AI agent frameworks

AI-Researcher is an LLM research automation framework and scientific workflow orchestrator designed to automate the end-to-end discovery process. It employs autonomous AI research agents to identify research gaps, formulate hypotheses, and execute scientific discovery workflows independently. The system integrates an automated literature review tool for gathering and analyzing academic papers and code repositories with an AI-driven manuscript generator that synthesizes research motivations and experimental results into full-length academic papers. The framework covers a modular research pipe

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

Feynman is an open-source AI research agent that coordinates multi-agent workflows to search papers, run experiments, and produce cited research briefs. It orchestrates parallel researcher agents that independently investigate subtopics, then synthesizes and verifies findings through a multi-step orchestration loop, enabling deep research across academic papers, web sources, and code. The tool distinguishes itself through several specialized capabilities, including paper claim verification that audits research paper claims against actual code implementations to identify mismatches and validat

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 collection of pre-configured Docker images that provide ready-to-run environments for interactive computing and data science. It functions as a scientific computing stack and a polyglot notebook server, bundling language interpreters and libraries for Python, R, and Julia within a containerized system to ensure reproducible research environments. The collection uses a layered image hierarchy to provide versioned software dependencies and support for hardware acceleration across different CPU architectures. It allows for the creation of custom images based on a foundation of

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

GPT Researcher is an autonomous agent framework designed to automate the process of gathering, synthesizing, and documenting information from diverse web and local sources. It functions as a research-oriented execution environment that orchestrates specialized agents to perform complex, multi-branch research tasks, transforming raw data into structured, factual, and cited reports. The project distinguishes itself through a graph-based orchestration layer that manages state transitions and information flow between specialized agents. It employs recursive tree-search execution to explore comple

ResearchKit is an open-source framework for building iOS applications that conduct medical research studies. It provides reusable components for creating study apps that collect participant data through surveys, sensor-driven active tasks, and digital informed consent workflows. The framework includes a step-based survey builder for constructing multi-step questionnaires, an active task engine that guides participants through structured physical and cognitive assessments while capturing device sensor data, and a visual consent workflow that guides participants through study details with on-de

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

Open-deep-research is an autonomous research orchestrator and language model-based agent designed to execute multi-step investigations and automated web research workflows. It explores complex topics systematically by generating targeted search queries, processing multiple sources simultaneously, and recursively feeding synthesized findings back into search planning loops to expand its research scope. The system coordinates these tasks through a graph-based agent orchestration model that routes state between modular nodes for searching, reading, and reasoning. It accelerates data collection v