TextGrad

Spec sheet

Product

TextGrad is a Python package for building LLM-gradient pipelines that optimize text.Read 5 Oct 2026 · textgrad.com

Intended users

The site says TextGrad mirrors PyTorch’s syntax and abstraction, and that people who know PyTorch will find it familiar.Read 4 Oct 2026 · textgrad.com

Integrations

The maker's GitHub README says a LiteLLM-based engine supports models available through LiteLLM, including Bedrock, Together, and Gemini.Read 5 Oct 2026 · github.com

Security

The official website and documentation pages reviewed do not state security or compliance features.Read 5 Oct 2026 · textgrad.com

Support

The official website links to API documentation and a public GitHub repository for source code.Read 5 Oct 2026 · textgrad.com

License

The maker's GitHub repository lists the project license as MIT.Read 5 Oct 2026 · github.com

Additional installs

The repository also documents conda-forge installation, a GitHub development install, and an optional vLLM extra.Read 4 Oct 2026 · github.com

API style

Its API resembles PyTorch and lets users define loss functions and optimize variables using text feedback.Read 5 Oct 2026 · textgrad.readthedocs.io

Credentials

The project’s tutorials say an OpenAI or Anthropic key is needed to run the LLMs in those tutorials.Read 4 Oct 2026 · github.com

How it works

The framework implements backpropagation through natural-language feedback provided by LLMs.Read 4 Oct 2026 · textgrad.readthedocs.io

Install

The documentation gives `pip install textgrad` as an installation command.Read 5 Oct 2026 · textgrad.readthedocs.io

Local model use

The maker's GitHub repository documents an external OpenAI-compatible client, with LM Studio as an example.Read 5 Oct 2026 · github.com

Maintainers

The website lists TextGrad team members affiliated with Stanford University.Read 5 Oct 2026 · textgrad.com

Method

It implements backpropagation through textual feedback provided by language models.Read 5 Oct 2026 · textgrad.readthedocs.io

Model access

The maker's GitHub README says its tutorials require an OpenAI or Anthropic key to run the language models.Read 5 Oct 2026 · github.com

Model providers

The repository says its experimental LiteLLM engine supports models available through LiteLLM, including Bedrock, Together, and Gemini.Read 4 Oct 2026 · github.com

Optimization targets

The documentation shows TextGrad optimizing text answers and prompts.Read 5 Oct 2026 · textgrad.readthedocs.io

Research status

The repository states that TextGrad was published in Nature on 19 March 2025.Read 4 Oct 2026 · github.com

Use cases

The maker lists coding, scientific problem solving, reasoning, chemistry, and medicine as applications.Read 5 Oct 2026 · textgrad.com

Where Specifiction places it

• Prompt Engineering Tools#21 of 256.7
• AI Prompt Generators#26 of 286.7