How to Use the Lamha MCP in AutoGen

Q: How do I assign Lamha tools to different agents in AutoGen?

When you create an AssistantAgent, you pass it a specific list of Lamha's functions. For example, your logistics agent gets the createorder tool, while your inventory agent gets the listinventory tool.

Model your entire logistics team with AutoGen. Agents collaborate using Lamha's Arabic e-commerce tools to make decisions.

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Connect Lamha MCP to AutoGen

Create your Vinkius account to connect Lamha 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 Lamha with AutoGen

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Build a Multi-Agent Logistics Team

Stop writing monolithic code. With AutoGen, you create a team of agents that talk to each other. One agent, the 'Fulfillment Specialist,' has access to `create_order` and `cancel_order`. Another, the 'Inventory Manager,' uses `list_inventory` and `list_warehouses`. When a user wants to place an order, they talk to a 'User Proxy' agent. That agent then coordinates the conversation between the specialists to check stock and create the shipment. It’s like modeling your real-world operations team in software.

Debate and Decide on Carrier Choice

AutoGen lets your agents negotiate. A 'Finance Agent' can call `list_carriers` and argue for the cheapest shipping option. At the same time, a 'Service Agent' can check the same list and push for the carrier with the fastest delivery time. They debate the trade-offs in a group chat. The agents reach a consensus based on the rules you give them before one of them is permitted to call `create_order`. This models the complex decisions businesses have to make every day.

Use Agent Conversation to Handle Errors

Build more resilient systems. What happens if a call to `create_order` fails? In a multi-agent setup, the 'Fulfillment Specialist' can report the failure to the group. Another agent, the 'Support Engineer,' can then take over. That agent can use `get_order` to investigate the problem, or `check_city_coverage` to see if the address was the issue. This conversational approach to error handling means your system can diagnose and recover from problems automatically.

Setup guide

Set up Lamha 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 Lamha 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="Lamha_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent Lamha data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lamha. 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 Lamha MCP in AutoGen

When you create an `AssistantAgent`, you pass it a specific list of Lamha's functions. For example, your logistics agent gets the `create_order` tool, while your inventory agent gets the `list_inventory` tool.

Yes. A user proxy agent can initiate a task, then an inventory agent can confirm stock with `list_inventory`, and finally a fulfillment agent can place the order with `create_order`. They collaborate in a sequence of conversational turns.

You define termination conditions for the group chat. This can be a specific number of turns or when a required tool, like `create_order`, has been successfully called. This ensures the conversation leads to a concrete action.

AutoGen is a powerful framework for creating sophisticated agent-based systems. You can definitely build production services with it, using Lamha's MCP server as the tool provider for all your Arabic-speaking e-commerce logistics.

The conversation containing order details happens within your controlled environment. The Vinkius platform only sees the individual, stateless tool calls from your agents to the Lamha server. The context and history of the agent chat, which may contain sensitive order data, do not leave your system.

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