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Agents, supercharged - Multi-agents, External tools, and more

What is an agent?

If you’re new to transformers.agents, make sure to first read the main agents documentation.

In this page we’re going to highlight several advanced uses of transformers.agents.

Multi-agents

Multi-agent has been introduced in Microsoft’s framework Autogen. It simply means having several agents working together to solve your task instead of only one. It empirically yields better performance on most benchmarks. The reason for this better performance is conceptually simple: for many tasks, rather than using a do-it-all system, you would prefer to specialize units on sub-tasks. Here, having agents with separate tool sets and memories allows to achieve efficient specialization.

You can easily build hierarchical multi-agent systems with transformers.agents.

To do so, encapsulate the agent in a ManagedAgent object. This object needs arguments agent, name, and a description, which will then be embedded in the manager agent’s system prompt to let it know how to call this managed agent, as we also do for tools.

Here’s an example of making an agent that managed a specific web search agent using our DuckDuckGoSearchTool:

from transformers.agents import ReactCodeAgent, HfApiEngine, DuckDuckGoSearchTool, ManagedAgent

llm_engine = HfApiEngine()

web_agent = ReactCodeAgent(tools=[DuckDuckGoSearchTool()], llm_engine=llm_engine)

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

manager_agent = ReactCodeAgent(
    tools=[], llm_engine=llm_engine, managed_agents=[managed_web_agent]
)

manager_agent.run("Who is the CEO of Hugging Face?")

For an in-depth example of an efficient multi-agent implementation, see how we pushed our multi-agent system to the top of the GAIA leaderboard.

Advanced tool usage

Directly define a tool by subclassing Tool, and share it to the Hub

Let’s take again the tool example from main documentation, for which we had implemented a tool decorator.

If you need to add variation, like custom attributes for your too, you can build your tool following the fine-grained method: building a class that inherits from the Tool superclass.

The custom tool needs:

An attribute name, which corresponds to the name of the tool itself. The name usually describes what the tool does. Since the code returns the model with the most downloads for a task, let’s name is model_download_counter.
An attribute description is used to populate the agent’s system prompt.
An inputs attribute, which is a dictionary with keys "type" and "description". It contains information that helps the Python interpreter make educated choices about the input.
An output_type attribute, which specifies the output type.
A forward method which contains the inference code to be executed.

The types for both inputs and output_type should be amongst Pydantic formats.

from transformers import Tool
from huggingface_hub import list_models

class HFModelDownloadsTool(Tool):
    name = "model_download_counter"
    description = """
    This is a tool that returns the most downloaded model of a given task on the Hugging Face Hub.
    It returns the name of the checkpoint."""

    inputs = {
        "task": {
            "type": "string",
            "description": "the task category (such as text-classification, depth-estimation, etc)",
        }
    }
    output_type = "string"

    def forward(self, task: str):
        model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
        return model.id

Now that the custom HfModelDownloadsTool class is ready, you can save it to a file named model_downloads.py and import it for use.

from model_downloads import HFModelDownloadsTool

tool = HFModelDownloadsTool()

You can also share your custom tool to the Hub by calling push_to_hub() on the tool. Make sure you’ve created a repository for it on the Hub and are using a token with read access.

tool.push_to_hub("{your_username}/hf-model-downloads")

Load the tool with the ~Tool.load_tool function and pass it to the tools parameter in your agent.

from transformers import load_tool, CodeAgent

model_download_tool = load_tool("m-ric/hf-model-downloads")

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:

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.

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'
)

The model adequately leverages the tool:

======== New task ========
Improve this prompt, then generate an image of it.
You have been provided with these initial arguments: {'prompt': 'A rabbit wearing a space suit'}.
==== Agent is executing the code below:
improved_prompt = StableDiffusionPromptGenerator(query=prompt)
while improved_prompt == "QUEUE_FULL":
    improved_prompt = StableDiffusionPromptGenerator(query=prompt)
print(f"The improved prompt is {improved_prompt}.")
image = image_generator(prompt=improved_prompt)
====

Before finally generating the image:

gradio-tools require textual inputs and outputs even when working with different modalities like image and audio objects. Image and audio inputs and outputs are currently incompatible.

Use LangChain tools

We love Langchain and think it has a very compelling suite of tools. To import a tool from LangChain, use the from_langchain() method.

Here is how you can use it to recreate the intro’s search result using a LangChain web search tool.

from langchain.agents import load_tools
from transformers import Tool, ReactCodeAgent

search_tool = Tool.from_langchain(load_tools(["serpapi"])[0])

agent = ReactCodeAgent(tools=[search_tool])

agent.run("How many more blocks (also denoted as layers) in BERT base encoder than the encoder from the architecture proposed in Attention is All You Need?")

Display your agent run in a cool Gradio interface

You can leverage gradio.Chatbotto display your agent’s thoughts using stream_to_gradio, here is an example:

import gradio as gr
from transformers import (
    load_tool,
    ReactCodeAgent,
    HfApiEngine,
    stream_to_gradio,
)

# Import tool from Hub
image_generation_tool = load_tool("m-ric/text-to-image")

llm_engine = HfApiEngine("meta-llama/Meta-Llama-3-70B-Instruct")

# Initialize the agent with the image generation tool
agent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)


def interact_with_agent(task):
    messages = []
    messages.append(gr.ChatMessage(role="user", content=task))
    yield messages
    for msg in stream_to_gradio(agent, task):
        messages.append(msg)
        yield messages + [
            gr.ChatMessage(role="assistant", content="⏳ Task not finished yet!")
        ]
    yield messages


with gr.Blocks() as demo:
    text_input = gr.Textbox(lines=1, label="Chat Message", value="Make me a picture of the Statue of Liberty.")
    submit = gr.Button("Run illustrator agent!")
    chatbot = gr.Chatbot(
        label="Agent",
        type="messages",
        avatar_images=(
            None,
            "https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png",
        ),
    )
    submit.click(interact_with_agent, [text_input], [chatbot])

if __name__ == "__main__":
    demo.launch()

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