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# Orchestrate a multi-agent system 🤖🤝🤖

[[open-in-colab]]

In this notebook we will make a **multi-agent web browser: an agentic system with several agents collaborating to solve problems using the web!**

It will be a simple hierarchy, using a `ManagedAgent` object to wrap the managed web search agent:

```
              +----------------+
              | Manager agent  |
              +----------------+
                       |
        _______________|______________
       |                              |
  Code interpreter   +--------------------------------+
       tool          |         Managed agent          |
                     |      +------------------+      |
                     |      | Web Search agent |      |
                     |      +------------------+      |
                     |         |            |         |
                     |  Web Search tool     |         |
                     |             Visit webpage tool |
                     +--------------------------------+
```
Let's set up this system. 

Run the line below to install the required dependencies:

```
!pip install markdownify duckduckgo-search smolagents --upgrade -q
```

Let's login in order to call the HF Inference API:

```py
from huggingface_hub import notebook_login

notebook_login()
```

⚡️ Our agent will be powered by [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) using `HfApiModel` class that uses HF's Inference API: the Inference API allows to quickly and easily run any OS model.

_Note:_ The Inference API hosts models based on various criteria, and deployed models may be updated or replaced without prior notice. Learn more about it [here](https://huggingface.co/docs/api-inference/supported-models).

```py
model = "Qwen/Qwen2.5-Coder-32B-Instruct"
```

### 🔍 Create a web search tool

For web browsing, we can already use our pre-existing [`DuckDuckGoSearchTool`](https://github.com/huggingface/smolagents/blob/main/src/smolagents/default_tools/search.py) tool to provide a Google search equivalent.

But then we will also need to be able to peak into the page found by the `DuckDuckGoSearchTool`.
To do so, we could import the library's built-in `VisitWebpageTool`, but we will build it again to see how it's done.

So let's create our `VisitWebpageTool` tool from scratch using `markdownify`.

```py
import re
import requests
from markdownify import markdownify
from requests.exceptions import RequestException
from smolagents import tool


@tool
def visit_webpage(url: str) -> str:
    """Visits a webpage at the given URL and returns its content as a markdown string.

    Args:
        url: The URL of the webpage to visit.

    Returns:
        The content of the webpage converted to Markdown, or an error message if the request fails.
    """
    try:
        # Send a GET request to the URL
        response = requests.get(url)
        response.raise_for_status()  # Raise an exception for bad status codes

        # Convert the HTML content to Markdown
        markdown_content = markdownify(response.text).strip()

        # Remove multiple line breaks
        markdown_content = re.sub(r"\n{3,}", "\n\n", markdown_content)

        return markdown_content

    except RequestException as e:
        return f"Error fetching the webpage: {str(e)}"
    except Exception as e:
        return f"An unexpected error occurred: {str(e)}"
```

Ok, now let's initialize and test our tool!

```py
print(visit_webpage("https://en.wikipedia.org/wiki/Hugging_Face")[:500])
```

## Build our multi-agent system 🤖🤝🤖

Now that we have all the tools `search` and `visit_webpage`, we can use them to create the web agent.

Which configuration to choose for this agent?
- Web browsing is a single-timeline task that does not require parallel tool calls, so JSON tool calling works well for that. We thus choose a `JsonAgent`.
- Also, since sometimes web search requires exploring many pages before finding the correct answer, we prefer to increase the number of `max_iterations` to 10.

```py
from smolagents import (
    CodeAgent,
    ToolCallingAgent,
    HfApiModel,
    ManagedAgent,
)
from smolagents.default_tools import DuckDuckGoSearchTool

model = HfApiModel(model)

web_agent = ToolCallingAgent(
    tools=[DuckDuckGoSearchTool(), visit_webpage],
    model=model,
    max_iterations=10,
)
```

We then wrap this agent into a `ManagedAgent` that will make it callable by its manager agent.

```py
managed_web_agent = ManagedAgent(
    agent=web_agent,
    name="search",
    description="Runs web searches for you. Give it your query as an argument.",
)
```

Finally we create a manager agent, and upon initialization we pass our managed agent to it in its `managed_agents` argument.

Since this agent is the one tasked with the planning and thinking, advanced reasoning will be beneficial, so a `CodeAgent` will be the best choice.

Also, we want to ask a question that involves the current year: so let us add `additional_authorized_imports=["time"]`

```py
manager_agent = CodeAgent(
    tools=[],
    model=model,
    managed_agents=[managed_web_agent],
    additional_authorized_imports=["time"],
)
```

That's all! Now let's run our system! We select a question that requires some calculation and 

```py
manager_agent.run("How many years ago was Stripe founded?")
```

Our agents managed to efficiently collaborate towards solving the task! ✅

💡 You can easily extend this to more agents: one does the code execution, one the web search, one handles file loadings...