openai_text-embedding-ada-002/fixed_chunks/_agents.txt_chunk_4.txt

You get the following:
text
======== New task ========
Can you give me the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub?
==== Agent is executing the code below:
most_downloaded_model = model_download_counter(task="text-to-video")
print(f"The most downloaded model for the 'text-to-video' task is {most_downloaded_model}.")
====
And the output:
"The most downloaded model for the 'text-to-video' task is ByteDance/AnimateDiff-Lightning."
Manage agent toolbox
If you have already initialized an agent, it is inconvenient to reinitialize it from scratch with a tool you want to use. With Transformers, you can manage an agent's toolbox by adding or replacing a tool.
Let's add the model_download_tool to an existing agent initialized with only the default toolbox.
thon
from transformers import CodeAgent
agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)
agent.toolbox.add_tool(model_download_tool)

Now we can leverage both the new tool and the previous text-to-speech tool:
python
agent.run(
    "Can you read out loud the name of the model that has the most downloads in the 'text-to-video' task on the Hugging Face Hub and return the audio?"
)
| Audio                                                                                                                                            |
|------------------------------------------------------------------------------------------------------------------------------------------------------|
|  |

[!WARNING]
Beware when adding tools to an agent that already works well because it can bias selection towards your tool or select another tool other than the one already defined.

Use the agent.toolbox.update_tool() method to replace an existing tool in the agent's toolbox.
This is useful if your new tool is a one-to-one replacement of the existing tool because the agent already knows how to perform that specific task.
Just make sure the new tool follows the same API as the replaced tool or adapt the system prompt template to ensure all examples using the replaced tool are updated.
Use a collection of tools
You can leverage tool collections by using the ToolCollection object, with the slug of the collection you want to use.
Then pass them as a list to initialize you agent, and start using them!

from transformers import ToolCollection, ReactCodeAgent
image_tool_collection = ToolCollection(collection_slug="huggingface-tools/diffusion-tools-6630bb19a942c2306a2cdb6f")
agent = ReactCodeAgent(tools=[*image_tool_collection.tools], add_base_tools=True)
agent.run("Please draw me a picture of rivers and lakes.")

To speed up the start, tools are loaded only if called by the agent.
This gets you this image:

Use gradio-tools
gradio-tools is a powerful library that allows using Hugging
Face Spaces as tools. It supports many existing Spaces as well as custom Spaces.
Transformers supports gradio_tools with the [Tool.from_gradio] method. For example, let's use the StableDiffusionPromptGeneratorTool from gradio-tools toolkit for improving prompts to generate better images.
Import and instantiate the tool, then pass it to the Tool.from_gradio method:
thon
from gradio_tools import StableDiffusionPromptGeneratorTool
from transformers import Tool, load_tool, CodeAgent
gradio_prompt_generator_tool = StableDiffusionPromptGeneratorTool()
prompt_generator_tool = Tool.from_gradio(gradio_prompt_generator_tool)

Now you can use it just like any other tool. For example, let's improve the prompt  a rabbit wearing a space suit.
thon
image_generation_tool = load_tool('huggingface-tools/text-to-image')
agent = CodeAgent(tools=[prompt_generator_tool, image_generation_tool], llm_engine=llm_engine)
agent.run(
    "Improve this prompt, then generate an image of it.", prompt='A rabbit wearing a space suit'
)