textgrad

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!

Past Updates:

We are introducing a new engine based on 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:

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:

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)

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