[How to start AI Agents] Agentic AI Frameworks Compared: LangGraph, Agno, SmolAgents, Mastra, and More

In the ever-evolving landscape of artificial intelligence, a new paradigm has emerged: Agentic AI. This refers to intelligent systems — usually built on large language models (LLMs) — that go beyond passive text generation. These systems are capable of initiating actions, invoking APIs, retrieving information, and even coordinating other agents to fulfill complex goals.

While commercial platforms like OpenAI’s Assistants API provide polished agent experiences, a growing number of open-source frameworks empower developers to build their own agentic systems. In the last year alone, over a dozen such frameworks have emerged, each tailored to different developer needs.

This article provides a deep comparative analysis of the leading open-source agentic AI frameworks, including LangGraph, Agno, SmolAgents, Mastra, PydanticAI, and Atomic Agents, with additional context drawn from established tools like CrewAI and AutoGen. We’ll explore the design philosophies behind each, their functionality, ease of use, and best-fit use cases.

What is Agentic AI?

From Language Models to Autonomous Agents

Agentic AI describes systems that combine reasoning, memory, tool-use, and autonomy. Rather than simply returning answers, these systems can evaluate goals, make decisions, route data, and act on behalf of users or applications. This is typically achieved through a combination of natural language interfaces, structured prompting, and backend toolchains.

The rise of Agentic AI marks a shift from static assistants to dynamic, task-performing entities. This requires robust architectural design, where tools like APIs, RAG (retrieval-augmented generation), memory, and decision trees come into play.

Why Use a Framework?

Building such systems from scratch is both time-consuming and brittle. Frameworks abstract away boilerplate code, manage complex prompt flows, and provide integrations for tools and memory. They also offer debugging tools, observability, and deployment pipelines that make scaling easier.

Core Functions of Agentic Frameworks

Agentic frameworks generally provide:

• Prompt Engineering Abstractions: Templates and parsing utilities.

• Tool Invocation Systems: Structured mechanisms to execute tools or API calls.

• Memory and State Management: Track what the model has done or said.

• RAG Integration: Combining search and generation.

• Error Handling & Logging: For resilient systems.

• Multi-Agent Orchestration: Enabling agents to collaborate and coordinate.

These are table-stakes in any mature framework. However, their implementation details, flexibility, and learning curve vary dramatically.

Comparison of Key Frameworks

🧭 LangGraph

LangGraph is a developer-centric framework using a graph-based structure to model agent workflows. Agents are defined as nodes, and logic flows through edges. It offers high flexibility and low-level access but comes with a steep learning curve and dense abstractions.

• 🔧 Ideal for: Advanced users building large, structured workflows.

• ❗ Considerations: Verbose setup; debugging can be difficult.

LangGraph
Build controllable agents with LangGraph, our low-level agent orchestration framework. Deploy and scale with LangGraph…www.langchain.com

✨ Agno (formerly Phi-Data)

Agno emphasizes a polished developer experience. It comes with clear documentation, well-structured abstractions, and plug-and-play tooling. It is particularly easy to onboard with, while still offering deep extensibility.

• 🔧 Ideal for: Developers seeking a smooth start with room to scale.

• ❗ Considerations: Still early in multi-agent and long-term memory support.

https://github.com/agno-agi/agno

🪶 SmolAgents

A lightweight, minimal framework with bare-bones logic routing done via executable code instead of schemas. It connects seamlessly to the Hugging Face model ecosystem and focuses on transparency.

• 🔧 Ideal for: Prototypers and researchers.

• ❗ Considerations: Not ideal for complex or scalable systems.

https://github.com/huggingface/smolagents

🌐 Mastra

Mastra, built by the creators of Gatsby, is a JavaScript-first framework aimed at frontend developers. It allows LLM agents to be directly embedded into web environments.

• 🔧 Ideal for: Frontend teams and web developers.

• ❗ Considerations: Limited visibility in AI/ML communities.

https://mastra.ai/

🧱 PydanticAI

A highly modular framework built on Pydantic for validation and type-safety. It’s focused on precision and predictability, best for those who value well-defined schemas.

• 🔧 Ideal for: Backend engineers and system architects.

• ❗ Considerations: Requires you to build orchestration manually.

https://ai.pydantic.dev/

🧩 Atomic Agents

Designed like LEGO blocks, Atomic Agents offer schema-driven modules that can be composed to form workflows. It is clean, modular, and built for those who value reproducibility.

• 🔧 Ideal for: Builders who want fine-grained modularity.

• ❗ Considerations: Smaller community; high DIY requirement.

https://github.com/BrainBlend-AI/atomic-agents

🧑‍🤝‍🧑 CrewAI

CrewAI uses very high-level abstractions to let you define agents and goals with minimal configuration. It’s fast and great for demos but sometimes difficult to customize.

• 🔧 Ideal for: Hackathons, POCs, early prototyping.

• ❗ Considerations: Hidden logic can hinder debugging.

https://www.crewai.com/

🧠 AutoGen

Built by Microsoft, AutoGen focuses on multi-agent systems and agent collaboration. It’s flexible in logic but abstracted in agent control, which can confuse new users.

• 🔧 Ideal for: Research environments and dynamic collaboration.

• ❗ Considerations: Complex prompt chaining logic.

https://github.com/microsoft/autogen

Shared Capabilities Across Frameworks

Despite differences in architecture, nearly all frameworks offer:

• ✅ Model Agnosticism: OpenAI, Anthropic, Cohere, Hugging Face, etc.

• ✅ Tooling Support: Structured function calling or external tool execution.

• ✅ Short-Term Memory: Managed via tokens or local state.

• ✅ RAG Support: Often through LangChain or vector DBs.

• ✅ Async and Streaming: For real-time interactions.

• ✅ Logging and Observability: Especially in LangGraph and Agno.

However, multimodal input (image, audio), long-term memory, and multi-hierarchical teams are inconsistently supported.

Key Differences Between Frameworks

🧱 Abstraction vs Control

• High Abstraction: CrewAI, Mastra, Agno

• Mid-level: LangGraph (abstracted but configurable)

• Low-level: PydanticAI, SmolAgents, Atomic Agents

🧠 Agent Autonomy Philosophy

• High Agency: AutoGen, SmolAgents (agents decide what to do)

• Low Agency: LangGraph, Agno, PydanticAI (tightly controlled tasks)

👨‍💻 Developer Experience

• Beginner Friendly: CrewAI, Mastra

• Mid-Level: Agno, SmolAgents

• Advanced: LangGraph, PydanticAI, Atomic Agents

Some are plug-and-play; others require setting up every node, tool, and route manually.

Conclusion: How to Choose the Right Framework

Every agentic framework is a trade-off between speed, control, complexity, and scalability.

As LLM tooling matures, hybrid solutions may emerge. For now, the best way to learn is to try one out, join the community, and start building.

Agentic AI is no longer theoretical — it’s here. And with the right framework, you can build agents that don’t just think, but act.