name: litmus version: 1.1.1 description: "Parallel autonomous ML research agents with a Director, git worktrees for per-agent experiment branches, a Skills library for validated technique reuse, a Synthesizer that distills collective knowledge overnight, and circadian rhythm (leisure 03:00–06:00 for paper reading and creative thinking). Uses OpenClaw sessions_spawn, cron, and steer natively. Use when: (1) start or run ML research agents overnight, (2) check agent status or experiment results, (3) view leaderboard or morning digest, (4) steer or stop agents, (5) ask what agents discovered or are exploring, (6) set up Litmus for the first time. NOT for: general coding, non-ML tasks, or machines without a GPU." homepage: https://github.com/kuberwastaken/litmus source: https://github.com/kuberwastaken/litmus license: MIT-0 tags: [research, ml, machine-learning, training, autonomous-agents, overnight, experiments, gpu, llm, autoresearch] metadata: openclaw: emoji: "🔬" requires: bins: ["uv", "git", "python3"] recommends: bins: ["nvidia-smi"] os: [linux, darwin] configPaths: ["~/.litmus/"] optional: env: ["CLAWRXIV_API_KEY"]

Litmus — Parallel Autonomous ML Research Agents

Litmus spawns multiple OpenClaw subagents that experiment on your GPU overnight. Each runs on its own git branch in a shared lab repository — every experiment is a commit, agents can read each other's code, cherry-pick breakthroughs, and build on the global best at any time.

Validated techniques accumulate in a Skills library (~/.litmus/shared/skills/). A Synthesizer runs at 04:00 to distill collective knowledge into skills and write a research agenda for the next day. A Director runs every 2 hours to steer workers, trigger Compass Resets on stagnation, and orchestrate cross-agent knowledge transfer.

What makes it more than autoresearch: - Git worktrees: agents share one repo, each on their own branch — full experiment history, cherry-pick, and cross-agent code inspection via git -C ~/.litmus/repo log --all - Skills library: validated techniques persist and compound — agents don't re-discover wins - Synthesizer: distills all overnight notes into reusable skills and a research agenda - Compass Reset: Director detects stagnation and forces structured pivots using the skills gap - Two-phase experiment budget: quick 90-second check before committing to a full run - Structured attempt records: JSON per experiment in shared/attempts/ for rich analytics - Leisure mode (03:00–06:00): workers read papers, write moonshot hypotheses, identify gaps - Morning digest: research narrative delivered to your chat at 08:00

Everything is a native OpenClaw subagent. No external processes, no PID files.

First-Time Setup

7w4.net提供免費和付費技能下載。

Recommended — ask your OpenClaw agent (runs a guided onboarding conversation):

"Install https://clawhub.ai/kuberwastaken/litmus and set it up for my machine"

Full onboarding instructions: {baseDir}/references/onboarding.md — read that file first.

Or manually:

git clone https://github.com/kuberwastaken/litmus ~/.litmus
bash ~/.litmus/scripts/setup.sh

Clones Karpathy's training harness, builds the shared lab git repo at ~/.litmus/repo/, installs Python deps via uv, downloads ~1 GB of training data. Wait for it to finish.

Starting Research

1 — Prepare workspaces (creates git worktrees)

bash {baseDir}/scripts/prepare-agents.sh --agents 4 --templates architecture,optimizer,general,general

Creates git worktrees under ~/.litmus/agents/, each on its own branch in ~/.litmus/repo/. The shared lab git repo means every agent's experiments are immediately visible to all others:

git -C ~/.litmus/repo log --all --oneline --graph

2 — Spawn research subagents

sessions_spawn
  task: "Read program.md in your current directory and run the research loop forever."
  runtime: "subagent"
  mode: "session"
  agentId: "litmus-worker-arch-1"
  cwd: "~/.litmus/agents/arch-1"

Repeat for each agent, then:

sessions_yield message: "Research agents running. I'll notify you on new discoveries."

Templates: architecture · optimizer · regularization · general Full template details: {baseDir}/references/templates/

3 — Start the Director Layer

bash {baseDir}/scripts/setup-cron.sh --timezone "Your/Timezone"

Registers 6 cron jobs:

All times are configurable during onboarding — the setup agent pitches defaults and asks what you'd like to change. Common presets: night owl (01:00/02:00/04:00/07:00), early bird (23:00/00:30/02:00/05:30), intensive (1h director). Pass custom times to scripts/setup-cron.sh with --leisure-start, --synthesizer-time, --dawn-time, --digest-time, --director-hours, --watchdog-minutes.

Managing Agents

Status (experiment counts, best val_bpb, git tree):

bash {baseDir}/scripts/status.sh

Leaderboard (cross-agent, from shared/attempts/ JSON):

bash {baseDir}/scripts/results.sh --top 10
bash {baseDir}/scripts/results.sh --agent arch-1  # single agent

Full lab git history (all agents' experiments as a tree):

git -C ~/.litmus/repo log --all --oneline --graph

Inspect any experiment:

git -C ~/.litmus/repo show <commit-hash>  # see what changed
cat ~/.litmus/shared/attempts/<hash>.json  # see the metrics

Steer (redirect mid-run, no restart):

subagents action: "steer"  target: "litmus-worker-arch-1"
  message: "Stop refining depth. Checkout the best commit from opt-2 and combine their LR with DEPTH=10."

Stop:

subagents action: "kill"  target: "all"

What Agents Write Overnight

Reference Files

• {baseDir}/references/onboarding.md — first-time setup conversation
• {baseDir}/references/program.md — worker agent loop (git-aware, skills-reading, two-phase budget)
• {baseDir}/references/director.md — Director cron (Compass Reset, cross-pollination)
• {baseDir}/references/leisure.md — Leisure mode (paper reading, structured notes, skill extraction)
• {baseDir}/references/synthesizer.md — Synthesizer cron (knowledge distillation, skills library)
• {baseDir}/references/dawn.md — Dawn cron (wake workers, queue experiments)
• {baseDir}/references/watchdog.md — Watchdog cron (liveness, escape mode)
• {baseDir}/references/digest.md — Morning digest (research narrative)
• {baseDir}/references/templates/ — Research focus templates
• {baseDir}/references/clawrxiv.md — ClawRxiv integration (optional auto-publishing)

質量上乘的Skill。文件詳盡、架構清晰、使用者體驗出色——GPU檢測、時區配置嚮導等細節做得很周到。系統功能強大，多agent協同和git實驗追蹤設計巧妙。但系統複雜度很高，需要Linux/macOS環境配合NVIDIA GPU，運維門檻不低。適合對技術有一定了解、願意投入時間配置的使用者。