How to Use the AutoGen MCP in LlamaIndex

Index your AutoGen agent outputs into LlamaIndex for RAG-based knowledge retrieval.

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Connect AutoGen MCP to LlamaIndex

Create your Vinkius account to connect AutoGen 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 AutoGen with LlamaIndex

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Index AutoGen sessions for RAG

Turn your agent message history into a searchable knowledge base. Use LlamaIndex to query past decisions made by your agents. Run `list_messages` to grab the raw text. Then, push that data into your vector store to stop your agents from hallucinating.

Query available AutoGen skills

Use `list_skills` to see what Python functions your agents can call. LlamaIndex treats these skills as metadata for your index. Your agent learns what it can do by searching the list. This keeps your RAG application grounded in the tools that actually exist.

Ground AutoGen decisions in data

Connect your LlamaIndex knowledge base to your agent workflow. The agent checks your index before sending a `create_message` command. This forces the agents to debate based on your actual documents. You get consensus that reflects your company data.

Setup guide

Set up AutoGen 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 AutoGen 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 AutoGen tools.",
)
response = await agent.run("List recent AutoGen data")

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Common questions about AutoGen MCP in LlamaIndex

You call the tool methods via McpToolSpec to get the history. Then, you load that text directly into your LlamaIndex vector store.

They can, by treating the session history as a document source. You index the output of `list_messages` so your agents have context during future debates.

They do, as the MCP server exposes the same tool set to both. You just use the LlamaIndex adapter to transform the schema for your agent.

It stays in the ephemeral server sandbox until you pull it out. Once indexed, your vector store holds the information you chose to save.

Your session logs remain within the secure sandbox environment. Only you control which parts of the conversation are indexed or moved to your storage.

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