--- title: "textgrad" type: "tool" slug: "zou-group-textgrad" canonical_url: "https://www.graphcanon.com/tools/zou-group-textgrad" github_url: "https://github.com/zou-group/textgrad" homepage_url: "http://textgrad.com/" stars: 3644 forks: 296 primary_language: "Python" license: "MIT" categories: ["ai-agents", "llm-frameworks"] tags: ["ai-optimization", "large-language-models", "textual-gradients", "compound-systems", "prompt-optimization"] updated_at: "2026-07-07T18:42:13.586033+00:00" --- # textgrad > TextGrad: Automatic Differentiation via Text A framework that uses large language models to backpropagate textual gradients for optimization of user-defined loss functions based on text feedback. ## Facts - Repository: https://github.com/zou-group/textgrad - Homepage: http://textgrad.com/ - Stars: 3,644 · Forks: 296 · Open issues: 66 · Watchers: 26 - Primary language: Python - License: MIT - Last pushed: 2025-07-25T14:30:39+00:00 ## Categories - [AI Agents](/categories/ai-agents.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags ai-optimization, large language models, textual-gradients, compound-systems, prompt-optimization ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. 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(★ 151,298) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,070) ## README (excerpt) ```text [][#license-gh-package] [][#arxiv-paper-package] [][#docs-package] [][#pypi-package] [][#pypi-package] [][#conda-forge-package] [][#conda-forge-package] [#license-gh-package]: https://lbesson.mit-license.org/ [#arxiv-paper-package]: https://arxiv.org/abs/2406.07496 [#docs-package]: https://textgrad.readthedocs.io/en/latest/?badge=latest [#pypi-package]: https://pypi.org/project/textgrad/ [#conda-forge-package]: https://anaconda.org/conda-forge/textgrad [#nature-link]: https://www.nature.com/articles/s41586-025-08661-4 ## TextGrad: Automatic ''Differentiation'' via Text An autograd engine -- for textual gradients! TextGrad is a powerful framework building automatic ``differentiation'' via text. TextGrad implements backpropagation through text feedback provided by LLMs, strongly building on the gradient metaphor We provide a simple and intuitive API that allows you to define your own loss functions and optimize them using text feedback. This API is similar to the Pytorch API, making it simple to adapt to your usecases. ### Updates: **19th March 2025** TextGrad published in [Nature](https://www.nature.com/articles/s41586-025-08661-4)! **Past Updates**: We are introducing a new engine based on [litellm](https://github.com/BerriAI/litellm). This should allow you to use any model you like, as long as it is supported by litellm. This means that now **Bedrock, Together, Gemini and even more** are all supported by TextGrad! This should be seen as experimental but we plan to depreciate the old engines in the future. In addition to this, with the new engines it should be easy to enable and disable caching. We are in the process of testing these new engines and deprecating the old engines. If you have any issues, please let us know! The new litellm engines can be loaded with the following code: An example of loading a litellm engine: ```python engine = get_engine("experimental:gpt-4o", cache=False) # this also works with set_backward_engine("experimental:gpt-4o", cache=False) ``` Be sure to set the relevant environment variables for the new engines! An example of forward pass: ```python import httpx from textgrad.engine_experimental.litellm import LiteLLMEngine LiteLLMEngine("gpt-4o", cache=True).generate(content="hello, what's 3+4", system_prompt="you are an assistant") image_url = "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg" image_data = httpx.get(image_url).content LiteLLMEngine("gpt-4o", cache=True).generate(content=[image_data, "what is this my boy"], system_prompt="you are an assistant") ``` In the examples folder you will find two new notebooks that show how to use the new engines. ## QuickStart If you know PyTorch, you know 80% of TextGrad. Let's walk through the key components with a simple example. Say we want to use GPT-4o to solve a simple reasoning problem. The question is *If it takes 1 hour to dry 25 shirts under the sun, how long will it take to dry 30 shirts under the sun? Reason step by step.* (Thanks, [Reddit User](https://www.reddit.com/r/OpenAI/comments/18q479x/comment/kf444es/)) ```python import textgrad as tg tg.set_backward_engine("gpt-4o", override=True) # Step 1: Get an initial response from an LLM. model = tg.BlackboxLLM("gpt-4o") question_string = ("If it takes 1 hour to dry 25 shirts under the sun, " "how long will it take to dry 30 shirts under the sun? " "Reason step by step") question = tg.Variable(question_string, role_description="question to the LLM", requires_grad=False) answer = model(question) ``` > :warning: **answer: To determine how long it will take to dry 30 shirts under the sun,** > **we can use a proportional relationship based on the given information.** > **Here’s the step-by-step reasoning: [.....]** > **So, it will take 1.2 hours (or 1 hour and 12 minutes) to dry 30 shirts under the sun.** As you can see, **the model's a ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/zou-group-textgrad`](/api/graphcanon/tools/zou-group-textgrad) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_