Dspy

If you're tired of manually tuning prompts or building fragile LM pipelines, DSPy from Stanford NLP gives you a declarative framework that optimizes them automatically. You define signatures (inputs to outputs), compose modules like ChainOfThought or ReAct, then run optimizers like BootstrapFewShot to improve performance using your training data. It's designed for complex systems like RAG pipelines, multi-hop reasoning, or agents where you need reliability and maintainability. The learning curve is real, but once you get past the abstraction layer, you stop writing brittle prompt strings and start building systems that actually improve themselves. Worth it if you're building production AI workflows that need to scale beyond one-off prompts.

skills.sh installs: 1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill dspy --agent claude-code

Installs into .claude/skills of the current project.

Files

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)

SKILL.md on GitHub

Details

Repository: github.com/orchestra-research/ai-research-skills
Categories: DevOps & CI/CD, AI & Agent Building, Data Science & ML
First seen: Jun 3, 2026