Auto Deep Researcher 24x7

Deep Researcher Agent 24/7 Autonomous Deep Learning Experiment Agent

An AI agent that autonomously runs your deep learning experiments 24/7 while you sleep.

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Recent Updates

2026-04-09

• Reduced token growth by resetting leader context between cycles.
• Added a lightweight fallback to avoid repeated no-progress loops.
• Hardened tool execution against path traversal and shell injection.

2026-04-08

• Added progress tracking exports for experiment monitoring.
• Supports optional Obsidian sync for a live dashboard plus daily notes.
• If no Obsidian vault is configured, progress falls back to project-local text files under workspace/progress_tracking/.

Start In 3 Steps

If you only want the shortest path to a working experiment loop, do this:

1. 1.Create a project folder with one file: PROJECT_BRIEF.md
2. 2.Run /auto-experiment --project /path/to/project --gpu 0
3. 3.Check progress with /experiment-status or optional Obsidian/local text notes

Prefer AI-guided setup? Open `AI_GUIDE.md` in Claude / ChatGPT / Codex and let the assistant walk you through it.

What You Actually Need

Minimum Working Example

The smallest project you can launch looks like this:

my-first-experiment/
├── PROJECT_BRIEF.md
└── workspace/                  # auto-created

Minimal PROJECT_BRIEF.md:

# Goal
Train a ResNet-50 on CIFAR-100 to reach 80%+ accuracy.

# Codebase
Create the training code from scratch in PyTorch.

# What to Try
- Start with a basic ResNet-50 baseline.
- If accuracy  80%, stop and report.

# Constraints
- Use GPU 0 only
- Max 100 epochs per run

That is enough to start. Everything else is optional refinement.

What This Project Is Good At

This project is for people who already know what experiment they want to run, but do not want to babysit the loop:

• edit code
• launch training
• monitor runs
• parse logs
• decide the next variation
• keep going while you sleep

It is not trying to replace the researcher. It is trying to take over the repetitive experiment-ops layer.

Why It Feels Different From A Simple Script

• It does not just launch one run. It keeps iterating.
• It does not just monitor. It reflects and decides the next step.
• It stays cheap because training-time monitoring makes zero LLM calls.
• It stays controllable because the human can override direction at any cycle.
• It now supports persistent progress notes in Obsidian or local text files.

How You Stay In Control