In a short Python script, you can install smolagents, connect a model, create a CodeAgent, and ask it to calculate a sum. The agent needs a model even when it has no tools. For a first example, you’ll use the Hugging Face inference client integration and an empty tools list.
Install smolagents
The official quick-start installation command is:
pip install 'smolagents[toolkit]'The [toolkit] extra installs default tools, including web search. You don’t need a search tool for the arithmetic example below, but this installation follows the quick-start and makes tools available for the later example. For a minimal installation without the extra, see the official quick-start and its installation guidance.
Build and run your first CodeAgent
Save this as a Python file, such as first_agent.py:
from smolagents import CodeAgent, InferenceClientModel
model = InferenceClientModel()
agent = CodeAgent(tools=[], model=model)
result = agent.run("Calculate the sum of numbers from 1 to 10")
print(result)from smolagents import ...imports the agent class and a model adapter.InferenceClientModel()initializes the Hugging Face inference-client model integration. The quick-start’s short example leaves the model configuration at its default.CodeAgent(tools=[], model=model)creates an agent with a model and an empty list of tools. The task is a calculation the model can express in code, so no external tool is needed.agent.run(...)sends the task to the agent;print(result)displays the returned result.
The model integration must be usable in your environment for the run to work. The example illustrates the setup pattern; it does not guarantee that an unspecified default model will always be available or establish a particular response time, cost, or result quality.
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Understand what CodeAgent executes
A CodeAgent formulates actions as generated Python code. That means it can use programming constructs such as loops and conditionals to combine steps, rather than being limited to a single tool call. The alternative ToolCallingAgent expresses actions as structured, JSON-like tool calls. Choose between them based on the action format your application needs; the documentation does not establish a general quality or performance winner. See the agent API reference and the guided tour for the current class details.
Add a tool when the task needs external information
A tool gives the agent a capability that is not supplied by the model alone. For example, web search is appropriate when the task depends on current information, unlike the fixed arithmetic task. With the toolkit extra installed, the quick-start demonstrates adding DuckDuckGo search:
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from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel
model = InferenceClientModel()
agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)
result = agent.run("Find recent information about ...")
print(result)Replace the ellipsis with a specific query. The important change is the tools list: the agent receives a search tool at initialization and can use it while working on the task. Check the quick-start for the current tool setup and example prompt.
Choose a model integration
The quick-start uses InferenceClientModel for its first example. The overview also documents LiteLLMModel for API-accessible models and TransformersModel for local models. Optional package extras are used for some integrations. These are setup alternatives, not a documented ranking: the cited documentation does not compare their price, speed, availability, or answer quality.
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Know where generated code runs
In the guided tour, generated code executes locally by default. Treat an agent’s generated Python as executable code: understand the environment and its access before giving it untrusted tasks, expanding imports, or working near sensitive local files.
Isolation is a separate execution choice, not something automatically provided by installing the package. The documentation describes configured execution options including Blaxel, E2B, and Docker; the project overview also identifies Modal. Each requires its own setup. Consult the secure code execution guide before choosing and configuring an executor.
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Check the current API before extending the example
The documentation snapshot identified v1.26.0 as the latest stable version at the time it was reviewed; that is a dated version note, not a claim about the latest release now. The API reference labels the API experimental and notes that behavior can vary with the API and underlying models. Check the quick-start, API reference, and guided tour for current installation, model, and agent details. The overview describes smolagents as an open-source Python library for building and running agents with a small amount of code.
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