textgrad
Automatic 'Differentiation' via Text using Large Language Models to Backpropagate Textual Gradients
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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 2d
- Provenance
- Not a fork · Organization account
- As of 2d
- Security (OSV)
- 19 low (19 low)
- As of 1mo
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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Tags
README
Installation
You can install TextGrad using any of the following methods.
With pip:
pip install textgrad
With conda:
conda install -c conda-forge textgrad
:bulb: The conda-forge package for
textgradis maintained here.
Bleeding edge installation with pip:
pip install git+https://github.com/zou-group/textgrad.git
Installing textgrad with vllm:
pip install textgrad[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.