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147 changes: 102 additions & 45 deletions docs/docs/en/user-guide/runtime/autogen/websurfer.md
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Expand Up @@ -38,11 +38,11 @@ The example starts by importing the necessary modules from **AutoGen** and **Fas
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] !}
```python hl_lines="4 8"
{!> docs_src/user_guide/runtime/autogen/websurfer_tool.py [ln:1-9] !}
```

For Mesop applications, import `MesopUI` to integrate with the Mesop web interface.
To enhance existing agents with web surfing capability, import `WebSurferTool` from FastAgency and `ConversableAgent` from AutoGen.


#### 2. **Configure the Language Model (LLM)**
Expand All @@ -62,42 +62,52 @@ Here, the large language model is configured to use the `gpt-4o` model, and the

- **WebSurferAgent**: This agent functions as a web surfer, with built-in capability to browse the web and fetch real-time data as required.


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.
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_agent` agent with the necessary instructions when web surfing is required, and the `user_agent` 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:
In this step, we create two agents and a web surfer tool:

- **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.
- **ConversableAgent**: This is the conversable agent to which we will be adding web surfing capabilities.

- **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/)**.
- **WebSurferTool**: The tool that gives the ConversableAgent the ability to browse the web after it has been registered.

```python
{!> docs_src/user_guide/runtime/autogen/websurfer.py [ln:21-51] !}
{!> docs_src/user_guide/runtime/autogen/websurfer_tool.py [ln:20-45] !}
```

Now, we need to register the WebSurferAgent with a caller and executor. This setup allows the caller to use the WebSurferAgent for performing real-time web interactions.

```python hl_lines="2 3"
{!> docs_src/user_guide/runtime/autogen/websurfer_tool.py [ln:46-50] !}
```

The `executor` can be either a single instance of `ConversableAgent` or a `list of ConversableAgent` instances.

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

#### 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:40-48] !}
```
=== "Using WebSurferAgent"

```python
{! docs_src/user_guide/runtime/autogen/websurfer.py [ln:39-48] !}
```

=== "Enhancing an existing agent"

```python
{! docs_src/user_guide/runtime/autogen/websurfer_tool.py [ln:51-60] !}
```

#### 5. **Create and Run the Application**
Finally, we create the FastAgency application and launch it using the console interface.
Expand All @@ -109,50 +119,97 @@ Finally, we create the FastAgency application and launch it using the console in
### Complete Application Code

<details>
<summary>websurfer.py</summary>
<summary>websurfer_agent.py</summary>
```python
{! docs_src/user_guide/runtime/autogen/websurfer.py!}
```
</details>

<details>
<summary>websurfer_tool.py</summary>
```python
{! docs_src/user_guide/runtime/autogen/websurfer_tool.py!}
```
</details>


### Running the Application

```bash
fastagency run websurfer.py
```

=== "Using WebSurferAgent"

```bash
fastagency run websurfer_agent.py
```

=== "Enhancing an existing agent"

```bash
fastagency run websurfer_tool.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 │
│ │
╰──────────────────────╯
=== "Using WebSurferAgent"

[INFO] Importing autogen.base.py
```console
╭── Python module file ───╮
│ │
│ 🐍 websurfer_agent.py │
│ │
╰─────────────────────────╯

[INFO] Importing autogen.base.py

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

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

╭─ Importable FastAgency app ─╮
│ │
│ from websurfer import app │
│ │
╰─────────────────────────────╯
=== "Enhancing an existing agent"

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

[INFO] Importing autogen.base.py

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

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

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

In the prompt, type **Search for information about Microsoft AutoGen and summarize the results** and press Enter.

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62 changes: 62 additions & 0 deletions docs/docs_src/user_guide/runtime/autogen/websurfer_tool.py
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import os

from autogen import UserProxyAgent
from autogen.agentchat import ConversableAgent

from fastagency import UI, FastAgency
from fastagency.runtime.autogen import AutoGenWorkflows
from fastagency.runtime.autogen.tools import WebSurferTool
from fastagency.ui.console import ConsoleUI

llm_config = {
"config_list": [
{
"model": "gpt-4o",
"api_key": os.getenv("OPENAI_API_KEY"),
}
],
"temperature": 0.0,
}

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
) -> str:
user_agent = UserProxyAgent(
name="User_Agent",
system_message="You are a user agent",
llm_config=llm_config,
human_input_mode="NEVER",
)
assistant_agent = ConversableAgent(
name="Assistant_Agent",
system_message="You are a useful assistant",
llm_config=llm_config,
human_input_mode="NEVER",
)

web_surfer = WebSurferTool(
name_prefix="Web_Surfer",
llm_config=llm_config,
summarizer_llm_config=llm_config,
)

web_surfer.register(
caller=assistant_agent,
executor=user_agent,
)

chat_result = user_agent.initiate_chat(
assistant_agent,
message=initial_message,
summary_method="reflection_with_llm",
max_turns=3,
)

return chat_result.summary # type: ignore[no-any-return]


app = FastAgency(wf=wf, ui=ConsoleUI())
28 changes: 28 additions & 0 deletions tests/docs_src/user_guide/runtime/autogen/test_websurfer_tool.py
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import pytest
from typer.testing import CliRunner

from fastagency.cli import app
from tests.conftest import InputMock

runner = CliRunner()

INPUT_MESSAGE = (
"Search for information about Microsoft AutoGen and summarize the results,"
)


@pytest.mark.openai
def test_main(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setattr("builtins.input", InputMock([INPUT_MESSAGE]))

result = runner.invoke(
app,
[
"run",
"docs/docs_src/user_guide/runtime/autogen/websurfer_tool.py",
"--single-run",
],
)
assert result.exit_code == 0
# assert INPUT_MESSAGE in result.stdout
assert "workflow -> user [workflow_completed]" in result.stdout

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