Framework Comparison

🔄 Framework Comparison

Detailed comparison of all supported agent frameworks to help you choose the right one.

Quick Comparison Table

Framework	Language	Best For	Complexity	Learning Curve	Community	Last Updated
SmolAgents	Python	Simple agents	Low	1-2 hours	Growing	Apr 2026
PydanticAI	Python	Type safety	Low-Med	2-3 hours	Growing	Apr 2026
OpenAI Agents	Python/TS	Multi-agent	Medium	4-6 hours	Growing	Apr 2026
CrewAI	Python	Role-based	Medium	4-6 hours	Active	Apr 2026
LangGraph	Python/TS	Complex flows	High	8-12 hours	Growing	Apr 2026
LlamaIndex	Python	RAG systems	Medium	6-8 hours	Active	Apr 2026
Haystack	Python	Search systems	Med-High	8-10 hours	Active	Apr 2026
AG2	Python	Research	Med-High	6-8 hours	Large	Apr 2026
Semantic Kernel	C#/Python	Enterprise	Medium	6-8 hours	Active	Apr 2026
Amazon Bedrock	Python	AWS-native	Medium	6-8 hours	Growing	Apr 2026
Google ADK	Python	GCP-native	Medium	6-8 hours	Growing	Apr 2026
Anthropic Claude	Python/TS	Claude API	Medium	4-6 hours	Growing	Apr 2026
Mistral	Python	Mistral API	Medium	4-6 hours	Growing	Apr 2026

Detailed Comparison

⭐ Best for Beginners: SmolAgents

Paradigm:      Code-first agents
Learning Time:  ~1-2 hours
Best For:       Simple automation, learning, rapid prototyping

Strengths:

Minimal setup required
Code-based (no JSON)
Supports 100+ LLM providers
Perfect for learning
Great documentation

Weaknesses:

Limited to single-agent workflows
Fewer advanced features
Smaller community

When to Choose:

You’re new to agents
You want simple automation
You need quick prototyping
You want a small, focused tool

📖 SmolAgents Guide

🔒 Best for Type Safety: PydanticAI

Paradigm:      Type-validated agents
Learning Time:  ~2-3 hours
Best For:       Structured outputs, validation, type-safe systems

Strengths:

Full type safety with Python typing
Validated outputs via Pydantic
Works with multiple models
Excellent for APIs
Production-ready

Weaknesses:

Less flexible than some alternatives
Smaller ecosystem
Fewer examples available

When to Choose:

You need validated structured outputs
You’re building APIs
Type safety is important
You want Pydantic integration

📖 PydanticAI Guide

🤖 Best for Multi-Agent: OpenAI Agents SDK

Paradigm:      Lightweight primitives
Learning Time:  ~4-6 hours
Best For:       Multi-agent coordination, research, flexible patterns

Strengths:

Powerful primitives (Agent, Runner, Handoff, Guardrail, Session)
Excellent for multi-agent systems
Rich feature set
Growing community
Great documentation

Weaknesses:

Requires OpenAI API key
More complex than SmolAgents
Steeper learning curve

When to Choose:

You need multi-agent coordination
You want powerful abstractions
You’re using OpenAI models
You need flexibility

📖 OpenAI Agents SDK Guide

👥 Best for Teams: CrewAI

Paradigm:      Role-based agent teams
Learning Time:  ~4-6 hours
Best For:       Team-based agents, orchestrated workflows

Strengths:

Intuitive role-based design
Great for team simulations
Active community
Good documentation
Task orchestration

Weaknesses:

Less flexible than LangGraph
Limited to CrewAI patterns
Smaller ecosystem

When to Choose:

You need role-based agents
You want team-like coordination
You need task orchestration
You like declarative patterns

📖 CrewAI Guide

🌊 Best for Complex Workflows: LangGraph

Paradigm:      Graph-based state machines
Learning Time:  ~8-12 hours
Best For:       Complex workflows, cycles, conditional routing

Strengths:

Handles complex workflows
Supports cycles and loops
Excellent for conditional logic
Great visualisation
Very flexible
Available in Python and TypeScript

Weaknesses:

Steepest learning curve
More verbose
Requires understanding of graphs
Larger codebase

When to Choose:

You need complex workflows
You have cycles/loops
You want maximum flexibility
You need conditional routing
You’re building TypeScript apps

LangGraph Guide | LangGraph TypeScript

📊 Best for RAG: LlamaIndex

Paradigm:      Data indexing and retrieval
Learning Time:  ~6-8 hours
Best For:       RAG systems, data indexing, knowledge retrieval

Strengths:

Excellent RAG support
Multiple vector stores
Great data connectors
Production-ready
Active community

Weaknesses:

Focused on RAG (not multi-agent)
Learning curve
Many options can be overwhelming

When to Choose:

You’re building RAG systems
You need data indexing
You want knowledge retrieval
You need multiple vector stores

📖 LlamaIndex Guide

🔍 Best for Search: Haystack

Paradigm:      Search & QA pipeline
Learning Time:  ~8-10 hours
Best For:       Search systems, Q&A, NLP pipelines

Strengths:

Production search systems
Flexible pipelines
Great for QA systems
Active community
Enterprise-ready

Weaknesses:

More complex than LlamaIndex
Steeper learning curve
Fewer examples

When to Choose:

You’re building search systems
You need QA pipelines
You want flexible composition
Enterprise requirements

📖 Haystack Guide

🧪 Best for Research: AG2 (AutoGen)

Paradigm:      Conversation-based agents
Learning Time:  ~6-8 hours
Best For:       Research, experimentation, flexible agent interactions

Strengths:

Very flexible
Great for research
GroupChat for multi-agent
Large community (original AutoGen)
Powerful abstractions

Weaknesses:

More complex setup
Learning curve
Community moving to AG2

When to Choose:

You’re doing research
You need maximum flexibility
You want conversation-based agents
You need GroupChat

📖 AG2 Guide

🏢 Best for Enterprise: Semantic Kernel

Paradigm:      Plugin-based architecture
Learning Time:  ~6-8 hours
Best For:       Enterprise integration, Microsoft ecosystem

Strengths:

Enterprise-ready
Plugin architecture
Microsoft integration
.NET support
Great for existing systems

Weaknesses:

Less Pythonic
Fewer examples
Enterprise focus may be overkill for small projects

When to Choose:

You need enterprise patterns
You’re using Microsoft services
You want plugin architecture
C# or .NET environment

📖 Semantic Kernel Guide

☁️ Cloud-Specific Frameworks

Amazon Bedrock Agents

AWS-native agent framework

Best for AWS deployments
Managed models
Knowledge bases
Tight AWS integration

📖 Amazon Bedrock Guide

Microsoft Agent Framework

Azure-native agent framework

Best for Azure deployments
Cognitive Services
Graph integration
Enterprise Azure customers

📖 Microsoft Agent Framework Guide

Google ADK

Google’s agent toolkit

Best for GCP deployments
Gemini integration
Google Cloud services
Search integration

📖 Google ADK Guide

🧠 Model-Specific Frameworks

Anthropic Claude (Python & TypeScript)

Claude API integration

Best for Claude model features
Vision capabilities
Extended context
Tool use

📖 Claude Python Guide | 📖 Claude TypeScript Guide

Mistral Agents API

Mistral LLM integration

Best for Mistral models
Cost-effective
Function calling
EU-based models

📖 Mistral Guide

OpenAI Agents SDK (TypeScript)

TypeScript implementation of OpenAI Agents

Best for TypeScript projects
Node.js environments
Browser-based agents
Same primitives as Python version

📖 OpenAI TypeScript Guide

Decision Tree

Choosing Your Framework

Start
  ├─ New to agents? → SmolAgents or PydanticAI
  │
  ├─ Need type safety? → PydanticAI
  │
  ├─ Need multi-agent coordination?
  │  ├─ Role-based teams → CrewAI
  │  ├─ Lightweight primitives → OpenAI Agents SDK
  │  └─ Flexible conversations → AG2
  │
  ├─ Need complex workflows?
  │  ├─ Cycles/loops → LangGraph
  │  └─ Simple chains → CrewAI or OpenAI
  │
  ├─ Building data/search systems?
  │  ├─ RAG focus → LlamaIndex
  │  └─ Search focus → Haystack
  │
  ├─ Enterprise requirements?
  │  ├─ Microsoft ecosystem → Semantic Kernel
  │  ├─ AWS deployment → Amazon Bedrock
  │  ├─ Azure deployment → Microsoft Agent Framework
  │  └─ GCP deployment → Google ADK
  │
  ├─ Specific model?
  │  ├─ Claude → Anthropic Claude
  │  ├─ Mistral → Mistral API
  │  └─ OpenAI → OpenAI Agents
  │
  └─ TypeScript?
     ├─ Complex workflows → LangGraph TS
     ├─ Claude → Anthropic Claude TS
     └─ OpenAI Agents → OpenAI SDK TS

Feature Comparison

Core Features

Feature	SmolAgents	OpenAI	CrewAI	LangGraph	AG2	LlamaIndex	Haystack
Single Agent	✅	✅	✅	✅	✅	⚠️	⚠️
Multi-Agent	⚠️	✅	✅	✅	✅	⚠️	⚠️
Tool Use	✅	✅	✅	✅	✅	✅	✅
Handoffs	❌	✅	⚠️	⚠️	✅	❌	❌
State Management	⚠️	✅	⚠️	✅	✅	✅	✅
Memory	⚠️	✅	✅	⚠️	✅	✅	✅
Streaming	✅	✅	⚠️	✅	⚠️	⚠️	⚠️
Vision	✅	✅	✅	✅	✅	⚠️	⚠️
Type Safety	⚠️	⚠️	⚠️	⚠️	⚠️	⚠️	⚠️

Advanced Features

Feature	SmolAgents	OpenAI	CrewAI	LangGraph	AG2	LlamaIndex	Haystack
Cycles/Loops	❌	⚠️	❌	✅	✅	⚠️	⚠️
Parallel Execution	❌	✅	✅	✅	✅	✅	✅
Conditional Routing	❌	⚠️	⚠️	✅	✅	✅	✅
GraphUI Visualisation	❌	⚠️	⚠️	✅	⚠️	❌	❌
RAG Integration	⚠️	⚠️	⚠️	⚠️	⚠️	✅	✅
Monitoring/Tracing	⚠️	✅	⚠️	✅	⚠️	✅	✅
Production Ready	✅	✅	✅	✅	✅	✅	✅

Use Case Recommendations

Chatbots & Assistants

Top Choice: OpenAI Agents SDK or SmolAgents

Lightweight coordination
Good tool support
Streaming support
User-friendly

Also Consider: CrewAI, PydanticAI

Customer Service

Top Choice: OpenAI Agents SDK or CrewAI

Handoff capability
Multi-agent support
Good memory
Escalation support

Also Consider: AG2, LangGraph

Research & Development

Top Choice: AG2 or LangGraph

Maximum flexibility
GroupChat support
Complex interactions
Experimentation-friendly

Also Consider: OpenAI Agents SDK

Data Analysis & Processing

Top Choice: LlamaIndex or Haystack

Data indexing
RAG support
Pipeline composition
Knowledge retrieval

Also Consider: LangGraph, SmolAgents

Knowledge Retrieval (RAG)

Top Choice: LlamaIndex

Multiple vector stores
Great data connectors
Optimised for RAG
Production-ready

Also Consider: Haystack, PydanticAI

Search Systems

Top Choice: Haystack

Pipeline framework
Search-specific
QA support
Enterprise-ready

Also Consider: LlamaIndex

Code Generation

Top Choice: LangGraph or PydanticAI

Structured outputs
Complex workflows
Type safety
Code-focused

Also Consider: CrewAI, SmolAgents

Team Simulations

Top Choice: CrewAI

Role-based design
Team dynamics
Natural coordination
Task orchestration

Also Consider: AG2, OpenAI Agents SDK

Complex Workflows

Top Choice: LangGraph

Cycle support
Conditional routing
State management
Maximum flexibility

Also Consider: AG2, Haystack

Cloud-Native Deployments

Top Choice: Amazon Bedrock, Microsoft Framework, or Google ADK

Native integration
Managed services
Built-in scaling
Cost optimisation

Migration Paths

If you outgrow a framework:

SmolAgents → OpenAI Agents SDK: Add multi-agent needs
OpenAI Agents SDK → LangGraph: Need complex workflows/cycles
CrewAI → LangGraph: Need more flexibility
LlamaIndex → Haystack: Need more search-specific features
Single Framework → LangGraph: Integrating multiple frameworks

Community Size & Support

Framework	GitHub Stars	Community	Docs	LLM Support
LangGraph	~23k	Large	Excellent	50+
AG2	~27k	Large	Good	20+
CrewAI	~16k	Active	Good	10+
LlamaIndex	~28k	Large	Excellent	30+
LangChain	~81k	Huge	Excellent	50+
Haystack	~11k	Active	Good	10+
OpenAI SDK	~23k	Growing	Excellent	3
SmolAgents	~4k	Growing	Good	100+
PydanticAI	~2k	Growing	Good	10+

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