From wicked-garden
Agentic framework landscape, comparison, and selection guide for choosing the right framework for your use case. Use when: "which agent framework", "compare frameworks", "LangGraph vs CrewAI", "framework selection"
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Comprehensive guide to agentic frameworks, their strengths, and how to choose the right one.
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Comprehensive guide to agentic frameworks, their strengths, and how to choose the right one.
| Framework | Language | Best For | Maturity | Learning Curve |
|---|---|---|---|---|
| Anthropic ADK | TypeScript | Claude-specific, production | High | Low |
| LangGraph | Python | Complex workflows, state | High | Medium |
| CrewAI | Python | Role-based teams | Medium | Low |
| AutoGen | Python | Multi-agent conversations | Medium | Medium |
| Pydantic AI | Python | Type-safe agents | Medium | Low |
| OpenAI Agents SDK | Python | OpenAI-specific | Low | Low |
| LlamaIndex Agents | Python | RAG-heavy applications | High | Medium |
| Haystack | Python | Production pipelines | High | Medium |
| Semantic Kernel | C#/Python | Microsoft ecosystem | Medium | Medium |
| LangChain | Python | Rapid prototyping | High | Medium-High |
| Agency Swarm | Python | OpenAI Assistants API | Low | Low |
| Dify | Low-code | No-code workflows | Medium | Very Low |
Simple Sequential:
Complex Workflows:
Team-Based:
No Built-in State: LangChain, Pydantic AI
Checkpointed State: LangGraph (built-in), ADK (context preservation)
Distributed State: Custom implementation needed for all
Extensive Tool Libraries: LangChain (largest ecosystem), LlamaIndex (RAG-focused), Haystack (production tools)
Easy Tool Definition: Pydantic AI (type-safe), ADK (TypeScript decorators), OpenAI Agents SDK (function calling)
Custom Tools: All frameworks support custom tools
Built-in Retry/Fallback: ADK (comprehensive), LangGraph (error handling nodes), Haystack (pipeline error handling)
Manual Error Handling: CrewAI, AutoGen, Pydantic AI
Native Tracing: LangSmith (for LangChain/LangGraph), Braintrust (for ADK)
Third-Party Integration: All support OpenTelemetry, most support LangFuse, Arize
See refs/framework-profiles-1.md (ADK, LangGraph, CrewAI) and refs/framework-profiles-2.md (AutoGen, Pydantic AI, LlamaIndex) for detailed profiles.
Best for: Production Claude applications Strengths: TypeScript with type safety, built-in context management, comprehensive error handling, delegated workflows Weaknesses: Claude-only, TypeScript/Node only, smaller community When to choose: Building on Claude exclusively, TypeScript/Node stack, need production-ready patterns
Best for: Complex stateful workflows Strengths: State machine abstraction, built-in checkpointing, human-in-the-loop support, time-travel debugging Weaknesses: Steeper learning curve, can be overkill, more boilerplate When to choose: Complex workflows with branches/loops, need state persistence, want human approval gates
Best for: Role-based agent teams Strengths: Intuitive role/task abstraction, simple API, good for hierarchical teams Weaknesses: Less mature, limited state management, fewer production features When to choose: Team-based workflows, quick prototyping, straightforward delegation
Best for: Multi-agent conversations Strengths: Flexible conversation patterns, group chat capabilities, human-in-the-loop Weaknesses: Can be verbose, conversation management complexity When to choose: Agents need to debate/collaborate, conversational workflows
Best for: Type-safe Python agents Strengths: Type safety via Pydantic, simple clean API, dependency injection, multi-provider Weaknesses: New/less mature, smaller ecosystem, limited orchestration patterns When to choose: Want type safety, simple agent use cases, already using Pydantic
Best for: RAG-heavy applications Strengths: Excellent retrieval capabilities, query planning, tool use with data Weaknesses: Best for RAG use cases, heavier framework When to choose: Heavy RAG requirements, complex data retrieval, query planning needs
Start: What's your primary use case?
├─ Complex stateful workflow with branches/loops
│ └─ Use: LangGraph
├─ Role-based team of agents
│ └─ Use: CrewAI or ADK
├─ RAG-heavy application
│ └─ Use: LlamaIndex Agents
├─ Multi-agent conversations/debates
│ └─ Use: AutoGen
├─ Simple sequential workflow
│ ├─ TypeScript?
│ │ └─ Use: ADK
│ └─ Python?
│ └─ Use: Pydantic AI or LangChain
├─ Production pipeline
│ └─ Use: Haystack or ADK
└─ Maximum flexibility
└─ Build from scratch or use LangGraph
Python Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Pydantic AI, LlamaIndex
TypeScript Frameworks: Anthropic ADK
C# Frameworks: Semantic Kernel
Multi-Provider (LLM-agnostic): LangChain, LangGraph, CrewAI, AutoGen, Pydantic AI
Single-Provider (Optimized): ADK (Claude), OpenAI Agents SDK (OpenAI)
Most Production-Ready: Anthropic ADK, LangGraph, Haystack, LlamaIndex
Good for Production: CrewAI, LangChain, AutoGen
Early/Experimental: Pydantic AI, OpenAI Agents SDK, Agency Swarm
Build from scratch if: very simple use case, specific requirements unmet, want maximum control, or learning exercise. Framework overhead not worth it for single LLM calls, static prompts, or no agent behavior.
Just getting started: CrewAI or Pydantic AI | State management: LangGraph | TypeScript: ADK | RAG: LlamaIndex | Team-based: CrewAI or ADK | Max flexibility: LangGraph | Production Claude: ADK
refs/framework-profiles-1.md - ADK, LangGraph, CrewAI detailed profilesrefs/framework-profiles-2.md - AutoGen, Pydantic AI, LlamaIndex profiles + comparison matrixrefs/migration-patterns-paths.md - Common migration paths between frameworksrefs/migration-patterns-testing.md - Migration testing, rollback, and effort estimates