How to Use the Gupshup Messaging MCP in AutoGen

Q: How do I handle interactive messages in AutoGen?

Your agents call sendinteractivemessage when the conversation reaches a point that requires user input. They build the button JSON together before dispatching it.

Coordinate multi-agent debates in AutoGen using Gupshup Messaging to manage WhatsApp channel interactions.

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Connect Gupshup Messaging MCP to AutoGen

Create your Vinkius account to connect Gupshup Messaging 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 Gupshup Messaging with AutoGen

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Debate messaging strategy with AutoGen

Assign one agent to monitor WhatsApp templates using `get_template_info` while another agent prepares the content. They negotiate the message structure before calling `send_template_message`. This ensures that every message sent meets your specific quality standards. The agents challenge each other until they reach a consensus on the payload.

Consensus-driven communication

Use `send_text_message` and `send_media_message` as tools in a multi-agent conversation. One agent critiques the media choice, and another validates the text, preventing mistakes before the API call. It turns messaging into a collaborative process. You get the benefit of multiple perspectives checking your outgoing communication for accuracy.

Automated reaction and interaction

Your security agent can use `send_reaction` to acknowledge user input after a safety check. This adds a layer of automated verification to your messaging flow. It keeps the conversation moving without human intervention. The agents work together to ensure every reaction is appropriate for the context of the chat.

Setup guide

Set up Gupshup Messaging 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 Gupshup Messaging 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="Gupshup Messaging_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Gupshup Messaging 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="Gupshup Messaging_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Gupshup Messaging 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 Gupshup. 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 Gupshup Messaging MCP in AutoGen

Use the MCP tool adapter to register the server tools with your agents. Once registered, the agents can negotiate their use during the multi-agent chat.

Absolutely, the agents evaluate the tool results and discuss the next steps. They can challenge each other on which template to send or whether an interactive message is needed.

It fits well, as the tools are exposed to the agent pool. You can define specific roles for agents to handle template management or media delivery.

Your agents call `send_interactive_message` when the conversation reaches a point that requires user input. They build the button JSON together before dispatching it.

The MCP server operates with an ephemeral memory model. It handles the secure transmission of your WhatsApp API keys and does not persist any message data after the request is finished.

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