DSPy
Program with language models instead of prompting them
DSPy is a framework for algorithmically optimizing language model pipelines. It replaces hand-crafted prompts with learnable modules that adapt to your task, reducing manual engineering.
DSPy abstracts LM calls into composable modules with automatic optimization. Define your task logic declaratively, then use DSPy's optimizers to learn prompt strategies and fine-tune weights across your pipeline. Supports multiple LM backends and includes built-in assertions for program correctness.
Pros
- Automate prompt engineering with data-driven optimization
- Compose modular LM programs with clean Python syntax
- Switch between LM providers without rewriting logic
- Track and improve program performance systematically
Cons
- Steeper learning curve than direct prompting
- Optimization requires labeled examples or metrics
- Abstraction overhead may complicate debugging
Best For
ML engineers and researchers building production LM systems who want programmable, optimizable pipelines over manual prompt iteration.
Pricing
Free Forever
- Core features
- Email support
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