joaopauloschuler · joaopauloschuler · Jan 12, 2025 · Jan 9, 2025 · Jan 9, 2025 · Jan 9, 2025
diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml
@@ -13,7 +13,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v2
         with:
-          python-version: "3.10"
+          python-version: "3.12"
 
       # Setup venv
       # TODO: revisit when https://github.com/astral-sh/uv/issues/1526 is addressed.

diff --git a/README.md b/README.md
@@ -38,6 +38,8 @@ limitations under the License.
 
 🌐 **Support for any LLM**: it supports models hosted on the Hub loaded in their `transformers` version or through our inference API, but also supports models from OpenAI, Anthropic and many others via our [LiteLLM](https://www.litellm.ai/) integration.
 
+Full documentation can be found [here](https://huggingface.co/docs/smolagents/index).
+
 > [!NOTE]  
 > Check the our [launch blog post](https://huggingface.co/blog/smolagents) to learn more about `smolagents`!
 

diff --git a/docs/source/en/examples/multiagents.md b/docs/source/en/examples/multiagents.md
@@ -48,10 +48,10 @@ Run the line below to install the required dependencies:
 
 Let's login in order to call the HF Inference API:
 
-```py
-from huggingface_hub import notebook_login
+```
+from huggingface_hub import login
 
-notebook_login()
+login()
 ```
 
 ⚡️ Our agent will be powered by [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) using `HfApiModel` class that uses HF's Inference API: the Inference API allows to quickly and easily run any OS model.

diff --git a/docs/source/en/examples/rag.md b/docs/source/en/examples/rag.md
@@ -137,7 +137,7 @@ _Note:_ The Inference API hosts models based on various criteria, and deployed m
 from smolagents import HfApiModel, CodeAgent
 
 agent = CodeAgent(
-    tools=[retriever_tool], model=HfApiModel("meta-llama/Llama-3.3-70B-Instruct"), max_steps=4, verbose=True
+    tools=[retriever_tool], model=HfApiModel("meta-llama/Llama-3.3-70B-Instruct"), max_steps=4, verbosity_level=2
 )
 ```
 

diff --git a/docs/source/en/guided_tour.md b/docs/source/en/guided_tour.md
@@ -113,8 +113,7 @@ The Python interpreter also doesn't allow imports by default outside of a safe l
 You can authorize additional imports by passing the authorized modules as a list of strings in argument `additional_authorized_imports` upon initialization of your [`CodeAgent`]:
 
 ```py
-from smolagents import CodeAgent
-
+model = HfApiModel()
 agent = CodeAgent(tools=[], model=model, additional_authorized_imports=['requests', 'bs4'])
 agent.run("Could you get me the title of the page at url 'https://huggingface.co/blog'?")
 ```
@@ -161,15 +160,15 @@ When the agent is initialized, the tool attributes are used to generate a tool d
 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 [`ToolCallingAgent`] if you initialize it with `add_base_tools=True`, since code-based agent can already natively execute Python code
+- **Python code interpreter**: runs your LLM generated Python code in a secure environment. This tool will only be added to [`ToolCallingAgent`] if you initialize it with `add_base_tools=True`, since code-based agent can already natively execute Python code
 - **Transcriber**: a speech-to-text pipeline built on Whisper-Turbo that transcribes an audio to text.
 
-You can manually use a tool by calling the [`load_tool`] function and a task to perform.
+You can manually use a tool by calling it with its arguments.
 
 ```python
-from smolagents import load_tool
+from smolagents import DuckDuckGoSearchTool
 
-search_tool = load_tool("web_search")
+search_tool = DuckDuckGoSearchTool()
 print(search_tool("Who's the current president of Russia?"))
 ```
 

diff --git a/docs/source/en/index.md b/docs/source/en/index.md
@@ -15,7 +15,7 @@ rendered properly in your Markdown viewer.
 
 # `smolagents`
 
-This library is the simplest framework out there to build powerful agents! By the way, wtf are "agents"? We provide our definition [in this page](conceptual_guides/intro_agents), whe're you'll also find tips for when to use them or not (spoilers: you'll often be better off without agents).
+This library is the simplest framework out there to build powerful agents! By the way, wtf are "agents"? We provide our definition [in this page](conceptual_guides/intro_agents), where you'll also find tips for when to use them or not (spoilers: you'll often be better off without agents).
 
 This library offers:
 

diff --git a/docs/source/en/reference/tools.md b/docs/source/en/reference/tools.md
@@ -39,10 +39,6 @@ contains the API docs for the underlying classes.
 
 [[autodoc]] Tool
 
-### Toolbox
-
-[[autodoc]] Toolbox
-
 ### launch_gradio_demo
 
 [[autodoc]] launch_gradio_demo

diff --git a/docs/source/en/tutorials/secure_code_execution.md b/docs/source/en/tutorials/secure_code_execution.md
@@ -30,7 +30,7 @@ Code is just a better way to express actions on a computer. It has better:
 - **Composability:** could you nest JSON actions within each other, or define a set of JSON actions to re-use later, the same way you could just define a python function?
 - **Object management:** how do you store the output of an action like `generate_image` in JSON?
 - **Generality:** code is built to express simply anything you can do have a computer do.
-- **Representation in LLM training corpuses:** why not leverage this benediction of the sky that plenty of quality actions have already been included in LLM training corpuses?
+- **Representation in LLM training corpus:** why not leverage this benediction of the sky that plenty of quality actions have already been included in LLM training corpus?
 
 This is illustrated on the figure below, taken from [Executable Code Actions Elicit Better LLM Agents](https://huggingface.co/papers/2402.01030).
 

diff --git a/docs/source/en/tutorials/tools.md b/docs/source/en/tutorials/tools.md
@@ -177,7 +177,7 @@ agent.run("How many more blocks (also denoted as layers) are in BERT base encode
 
 ### Manage your agent's toolbox
 
-You can manage an agent's toolbox by adding or replacing a tool.
+You can manage an agent's toolbox by adding or replacing a tool in attribute `agent.tools`, since it is a standard dictionary.
 
 Let's add the `model_download_tool` to an existing agent initialized with only the default toolbox.
 
@@ -187,7 +187,7 @@ from smolagents import HfApiModel
 model = HfApiModel("Qwen/Qwen2.5-Coder-32B-Instruct")
 
 agent = CodeAgent(tools=[], model=model, add_base_tools=True)
-agent.toolbox.add_tool(model_download_tool)
+agent.tools[model_download_tool.name] = model_download_tool
 ```
 Now we can leverage the new tool:
 
@@ -202,11 +202,6 @@ agent.run(
 > Beware of not adding too many tools to an agent: this can overwhelm weaker LLM engines.
 
 
-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.