by stanfordnlp · Agent Tool · ★ 34.4k
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DSPy: Programming—not prompting—Foundation Models Documentation: DSPy Docs 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. Documentation: dspy.ai Please go to the DSPy Docs at dspy.ai Installation To install the very latest from : 📜 Citation & Reading More If you're looking to understand the framework, please go to the DSPy Docs at dspy.ai. If you're looking to understand the underlying
| Stars | 34,378 |
| Forks | 2,884 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 44.026/100 |
| Open Issues | 465 |
| Last Updated | 2026-05-12 |
| Created | 2023-01-09 |
| Platforms | python |
| Est. Tokens | ~11557k |
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dspy is DSPy: The framework for programming—not prompting—language models. It is categorized as a Agent Tool with 34.4k GitHub stars.
dspy is primarily written in Python.
You can find installation instructions and usage details in the dspy GitHub repository at github.com/stanfordnlp/dspy. The project has 34.4k stars and 2884 forks, indicating an active community.
dspy is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to dspy on Agent Skills Hub include ollama, langflow, langchain. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.