Agent Framework Landscape

Agent frameworks abstract complexity—tool calling, memory, orchestration—letting you focus on logic

The ecosystem exploded from 2022-2024: LangChain, AutoGen, CrewAI, Semantic Kernel dominate

LangChain offers max flexibility and integrations but has a steep learning curve

AutoGen excels at conversational multi-agent systems with built-in code execution

CrewAI provides cleanest API for role-based teams with intuitive task delegation

Semantic Kernel is best for enterprise/Microsoft ecosystems with multi-language support

Feature comparison matters: evaluate core features, integrations, and developer experience

LangChain leads in RAG, vector DB support, and LLM provider options (5/5 scores)

AutoGen and CrewAI score highest (5/5) in multi-agent coordination capabilities

CrewAI has the gentlest learning curve (4/5), LangChain the steepest (2/5)

Sequential chains work for 60% of use cases—simple, fast, debuggable

ReAct agents add flexibility and tool selection but increase latency 2-5x

Multi-agent teams shine for complex tasks benefiting from specialization

Graph workflows enable conditionals, parallel execution, and loops for advanced logic

Start simple with chains, add complexity only when needed—measure and iterate