What Can You Learn from the AI Frameworks Mud Fight?
Frameworks won’t save you — focus on what only you can build.
In the increasingly crowded world of AI tooling, even the friendliest feature comparison can spark a “mud fight” — and that’s exactly what happened when Harrison Chase put LangGraph head-to-head with CrewAI, Pydantic AI, LlamaIndex and others. What started as a straightforward review quickly laid bare the pressures of VC-fuelled saturation, looming competition from the likes of OpenAI and Google, and the race to stay relevant as native LLM capabilities evolve.
Adopters should prioritise increasingly capable native LLM features, critically vet any external frameworks for community health and commercial viability, and centre development on their own unique application logic rather than on fleeting framework quirks. Builders, meanwhile, must differentiate by carving out narrow vertical specialisations, leveraging proprietary datasets or deep domain expertise, and targeting well-defined user segments to avoid being commoditised or absorbed into base models.
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