dex

OpenAI recently released a guide on building agents which contains some misguided takes There's a lot of FUD, confusion, hype, and noise around agents I wrote a blog on how to think about agent frameworks. Includes: Background Info - What is an agent? - What is hard about building agents? - What is LangGraph? Flavors of agentic frameworks - “Agents” vs “workflows” - Declarative vs non-declarative - Agent abstractions - Multi agent Common Questions - What is the value of a framework? - As the models get better, will everything become agents instead of workflows? - What did OpenAI get wrong in their take? - How do all the agent frameworks compare?

"12 Factor Agents - Principles for building reliable LLM applications" Great piece from @dexhorthy. A lot of the principles really resonate with LangGraph ethos. Would people be interested in a blog on how LangGraph enables these? #2: Own your prompts (no hidden prompts in LangGraph) #3: Own your context windows (in LangGraph you control exactly what goes into LLM!) #5: Unify execution state and business state (we invested a lot in a production-ready persistence layer) #6: Launch/Pause/Resume with simple APIs (this persistence layer enables these interaction patterns) #8: Own your control flow (this is literally exactly the motivating reason for LangGraph) #10: Small, focused agents (subgraphs!)