LLM agents that autonomously execute multi-step tasks represent the next evolution of AI applications. Multiple frameworks have emerged to simplify agent development, each with distinct philosophies and tradeoffs. Understanding these differences helps teams select frameworks aligned with their requirements and expertise.
Framework Philosophies
LangChain emphasizes composability with extensive integrations and chain abstractions. AutoGen focuses on multi-agent conversations where agents collaborate to solve problems. CrewAI provides role-based agent orchestration inspired by team dynamics. These philosophical differences shape how developers structure agent applications.
- LangChain offers the broadest ecosystem with hundreds of integrations and tools
- AutoGen excels at complex multi-agent scenarios requiring negotiation and collaboration
- CrewAI provides intuitive role-based abstractions mapping to business workflows
- All frameworks support major LLM providers including OpenAI, Anthropic, and open-source models
- Framework maturity and community support vary significantly
Production Considerations
Moving agents to production requires reliability, observability, and cost control. Evaluate frameworks on error handling, retry mechanisms, and debugging capabilities. Consider how easily you can monitor agent behavior and token usage. Production-ready agent systems need guardrails preventing runaway execution and cost overruns.