It is a framework for programming language models (LMs) rather than relying on traditional prompting methods. DSPy, which stands for Declarative Self-improving Python, enables users to build modular AI systems quickly and efficiently by writing compositional Python code instead of crafting brittle prompts. It provides algorithms for optimizing prompts and weights, making it suitable for tasks ranging from simple classifiers to complex Retrieval-Augmented Generation (RAG) pipelines and Agent loops.
DSPy shifts the focus from manually tweaking prompt strings to programming with structured, declarative natural-language modules. Users define input/output behaviors as signatures and assign strategies for invoking LMs using modules like `dspy.Predict`, `dspy.ChainOfThought`, or `dspy.ReAct`. These modules are designed to be ergonomic, portable, and optimizable, allowing users to compose them into reliable AI systems. DSPy automatically expands signatures into prompts and parses typed outputs, decoupling system design from specific LM or prompting choices.
The framework supports integration with various LM providers, including OpenAI, Anthropic, and Databricks, through environment variables or API keys. It also works with local LMs via OpenAI-compatible endpoints, such as Ollama or SGLang. DSPy leverages LiteLLM to support dozens of LLM providers, offering a unified API for seamless interaction.
DSPy includes optimizers like `dspy.BootstrapRS`, `dspy.MIPROv2`, and `dspy.BootstrapFinetune`, which improve LM performance by synthesizing few-shot examples, refining natural-language instructions, or finetuning LM weights. These optimizers can be composed or combined into ensembles, enabling systematic scaling of inference-time and pre-inference-time compute. Optimization runs are cost-effective, typically ranging from a few cents to tens of dollars, depending on the LM and dataset.
The framework originated at Stanford NLP in February 2022, evolving from early compound LM systems like ColBERT-QA and Baleen. Since its release, DSPy has grown into a robust ecosystem with contributions from over 250 developers, advancing open-source AI research through optimizers, program architectures, and applications in diverse domains. DSPy empowers users to build, iterate, and optimize AI systems faster and with greater control, ensuring their programs improve over time with the latest advancements.
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