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SylphAI-Inc/AdalFlow

⭐ 4213 Python repository created 2024-04-19

AdalFlow is a cutting-edge library designed to build and automatically optimize large language model (LLM) applications. It offers a PyTorch-like framework that enables developers to create and fine-tune various LLM workflows, including chatbots, retrieval-augmented generation (RAG), autonomous agents, and classical natural language processing (NLP) tasks such as text classification and named entity recognition. The library's core strength lies in its ability to eliminate manual prompting by providing a unified auto-differentiative framework for zero-shot and few-shot prompt optimization, leveraging advanced research techniques like LLM-AutoDiff and Learn-to-Reason Few-shot In Context Learning. These methods have demonstrated superior accuracy compared to other auto-prompt optimization libraries. AdalFlow is model-agnostic, allowing users to switch between different LLMs seamlessly through configuration changes without rewriting code. This flexibility supports a wide range of models and providers via a consistent interface, helping to avoid vendor lock-in. The library emphasizes modularity and minimal abstraction, giving developers maximum control over prompt templates, model selection, and output parsing. It also supports auto-optimization of task pipelines by defining parameters and using AdalFlow's Generator for prompt tuning and few-shot demonstration optimization. The project is well-documented, with quickstart guides and tutorials available to help users get started quickly. It is actively maintained and supported by collaborations with academic groups such as the VITA Group at the University of Texas at Austin. AdalFlow is distributed under the MIT license and has a growing user base, as indicated by its GitHub stars and PyPI downloads. Overall, AdalFlow is a powerful tool for AI researchers, product teams, and software engineers aiming to develop sophisticated LLM applications with automated optimization capabilities, reducing the complexity and manual effort typically involved in prompt engineering and model integration.

https://github.com/SylphAI-Inc/AdalFlow

adalflowagentaiai-researchauto-optimizationauto-promptingautonomous-agentsbm25chatbotchatbotsfaissfew-shot-demonstrationsfew-shot-learningflexibleframeworkgenerative-aiinformation-retrievallearn-to-reasonllmllm-applicationsllm-autodiffmachine-learningmit-licensemodel-agnosticmodularnamed-entity-recognitionnlpnlp-tasksoptimizerprompt-optimizationprompt-tuningpythonpytorch-like-libraryquestion-answeringragrerankerretrieval-augmented-generationretrieversummarizationtext-classificationtrainerzero-shot-learning