dspy

Build modular RAG pipelines by declaratively composing prompts, reasoning steps, and tools into reusable components. This approach replaces ad-hoc prompt engineering with structured signatures and modules, enabling robust retrieval-augmented generation across projects.

Declarative language model programming assembles complex AI systems by defining signatures, modules, and workflows like ChainOfThought or ReAct. Use it to reduce ad-hoc prompt engineering and enable reusable, type-safe pipelines for research, agents, and classification tasks.

Optimize prompts automatically by integrating teleprompters like BootstrapFewShot, MIPRO, or COPRO into your declarative workflows. These optimizers improve your language model prompts systematically using data, enhancing reasoning and tool integration without manual tuning.

Yes, you can use reasoning modules like ChainOfThought and ReAct with OpenAI or Anthropic providers. The system requires a Python environment with optional language model providers configured, enforcing type-safety and reproducibility through structured outputs.

The best way to structure multi-stage reasoning workflows is by declaratively composing modules like Predict, ChainOfThought, ReAct, and ProgramOfThought. This modular approach provides clean interfaces for building tool-using agents and complex reasoning steps.

Yes, you need a Python environment with the DSPy framework installed and optional language model providers like OpenAI or Anthropic configured. This setup enforces safety, type-safety, and reproducibility through structured outputs and defined signatures.