AI agents have redefined automation, data structuring, and knowledge retrieval, making them essential across industries. From streamlining real-time support to handling large-scale data processing, they’re changing how systems operate.
SmolAgents keeps automation fast and flexible, making it easy to implement real-time AI workflows. PydanticAI ensures data is structured, validated, and reliable, which is critical for compliance-heavy industries like finance and healthcare. LlamaIndex Workflows takes automation further by managing complex, multi-step processes, allowing organizations to orchestrate tasks at scale.
Each of these solutions is built for a different kind of challenge—one is ideal for quick automation, another ensures accuracy in structured data, and the last specializes in handling large-scale coordination flexibly. The best choice depends on what you need to accomplish.
The best choice comes down to what you need to get done. AI agents are making workflows smarter and more efficient.
Let’s dive into the blog to break down how to choose the right agentic AI framework for your needs.
What are AI agents
AI agents are advanced software systems that interact with digital data, execute algorithmic decision-making, and perform targeted actions.
An AI agent perceives its environment through sensors (or data inputs), processes information using sophisticated algorithms, and triggers responses via actuators (or output mechanisms) to achieve specific objectives—often operating autonomously and adaptively.
Depending on the complexity of the task, these agents can either offer strategic recommendations or fully manage process AgentWorkflows without human oversight.
Agentic AI refines this concept further by employing a step-by-step decision-making framework that integrates external tools and leverages historical and contextual data to enhance its responses.
For example, an autonomous customer support agent might automatically verify order statuses, process refunds, and notify customers without any human intervention.
In essence, well-engineered AI agents enhance operational efficiency and responsiveness, evolving into dynamic problem-solving entities that integrate seamlessly into digital agent workflows.
Core characteristics of agentic AI
Most AI answers questions. Agentic AI gets things done.
Think of it like the difference between a search engine and a personal assistant. A search engine gives you links.
A real assistant books the flight, checks your schedule, and reminds you to pack an umbrella because it sees rain at your destination.
- Autonomous decision-making – These AI systems think through problems step by step, adjusting as they go. They solve, not just respond.
- Tool integration – They connect with APIs, databases, and external tools to pull real-time data, run computations, and generate reports.
- Memory and context retention – They track past interactions, refine responses, and make informed decisions.
- Recursive execution – They iterate, checking their output and improving results until they get it right.
When to use agents vs. when to avoid them
AI doesn’t always need to reason through every step. Sometimes a structured workflow is best. Other times, AI agents provide the flexibility needed for dynamic, multi-step tasks.
When structured AgentWorkflows work best
For clear, predictable Agent workflow, a traditional system ensures efficiency.
Take a customer service bot for a surfing trip website. Users typically have two main needs:
- If they want trip details, direct them to an FAQ or search tool.
- If they want to book a trip, connect them with a form or sales rep.
Since the process is well-defined, using structured logic ensures fast responses and a reliable experience.
When to use AI agents
Now, imagine a customer asks:
“I can come on Monday, but I forgot my passport, so I might be delayed until Wednesday. Can I surf Tuesday morning, and will my insurance cover cancellations?”
This question involves multiple factors:
- Surf conditions on Tuesday
- Instructor availability
- Travel insurance policies
- Travel time from the airport
A structured system might not cover every variation, but an AI agent can:
- Check a weather API for surf conditions
- Pull Google Maps data for travel time
- Query an employee scheduling system
- Search policy documents for insurance details
Since the workflow is well-structured, using clear logic ensures fast responses and a reliable experience.
SmolAgents: light and flexible agentic AI
SmolAgents, developed by Hugging Face, is built for simplicity and efficiency in AI agent development. It follows the ReAct paradigm, enabling agents to interact with tools, refine their reasoning through iteration, and dynamically improve their responses.
With a minimalist approach and broad compatibility, SmolAgents streamlines the process of building AI agents while maintaining flexibility.
How it works
Minimalist architecture – With just around 1,000 lines of core logic, SmolAgents keeps the framework lightweight while ensuring a clean and efficient implementation.
This approach makes it easy to work with while offering powerful functionality.
Code-first design – Instead of relying on structured JSON for tool interactions, SmolAgents uses Python Code Agents that write and execute actions directly in Python.
This reduces processing overhead and improves efficiency in handling complex tasks.
Broad model support – SmolAgents is designed to work seamlessly with multiple AI models, including those from Hugging Face Hub, OpenAI, and Anthropic.
This flexibility allows developers to integrate the best models for their specific use cases.
Secure execution – To ensure safe execution of AI-generated code, SmolAgents provides multiple runtime environments, including a Secure Python Interpreter and an E2B Sandboxed Environment.
These options allow for secure and controlled interactions while maintaining performance.
With its lightweight structure, adaptable design, and strong security features, SmolAgents makes AI agent development both efficient and accessible, enabling smooth interaction between models, tools, and structured processes.
Code Example :
# Example of creating a SmolAgent
agent = SmolAgent(model='huggingface-model', tools=[web_search_tool, code_execution_tool])
agent.run("Find the number of software engineers needed in 5 years.")
Strengths
Minimalist yet powerful – With a streamlined codebase, developers can quickly understand and modify the framework while maintaining efficiency. Its clean design makes AI agent development more accessible without added complexity.
Efficient code execution – By allowing direct Python execution instead of relying on intermediate tool-call formats, SmolAgents optimizes processing. This reduces LLM calls, improving response speed and lowering costs.
Secure execution – SmolAgents supports isolated execution environments, ensuring safe operations while handling code-based actions. This adds an extra layer of protection when working with AI-generated code.
Weakness
Optimized for lightweight tasks – SmolAgents is designed for streamlined execution, making it well-suited for focused AI Agent workflows. For distributed execution and large-scale parallel processing, external solutions may be required.
Flexible memory integration – While SmolAgents does not include built-in memory handling, it works well with external tools for state persistence. This allows developers to tailor long-term context retention to specific project needs.
With its balance of simplicity, efficiency, and security, SmolAgents provides a strong foundation for AI agent development while allowing flexibility for more advanced use cases.
Advantages
- Easy to get started – SmolAgents is built for quick setup with a minimal learning curve. Developers can go from installation to working AI agents in about an hour, making it ideal for fast development.
- Built-in open-source telemetry – SmolAgents includes native support for open-source telemetry tools, making it easier to visualize, debug, and track agent behaviour. This adds transparency to Agent workflows, unlike PydanticAI, which requires additional setup for similar functionality.
- Flexible model selection – Developers aren’t locked into a single model. SmolAgents supports multiple AI providers, including Hugging Face transformers, OpenAI, and Anthropic, allowing teams to mix and match based on their specific needs.
- Great for prototyping – SmolAgents is optimized for experimentation, making it a strong choice for proof-of-concept projects, MVPs, and hackathons. Its lightweight design and ease of use allow for rapid iteration without unnecessary complexity.
With its balance of simplicity, flexibility, and built-in tools, SmolAgents makes AI agent development faster and more accessible while maintaining the adaptability needed for different processes.
Best use cases
- Development automation – Ideal for rapidly prototyping AI-powered automation tools with minimal setup. Its streamlined design makes it easy to integrate AI-driven Agent workflow without extra overhead.
- AI research & experimentation – Designed for quick iteration, SmolAgents allows researchers to test AI behaviours in a flexible, code-driven environment. Its simplicity makes experimentation more efficient.
- Production deployments – With its lightweight architecture, SmolAgents is well-suited for low-latency applications that require scalability and efficiency. Its minimal footprint ensures smooth integration into production environments.
- Whether for automation, research, or deployment, SmolAgents provides an adaptable framework that balances speed, simplicity, and performance.
PydanticAI: structured and compliant agentic AI
The agent approach can go even further by incorporating Pydantic models to enforce structured output. This is especially useful in AI applications that require validation, compliance, or structured data processing at any stage.
By integrating Pydantic, agents can ensure consistency and reliability, making them a strong fit for scenarios where data integrity is essential.
Architecture
Schema validation for LLMs – PydanticAI integrates OpenAI-compatible language models while enforcing structured output validation, ensuring data consistency and reliability.
Pydantic-based validation layer – Agents generate outputs that follow predefined schemas, making data handling more structured and reducing inconsistencies.
Modular design with dependency injection – The framework allows different components to be injected as dependencies, making Agent workflows more flexible and scalable.
Asynchronous execution for performance – With built-in async support, agents can handle multiple tasks at once, improving efficiency in real-time applications.
By combining structured validation, modular workflows, and high-performance execution, PydanticAI provides a strong foundation for building reliable and scalable systems.
Code Example:
from pydantic_ai import Agent, RunContext
roulette_agent = Agent(
'openai:gpt-4o',
deps_type=int,
result_type=bool,
system_prompt=(
'Use the `roulette_wheel` function to see if the '
'customer has won based on the number they provide.'
),
)
Strengths
Ensures data integrity – Pydantic validation enforces structured, predictable outputs, ensuring consistency across AI-driven workflows.
Scalable by design – With built-in async execution and modular dependency management, PydanticAI efficiently handles high-performance applications.
Compliance-ready – Ideal for industries with strict regulations, PydanticAI ensures structured validation for reliable, audit-ready data processing.
Weakness
Structured validation expertise – Familiarity with Pydantic and schema-based validation enhances the development process, making it valuable for structured AI applications.
Flexible memory integration – While PydanticAI doesn’t include built-in memory, it works well with external state management solutions to support long-term context retention.
Advantages
Structured output enforcement – PydanticAI ensures strict validation of model outputs using predefined schemas, a feature not natively available in SmolAgents or LlamaIndex.
Scalability for high-performance applications – Async execution and streaming capabilities make it well-suited for real-time, data-intensive Agent workflows.
Flexibility for complex systems – Graph-based dependencies and dependency injection allow for handling intricate Agent workflows with greater adaptability.
Production-ready reliability – With structured validation at its core, PydanticAI is an excellent choice for industries requiring compliance and data consistency.
Best use cases
Healthcare AI applications – Generate structured patient reports while maintaining regulatory compliance.
Financial data processing – Automate the analysis of invoices, reports, and financial documents with validated output schemas.
Customer support chatbots – Implement scalable AI-driven chatbots that process real-time queries while enforcing structured response formats.
With its structured approach, scalability, and compliance focus, PydanticAI is a strong choice for applications that demand precision, reliability, and flexibility.
LlamaIndex workflows: the orchestrator
LlamaIndex Agent Workflows is designed to handle complex tasks using a modular, event-driven approach.
The framework also supports flexible agents like ReactAgent and FunctionalAgent. ReactAgent reacts to changes in real-time, quickly adjusting to new data and events.
On the other hand, FunctionalAgent splits tasks into small, manageable parts that are simple to test and maintain. This approach ensures each part works well on its own and contributes smoothly to the overall process.
How it works
Step-by-step execution – Tasks are broken into smaller steps, each triggered by specific conditions to keep Agent Workflows smooth and fault-tolerant.
Smart progress tracking – Intermediate results are saved so long-running tasks can resume if interrupted, ensuring reliability.
Scalable performance – Different Agent workflow steps can run in parallel, making processing faster and optimizing resource use.
Easy integrations – Connects with APIs, databases, and external tools to pull in data and handle complex operations seamlessly.
LlamaIndex Workflows simplifies AI-driven automation, making it easier to manage large-scale, multi-step processes with speed and flexibility.
Code Example:
workflow = AgentWorkflow.from_tools_or_functions(
[search_web],
llm=llm,
system_prompt=(
"You are a helpful assistant that can search the web for information."
)
)
Strengths
Built for scale –Designed to run processes in parallel, making it easy to handle large amounts of data without slowing down..
Reliable orchestration – Supports checkpointing, fault tolerance, and API integrations, ensuring smooth execution even for long-running tasks.
Great for complex Agent workflows – Ideal for multi-step AI reasoning, where different tasks need to be managed efficiently in a structured way.
Weakness
Best suited for structured Agent workflows – Works well for enterprise-level automation, though simpler AI tasks might be better handled with lightweight solutions.
Requires an Agent workflow-first approach – Understanding event-driven architectures helps unlock its full potential.
Advantages
Step-by-step orchestration – Unlike SmolAgents or PydanticAI, LlamaIndex is designed to break Agent workflows into organized steps, making it ideal for complex, multi-stage processes.
Scalability at its core – Agent Workflow steps can scale independently, ensuring efficient resource use in production environments.
Seamless integrations – Connects easily with third-party tools, AI models, and monitoring platforms for enterprise-ready automation.
Advanced reliability features – Checkpointing and human-in-the-loop options provide oversight and control in high-stakes processes.
Where it works best
Enterprise automation – Automates processes like shipment tracking, inventory management, and logistics optimization.
Real-time event monitoring – Supports incident response agent workflow, alert systems, and continuous monitoring.
Large-scale data processing – Handles document analysis, content moderation, and AI-driven decision-making.
LlamaIndex Workflows is a strong choice for businesses that need structured, scalable automation that integrates seamlessly with existing tools and systems.
Comparison table
Which framework is right for you?
Each framework has its strengths—choosing the right one depends on what you're building. Here’s a simple breakdown:
SmolAgents: best for lightweight, code-first AI agents
If you need something simple, fast, and efficient, SmolAgents is a great pick.
- Minimal setup, fast execution – Runs on a small, efficient codebase (~1,000 lines), making it ideal for lightweight applications.
- Code-first design – Agents write and execute Python code directly, skipping complex tool-call formats.
- Focused on small tasks – Works well for quick automation but isn’t built for large-scale Agent workflows or long-term memory storage.
Perfect for: quick prototypes, small AI agents, and tasks where speed matters more than complexity.
PydanticAI: best for structured, compliance-ready AI applications
If data integrity and structured outputs matter, PydanticAI is the way to go.
- Strict validation – Uses Pydantic to ensure AI-generated responses follow predefined schemas, which is great for compliance-heavy industries.
- Modular Agent workflows – Supports dependency injection, making it easier to build and manage complex AI applications.
- Real-time structured output – Optimized for scenarios where AI responses must be predictable and reliable.
Perfect for: financial reports, healthcare applications, and any AI system that needs strict data validation.
LlamaIndex Workflows: best for orchestrating complex, multi-step AI pipelines
For large-scale automation and AI-driven Agent workflow, LlamaIndex shines.
- Built for complex Agent Workflows – Manages long-running, multi-step processes with checkpointing and fault tolerance.
- Scales effortlessly – Runs tasks in parallel, integrates with APIs, and tracks progress across different steps.
- Memory-aware AI – Keeps context across multiple steps, making it great for intelligent automation and document processing.
Perfect for: enterprise automation, event monitoring, and large-scale AI-powered processes.
Wrapping up
There’s no single “best” AI framework—just the one that fits what you need.
- If you’re hacking together an MVP or running a small, focused AI task, SmolAgents will feel like a breath of fresh air.
- If your data needs to be structured and validated, PydanticAI is the right tool for the job.
- If you’re working on a large-scale AI system that needs to coordinate multiple moving parts, LlamaIndex AgentWorkflows is the powerhouse that brings it all together.
The real takeaway? AI development is about using what makes the most sense for your Agent workflow.
Whether you're experimenting, fine-tuning structured AI models, or orchestrating complex automation, the right framework makes the difference between AI that simply processes data and AI that truly works.
W.W. AI Adventures, Which Agentic AI Framework to Pick? SmolAgents vs. PydanticAI vs. LlamaIndex Workflows
Hugging Face, huggingface / smolagents
Llamaindex, Introducing AgentWorkflow: A Powerful System for Building AI Agent Systems
Pydantic, Official documentation