Python vs JavaScript
- JavaScript/TypeScript: Web-first; one language across UI, server, and edge. Rich frameworks for real-time UX.
- Python: Data/ML-first; dominant in notebooks, research, and model-adjacent tooling.
Developer Experience: TypeScript vs Python Typing
- TypeScript gives end-to-end types (client ↔ server ↔ LLM schema) with minimal friction.
- Python typing is solid, but static types aren’t as “first-class” in day-to-day DX.
Tool/Function Calling & Routers
- Both ecosystems map tool definitions → JSON-Schema well.
- TS ergonomics: infer types from Zod and auto-wire handlers.
- Python ergonomics: Pydantic models as contracts; dataclass mental model; strong validators.
Frameworks & Libraries
- JS/TS strengths:
- Vercel AI SDK / tiny inference clients; tight UI integration.
- LangChain.js and LlamaIndex TS (good parity for common patterns).
- Python strengths:
- Larger/earlier ecosystem for data/ML (Transformers, evaluation libs, local model servers).
- Agentic frameworks and eval tools tend to land first/mature faster in Python.
Performance & Cost Considerations
- I/O-bound chat orchestration suits either runtime; JS often simpler at high concurrency.
- CPU/GPU-bound tasks (reranking, embedding at scale, local models) favor Python stacks.
- Keep compute separate from chat orchestration to control costs (microservices/queues).
Three Reference Architectures
- All-in JS/TS:
- Next/SvelteKit + Vercel AI SDK, Zod schemas, serverless RAG calls, hosted models.
- Pros: speed to market; Cons: less ideal for heavy offline/eval.
- All-in Python:
- FastAPI + Pydantic, Celery/Redis workers, strong eval and local model serving.
- Pros: ML depth; Cons: more front-end plumbing for streaming UX.
- Hybrid (recommended default for teams like yours):
- TS front + gateway (schemas in Zod → JSON-Schema).
- Python services for embeddings/reranking/eval; communicate via HTTP/gRPC/queues.
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