Awesome GitHub RepositoriesResearch Agent Frameworks

This project is an agentic workflow orchestrator designed for building and deploying autonomous systems that perform multi-step reasoning. It functions as a tool-augmented engine, enabling developers to chain model calls with external function execution to complete complex, user-defined tasks. By integrating large language models with persistent memory and stateful logic, the framework supports the creation of intelligent applications capable of independent operation. The platform distinguishes itself through graph-based state orchestration, which allows developers to define logic steps and t

The Gemini Cookbook is a comprehensive collection of implementation patterns, code samples, and development guides designed for building applications with Google Gemini models. It serves as a central resource for developers to integrate multimodal generative artificial intelligence into their software, providing the necessary frameworks to manage model interactions, stateful workflows, and structured data extraction. The repository distinguishes itself by offering specialized toolkits for autonomous agent orchestration, enabling the construction of agents that can execute code, browse the web

oh-my-pi is an agentic workflow automation platform and AI coding agent orchestrator designed for autonomous software engineering. It functions as a multi-model LLM router and an LSP-integrated development environment, coordinating specialized AI agents to perform codebase analysis, automated refactoring, and complex task execution. The system distinguishes itself through the use of subagent coordination to execute parallel tasks within isolated environments and an auto-research framework for iterative experiments. It employs AST-driven structural search for code discovery and content-hash an

Open Deep Research is an artificial intelligence framework designed to automate complex, multi-step research workflows. It functions as an autonomous agent that performs iterative web searches, analyzes retrieved data, and synthesizes information into structured reports. By decomposing broad queries into smaller sub-tasks, the system builds a comprehensive knowledge base to address open-ended questions. The platform distinguishes itself through an agentic loop that dynamically refines research strategies based on previous findings. It manages long-form data by compressing and summarizing cont

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

This project is a scientific agent framework and workflow orchestrator designed to extend large language models with specialized tools for genomic, chemical, and biological research. It provides a system for planning research hypotheses and executing automated workflows by integrating scientific databases with dynamic code execution. The framework includes a cheminformatics modeling suite for predicting molecular bioactivity and performing virtual screening, alongside a bioinformatics analysis toolkit for processing genomic sequences and single-cell data. It also features an academic document

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

Oasis es un simulador social multi-agente impulsado por LLM y una herramienta de investigación diseñada para estudiar fenómenos sociales sintéticos. Funciona como una plataforma de red social sintética, replicando la infraestructura de sitios sociales, incluyendo perfiles de usuario, relaciones de seguimiento y mecanismos de descubrimiento de contenido para modelar comportamientos sociales similares a los humanos a escala. El framework orquesta poblaciones de agentes a gran escala, soportando hasta un millón de agentes autónomos. Se distingue por traducir las salidas de los modelos de lenguaje en acciones sociales concretas y ejecuciones de herramientas externas a través de un orquestador de llamadas a herramientas, mientras utiliza un reloj de simulación acelerado en el tiempo para desacoplar las secuencias de eventos del tiempo real. El sistema cubre amplias áreas de capacidad, incluyendo el modelado de plataformas sociales, el mapeo de redes sociales basado en grafos y la recomendación de contenido basada en algoritmos. Proporciona herramientas de investigación especializadas para el modelado de propagación de información, análisis de polarización grupal y entrevistas a agentes, respaldadas por un registro de actividad persistente para el análisis de datos retrospectivo. El proyecto está implementado en Python.

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