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<!-- <h4 align="center"> <img alt="AdalFlow logo" src="docs/source/_static/images/adalflow-logo.png" style="width: 100%;"> </h4> --> <h4 align="center"> <img alt="AdalFlow logo" src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/adalflow-logo.png" style="width: 100%;"> </h4> <h2> <p align="center"> ⚑ The Library to Build and Auto-optimize LLM Applications ⚑ </p> </h2> <p align="center"> <a href="https://colab.research.google.com/drive/1TKw_JHE42Z_AWo8UuRYZCO2iuMgyslTZ?usp=sharing"> <img alt="Try Quickstart in Colab" src="https://colab.research.google.com/assets/colab-badge.svg"> </a> </p> <h4 align="center"> <p> <a href="https://adalflow.sylph.ai/">All Documentation</a> | <a href="https://adalflow.sylph.ai/apis/components/components.model_client.html">Models</a> | <a href="https://adalflow.sylph.ai/apis/components/components.retriever.html">Retrievers</a> | <a href="https://adalflow.sylph.ai/apis/components/components.agent.html">Agents</a> | <a href="https://adalflow.sylph.ai/tutorials/evaluation.html"> LLM evaluation</a> | <a href="https://adalflow.sylph.ai/use_cases/question_answering.html">Trainer & Optimizers</a> <p> </h4> <p align="center"> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Version" src="https://img.shields.io/pypi/v/adalflow?style=flat-square"> </a> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Downloads" src="https://static.pepy.tech/badge/adalflow"> </a> <a href="https://pypi.org/project/adalflow/"> <img alt="PyPI Downloads" src="https://static.pepy.tech/badge/adalflow/month"> </a> <a href="https://star-history.com/#SylphAI-Inc/AdalFlow"> <img alt="GitHub stars" src="https://img.shields.io/github/stars/SylphAI-Inc/AdalFlow?style=flat-square"> </a> <a href="https://github.com/SylphAI-Inc/AdalFlow/issues"> <img alt="Open Issues" src="https://img.shields.io/github/issues-raw/SylphAI-Inc/AdalFlow?style=flat-square"> </a> <a href="https://opensource.org/license/MIT"> <img alt="License" src="https://img.shields.io/github/license/SylphAI-Inc/AdalFlow"> </a> <a href="https://discord.gg/ezzszrRZvT"> <img alt="discord-invite" src="https://dcbadge.vercel.app/api/server/ezzszrRZvT?style=flat"> </a> </p> <h4> <p align="center"> For AI researchers, product teams, and software engineers who want to learn the AI way. </p> </h4> <!-- <a href="https://colab.research.google.com/drive/1PPxYEBa6eu__LquGoFFJZkhYgWVYE6kh?usp=sharing"> <img alt="Try Quickstart in Colab" src="https://colab.research.google.com/assets/colab-badge.svg"> </a> --> <!-- <a href="https://pypistats.org/packages/lightrag"> <img alt="PyPI Downloads" src="https://img.shields.io/pypi/dm/lightRAG?style=flat-square"> </a> -->

Why AdalFlow

  1. Embracing a design pattern similar to PyTorch, AdalFlow is powerful, light, modular, and robust. AdalFlow provides Model-agnostic building blocks to build LLM task pipelines, ranging from RAG, Agents to classical NLP tasks like text classification and named entity recognition. It is easy to get high performance only using manual prompting.
  2. AdalFlow provides a unified auto-differentiative framework for both zero-shot prompt optimization and few-shot optimization. It advances existing auto-optimization research, including Text-Grad and DsPy. Through our research, Text-Grad 2.0 and Learn-to-Reason Few-shot In Context Learning, AdalFlow Trainer achieves the highest accuracy while being the most token-efficient.
<!-- It advances existing auto-optimization research, including Text-Grad and DsPy. Through our research, Text-Grad 2.0, and Learn-to-Reason Few-shot In-Context Learning, AdalFlow Trainer achieves the highest accuracy while being the most token-efficient. --> <!-- AdalFlow not only helps developers build model-agnostic LLM task pipelines with full control over prompts and output processing, but it also auto-optimizes these pipelines to achieve SOTA accuracy. --> <!-- Embracing a design pattern similar to PyTorch, AdalFlow is powerful, light, modular, and robust. -->

Here is an optimization demonstration on a text classification task:

<!-- <p align="center"> <img src="docs/source/_static/images/classification_training_map.png" alt="AdalFlow Auto-optimization" style="width: 80%;"> </p> <p align="center"> <img src="docs/source/_static/images/classification_opt_prompt.png" alt="AdalFlow Auto-optimization" style="width: 80%;"> </p> --> <p align="center" style="background-color: #f0f0f0;"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/classification_training_map.png" style="width: 80%;" alt="AdalFlow Auto-optimization"> </p> <p align="center" style="background-color: #f0f0f0;"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/classification_opt_prompt.png" alt="AdalFlow Optimized Prompt" style="width: 80%;"> </p>

Among all libraries, AdalFlow achieved the highest accuracy with manual prompting (starting at 82%) and the highest accuracy after optimization.

Further reading: Optimize Classification

Light, Modular, and Model-Agnostic Task Pipeline

LLMs are like water; AdalFlow help you quickly shape them into any applications, from GenAI applications such as chatbots, translation, summarization, code generation, RAG, and autonomous agents to classical NLP tasks like text classification and named entity recognition.

AdalFlow has two fundamental, but powerful, base classes: Component for the pipeline and DataClass for data interaction with LLMs. The result is a library with minimal abstraction, providing developers with maximum customizability.

You have full control over the prompt template, the model you use, and the output parsing for your task pipeline.

<p align="center"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/AdalFlow_task_pipeline.png" alt="AdalFlow Task Pipeline"> </p> <!-- LLMs are like water; they can be shaped into anything, from GenAI applications such as chatbots, translation, summarization, code generation, and autonomous agents to classical NLP tasks like text classification and named entity recognition. They interact with the world beyond the model’s internal knowledge via retrievers, memory, and tools (function calls). Each use case is unique in its data, business logic, and user experience. Because of this, no library can provide out-of-the-box solutions. Users must build towards their own use case. This requires the library to be modular, robust, and have a clean, readable codebase. The only code you should put into production is code you either 100% trust or are 100% clear about how to customize and iterate. --> <!-- This is what AdalFlow is: light, modular, and robust, with a 100% readable codebase. -->

Further reading: How We Started, <!-- [Introduction](https://adalflow.sylph.ai/), -->Design Philosophy and Class hierarchy.

<!-- **PyTorch** ```python import torch import torch.nn as nn class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.dropout1(x) x = self.dropout2(x) x = self.fc1(x) return self.fc2(x) ``` -->

Unified Framework for Auto-Optimization

AdalFlow provides token-efficient and high-performing prompt optimization within a unified framework. To optimize your pipeline, simply define a Parameter and pass it to AdalFlow's Generator. Whether you need to optimize task instructions or run some few-shot demonstrations, AdalFlow's unified framework offers an easy way to diagnose, visualize, debug, and train your pipeline.

This Dynamic Computation Graph demonstrates how our auto-differentiation and the dynamic computation graph work.

No need to manually defined nodes and edges, AdalFlow will automatically trace the computation graph for you.

Trainable Task Pipeline

Just define it as a Parameter and pass it to AdalFlow's Generator.

<p align="center"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/LightRAG/main/docs/source/_static/images/Trainable_task_pipeline.png" alt="AdalFlow Trainable Task Pipeline"> </p>

AdalComponent & Trainer

AdalComponent acts as the 'interpreter' between task pipeline and the trainer, defining training and validation steps, optimizers, evaluators, loss functions, backward engine for textual gradients or tracing the demonstrations, the teacher generator.

<p align="center"> <img src="https://raw.githubusercontent.com/SylphAI-Inc/AdalFlow/main/docs/source/_static/images/trainer.png" alt="AdalFlow AdalComponent & Trainer"> </p>

Quick Install

Install AdalFlow with pip:

pip install adalflow

Please refer to the full installation guide for more details.

Documentation

AdalFlow full documentation available at adalflow.sylph.ai:

AdalFlow: A Tribute to Ada Lovelace

AdalFlow is named in honor of Ada Lovelace, the pioneering female mathematician who first recognized that machines could go beyond mere calculations. As a team led by a female founder, we aim to inspire more women to pursue careers in AI.

Contributors

contributors

Acknowledgements

Many existing works greatly inspired AdalFlow library! Here is a non-exhaustive list:

Citation

@software{Yin2024AdalFlow,
  author = {Li Yin},
  title = {{AdalFlow: The Library for Large Language Model (LLM) Applications}},
  month = {7},
  year = {2024},
  doi = {10.5281/zenodo.12639531},
  url = {https://github.com/SylphAI-Inc/AdalFlow}
}
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