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‎docs/SmolaAgents/01_multistepagent.md‎

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# Chapter 1: The MultiStepAgent - Your Task Orchestrator
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Welcome to the SmolaAgents library! If you're looking to build smart AI agents that can tackle complex problems, you're in the right place.

‎docs/SmolaAgents/02_model_interface.md‎

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# Chapter 2: Model Interface - Your Agent's Universal Translator
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Welcome back! In [Chapter 1: The MultiStepAgent - Your Task Orchestrator](01_multistepagent.md), we met the `MultiStepAgent`, our AI project manager. We learned that it follows a "Think -> Act -> Observe" cycle to solve tasks. A crucial part of the "Think" phase is consulting its "brain" – a Large Language Model (LLM).

‎docs/SmolaAgents/03_tool.md‎

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# Chapter 3: Tool - Giving Your Agent Superpowers
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Welcome back! In [Chapter 2: Model Interface](02_model_interface.md), we learned how our `MultiStepAgent` uses a "universal remote" (the Model Interface) to talk to its LLM "brain". The LLM thinks and suggests what the agent should do next.

‎docs/SmolaAgents/04_agentmemory.md‎

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# Chapter 4: AgentMemory - The Agent's Notepad
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Welcome back! In [Chapter 3: Tool](03_tool.md), we equipped our agent with "superpowers" – tools like web search or calculators that let it interact with the world and perform actions. We saw how the agent's "brain" (the LLM) decides which tool to use, and the agent executes it.

‎docs/SmolaAgents/05_prompttemplates.md‎

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# Chapter 5: PromptTemplates - Crafting Your Agent's Script
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Welcome back! In [Chapter 4: AgentMemory](04_agentmemory.md), we learned how our agent uses its "logbook" (`AgentMemory`) to remember the task, its past actions, and observations. This memory is crucial for deciding the next step.

‎docs/SmolaAgents/06_pythonexecutor.md‎

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# Chapter 6: PythonExecutor - Running Code Safely
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Welcome back! In [Chapter 5: PromptTemplates](05_prompttemplates.md), we saw how agents use templates to create clear instructions for their LLM brain. These instructions often involve asking the LLM to generate code, especially for agents like `CodeAgent`, which are designed to solve problems by writing and running Python.

‎docs/SmolaAgents/07_agenttype.md‎

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# Chapter 7: AgentType - Handling More Than Just Text
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Welcome back! In the previous chapters, especially when discussing [Tools](03_tool.md) and the [PythonExecutor](06_pythonexecutor.md), we saw how agents can perform actions and generate results. So far, we've mostly focused on text-based tasks and results.

‎docs/SmolaAgents/08_agentlogger___monitor.md‎

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# Chapter 8: AgentLogger & Monitor - Observing Your Agent in Action
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Welcome to the final chapter of the SmolaAgents tutorial! In [Chapter 7: AgentType](07_agenttype.md), we saw how `SmolaAgents` handles different kinds of data like text, images, and audio using specialized containers. Now that our agent can perform complex tasks ([Chapter 1: MultiStepAgent](01_multistepagent.md)), use various [Tools](03_tool.md), remember its progress ([Chapter 4: AgentMemory](04_agentmemory.md)), and even handle diverse data types, a new question arises: **How do we actually see what the agent is doing?**
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**Example Output (Simulated)**
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The `AgentLogger` uses `rich` to make the output colorful and easy to read. Here’s a simplified idea of what you might see in your console for our "Capital and Weather" example:
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The `AgentLogger` uses `rich` to make the output colorful and easy to read. Here's a simplified idea of what you might see in your console for our "Capital and Weather" example:
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```console
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╭─[bold] New run ─ ToolCallingAgent [/bold]────────────────────────────────╮

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