DSPy
stanfordnlp/dspy
Program agents with optimized prompts/pipelines — don’t only hand-write prompts. GitHub: stanfordnlp/dspy (~37k stars).
GitHub repository
Overview
DSPy (stanfordnlp/dspy) is listed in LimeDock Directories as an AI agent resource. Program agents with optimized prompts/pipelines — don’t only hand-write prompts. Group: memory-infra. Community size: ~37k GitHub stars. Repo (copy/paste): https://github.com/stanfordnlp/dspy This page is a plain-English guide so searches for the GitHub project can land on how teams actually use it.
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Link
Installation guide
Open the GitHub repository, then follow the README for your stack.
Official GitHub repository: https://github.com/stanfordnlp/dspy GitHub path: stanfordnlp/dspy
How to open it: 1. Copy: https://github.com/stanfordnlp/dspy 2. Clone or follow the README for your OS/stack. 3. Start with the smallest example before production use.
How to use it
**Simple example** Prompt spaghetti isn’t scaling; you want programmed, optimizable LLM pipelines.
1. Install DSPy. 2. Define a signature/module. 3. Optimize on a small dataset of examples.
Example prompts
- “Rewrite our FAQ bot as a DSPy module.”
- “DSPy vs raw LangChain prompts?”
- “How does optimization work intuitively?”
Use cases and examples
- Evaluate stanfordnlp/dspy as an AI agent building block
- Pilot DSPy on one staging workflow
- Compare against agents you already pay for
- Document a team playbook after the pilot
Prerequisites
- Python
- Labeled examples help
Tips
- Examples beat vibes.
- Keep a held-out eval set.