wicked-garden-agentic-frameworks

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 profiles
• refs/framework-profiles-2.md - AutoGen, Pydantic AI, LlamaIndex profiles + comparison matrix
• refs/migration-patterns-paths.md - Common migration paths between frameworks
• refs/migration-patterns-testing.md - Migration testing, rollback, and effort estimates