zou-group/textgrad

Health v2 · maintenance only

Adoption not part of the score

3706 stars · 293 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TextGrad is a Python framework that implements automatic 'differentiation' via text, using LLM-generated natural language feedback as gradients to optimize prompts, solutions, and compound AI systems. Its API mirrors PyTorch, letting users define losses and backpropagate textual feedback through arbitrary pipelines of agents and tools.

Use cases

• optimize llm prompts automatically
• improve gpt-4o solutions to hard coding problems
• refine answers to scientific questions at test time
• design new molecules with desired properties
• optimize multi-agent llm pipelines with text feedback
• boost reasoning benchmark scores via llm critique

When to choose

• you want to optimize prompts or LLM outputs using natural-language feedback instead of numeric gradients
• you already know PyTorch and want a familiar API for text-based optimization
• your system contains black-box components like LLMs or tools that standard gradient descent cannot tune
• you want model-provider flexibility via litellm (OpenAI, Bedrock, Gemini, Together, etc.)

When to avoid

• you need traditional numerical gradient-based training of neural network weights
• you cannot afford the LLM API costs of iterative critique-and-revise loops
• you need a fully stable API - the litellm-based engines are still experimental and old engines are being deprecated
• your optimization target is fully differentiable and standard autograd frameworks suffice

Facets

library · maturity active

llm-inference prompt-engineering agent-framework machine-learning large-language-models artificial-intelligence python cross-platform textual-gradients autograd prompt-optimization llm-feedback compound-ai-systems pytorch-like-api litellm ai-agents natural-language-processing

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