Exploring Open-Source Agentic AI Frameworks: A Comparative Overview
Most developers have heard of CrewAI and AutoGen, but fewer realize there are dozens of open-source agentic frameworks available—many released just in the past year.
To understand how these frameworks work and how easy they are to use, several of the more popular options were briefly tested. This article explores what each one offers, comparing them to the more established CrewAI and AutoGen.
The focus is on LangGraph, Agno, SmolAgents, Mastra, PydanticAI, and Atomic Agents, examining their features, design choices, and underlying philosophies.
What Agentic AI Entails
Agentic AI revolves around building systems that enable large language models (LLMs) to access accurate knowledge, process data, and take action. Essentially, it uses natural language to automate tasks and workflows.
While natural language processing (NLP) for automation isn’t new, the key advancement is the level of autonomy now possible. LLMs can handle ambiguity, make dynamic decisions, and adapt to unstructured tasks—capabilities that were previously limited.
However, just because LLMs understand language doesn’t mean they inherently grasp user intent or execute tasks reliably. This is where engineering comes into play—ensuring systems function predictably.
For those new to the concept, deeper explanations of Agentic AI can be found here and here.
The Role of Frameworks
At their very core, agentic frameworks assist with prompt engineering and data routing to and from LLMs. They also provide abstractions that simplify development.
Without a framework, developers would manually define system prompts, instructing the LLM to return structured responses (e.g., API calls to execute). The framework then parses these responses and routes them to the appropriate tools.
Frameworks typically help in two ways:
- Structuring prompts to ensure LLMs respond in the correct format.
- Parsing and routing responses to tools, APIs, or other external systems.
Additionally, they may assist with:
- Knowledge integration (chunking, embedding, and storing documents for RAG).
- Error handling, validation, and structured outputs.
- Observability, deployment, and multi-agent orchestration.
However, some argue that full frameworks can be overkill. If an LLM misuses a tool or the system breaks, debugging becomes difficult due to abstraction layers. Switching models can also be problematic if prompts are tailored to a specific one.
This is why some developers end up customizing framework components—such as create_react_agent in LangGraph—for finer control.
Popular Frameworks
The most well-known frameworks are CrewAI and AutoGen:
- CrewAI offers high-level abstractions, enabling rapid development by hiding low-level details.
- AutoGen focuses on autonomous, asynchronous collaboration, making it ideal for research and experimentation.
LangGraph, while less mainstream, is a powerful choice for developers. It uses a graph-based approach, where nodes represent agents or workflows connected via edges. Unlike AutoGen, it emphasizes structured control over agent behavior, making it better suited for deterministic workflows.
That said, some criticize LangGraph for overly complex abstractions and a steep learning curve.
Emerging Frameworks
Several newer frameworks are gaining traction:
- Agno (formerly Phi-Data) prioritizes developer experience, with clean documentation and plug-and-play functionality.
- SmolAgents is minimalistic, introducing CodingAgent, which routes data via executable code rather than JSON. It also integrates directly with Hugging Face’s model library.
- PydanticAI builds on Pydantic for strict type safety and validation, appealing to developers who prefer fine-grained control.
- Atomic Agents adopts a schema-driven, Lego-like approach, emphasizing structure and predictability.
- Mastra, developed by the Gatsby team, is a JavaScript-based framework for frontend developers.
Common Features
Most frameworks share core functionalities:
- Model Agnosticism – Support for multiple LLM providers (though prompt structures may favor certain models).
- Tool Integration – Essential for enabling agent actions, with easy custom tool definitions.
- State Management – Short-term memory for retaining context between LLM calls.
- RAG Support – Built-in or customizable document retrieval.
- Asynchronous Calls, Structured Outputs, and Streaming – Standard in most frameworks.
Key Differences
Frameworks vary in several areas:
- Multimodality – Some support text, images, and voice natively; others require manual implementation.
- Long-Term Memory – While all handle short-term state, long-term memory solutions differ.
- Multi-Agent Systems – Scaling to hierarchical or collaborative teams varies:
- LangGraph excels in complex, multi-layered workflows.
- Agno recently added team support but lacks extensive examples.
- SmolAgents and Mastra resemble CrewAI in agent team structuring.
- PydanticAI and Atomic Agents require manual orchestration.
Abstraction vs. Control
Frameworks differ in abstraction levels and developer control:
- High Abstraction (Easy Start): Mastra, CrewAI, Agno.
- Balanced (Flexible but Structured): LangGraph.
- Low Abstraction (Full Control): PydanticAI, SmolAgents, Atomic Agents.
They also vary in agent autonomy:
- High Agency: AutoGen, SmolAgents (agents decide workflows).
- Controlled Agency: Most others (step-by-step task guidance).
Developer Experience
- Beginner-Friendly: CrewAI, Agno, Mastra.
- Intermediate: SmolAgents (simple cases), LangGraph (steep learning curve).
- Advanced: PydanticAI, Atomic Agents (manual orchestration required).
Debugging challenges exist:
- CrewAI/AutoGen can be opaque.
- SmolAgents’ CodeAgent is innovative but inconsistent.
- LangGraph’s abstractions sometimes require rebuilding.
- Agno/Mastra may encounter looping issues.
Final Thoughts
The best way to learn is to experiment. While this overview highlights key differences, factors like enterprise scalability and operational robustness require deeper evaluation.
Some developers argue that agent frameworks introduce unnecessary complexity compared to raw SDK usage. However, for those building structured AI systems, these tools offer valuable scaffolding—if chosen wisely.
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