DSPy

DSPy

DSPy (Declarative Self-improving Python) is Stanford NLP's framework for algorithmically optimizing LLM prompts and weights. Instead of hand-crafting prompts, you write Python modules with typed signatures; DSPy's optimizers ("teleprompters") automatically generate, evaluate, and refine the best prompts for your task and model.

Key Features

• Signatures — declare input/output fields with descriptions; DSPy handles prompt construction
• Modules — dspy.Predict, dspy.ChainOfThought, dspy.ReAct, dspy.ProgramOfThought, and more
• Optimizers — BootstrapFewShot, MIPRO, BayesianSignatureOptimizer auto-tune prompts with labeled examples
• Assertions — declare constraints and DSPy retries until they're satisfied
• Model-agnostic — works with OpenAI, Anthropic, Ollama, HuggingFace, Databricks, and 20+ providers
• Composable — nest modules into complex multi-stage pipelines that optimize end-to-end

Quick Start

pip install dspy

import dspy

lm = dspy.LM("openai/gpt-4o-mini")
dspy.configure(lm=lm)

class QA(dspy.Signature):
    question: str = dspy.InputField()
    answer: str = dspy.OutputField(desc="concise factual answer")

predict = dspy.Predict(QA)
result = predict(question="What year was Python created?")
print(result.answer)

Install via ai-supply

npx ai-supply add dspy-llm-programming

Curated mirror of the open-source DSPy project (MIT). Install upstream from the repository.