stanfordnlp

    stanfordnlp/dspy

    DSPy: The framework for programming—not prompting—language models

    backend
    Python
    MIT
    38.9K stars
    3.5K forks
    38.9K GitHub watchers
    Updated Oct 31, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    5 releases in the last 90 days, the latest 18 days ago, with 411 contributors on GitHub.

    Peaked at #48 in the weekly ranking the week of Jul 5, its best in the past year.

    Use Cases & Benefits

    • Provides a Python framework for programming language models through compositional code instead of prompt engineering.
    • Enables fast iteration and optimization of modular AI systems by combining prompt and weight tuning for improved output quality.
    • Use for building complex retrieval-augmented generation pipelines that integrate multiple language model components.
    • Use for developing self-improving agent loops that refine their behavior through declarative programming.
    • Use for creating customizable classifiers and AI workflows that benefit from automated prompt and parameter optimization.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    MIT

    Permissive: commercial use, modification and redistribution are allowed with attribution.

    Community files

    • README: present
    • Contributing guide: present

    Watch-outs

    No watch-outs from GStars' checks.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    DSPy is the framework for programming—rather than prompting—language models. It allows you to iterate fast on building modular AI systems and offers algorithms for optimizing their prompts and weights, whether you're building simple classifiers, sophisticated RAG pipelines, or Agent loops.

    DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, you write compositional Python code and use DSPy to teach your LM to deliver high-quality outputs. Learn more via our official documentation site or meet the community, seek help, or start contributing via this GitHub repo and our Discord server.

    Please go to the DSPy Docs at dspy.ai

    Excerpt from the stanfordnlp/dspy README on GitHub (MIT license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Aug 10: +160Aug 11: +78Aug 12: +38Aug 13: +12Aug 14: +30Aug 15: +8

    +2,342 stars over 90 days across 13 snapshot days; best day +160 on Aug 10.

    Forks

    3.5K+307

    Open issues

    739+111

    Compared with peers in Backend

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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