dspy

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## DSPy: _Programming_—not prompting—Foundation Models

**Documentation:** [DSPy Docs](https://dspy.ai/)

[![PyPI Downloads](https://static.pepy.tech/personalized-badge/dspy?period=monthly)](https://pepy.tech/projects/dspy)


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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](https://dspy.ai/) or meet the community, seek help, or start contributing via this GitHub repo and our [Discord server](https://discord.gg/XCGy2WDCQB).


## Documentation: [dspy.ai](https://dspy.ai)


**Please go to the [DSPy Docs at dspy.ai](https://dspy.ai)**


## Installation


```bash
pip install dspy
```

To install the very latest from `main`:

```bash
pip install git+https://github.com/stanfordnlp/dspy.git
```

The current LM integration uses bundled native lm15 engines, without a LiteLLM
runtime dependency or fallback. See the [LM migration guide](docs/docs/community/normalized-lm-api-migration.md)
for breaking changes to provider options, custom LMs, text completions, and embeddings.



## 📜 Citation & Reading More

If you're looking to understand the framework, please go to the [DSPy Docs at dspy.ai](https://dspy.ai).

If you're looking to understand the underlying research, this is a set of our papers:

**[Jul'25] [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://arxiv.org/abs/2507.19457)**       
**[Jun'24] [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://arxiv.org/abs/2406.11695)**       
**[Oct'23] [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://arxiv.org/abs/2310.03714)**     
[Jul'24] [Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together](https://arxiv.org/abs/2407.10930)     
[Jun'24] [Prompts as Auto-Optimized Training Hyperparameters](https://arxiv.org/abs/2406.11706)    
[Feb'24] [Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models](https://arxiv.org/abs/2402.14207)         
[Jan'24] [In-Context Learning for Extreme Multi-Label Classification](https://arxiv.org/abs/2401.12178)       
[Dec'23] [DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines](https://arxiv.org/abs/2312.13382)   
[Dec'22] [Demonstrate-Search-Predict: Composing Retrieval & Language Models for Knowledge-Intensive NLP](https://arxiv.org/abs/2212.14024.pdf)

To stay up to date or lea