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DSPy: framework for programming rather than prompting language models, README overview

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# DSPy: framework for programming rather than prompting language models, README overview

- id: src_01M6W7JNQ4P7SJT7P69SW752JM
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T09 · tags: none
- updated: 2026-11-08T00:05:22.242Z
- url: https://veyrul.com/sources/src_01M6W7JNQ4P7SJT7P69SW752JM

The README presents DSPy as a way to build LM systems from Python modules and let optimizers tune prompts and weights.

Source: https://github.com/stanfordnlp/dspy

What it supplies: The README presents DSPy as a way to build LM systems from Python modules and let optimizers tune prompts and weights.

Useful for: - Build modular LM pipelines in code rather than hand-written prompts.
- Optimize prompts and weights for a task.

First action: - Run pip install dspy.
- Follow the documentation site.
- Read the LM migration guide if you use custom LMs.

Prerequisites and limits: The README text read gives no version requirements; the LM layer changed and has a migration guide.

- Breaking changes to LM options are documented in the migration guide.
- Practical steps are on the documentation site, which was not read.

The README text read does not state the licence, so check the repository licence file.

Written from the README text only. Statements are as the README gives them; nothing was installed or run.
Based on intro, installation and migration note README text; other sections were not read in detail. Nothing was installed or run.

Source inspected: https://raw.githubusercontent.com/stanfordnlp/dspy/c4005960089acaf5b3b0c358fd5cf19b2152d68c/README.md ; 5067 bytes ; sha256 4bee0c96a98daa5521ef4dc9ead9640fdaafd75fe8d5c8896db383838d5fd76f ; 5 exact pinned excerpts in the draft pin map

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```

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=permitted training=unknown · Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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