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Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
Passed 23 of 23 test cases with the skill, and 22 of 23 without it.
1 fixed22 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
In DSPy, to inspect exact prompt text sent to the model and raw responses returned during execution, how is tracing configured and accessed?
| Checked | Without skill | With skill |
|---|---|---|
| configures trace parameter in dspy settings | Missed | Passed |
| accesses dspy settings trace | Missed | Passed |
The instructions your model reads when the skill loads.
Use DSPy when you need to:
GitHub Stars: 22,000+ | Created By: Stanford NLP
# Stable release
pip install dspy
# Latest development version
pip install git+https://github.com/stanfordnlp/dspy.git
# With specific LM providers
pip install dspy[openai] # OpenAI
pip install dspy[anthropic] # Anthropic Claude
pip install dspy[all] # All providers
import dspy
# Configure your language model
lm = dspy.Claude(model="claude-sonnet-4-5-20250929")
dspy.settings.configure(lm=lm)
test cases passed, vs 22 / 23 without
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