research-dspy

DSPy: Declarative Language Model Programming

When to Use This Skill

Use DSPy when you need to:

• Build complex AI systems with multiple components and workflows
• Program LMs declaratively instead of manual prompt engineering
• Optimize prompts automatically using data-driven methods
• Create modular AI pipelines that are maintainable and portable
• Improve model outputs systematically with optimizers
• Build RAG systems, agents, or classifiers with better reliability

GitHub Stars: 22,000+ | Created By: Stanford NLP

Installation

# Stable release
pip install dspy

# Latest development version
pip install git+https://github.com/stanfordnlp/dspy.git

# With specific LM providers
pip install dspy[openai]        # OpenAI
pip install dspy[anthropic]     # Anthropic Claude
pip install dspy[all]           # All providers

Quick Start

Basic Example: Question Answering

import dspy

# Configure your language model
lm = dspy.Claude(model="claude-sonnet-4-5-20250929")
dspy.settings.configure(lm=lm)

# Define a signature (input → output)
class QA(dspy.Signature):
    """Answer questions with short factual answers."""
    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

# Create a module
qa = dspy.Predict(QA)

# Use it
response = qa(question="What is the capital of France?")
print(response.answer)  # "Paris"

Chain of Thought Reasoning

import dspy

lm = dspy.Claude(model="claude-sonnet-4-5-20250929")
dspy.settings.configure(lm=lm)

# Use ChainOfThought for better reasoning
class MathProblem(dspy.Signature):
    """Solve math word problems."""
    problem = dspy.InputField()
    answer = dspy.OutputField(desc="numerical answer")

# ChainOfThought generates reasoning steps automatically
cot = dspy.ChainOfThought(MathProblem)

response = cot(problem="If John has 5 apples and gives 2 to Mary, how many does he have?")
print(response.rationale)  # Shows reasoning steps
print(response.answer)     # "3"