DSPy is a Python framework for programming language models (LMs) rather than prompting them. It provides a declarative API for defining LM programs using typed signatures, predictors (e.g., Predict, ChainOfThought, ReAct, RLM), and adapters (Chat, JSON, XML) that format and parse LM interactions. The framework includes a rich optimization suite (teleprompters) that can bootstrap demonstrations, optimize instructions (MIPROv2, COPRO), fine-tune models, and perform code-level optimization (GEPA) for Flex modules. It supports multiple LM backends (LiteLLM, native lm15) with caching, streaming, error handling, and cost tracking. The architecture is modular: clients manage LM connections, engines abstract execution backends, adapters handle message formatting/parsing, predictors implement reasoning patterns, signatures define I/O contracts, and utilities provide caching, callbacks, parallelism, and other cross-cutting concerns. The codebase demonstrates high cohesion and separation of concerns, with extensive test coverage and documentation. However, it is a large project with some experimental features, deprecation warnings, and a vendored lm15 library that adds maintenance overhead. Overall, it is a production-ready research framework widely used in the NLP community for building and optimizing LM programs.