Auto-Deep-Research Files

Your Fully-Automated Personal AI Assistant

This is an exact mirror of the Auto-Deep-Research project, hosted at https://github.com/HKUDS/Auto-Deep-Research. SourceForge is not affiliated with Auto-Deep-Research.

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size
Auto-Deep-Research source code.tar.gz	2025-10-16	1.6 MB
Auto-Deep-Research source code.zip	2025-10-16	1.6 MB
Totals: 2 Items		3.2 MB

Other Useful Business Software

Find Hidden Risks in Windows Task Scheduler

Free diagnostic script reveals configuration issues, error patterns, and security risks. Instant HTML report.

Windows Task Scheduler might be hiding critical failures. Download the free JAMS diagnostic tool to uncover problems before they impact production—get a color-coded risk report with clear remediation steps in minutes.

Download Free Tool

AI-generated apps that pass security review

Stop waiting on engineering. Build production-ready internal tools with AI—on your company data, in your cloud.

Retool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.

Try Retool free

Recommended Projects

Research From AI
# Research Archive see README.md ## Overview This directory is the working archive for PXOS, analog/pixel-native computing, and related research initiatives. It currently holds more than 2,800 source notes, design briefs, and exploratory writeups created between successive project sprints. Content spans architecture proposals, roadmap experiments, ethics frameworks, technical deep dives, and supporting assets such as workspace settings and compressed bundles.
CNN for Image Retrieval
cnn-for-image-retrieval is a research-oriented project that demonstrates the use of convolutional neural networks (CNNs) for image retrieval tasks. The repository provides implementations of CNN-based methods to extract feature representations from images and use them for similarity-based retrieval. It focuses on applying deep learning techniques to improve upon traditional handcrafted descriptors by learning features directly from data. The code includes training and evaluation scripts that can be adapted for custom datasets, making it useful for experimenting with retrieval systems in computer vision. By leveraging CNN architectures, the project showcases how learned embeddings can capture semantic similarity across varied images. This resource serves as both an educational reference and a foundation for further exploration in image retrieval research.
PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
BETR-Research
BETR-Research (BETRS) is a modelling framework to create spatially resolved multimedia models for the environmental fate- and transport of chemical contaminants. It is intended to serve the research community and can be used as a teaching tool.
DeerFlow
Deep Research framework, combining language models with tools

Auto-Deep-Research Files

Your Fully-Automated Personal AI Assistant

Get an email when there's a new version of Auto-Deep-Research