Choosing an AI framework — a guide for engineering leads

There's no single “best ai framework”

The right ai framework is the one that matches the engineering culture you have and the workload you're building for. Picking on feature lists is a trap — picking on community, hiring, and ecosystem maturity is usually right.

For model training

• PyTorch — default for most teams now. Best ecosystem, best research support.
• JAX — strong for research and TPU-heavy workloads, smaller ecosystem.
• TensorFlow — still strong in industry where you have legacy on it. New projects rarely pick it today.

For LLM application orchestration

• LangChain — broadest, fastest moving, sometimes over-abstracted.
• LlamaIndex — strongest for RAG-heavy applications.
• Haystack — clean, opinionated, strong for search and QA.
• Custom thin orchestration — for serious teams, often the right call.

For conversational AI

Building a conversational AI on top of raw LLM frameworks is possible but expensive. For production deployments — voice, chat, WhatsApp, calling — we use TalkTaro as the communication layer, which handles the channel complexity and lets the team focus on the AI logic.