Field notes, protocols and teardowns on agentic AI, cloud architecture, LLM economics and DevOps practice — written by the crew of Sector 7G.
Every agent framework claims to do the same thing, and none of them do. This is a map of what each one is actually for — sorted by the shape of your problem, not by the vendor's pitch.
If you are building an AI feature into a TypeScript web application, these are the two obvious candidates — and they were designed for quite different jobs.
One is the validation library half of Python already depends on. The other is an agent framework built on it by the same team. The confusion is understandable and the answer is short.
OpenAI's own agent framework is notable for how little it does. Four concepts, a short learning curve, and a very direct relationship with one provider.
They are not competitors, and picking between them is a category error. LangChain gives you the parts; LangGraph decides the order they run in — and lets you see it.
Google ships two things that sound like the same thing. ADK is about agents and their orchestration; Genkit is about building AI features into an application.
Often, no. A while loop, a switch statement and a well-typed tool schema will take you further than the discourse suggests — and leave you with code you can debug.