Optimize prompt and agent pipelines with DSPy programs and evaluators
Use DSPy to define modular LLM programs, metrics, and evaluation sets so an agent can optimize prompts and pipeline behavior with measurable feedback instead of ad hoc prompt editing.
Prerequisites
Python, DSPy, task examples, scoring metric or evaluator, target LLM provider credentials
Installation
Use the upstream install or setup path that matches your environment:
- pip install dspy
- pip install git+https://github.com/stanfordnlp/dspy.git
Requirements and caveats from upstream:
- 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 docu...
Basic usage or getting-started notes:
bash
To install the very latest from main:
📜 Citation & Reading More
Extracted from upstream docs: https://raw.githubusercontent.com/stanfordnlp/dspy/HEAD/README.md