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147 changes: 125 additions & 22 deletions docs/docs/en/user-guide/runtime/autogen/websurfer.md
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# WebSurfer Tool
# WebSurfer

The `WebSurferTool` from **FastAgency** lets developers create agents that search, navigate, and gather real-time web data. It supports workflows with live browsing, automatic data retrieval, and tasks requiring current web information, making it easy to add web capabilities to your AI agents.
FastAgency allows you to quickly create workflows with capabilities like live browsing, automatic data retrieval, and tasks requiring up-to-date web information, making it easy to integrate web functionality.

Let’s see how the `WebSurferTool` works with an example: “Search for information about Microsoft AutoGen and summarize the results,” highlighting its ability to browse and collect data in real time.
## Adding Web Surfing Capabilities to Agents

FastAgency provides two ways to add web surfing capabilities to agents. You can either:

1. Use a WebSurferAgent, which comes with built-in web surfing capabilities (recommended)
2. Enhance an existing agent with web surfing capability

In this guide, we'll demonstrate both methods with a real-world example. We’ll create a workflow where agents search the web for real-time data.

We’ll build agents and assign them the task: “Search for information about Microsoft AutoGen and summarize the results” to showcase its ability to browse and gather real-time data in action.

## Installation

Expand All @@ -12,7 +21,7 @@ Before getting started, make sure you have installed FastAgency with support for
pip install "fastagency[autogen]"
```

This installation includes the AutoGen runtime, allowing you to build multi-agent workflows and integrate external APIs seamlessly.
This command installs FastAgency with support for the Console interface and AutoGen framework.

## Example: Search for information about Microsoft AutoGen and summarize the results

Expand All @@ -21,47 +30,83 @@ This installation includes the AutoGen runtime, allowing you to build multi-agen
#### 1. **Import Required Modules**
The example starts by importing the necessary modules from **AutoGen** and **FastAgency**. These imports lay the foundation for building and running multi-agent workflows.

```python
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:1-9] !}
```
=== "Using WebSurferAgent"
```python hl_lines="7"
{!> docs_src/user_guide/runtime/autogen/websurfer.py [ln:1-9] !}
```

To create a new web surfing agent, simply import `WebSurferAgent`, which comes with built-in web surfing capabilities, and use it as needed.

=== "Enhancing an existing agent"
```python hl_lines="7"
{!> docs_src/user_guide/runtime/autogen/websurfer.py [ln:1-9] !}
```

For Mesop applications, import `MesopUI` to integrate with the Mesop web interface.


#### 2. **Configure the Language Model (LLM)**
Here, the large language model is configured to use the `gpt-4o` model, and the API key is retrieved from the environment. This setup ensures that both the user and websurfer agents can interact effectively.

```python
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:11-19] !}
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:10-19] !}
```

#### 3. **Define the Workflow and Agents**
In this step, we create two agents:

- **UserProxyAgent**: This agent simulates the user interacting with the system.
=== "Using WebSurferAgent"

- **ConversableAgent**: This agent serves as the websurfer and has access to the `WebSurferTool`, using it whenever real-time web data is needed.
In this step, we are going to create two agents:

- **WebSurferTool**: An instance of the `WebSurferTool` is registered with the caller as `ConversableAgent` and with the executor as `UserProxyAgent`. This setup allows the `ConversableAgent` to use the `WebSurferTool`, giving it the ability to perform real-time web interactions.
- **UserProxyAgent**: This agent simulates the user interacting with the system.

The workflow is registered using **[AutoGenWorkflows](../../../api/fastagency/runtime/autogen/AutoGenWorkflows/)**.
- **WebSurferAgent**: This agent functions as a web surfer, with built-in capability to browse the web and fetch real-time data as required.

```python
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:21-51] !}
```

The workflow is registered using **[AutoGenWorkflows](../../../api/fastagency/runtime/autogen/AutoGenWorkflows.md)**.

```python hl_lines="18"
{!> docs_src/user_guide/runtime/autogen/websurfer.py [ln:20-38] !}
```

When initiating the `WebSurferAgent`, the executor parameter must be provided. This can be either a single instance of `ConversableAgent` or a `list of ConversableAgent` instances.

The `WebSurferAgent` relies on the executor agent(s) to execute the web surfing tasks. In this example, the `web_surfer` agent will call the `user_proxy` agent with the necessary instructions when web surfing is required, and the `user_proxy` will execute those instructions.

=== "Enhancing an existing agent"
```python hl_lines="7"
{!> docs_src/user_guide/runtime/autogen/websurfer.py [ln:1-9] !}
```

In this step, we create two agents:

- **UserProxyAgent**: This agent simulates the user interacting with the system.

- **ConversableAgent**: This agent serves as the websurfer and has access to the `WebSurferTool`, using it whenever real-time web data is needed.

- **WebSurferTool**: An instance of the `WebSurferTool` is registered with the caller as `ConversableAgent` and with the executor as `UserProxyAgent`. This setup allows the `ConversableAgent` to use the `WebSurferTool`, giving it the ability to perform real-time web interactions.

The workflow is registered using **[AutoGenWorkflows](../../../api/fastagency/runtime/autogen/AutoGenWorkflows/)**.

```python
{!> docs_src/user_guide/runtime/autogen/websurfer.py [ln:21-51] !}
```

#### 4. **Enable Agent Interaction and Chat**
Here, the user agent starts a conversation with the websurfer agent, which performs a web search and returns summarized information. The conversation is then summarized using a method provided by the LLM.

```python
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:52-59] !}
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:40-48] !}
```

#### 5. **Create and Run the Application**
Finally, we create the FastAgency application and launch it using the console interface.

```python
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:62] !}
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:50] !}
```

## Complete Application Code
### Complete Application Code

<details>
<summary>websurfer.py</summary>
Expand All @@ -79,8 +124,66 @@ fastagency run websurfer.py

Ensure you have set your OpenAI API key in the environment. The command will launch a console interface where users can input their requests and interact with the websurfer agent.

---
### Output

Once you run it, FastAgency automatically detects the appropriate app to execute and runs it. The application will then prompt you with: "Please enter an initial message:".

```console
╭─ Python module file ─╮
│ │
│ 🐍 websurfer.py │
│ │
╰──────────────────────╯

[INFO] Importing autogen.base.py

╭─ Importable FastAgency app ─╮
│ │
│ from websurfer import app │
│ │
╰─────────────────────────────╯

╭─ FastAgency -> user [text_input] ────────────────────────────────────────────╮
│ │
│ Starting a new workflow 'simple_websurfer' with the following │
│ description: │
│ │
│ WebSurfer chat │
│ │
│ Please enter an initial message: │
╰──────────────────────────────────────────────────────────────────────────────╯
```

This example demonstrates the power of the **AutoGen** runtime within FastAgency, showing how easy it is to integrate LLM-powered agents with real-time API services. By leveraging FastAgency, developers can quickly create interactive, scalable applications that interact with external data sources in real-time.
In the prompt, type **Search for information about Microsoft AutoGen and summarize the results** and press Enter.

This will initiate the task, allowing you to see the real-time conversation between the agents as they collaborate to complete it. Once the task is finished, you’ll see an output similar to the one below.

```console
╭─ workflow -> user [workflow_completed] ──────────────────────────────────────╮
│ │
│ { │
│ "result": "Microsoft AutoGen is an open-source framework designed │
│ to simplify the orchestration, optimization, and automation of large │
│ language model (LLM) workflows. It features customizable agents, │
│ multi-agent conversations, tool integration, and human involvement, │
│ making it suitable for complex AI applications. Key resources include │
│ the Microsoft Research Blog and the GitHub repository for AutoGen." │
│ } │
╰──────────────────────────────────────────────────────────────────────────────╯

╭─ FastAgency -> user [text_input] ────────────────────────────────────────────╮
│ │
│ Starting a new workflow 'simple_websurfer' with the following │
│ description: │
│ │
│ WebSurfer chat │
│ │
│ Please enter an initial message: │
╰──────────────────────────────────────────────────────────────────────────────╯
```

The agent will summarize its findings and then prompt you again with "Please enter an initial message:", allowing you to continue the conversation with the web surfer agent.

---

For more detailed documentation, visit the [AutoGen Reference](../../../api/fastagency/runtime/autogen/AutoGenWorkflows/).
This example demonstrates the power of the AutoGen runtime within FastAgency, showcasing how easily LLM-powered agents can be integrated with browsing capabilities to fetch and process real-time information. By leveraging FastAgency, developers can quickly build interactive, scalable applications that interact with live data sources.
1 change: 0 additions & 1 deletion docs/docs_src/user_guide/runtime/autogen/websurfer.py
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wf = AutoGenWorkflows()


@wf.register(name="simple_websurfer", description="WebSurfer chat") # type: ignore[type-var]
def websurfer_workflow(
wf: AutoGenWorkflows, ui: UI, initial_message: str, session_id: str
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