cancelrun. This stops the remote execution immediately and frees up your agent to pursue a different query.

Index remote agent memory directly into your LlamaIndex RAG applications.

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Connect LangGraph Cloud (Stateful AI Agents) MCP to LlamaIndex

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

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State Extraction and Indexing

The `get_thread_state` tool pulls the current messages array and structured outputs from your remote graphs. LlamaIndex takes those raw variables and embeds them straight into your vector store. You stop querying static documents and start querying live agent memory. Tracking execution history works the same way through this MCP integration. Calling `list_runs` retrieves the exact path a specific thread took. Your RAG application indexes that metadata, letting users ask questions like what steps the agent took to generate a report, and get grounded answers.

Triggering LlamaIndex Agent Runs

You use `list_assistants` to discover available graph configurations on the remote server. Your FunctionAgent reads those options and decides which one fits the user prompt. It then fires `create_run` to execute the payload. This MCP Server handles the remote execution while LlamaIndex manages the local reasoning. If the graph needs more time, your agent polls `get_run` to fetch status updates. You keep the heavy lifting on the remote infrastructure and the semantic search local.

Manual Overrides and Interruptions

RAG applications sometimes generate bad inputs that break remote workflows over the MCP Server connection. The `update_thread_state` tool lets your local application manually overwrite the graph variables to fix the error. You correct the trajectory without restarting the entire process. Runaway processes cost money. Your agent can call `cancel_run` to kill an ongoing execution if the user changes their mind. You also get visibility into automated tasks by checking `list_crons` to see what jobs are scheduled to run against your indexes.

Setup guide

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

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all LangGraph Cloud (Stateful AI Agents) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to LangGraph Cloud (Stateful AI Agents) tools.",
)
response = await agent.run("List recent LangGraph Cloud (Stateful AI Agents) data")

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

Install llama-index-tools-mcp and set up a BasicMCPClient. You convert the tools using McpToolSpec and pass them to your agent.

Your agent uses `list_threads` to find active conversations. It then pulls the data with `get_thread_state` and indexes the text into your vector database for semantic search.

Execute the `list_assistants` tool. It returns the available graph configurations so your RAG application knows exactly what remote capabilities exist.

You interrupt active tasks by calling `cancel_run`. This stops the remote execution immediately and frees up your agent to pursue a different query.

The server reads graph variables and structured outputs. Vinkius isolates this data flow inside a zero-trust sandbox environment. The connection drops immediately after the tool call finishes, leaving zero residual data on the host.

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