In our previous explorations of agentic frameworks, we saw how systems like LangGraph and CrewAI enable multiple AI agents to collaborate on complex tasks. Each framework takes a unique approach – LangGraph offers a rich graph-based orchestration, while CrewAI emphasizes simplicity with structured pipelines. SmolAgents is another intriguing player in this space, one that pushes minimalism to the forefront. Instead of elaborate workflows or heavy abstractions, SmolAgents opts for a barebones design philosophy centered on autonomy and minimal overhead. The result is a framework that feels lightweight yet surprisingly capable, letting you spin up intelligent agents in just a few lines of code.
SmolAgents is relatively new (introduced in late 2024 by Hugging Face) and comes with its own design trade-offs. In this post, we’ll put SmolAgents under the microscope – examining its guiding philosophy of simplicity and autonomy, diving into core features like lightweight agent creation, self-reflection cycles, and scratchpad memory, and walking through a bit of code to see it in action. We’ll also weigh its strengths (agility, very low overhead, rapid iteration) against its weaknesses (limited scalability, lack of complex coordination primitives) and compare how SmolAgents stands relative to frameworks like LangGraph and CrewAI.
Less Is More: The SmolAgents Philosophy
SmolAgents’ motto could well be “keep it simple.” This framework is built on the idea that enabling agent capabilities shouldn’t require an army of classes or thousands of lines of boilerplate. In fact, the entire SmolAgents logic is only on the order of 1,000 lines of code – a stark contrast to some larger frameworks (although not the smallest – that title may be held by PocketFlow, which advertises being a complete agent framework in exactly 100 lines). The developers deliberately kept abstractions minimal , so using SmolAgents feels closer to writing plain Python than orchestrating a complex system. This minimalist core makes it easy to understand what’s happening under the hood and to modify or extend behavior if needed.
Another pillar of SmolAgents’ philosophy is autonomy. The framework aims to let each agent operate with as much independence as possible, deciding its own steps in pursuit of a goal. Rather than predefining a rigid sequence of actions, you give a SmolAgent an objective and some tools, then allow the agent to figure out the workflow on its own via an internal reasoning loop. This stands in contrast to something like LangGraph, which explicitly manages workflow via graphs and state transitions. SmolAgents shifts that responsibility to the agent itself – embodying the “agents that think in code” ethos.
Core Features of SmolAgents
Despite its small footprint, SmolAgents packs in several important features that empower its minimalist agents. Let’s break down some of the core aspects of how SmolAgents works and what it offers:
Lightweight Agent Creation: One of SmolAgents’ biggest draws is how easy it is to create and run an agent. There’s no need to set up complex graphs or workflows; you can get started with just a couple of objects. For example, to make an agent you typically instantiate a CodeAgent with a language model and some tools, then call agent.run(...) with your task. That’s it – the framework handles the rest.
Code-First Actions (The CodeAgent): SmolAgents provides different agent types, but its flagship is the CodeAgent . A CodeAgent writes its actions in code rather than just outputting natural language instructions. This is a subtle but powerful distinction. Instead of an agent telling you what needs to be done (for example “I should calculate X or call Y”), the CodeAgent will produce a snippet of code that performs the action, and then that code is executed. This unlocks a lot of flexibility: the agent isn’t limited to a fixed set of tools or APIs; it can invent new solution steps as needed by writing code.
Scratchpad Memory (Iterative Reasoning State): SmolAgents handle multi-step reasoning through a scratchpad memory loop modeled after the ReAct (Reason+Act) pattern. The agent records each action and observation as it works, feeding this growing history back into the prompt with each new step. This allows the agent to “remember” past actions and dynamically adjust its reasoning in real time. The process continues until the task is complete or a predefined step limit is reached. This straightforward mechanism enables SmolAgents to solve complex tasks efficiently within a single execution flow while staying lightweight and aligned with the natural conversational context handling of LLMs.
Self-Reflection and Correction Cycles: Through its iterative loop and scratchpad memory, SmolAgents naturally enable self-reflection. After each action, the agent evaluates the result and adjusts its strategy if needed. For example, if a tool output or generated code is incorrect, the agent can detect errors in the observations and refine its approach in subsequent steps. This fosters an autonomous trial-and-error process where the agent continuously executes, observes, and revises until the task is successfully completed or deemed unsolvable, making it well-suited for open-ended or ambiguous problems.
Tool Integration and Extensibility: While SmolAgents emphasizes code-writing for problem-solving, it also supports seamless tool integration. Agents can use custom Python functions, tools from other ecosystems like LangChain, or even external services such as Hugging Face Spaces. Tools are easily defined with a simple decorator and are automatically available for the agent to call during reasoning. Built-in tools like DuckDuckGoSearchTool enable immediate functionality without extra coding.
Let’s see how these pieces come together with an example.
SmolAgents in Action: A Minimal Agent Solving a Multi-Step Task
Recall the kind of question we tackled using LangGraph’s planner+executor setup: “What is the hometown of the men’s 2024 Australian Open winner?” In that example, we set up a dedicated planner agent and a researcher agent to find the answer in steps. With SmolAgents, we can attempt the same multi-step challenge using just a single agent – one CodeAgent equipped with a web search tool.
Scenario: We want the hometown of the men’s 2024 Australian Open champion. That likely requires (1) finding out who won the 2024 Australian Open (men’s singles), and (2) finding that person’s hometown.
First, we set up the agent in code. We’ll give it access to a search tool (so it can look things up on the web) and choose an LLM to power its reasoning.
from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel
# Initialize a language model (using Hugging Face Inference API; you can plug in any model or provider)
model = OpenAIServerModel(model_id="gpt-4.1")
# Initialize the agent with a web search tool
agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)
question = "What is the hometown of the men's 2024 Australian Open winner?"
result = agent.run(question)
print(result)
The agent will take this question and start its reasoning loop. The agent doesn’t know the answer outright, so it will likely use the DuckDuckGoSearchTool to find information. One of its first actions might be a search for “2024 Australian Open men’s winner”.
Suppose that search returns a result like “Novak Djokovic won the 2024 Australian Open”. The agent will add that to its memory and then realize it needs Djokovic’s hometown.
Next, it could issue another search for “Novak Djokovic hometown”. That might return something like “Novak Djokovic was born in Belgrade, Serbia”.
The final result printed might be a sentence such as:
“The men’s 2024 Australian Open champion, Novak Djokovic, hails from Belgrade in Serbia.”
All of this happens within the single agent.run() call. Under the hood, the CodeAgent might have generated and executed code akin to:
# Pseudocode representation of what the agent might do internally:
search("2024 Australian Open men's winner")
# -> observation: "The 2024 Australian Open men's singles title was won by Novak Djokovic."
search("Novak Djokovic hometown")
# -> observation: "Novak Djokovic was born in Belgrade, Serbia."
# Formulate final answer
return "Novak Djokovic is from Belgrade, Serbia."
With LangGraph we had to define agent roles and a state graph to handle the planning loop. With CrewAI, we would need to define tasks and possibly use its planner/executor mode to break down the query. SmolAgents simply said “I’ve got this – I’ll search, then search again, then answer.”
Strengths of SmolAgents
SmolAgents’ strengths largely derive from its simplicity and the autonomy it grants to agents. Here are some of the key advantages of this framework:
Ultra-Low Overhead: As we’ve highlighted, SmolAgents is incredibly lightweight in both code and concept.
Agility and Flexibility: SmolAgents doesn’t prescribe how you structure your agent beyond the basic loop. You have full control to integrate it into your own application logic.
First-Class Code Execution: Many agent frameworks allow calling code or tools, but SmolAgents’ philosophy of treating code generation as a first-class action turns the agent into a sort of on-the-fly programmer.
Community and Integration Friendly: Being part of the Hugging Face ecosystem, SmolAgents benefits from easy integration with the Hub (for sharing agents and tools) and a community focus.
Straightforward Debugging and Transparency: Debugging multi-agent systems can be tough, but since the agent’s thought process is basically constructing a chain of actions (which you can log or inspect), you can usually trace what went wrong if the agent gets stuck or does something odd.
In summary, SmolAgents’ strengths make it an excellent choice for small to medium complexity tasks where quick development and iteration is paramount.
Weaknesses & Limitations
No framework is perfect, and SmolAgents’ minimalist approach comes with trade-offs that might become pain points in certain scenarios.
Limited Scalability for Complex Workflows: SmolAgents doesn’t have a built-in concept of coordinating many agents or tasks in parallel. If you envision a scenario with a dozen specialized agents working concurrently on sub-problems, SmolAgents will not provide an out-of-the-box orchestration layer for that.
Lack of Structured Coordination Primitives: SmolAgents lacks higher-level constructs like managed workflows, dependency graphs, or explicit state machines. It essentially provides a loop and leaves the strategy to the agent’s LLM reasoning. This means less control over the process for the developer.
Potential Reliability Issues: Since SmolAgents relies heavily on the LLM’s own reasoning to drive the agent, the quality and reliability of the agent are directly tied to the quality of the model and prompt. If the model is prone to hallucination or error, the agent might make nonsensical moves.
Debugging by prompt is a bit of an art; you might need to iterate on the system message or few-shot examples to get the agent to behave consistently.
Less Out-of-the-Box Functionality: Some might find that SmolAgents is “too minimal” for their needs. Other frameworks often come with batteries included – e.g., LangGraph integrates seamlessly with LangChain’s abundant utilities, and CrewAI has modes that implement patterns like sequential workflows or manager-worker setups for you.
Maturity and Community Support: As a newer framework, SmolAgents is still maturing. While Hugging Face’s backing is a strength, it doesn’t yet have the extensive community usage that something like LangChain (and by extension LangGraph) has.
Comparison with LangGraph & CrewAI
Now that we’ve seen SmolAgents in detail, it’s worth reflecting on how it compares with the other frameworks we’ve discussed in this series. Each of these agentic frameworks – LangGraph, CrewAI, and SmolAgents – occupies a slightly different niche and philosophy:
LangGraph vs. SmolAgents: LangGraph can be thought of as the master planner approach. It’s all about explicitly modeling the workflow as a graph of nodes and edges, where each node could be an agent or function, and edges define the flow of information or triggering conditions. SmolAgents, on the other hand, is more like letting the agent improvise. Rather than orchestrating at a high level, you let the LLM handle control flow. If your problem truly needs multiple agents working in tandem with intricate interactions, LangGraph provides a scaffolding that SmolAgents lacks.
CrewAI vs. SmolAgents: Compared to SmolAgents, CrewAI sits somewhat in the middle of the spectrum. It doesn’t have the heavy graph machinery of LangGraph, but it does impose a bit more structure than SmolAgents. For example, CrewAI’s planner/executor mode explicitly separates the planning step (manager agent) from execution, whereas SmolAgents would merge those implicitly unless you create two separate SmolAgents and orchestrate them yourself. If the problem fits a known pattern, CrewAI might get you there slightly quicker with more guardrails. If the problem is unusual or you want maximum flexibility for the agent to handle it, SmolAgents gives you that freedom.
In terms of technical trade-offs, SmolAgents relies more on the LLM’s competence (so using a strong model is important) whereas LangGraph and CrewAI try to compensate by providing structure that even a weaker model can follow.
In summary, SmolAgents distinguishes itself by being even more minimalist and autonomous than CrewAI, and far simpler in structure than LangGraph. It’s like the lightweight sports car compared to the family sedan and the bus: fast and nimble, but not meant to carry a huge load.
One great aspect of having all these frameworks at our disposal is that you can choose the level of abstraction that’s appropriate for the task:
- If you need full control and have a complex workflow → LangGraph might be the go-to.
- If you want quick setup with common patterns → CrewAI offers a nice balance.
- If you desire minimal overhead and trust the AI to organize itself → SmolAgents is very appealing.
It’s not inconceivable to even use them in combination for different parts of a project. For instance, one could imagine using LangGraph to coordinate at a high level (especially if integrating with other systems), but within a particular node, spin up a SmolAgent to handle an open-ended subtask that wasn’t easy to hardcode. The modular nature of these tools means interoperability is possible, though one must be careful to not over-engineer a Frankenstein monster and be mindful of connection points.
Final Thoughts
SmolAgents demonstrates that sometimes simpler is better – especially in the rapidly evolving world of AI agents. By stripping the concept down to its essentials (an LLM, a loop, and some tools), SmolAgents reminds us that a huge amount can be accomplished with very little. Its design philosophy of minimalism and autonomy leads to a framework that feels refreshingly light, especially if you’ve waded through heavier orchestration libraries.
If you’re already familiar with agentic AI and have experimented with frameworks like LangGraph or CrewAI, giving SmolAgents a try will be an interesting experience. You might be surprised at how quickly you can get something working, and how the agent’s chain-of-thought unfolds when it’s given free rein. It encourages a style of development that’s more about prompt design and tool provision than coding the process itself. This can feel liberating – though don’t throw away your unit tests and oversight just yet!
As with any technology, understanding its strengths and weaknesses is key to using it effectively. With that knowledge, you can make an informed decision about when to grab this particular Swiss Army knife from your toolkit for your next AI adventure.
Next time, join us as we go through Autogen – one of the earliest and still very popular frameworks from Microsoft!
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