Xiangyue-Zhang/auto-deep-researcher-24x7
🔥 An autonomous AI agent that runs your deep learning experiments 24/7 while you sleep. Zero-cost monitoring, Leader-Worker architecture, constant-size memory. observed · 2026-08-28
Health v2 · maintenance only
52/100
- Activity 85
- Release rhythm 35
- Longevity 10
Flags: no_releases young
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 147
- days_rel: n/a
- days_push: 91
- n_releases_24m: 0
Adoption not part of the score
1283 stars · 112 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
An open-source Python framework where LLM agents autonomously run deep learning experiments 24/7, covering hypothesis formation, code implementation, training, and analysis. It uses a Leader-Worker multi-agent architecture, constant-size memory, and zero-cost process-level monitoring to keep long-running autonomous experimentation cheap.
Use cases
- run deep learning experiments autonomously overnight
- automate hyperparameter tuning with an LLM agent
- monitor GPU training jobs without paying for LLM API calls
- run ML experiments on a Slurm cluster automatically
- iterate on research experiments 24/7 while I sleep
- keep agent memory bounded during long autonomous runs
When to choose
- you want fully autonomous end-to-end deep learning experiment cycles
- you need low-cost long-running agent deployments (days to weeks)
- you train PyTorch models locally or on Slurm clusters
- you want to plug in Claude Code, Codex CLI, or OpenAI-compatible LLM APIs like DeepSeek or Qwen
When to avoid
- you need a general-purpose coding assistant rather than an experiment loop
- your workloads are not deep learning training runs
- you require a managed SaaS with a web dashboard rather than a local Python framework
- you cannot grant an agent permission to execute training code on your machine or cluster
Facets
framework · maturity active
agent-framework llm-inference machine-learning monitoring workflow-automation scheduling artificial-intelligence machine-learning deep-learning developer-tools python cli autonomous-agent experiment-automation mlops hyperparameter-tuning pytorch claude-code leader-worker-architecture zero-cost-monitoring slurm research-automation ai-agents automation linux gpu
6 sources
- readme: https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7 · fetched 2026-08-28 · 25b991c2739c
- homepage: https://arxiv.org/abs/2604.05854 · fetched 2026-08-29 · 50cc067a6af4
- site_page: https://info.arxiv.org/about/donate.html · fetched 2026-08-29 · cca9c3a11c56
- site_page: https://info.arxiv.org/about/ourmembers.html · fetched 2026-08-29 · 47cbc55ff1de
- site_page: https://info.arxiv.org/about · fetched 2026-08-29 · a1f16f915a9a
- site_page: https://info.arxiv.org/labs/index.html · fetched 2026-08-29 · b14a8d05a0ec
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Xiangyue-Zhang/auto-deep-researcher-24x7 | main | 52 |
For agents
markdown · JSON · MCP: product_card(name="Xiangyue-Zhang/auto-deep-researcher-24x7")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem