Overview ↗

This section demonstrates practical applications of DSPy across different domains and use cases. Each tutorial shows how to build production-ready AI systems using DSPy’s modular programming approach.

Featured Examples#

📄 Generating llms.txt#

Learn how to create AI-powered documentation generators that analyze codebases and produce structured, LLM-friendly documentation following the llms.txt standard.

Key Concepts: Repository analysis, meta-programming, documentation generation

📧 Email Information Extraction#

Build intelligent email processing systems that classify messages, extract entities, and identify action items using DSPy’s structured prediction capabilities.

Key Concepts: Information extraction, classification, text processing

🧠 Memory-Enabled ReAct Agents with Mem0#

Create conversational agents with persistent memory using DSPy ReAct and Mem0 integration for context-aware interactions across sessions.

Key Concepts: Memory systems, conversational AI, agent persistence

💰 Financial Analysis with Yahoo Finance#

Develop financial analysis agents that fetch real-time market data, analyze news sentiment, and provide investment insights using LangChain tool integration.

Key Concepts: Tool integration, financial data, real-time analysis

🔄 Automated Code Generation from Documentation#

Build a system that automatically fetches documentation from URLs and generates working code examples for any library using DSPy’s intelligent analysis.

Key Concepts: Web scraping, documentation parsing, automated learning, code generation

🎮 Building a Creative Text-Based AI Game#

Create an interactive text-based adventure game with dynamic storytelling, AI-powered NPCs, and adaptive gameplay using DSPy’s modular programming approach.

Key Concepts: Interactive storytelling, game state management, character progression, AI-driven narratives