In depth
Agent frameworks save you from re-implementing the loop, the tool registry, the cancellation logic, and the multi-agent dispatch. The choice between frameworks is mostly a choice about how you express the agent's shape: code (LangChain, CrewAI), graph (LangGraph), config (Digitorn). Each style has trade-offs. The honest take is in the comparison piece linked below.
Related concepts
LangChainThe most widely used Python framework for building LLM agents. Class-based, code-first.CrewAIA Python multi-agent framework that organises agents into role-based crews.YAML-first configDefining agents declaratively in a YAML file the runtime executes, instead of imperatively in framework code.
Read the deep dive
Digitorn vs LangChain: an honest comparison
Read article
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Engineering notes from the Digitorn team. No marketing, no launch announcements, no "10 prompts that will change your life". Just the things we write that we'd want to read.