Awesome GitHub RepositoriesResearch Workflow Automation

PDFMathTranslate is a document translation tool designed to convert technical and scientific files into multiple languages while preserving their original visual layout. It functions as a specialized processor for academic research papers, ensuring that complex mathematical notation and technical formatting remain intact throughout the translation process. The system utilizes a layout-preserving parsing engine that extracts text and structural metadata while maintaining the spatial coordinates of every document element. To handle the translation of technical content, it employs an intermediat

This project is an LLM research workflow framework and academic writing automation tool designed to coordinate the research, drafting, and peer-review processes of scholarly papers. It functions as a scientific manuscript auditor and an AI peer review system that uses multi-agent evaluation to verify citation integrity and score manuscripts against quality rubrics. The system distinguishes itself through a verification suite that employs vision models for figure fidelity auditing and anchor links for claim support verification. It includes a writing style calibration utility that analyzes pre

DeepCode is an agentic development framework designed to orchestrate autonomous AI agents for software engineering tasks. It functions as a multi-agent workflow orchestrator that translates natural language requirements into functional codebases by coordinating specialized agents for architectural planning, intent analysis, and implementation. The platform integrates multiple language models to power these automated routines, providing a unified environment for complex development projects. The system distinguishes itself through its ability to transform academic research papers into executab

AutoResearchClaw is an agentic system designed to automate the scientific research process. It functions as an autonomous research agent and workflow automator that manages the entire lifecycle of a project, from initial hypothesis generation and literature review to experimental execution and the production of LaTeX-formatted academic papers. The system distinguishes itself through a multi-agent research pipeline that utilizes structured debates for hypothesis refinement and peer review. It employs a branch-and-merge architecture to explore parallel research directions and integrates human-i

This project is a machine learning research automation system designed to manage the full research lifecycle, from idea discovery to final paper submission. It utilizes markdown-based skill templates to execute autonomous research tasks and manage iterative loops of deep review and experimentation. The system distinguishes itself through integrated capabilities for academic communication and integrity auditing. It can automate the generation of LaTeX papers, conference slide decks, and evidence-grounded peer review rebuttals. To ensure rigor, it employs cross-model review routing and adversar

SpeechBrain is an all-in-one deep learning toolkit designed for speech and audio processing. Built as a modular library, it provides a structured environment for developing, training, and deploying neural network models across a wide range of tasks, including automatic speech recognition, speaker identification, and audio enhancement. The framework distinguishes itself through a configuration-driven approach that separates model architecture and training hyperparameters from application logic. By utilizing externalized configuration files and standardized recipes, it enables reproducible rese

RD-Agent is an autonomous framework designed to orchestrate multi-step software engineering and data science workflows. By leveraging large language models, the system decomposes complex technical requirements into actionable research, planning, and execution phases, ultimately generating and running code to solve specific development tasks. The platform distinguishes itself through a containerized execution sandbox that ensures secure dependency management and system stability for all autonomously generated code. It employs multi-agent orchestration to manage iterative feedback loops, allowi

This project is a comprehensive AI research workflow framework and skill library designed to transform general large language models into specialized AI research agents. It provides an agentic toolset for academic writing, a knowledge base for AI engineering, and a system for analyzing research artifacts by converting documents and repositories into structured claims and evidence graphs. The framework employs a two-loop orchestration architecture to manage the research lifecycle from ideation and literature surveys to final paper drafting. It distinguishes itself through a modular skill injec

zotero-gpt is an extension that integrates large language models with a reference management system to assist in the analysis and summarization of academic research papers. It functions as a research paper AI assistant capable of querying PDF documents and extracting insights directly from academic libraries. The tool features a contextual research search system that locates items within a library based on the semantic meaning of selected text. It includes a visual interface that renders AI-generated responses using Markdown and supports the display of complex mathematical formulas. The syst

AgentLaboratory is a multi-agent research system that automates the entire scientific experimentation process, from literature review through experiment execution to report generation, using a sequence of specialized AI agents. The system orchestrates a team of language-model-driven agents—a literature reviewer, experimental planner, executor, and report writer—to autonomously complete an end-to-end research workflow. The system distinguishes itself by saving progress at every checkpoint, enabling seamless recovery and continuation after interruptions or failures. Agents build on each other's

UltraRAG is an LLM RAG orchestration platform and AI agent research framework designed to coordinate complex retrieval-augmented generation workflows. It functions as a multimodal RAG engine capable of retrieving and generating responses using text, images, and diverse data types, while providing tools for vector database management and RAG performance evaluation. The platform features a visual RAG pipeline builder that uses a canvas interface to construct and debug data flows, synchronizing visual designs directly with underlying code. It distinguishes itself through an autonomous research s

Learn_Prompting est un projet éducatif axé sur le prompt engineering, fournissant les principes et techniques nécessaires pour concevoir des entrées efficaces et améliorer la qualité des sorties d'IA générative. Le projet couvre des stratégies de prompting avancées pour améliorer le raisonnement, la fiabilité et la qualité des résultats. Cela inclut des techniques de décomposition de tâches, le raisonnement par chaîne de pensée (chain-of-thought), et l'utilisation de guidage few-shot et zero-shot. Il aborde également la sécurité des modèles à travers l'étude du prompt hacking, l'analyse de vulnérabilité et l'audit de confidentialité pour prévenir les fuites de données sensibles. La portée s'étend à l'application pratique de l'IA générative à travers divers médias et workflows, incluant la génération de texte, la création d'images photoréalistes et la production audiovisuelle. Il couvre en outre le développement d'agents autonomes, la programmation assistée par IA et l'automatisation des workflows métier pour le marketing et la communication. Le projet fournit des ressources pour l'optimisation des modèles, l'évaluation et la gestion des cycles de vie des prompts au sein d'un environnement d'expérimentation interactif.

This project is an LLM research orchestrator and autonomous AI agent framework designed to automate the scientific lifecycle. It functions as an end-to-end research pipeline and model training toolkit, managing everything from initial literature reviews and hypothesis testing to the final drafting of academic papers. The system is distinguished by its ability to convert unstructured academic PDFs into machine-executable knowledge layers, allowing agents to reproduce and extend research findings. It employs a two-loop orchestration architecture and a specialized research engineering skill libr

This project is a parallel simulation engine and molecular dynamics simulator designed to model the physical movements of atoms and molecules. It functions as an interatomic potential framework for calculating forces between particles and a materials analysis tool for computing thermodynamic, structural, and transport properties of solids and fluids. The engine is distinguished by its high-performance computing capabilities, utilizing spatial-domain decomposition and message-passing interface communication to distribute workloads across processors. It supports multi-backend GPU acceleration v

This project is an AI agent orchestration framework that installs a complete, spec-driven software development lifecycle across multiple AI coding agents. It coordinates agents through sequential stages from discovery and requirements through design to autonomous implementation, with each task receiving independent review. The system distinguishes itself by spawning a fresh implementer and reviewer per task, running test-driven development behind feature flags, and automatically debugging on failure. New code is introduced behind feature flags to isolate changes and enable safe rollback, whil