How to select the right AI Agentic Framework?
Select the right Agentic Framework among AutoGen,CrewAI,LangGraph,SmolAgents, LlamaIndex,PydanticAI based on parameters like LearningCurve, Integrations,Scalability,Flexibility,Documentation,Features
Since my blog post on the best frameworks for building AI agents, I have been thinking about writing comparative analysis and insights about the frameworks. After conducting research and watching videos, I came across a wonderful analysis by William White on YouTube.
I thought it would be better to share that information with you all instead of doing my research again, so I decided to summarise that analysis.
I would like to thank William White for this analysis.
What are Agentic AI Frameworks?
"Agentic frameworks" are structured systems for building multi-agent, task-oriented, and conversational applications. These frameworks empower developers to orchestrate models, tools, and data flows into cohesive systems that can reason, delegate tasks, and even interact with humans when necessary.
In this blog, we will be evaluating the below popular agentic frameworks:
Microsoft AutoGen
Crew AI
LangGraph
Smol Agents by Hugging Face
LlamaIndex Workflows
PydanticAI Agents
Evaluation Parameters for Agentic Frameworks
William has evaluated each framework based on six key dimensions and to provide a visual representation of each framework's capabilities, he has created spider graphs that illustrate their performance across key metrics.
Radar Chart Explanation
The spider graphs evaluate each framework on six key dimensions:
Learning Curve: Ease of getting started and mastering the framework
Integrations: Compatibility with different tools, models, and ecosystems
Scalability: Ability to handle complex, production-level scenarios
Flexibility: Adaptability to different use cases and customization options
Documentation: Quality, comprehensiveness, and clarity of documentation
Feature Support: Advanced capabilities like streaming, human interaction, Low Code Development, debugging etc…
Check out my previous articles here:
Detailed Framework Analysis
1. Microsoft Autogen
Overview: An open-source, actor model-based framework backed by Microsoft that enables asynchronous distributed communication between agents.
Documentation:
Languages Supported: Python, .NET
Key Features:
Actor model-based design for asynchronous messaging
Agent conversations with structured termination conditions
Low-code interface via Autogen Studio
Quick setup with minimal code (around 6 lines)
Evaluation:
Learning Curve: 10/10 - Fast learning with simple concepts
Integrations: 6/10 - Strong Microsoft ecosystem integration, requires proxy for non-OpenAI models
Scalability: 7/10 - Built for distributed systems
Flexibility: 4/10 - Limited customization options
Documentation: 7/10 - Comprehensive but Microsoft-centric
Feature Support: 5/10
Best For: Microsoft-centric, task-based systems requiring asynchronous communication
2. Crew AI
Overview: A task-driven, multi-agent system backed by deeplearning.ai and Andrew Ng, focusing on decomposing complex problems into manageable subtasks.
Documentation:
Languages Supported: Python
Key Features:
YAML-based configuration
Built-in memory management
Strong LangChain integration
Production-ready monitoring tools
Evaluation:
Learning Curve: 7/10 - Declarative configuration helps quick start
Integrations: 8/10 - Extensive ecosystem support
Scalability: 8/10 - Production-focused design
Flexibility: 7/10 - Task-focused limitations
Documentation: 10/10 - Comprehensive with practical examples
Feature Support: 7/10
Best For: Production environments requiring robust task management and state handling
3. LangGraph
Overview: A flexible, graph-based framework by the LangChain team, offering complete control over agent orchestration through interconnected nodes.
Documentation:
Languages Supported: Python
Key Features:
Graph-based architecture
Time travel debugging
Human-in-the-loop capabilities
Multi-language support
Evaluation:
Learning Curve: 3/10 - Steeper due to graph complexity
Integrations: 8/10 - Strong LangChain ecosystem integration
Scalability: 9/10 - Highly flexible for complex workflows
Flexibility: 10/10 - Customizable graph architecture
Documentation: 8/10 - Initially complex but improving
Feature Support: 8/10
Best For: Complex workflows requiring custom agent orchestration and detailed debugging
4. SMOL Agents (Hugging Face)
Overview: A framework implementing REACT agents, focusing on simplicity and code generation capabilities.
Documentation:
Languages Supported: Python
Key Features:
Three agent types: Code, Tool, and Managed Agents
Single-line agent creation
Sandbox code execution via E2B
OpenTelemetry integration
Evaluation:
Learning Curve: 10/10 - Simplest framework to get started
Integrations: 8/10 - Strong HuggingFace ecosystem support
Scalability: 2/10 - Limited by lack of async execution
Flexibility: 6/10 - Limited customization options
Documentation: 10/10 - Sufficient for core features
Feature Support: 0/10
Best For: Research projects, proofs of concept, and educational purposes
5. LlamaIndex Workflows
Overview: A workflow-based framework that leverages event-driven execution for task orchestration.
Documentation:
Languages Supported: Python
Key Features:
Event-driven workflow execution
Scalable independent workflow steps
Simple, clear orchestration model
Evaluation:
Learning Curve: 8/10 - Intuitive event-driven model
Integrations: 9/10 - Limited to Python ecosystem
Scalability: 9/10 - Scalable workflow steps
Flexibility: 9/10 - Limited customization options
Documentation: 6/10 - Good but improving
Feature Support: 7/10
Best For: Orchestrating parallel task flows with clear sequential steps
6. PydanticAI Agents
Overview: A framework that builds upon the REACT agent model, incorporating features like dependency injection and asynchronous structured output streaming.
Documentation:
Languages Supported: Python
Key Features:
Dual-level APIs for quick start and deep customization
Asynchronous execution with streaming structured outputs
Dependency injection
Evaluation:
Learning Curve: 7/10 - Moderate complexity
Integrations: 6/10 - Strong support for various tools
Scalability: 10/10 - Asynchronous execution for real-time responses
Flexibility: 9/10 - Customizable APIs
Documentation: 7/10 - Comprehensive with practical examples
Feature Support: 6/10
Best For: Real-time, scalable multi-agent systems requiring structured responses
Framework Comparison Matrix
Below is a quick snapshot of the analysis.
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Conclusion
Each framework offers unique advantages and trade-offs. The choice depends heavily on specific requirements:
For production Microsoft environments: Autogen
For robust task management: Crew AI
For complex custom workflows: Lang Graph
For quick prototypes and research: SMOL Agents
For parallel task flows: Llama Index Workflows
For real-time, scalable multi-agent systems: PydanticAI Agents
Consider factors like ecosystem compatibility, scalability needs, and development team expertise when making your selection.
Credits and Inspiration
Research Sources
This comprehensive analysis is based on in-depth research and insights from the following video presentations:
Agentic Frameworks Comparison
Creator: William White
LinkedIn: William White - Data Scientist
Video: Which Agentic AI Framework to Pick? SmolAgents vs. PydanticAI vs. LlamaIndex Workflows
Video: Which Agentic AI Framework to Pick? LangGraph vs. CrewAI vs. AutoGen
Glossary of Terms
React Agent
React Agent is an approach to building AI agents that integrate reasoning and action within an interactive environment. It follows a "Reasoning + Acting" (ReAct) paradigm, enabling agents to dynamically think, plan, and execute tasks based on external feedback. Unlike traditional AI pipelines that separate reasoning and execution, React Agents interleave thought processes with actions, allowing them to refine decisions in real time based on observations.
React Agents are commonly used in LLM-powered applications, multi-agent collaboration, and decision-making frameworks. They are particularly effective in complex environments where adaptability and iterative learning are required, such as task automation, conversational AI, and interactive problem-solving systems.
Actor Model
The Actor Model is a computational framework for designing distributed and concurrent systems. It defines an architecture where actors are fundamental units of computation that communicate asynchronously by sending and receiving messages. Each actor operates independently, processes messages sequentially, and can create new actors, send messages, and change its internal state.
This model is widely used in parallel computing, distributed systems, and multi-agent AI frameworks because it eliminates shared state, reducing issues like race conditions and deadlocks. Read more
Note: Framework capabilities and agent architectures evolve rapidly. Always consult the latest research and documentation.
AutoGen, was also spun out by its founders.