smolagents/docs/source/quicktour.md

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# Agents and tools

[[open-in-colab]]

### What is an agent?

Large Language Models (LLMs) trained to perform [causal language modeling](./tasks/language_modeling) can tackle a wide range of tasks, but they often struggle with basic tasks like logic, calculation, and search. When prompted in domains in which they do not perform well, they often fail to generate the answer we expect them to.

One approach to overcome this weakness is to create an *agent*.

An agent is a system that uses an LLM as its engine, and it has access to functions called *tools*.

These *tools* are functions for performing a task, and they contain all necessary description for the agent to properly use them.

For example, here is how a Code agent with access to a `web_search` tool would work its way through the following question.

```py3
agent.run(
"""How many more blocks (also denoted as layers) are there in BERT base encoder than in the encoder from the architecture proposed in Attention is All You Need?"""
)
```
```text
=====New task=====
How many more blocks (also denoted as layers) are there in BERT base encoder than in the encoder from the architecture proposed in Attention is All You Need?
====Agent is executing the code below:
bert_blocks = web_search(query="number of blocks in BERT base encoder")
print("BERT blocks:", bert_blocks)
====
Print outputs:
BERT blocks: twelve encoder blocks

====Agent is executing the code below:
attention_layer = web_search(query="number of layers in Attention is All You Need")
print("Attention layers:", attention_layer)
====
Print outputs:
Attention layers: Encoder: The encoder is composed of a stack of N = 6 identical layers. Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position- 2 Page 3 Figure 1: The Transformer - model architecture.

====Agent is executing the code below:
bert_blocks = 12
attention_layers = 6
diff = bert_blocks - attention_layers
print("Difference in blocks:", diff)
final_answer(diff)
====

Print outputs:
Difference in blocks: 6

Final answer: 6
```

### How can I build an agent?

To initialize an agent, you need these arguments:

- an LLM to power your agent - the agent is not exactly the LLM, it’s more like the agent is a program that uses an LLM as its engine.
- a system prompt: what the LLM engine will be prompted with to generate its output
- a toolbox from which the agent pick tools to execute
- a parser to extract from the LLM output which tools are to call and with which arguments

Upon initialization of the agent system, the tool attributes are used to generate a tool description, then baked into the agent’s `system_prompt` to let it know which tools it can use and why.

To start with, please install the `agents` extras in order to install all default dependencies.

```bash
pip install agents
```

Build your LLM engine by defining a `llm_engine` method which accepts a list of [messages](./chat_templating) and returns text. This callable also needs to accept a `stop` argument that indicates when to stop generating.

```python
from huggingface_hub import login, InferenceClient

login("<YOUR_HUGGINGFACEHUB_API_TOKEN>")

model_id = "Qwen/Qwen2.5-72B-Instruct"

client = InferenceClient(model=model_id)

def llm_engine(messages, stop_sequences=["Task"]) -> str:
    response = client.chat_completion(messages, stop=stop_sequences, max_tokens=1000)
    answer = response.choices[0].message.content
    return answer
```

You could use any `llm_engine` method as long as:
1. it follows the [messages format](./chat_templating) (`List[Dict[str, str]]`) for its input `messages`, and it returns a `str`.
2. it stops generating outputs at the sequences passed in the argument `stop_sequences`

Additionally, `llm_engine` can also take a `grammar` argument. In the case where you specify a `grammar` upon agent initialization, this argument will be passed to the calls to llm_engine, with the `grammar` that you defined upon initialization, to allow [constrained generation](https://huggingface.co/docs/text-generation-inference/conceptual/guidance) in order to force properly-formatted agent outputs.

You will also need a `tools` argument which accepts a list of `Tools` - it can be an empty list. You can also add the default toolbox on top of your `tools` list by defining the optional argument `add_base_tools=True`.

Now you can create an agent, like [`CodeAgent`], and run it. You can also create a [`TransformersEngine`] with a pre-initialized pipeline to run inference on your local machine using `transformers`.
For convenience, since agentic behaviours generally require strong models that are harder to run locally for now, we also provide the [`HfApiEngine`] class that initializes a `huggingface_hub.InferenceClient` under the hood. 

```python
from agents import CodeAgent, HfApiEngine

llm_engine = HfApiEngine(model=model_id)
agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)

agent.run(
    "Could you translate this sentence from French, say it out loud and return the audio.",
    sentence="Où est la boulangerie la plus proche?",
)
```

This will be handy in case of emergency baguette need!
You can even leave the argument `llm_engine` undefined, and an [`HfApiEngine`] will be created by default.

```python
from agents import CodeAgent

agent = CodeAgent(tools=[], add_base_tools=True)

agent.run(
    "Could you translate this sentence from French, say it out loud and give me the audio.",
    sentence="Où est la boulangerie la plus proche?",
)
```

Note that we used an additional `sentence` argument: you can pass text as additional arguments to the model.

You can also use this to indicate the path to local or remote files for the model to use:

```py
from agents import CodeAgent

agent = CodeAgent(tools=[], llm_engine=llm_engine, add_base_tools=True)

agent.run("Why does Mike not know many people in New York?", audio="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/recording.mp3")
```


The prompt and output parser were automatically defined, but you can easily inspect them by calling the `system_prompt_template` on your agent.

```python
print(agent.system_prompt_template)
```

It's important to explain as clearly as possible the task you want to perform.
Every [`~Agent.run`] operation is independent, and since an agent is powered by an LLM, minor variations in your prompt might yield completely different results.
You can also run an agent consecutively for different tasks: each time the attributes `agent.task` and `agent.logs` will be re-initialized.


#### Code execution

A Python interpreter executes the code on a set of inputs passed along with your tools.
This should be safe because the only functions that can be called are the tools you provided (especially if it's only tools by Hugging Face) and the print function, so you're already limited in what can be executed.

The Python interpreter also doesn't allow imports by default outside of a safe list, so all the most obvious attacks shouldn't be an issue.
You can still authorize additional imports by passing the authorized modules as a list of strings in argument `additional_authorized_imports` upon initialization of your [`CodeAgent`] or [`CodeAgent`]:

```py
from agents import CodeAgent

agent = CodeAgent(tools=[], additional_authorized_imports=['requests', 'bs4'])
agent.run("Could you get me the title of the page at url 'https://huggingface.co/blog'?")
```
This gives you at the end of the agent run:
```text
'Hugging Face – Blog'
```
The execution will stop at any code trying to perform an illegal operation or if there is a regular Python error with the code generated by the agent.

> [!WARNING]
> The LLM can generate arbitrary code that will then be executed: do not add any unsafe imports!

### The system prompt

An agent, or rather the LLM that drives the agent, generates an output based on the system prompt. The system prompt can be customized and tailored to the intended task. For example, check the system prompt for the [`CodeAgent`] (below version is slightly simplified).

```text
You will be given a task to solve as best you can.
You have access to the following tools:
{{tool_descriptions}}

To solve the task, you must plan forward to proceed in a series of steps, in a cycle of 'Thought:', 'Code:', and 'Observation:' sequences.

At each step, in the 'Thought:' sequence, you should first explain your reasoning towards solving the task, then the tools that you want to use.
Then in the 'Code:' sequence, you should write the code in simple Python. The code sequence must end with '/End code' sequence.
During each intermediate step, you can use 'print()' to save whatever important information you will then need.
These print outputs will then be available in the 'Observation:' field, for using this information as input for the next step.

In the end you have to return a final answer using the `final_answer` tool.

Here are a few examples using notional tools:
---
{examples}

Above example were using notional tools that might not exist for you. You only have acces to those tools:
{{tool_names}}
You also can perform computations in the python code you generate.

Always provide a 'Thought:' and a 'Code:\n```py' sequence ending with '```<end_code>' sequence. You MUST provide at least the 'Code:' sequence to move forward.

Remember to not perform too many operations in a single code block! You should split the task into intermediate code blocks.
Print results at the end of each step to save the intermediate results. Then use final_answer() to return the final result.

Remember to make sure that variables you use are all defined.

Now Begin!
```

The system prompt includes:
- An *introduction* that explains how the agent should behave and what tools are.
- A description of all the tools that is defined by a `{{tool_descriptions}}` token that is dynamically replaced at runtime with the tools defined/chosen by the user.
    - The tool description comes from the tool attributes, `name`, `description`, `inputs` and `output_type`,  and a simple `jinja2` template that you can refine.
- The expected output format.

You could improve the system prompt, for example, by adding an explanation of the output format.

For maximum flexibility, you can overwrite the whole system prompt template by passing your custom prompt as an argument to the `system_prompt` parameter.

```python
from agents import JsonAgent, PythonInterpreterTool, JSON_SYSTEM_PROMPT

modified_prompt = JSON_SYSTEM_PROMPT

agent = JsonAgent(tools=[PythonInterpreterTool()], system_prompt=modified_prompt)
```

> [!WARNING]
> Please make sure to define the `{{tool_descriptions}}` string somewhere in the `template` so the agent is aware 
of the available tools.


### Inspecting an agent run

Here are a few useful attributes to inspect what happened after a run:
- `agent.logs` stores the fine-grained logs of the agent. At every step of the agent's run, everything gets stored in a dictionary that then is appended to `agent.logs`.
- Running `agent.write_inner_memory_from_logs()` creates an inner memory of the agent's logs for the LLM to view, as a list of chat messages. This method goes over each step of the log and only stores what it's interested in as a message: for instance, it will save the system prompt and task in separate messages, then for each step it will store the LLM output as a message, and the tool call output as another message. Use this if you want a higher-level view of what has happened - but not every log will be transcripted by this method.

## Tools

A tool is an atomic function to be used by an agent.

You can for instance check the [`PythonInterpreterTool`]: it has a name, a description, input descriptions, an output type, and a `__call__` method to perform the action.

When the agent is initialized, the tool attributes are used to generate a tool description which is baked into the agent's system prompt. This lets the agent know which tools it can use and why.

### Default toolbox

Transformers comes with a default toolbox for empowering agents, that you can add to your agent upon initialization with argument `add_base_tools = True`:

- **DuckDuckGo web search***: performs a web search using DuckDuckGo browser.
- **Python code interpreter**: runs your the LLM generated Python code in a secure environment. This tool will only be added to [`JsonAgent`] if you initialize it with `add_base_tools=True`, since code-based agent can already natively execute Python code

You can manually use a tool by calling the [`load_tool`] function and a task to perform.

```python
from transformers import load_tool

search_tool = load_tool("web_search")
print(search_tool("Who's the current president of Russia?"))
```


### Create a new tool

You can create your own tool for use cases not covered by the default tools from Hugging Face.
For example, let's create a tool that returns the most downloaded model for a given task from the Hub.

You'll start with the code below.

```python
from huggingface_hub import list_models

task = "text-classification"

model = next(iter(list_models(filter=task, sort="downloads", direction=-1)))
print(model.id)
```

This code can quickly be converted into a tool, just by wrapping it in a function and adding the `tool` decorator:


```py
from transformers import tool

@tool
def model_download_tool(task: str) -> str:
    """
    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.

    Args:
        task: The task for which
    """
    model = next(iter(list_models(filter="text-classification", sort="downloads", direction=-1)))
    return model.id
```

The function needs:
- A clear name. The name usually describes what the tool does. Since the code returns the model with the most downloads for a task, let's put `model_download_tool`.
- Type hints on both inputs and output
- A description, that includes an 'Args:' part where each argument is described (without a type indication this time, it will be pulled from the type hint).
All these will be automatically baked into the agent's system prompt upon initialization: so strive to make them as clear as possible!

> [!TIP]
> This definition format is the same as tool schemas used in `apply_chat_template`, the only difference is the added `tool` decorator: read more on our tool use API [here](https://huggingface.co/blog/unified-tool-use#passing-tools-to-a-chat-template).

Then you can directly initialize your agent:
```py
from agents import CodeAgent
agent = CodeAgent(tools=[model_download_tool], llm_engine=llm_engine)
agent.run(
    "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?"
)
```

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_tool(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."`

## Multi-agents

Multi-agent has been introduced in Microsoft's framework [Autogen](https://huggingface.co/papers/2308.08155).
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 `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`]:

```py
from agents import CodeAgent, HfApiEngine, DuckDuckGoSearchTool, ManagedAgent

llm_engine = HfApiEngine()

web_agent = CodeAgent(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 = CodeAgent(
    tools=[], llm_engine=llm_engine, managed_agents=[managed_web_agent]
)

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

> [!TIP]
> 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](https://huggingface.co/blog/beating-gaia).


## Display your agent run in a cool Gradio interface

You can leverage `gradio.Chatbot` to display your agent's thoughts using `stream_to_gradio`, here is an example:

```py
import gradio as gr
from transformers import (
    load_tool,
    CodeAgent,
    HfApiEngine,
    stream_to_gradio,
)

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

llm_engine = HfApiEngine(model_id)

# Initialize the agent with the image generation tool
agent = CodeAgent(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()
```