| name | wicked-garden-agentic-frameworks |
| description | Use when selecting or comparing agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, etc.) —
curated comparison by use case, language, and maturity. Gets latest context via Context7 when available.
NOT for reviewing existing agentic code (use review-methodology) or architecture patterns (use agentic-patterns).
|
| portability | portable |
| phase_relevance | ["design","review"] |
| archetype_relevance | ["*"] |
Agentic Frameworks
Comprehensive guide to agentic frameworks, their strengths, and how to choose the right one.
Quick Comparison Table
| 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 |
Selection Criteria
1. Orchestration Capabilities
Simple Sequential:
- LangChain (chains)
- Haystack (pipelines)
- Pydantic AI
Complex Workflows:
- LangGraph (state machines)
- ADK (delegated workflows)
- AutoGen (conversation patterns)
Team-Based:
- CrewAI (role-based)
- Agency Swarm (org structure)
2. State Management
No Built-in State: LangChain, Pydantic AI
Checkpointed State: LangGraph (built-in), ADK (context preservation)
Distributed State: Custom implementation needed for all
3. Tool Integration
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
4. Error Handling
Built-in Retry/Fallback: ADK (comprehensive), LangGraph (error handling nodes), Haystack (pipeline error handling)
Manual Error Handling: CrewAI, AutoGen, Pydantic AI
5. Observability
Native Tracing: LangSmith (for LangChain/LangGraph), Braintrust (for ADK)
Third-Party Integration: All support OpenTelemetry, most support LangFuse, Arize
Framework Profiles
See refs/framework-profiles-1.md (ADK, LangGraph, CrewAI) and refs/framework-profiles-2.md (AutoGen, Pydantic AI, LlamaIndex) for detailed profiles.
Anthropic Agent Developer Kit (ADK)
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
LangGraph
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
CrewAI
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
AutoGen
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
Pydantic AI
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
LlamaIndex Agents
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
Decision Tree
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
Language Considerations
Python Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Pydantic AI, LlamaIndex
- Largest ecosystem, most tutorials/examples, best for data science/ML integration
TypeScript Frameworks: Anthropic ADK
- Better type safety, Node.js ecosystem, good for web applications
C# Frameworks: Semantic Kernel
- Microsoft ecosystem, .NET integration
Multi-Provider vs Single-Provider
Multi-Provider (LLM-agnostic): LangChain, LangGraph, CrewAI, AutoGen, Pydantic AI
- Can switch between OpenAI, Anthropic, etc.
- More flexibility but may not leverage provider-specific features
Single-Provider (Optimized): ADK (Claude), OpenAI Agents SDK (OpenAI)
- Better integration with specific provider
- Access to provider-specific features but less flexibility
Production Readiness
Most Production-Ready: Anthropic ADK, LangGraph, Haystack, LlamaIndex
Good for Production: CrewAI, LangChain, AutoGen
Early/Experimental: Pydantic AI, OpenAI Agents SDK, Agency Swarm
When NOT to Use a Framework
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.
Quick Recommendations
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
References
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
