textgrad
Automatic 'Differentiation' via Text using Large Language Models to Backpropagate Textual Gradients
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
TextGrad optimizes prompts using large language models to backpropagate textual gradients.
Good fit when
- When optimizing complex prompting for large language models in production due to its published effectiveness.
- For cutting-edge prompt optimization research, thanks to its integration options beyond just pip.
Avoid when
- If only basic and traditional manual tuning methods are needed for simpler use cases.
- Avoid if strict version control is required since the bleeding edge installation points to GitHub directly.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (388d since push)
- As of Aug 18, 2026
- Provenance
- Not a fork · Organization account
- As of Aug 18, 2026
- Security (OSV)
- 19 low (19 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install textgradHow it fits your stack(8)
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Evidence and technical details
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Overview
TextGrad is a tool that leverages large language models to optimize prompts by backpropagating textual gradients.
Capability facts
- Languages
- python
Source: github.language · Aug 18, 2026
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Compatibility
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README
Installation You can install TextGrad using any of the following methods. With : With : :bulb: The conda forge package for is maintained here. Bleeding edge installation with : Installing textgrad with vllm : See here for more details on various methods of pip installation.
For agents
This page has a .md twin and JSON over the API.