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.
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 research, this is a set of our papers:
To stay up to date or learn more, follow @DSPyOSS on Twitter or the DSPy page on LinkedIn.
The DSPy logo is designed by Chuyi Zhang.
@inproceedings{khattab2024dspy,
title={DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines},
author={Khattab, Omar and Singhvi, Arnav and Maheshwari, Paridhi and Zhang, Zhiyuan and Santhanam, Keshav and Vardhamanan, Sri and Haq, Saiful and Sharma, Ashutosh and Joshi, Thomas T. and Moazam, Hanna and Miller, Heather and Zaharia, Matei and Potts, Christopher},
journal={The Twelfth International Conference on Learning Representations},
year={2024}
}
@article{khattab2022demonstrate,
title={Demonstrate-Search-Predict: Composing Retrieval and Language Models for Knowledge-Intensive {NLP}},
author={Khattab, Omar and Santhanam, Keshav and Li, Xiang Lisa and Hall, David and Liang, Percy and Potts, Christopher and Zaharia, Matei},
journal={arXiv preprint arXiv:2212.14024},
year={2022}
}