M4A

DSPy

Stanford's framework for programming with language models

open-sourcebetapythonstanfordoptimizationopen-sourceresearch
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Memory Types

semantic

Integrations

openai, anthropic, cohere, together, ollama


Overview


DSPy (Demonstrate, Search, Predict) is a framework from Stanford NLP that fundamentally rethinks how to program with language models. Instead of manually crafting prompts, DSPy treats prompts as parameters to be optimized. It introduces a programming model where you declare what you want to do (signatures) and let the framework optimize how to do it (prompts).


The framework uses compilation and optimization techniques to automatically generate and refine prompts based on your data and metrics. This approach leads to more robust LLM programs that adapt to different models and datasets without manual prompt engineering.


Key Features


  • **Signature-Based Programming**: Declare tasks instead of writing prompts
  • **Automatic Optimization**: Optimizes prompts based on metrics
  • **Modular Components**: Composable building blocks for LLM programs
  • **Teleprompters**: Compile programs to optimize performance
  • **Few-Shot Learning**: Automatically generates examples
  • **Model Agnostic**: Works across different LLMs
  • **Evaluation Framework**: Built-in metrics and evaluation
  • **Research-Driven**: Based on latest Stanford research

    • When to Use DSPy


    DSPy is ideal for:

  • Research projects exploring LLM capabilities
  • Applications requiring systematic prompt optimization
  • Teams wanting to minimize manual prompt engineering
  • Projects needing to work across multiple LLMs
  • Complex pipelines that are hard to prompt manually
  • Applications where performance metrics are well-defined

    • Pros


  • Eliminates manual prompt engineering
  • Systematic optimization approach
  • Strong research foundation from Stanford
  • Works across different models
  • Reduces brittleness of prompt-based systems
  • Novel and innovative approach
  • Active research and development
  • Good for reproducible LLM research

    • Cons


  • Still relatively new and evolving
  • Steeper learning curve due to new paradigm
  • Less production battle-testing
  • Smaller community and ecosystem
  • Optimization can be time-consuming
  • May not beat expert manual prompts for all tasks
  • Documentation still developing
  • Requires good evaluation metrics

    • Pricing


  • **Open Source**: Free, MIT license
  • **Self-Hosted**: Free to use anywhere
  • **No Commercial Offering**: Pure open-source project

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