Direct SDKs (no framework) vs DSPy

The strongest LangChain alternative today is often no framework at all. Modern models ship native tool calling, structured outputs, and long context — the very things LangChain was built to scaffold — so the official Anthropic/OpenAI/Mistral SDKs (MIT/Apache-licensed) plus a small amount of your own orchestration code covers most real applications. You keep full debuggability (a stack trace is your code, not five layers of framework), zero dependency churn, and total freedom to swap providers. Engineering write-ups since 2024 keep landing on the same conclusion: start direct, add a framework only when a specific need demands it — not the other way around.

DSPy (MIT, from Stanford NLP, ~34k stars) replaces hand-tuned prompt strings with something closer to software engineering: you declare what a step takes in and produces (a Signature), compose modules, and let an optimizer compile the best prompts and few-shot examples against your own metric and data. When quality matters and you're tired of prompt whack-a-mole, DSPy turns the tuning into a reproducible build step. It's the most intellectually distinct alternative on this list.