researchloop 0.3.2

Automated research sprint platform for HPC clusters

ResearchLoop

Run AI-automated research experiments on your HPC cluster. Monitor from anywhere.

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ResearchLoop submits AI-powered research experiments to your SLURM or SGE cluster, then reports back the results. You describe a research idea in natural language, it handles the rest: submitting the job, running a multi-step pipeline with Claude Code, red-teaming the results, generating a report, and notifying you when it's done.

pip install researchloop

# Submit an experiment to your cluster
researchloop sprint run "Investigate whether batch normalization improves convergence" --study my-project

# Start an auto-loop: 5 experiments, each building on the last
researchloop loop start --study my-project --count 5 --context "Focus on improving F1 score"

Monitor everything from a web dashboard, Slack, or the CLI -- no need to SSH in and check on jobs.

Why ResearchLoop?

If you run experiments on shared HPC clusters, you know the pain: SSH in, write a script, submit with sbatch, wait, check logs, repeat. ResearchLoop automates this loop:

1. You describe what to investigate (via CLI, dashboard, or Slack)
2. ResearchLoop submits a job to your cluster via SSH
3. Claude runs the full experiment -- writes code, runs it, analyzes results
4. A red-team step critiques the work and Claude fixes any issues
5. You get a report with a summary, PDF, and all artifacts

The auto-loop feature takes this further: after each experiment, Claude analyzes the results and proposes the next one. You set how many iterations, and walk away.

Get started in 5 minutes

Prerequisites: Python 3.10+, SSH access to an HPC cluster, Claude Code installed on the cluster.

1. Install and initialize

pip install researchloop
researchloop init

2. Edit researchloop.toml

shared_secret = "pick-a-secret"
orchestrator_url = "http://localhost:8080"

[[cluster]]
name = "my-cluster"
host = "login.cluster.example.com"
user = "researcher"
key_path = "~/.ssh/id_ed25519"
scheduler_type = "slurm"
working_dir = "/scratch/researcher/researchloop"

[cluster.job_options]
gres = "gpu:1"
mem = "64G"
cpus-per-task = "8"

[[study]]
name = "my-project"
cluster = "my-cluster"
description = "Investigating X"

3. Start the server and run your first sprint

researchloop serve &
researchloop connect http://localhost:8080
researchloop sprint run "Try approach X on dataset Y" --study my-project

That's it. ResearchLoop SSHes to your cluster, submits the job, and you can monitor progress from the dashboard at http://localhost:8080/dashboard/.

Three ways to interact

Web dashboard

Browse to /dashboard/ to see all your studies, sprints, and loops. Submit new sprints, start loops with custom GPU/memory settings, refresh live status from the cluster, and read reports -- all from the browser.

Slack bot

Get sprint notifications in your Slack channel and run commands from a thread:

sprint run my-project "investigate feature X under condition Y"
sprint list
loop start my-project 5
help

See the Slack setup guide for configuration.

CLI

researchloop sprint run "idea" --study my-project   # Submit a sprint
researchloop sprint list                             # List recent sprints
researchloop sprint show sp-a3f7b2                   # View details
researchloop loop start --study my-project --count 5 # Auto-loop
researchloop loop stop loop-b4e1c9                   # Stop a loop

Customizing your studies

Each study can have its own context, cluster settings, and configuration:

[[study]]
name = "sae-research"
cluster = "my-cluster"
max_sprint_duration_hours = 12
red_team_max_rounds = 2
allow_loop = true

# Tell Claude what this study is about and how to approach it
context = """
You are researching sparse autoencoder architectures.
Always train for 200M samples. Use batch size 1024.
Validate on the variation models listed in ~/reference/models.txt.
"""

# Or point to a file with detailed instructions
claude_md_path = "./studies/sae-research/CLAUDE.md"

# Override GPU/memory for this study
[study.job_options]
gres = "gpu:a100:2"
mem = "128G"

The context hierarchy is: global > cluster > study. All levels are merged and included in every sprint's prompt.

Deployment

For production, deploy the orchestrator as a Docker container on Fly.io, Railway, or any platform that supports persistent volumes:

pip install researchloop
# See deployment guide for Docker/Fly.io setup

Full deployment guide: researchloop.github.io/researchloop/deployment

Documentation

Full docs at researchloop.github.io/researchloop, including:

• Configuration reference -- all TOML options and environment variables
• Deployment guide -- Docker, Fly.io, SSH key setup
• Dashboard guide -- web UI features and authentication
• Slack integration -- setup, commands, notifications
• CLI reference -- all commands with examples
• Security -- authentication, CSRF, webhook tokens
• Development -- contributing, testing, architecture

Contributing

git clone https://github.com/researchloop/researchloop.git
cd researchloop
uv sync
uv run pytest tests/ -m "not integration"   # Unit tests
uv run ruff check . && uv run pyright researchloop/  # Lint + type check

Integration tests run against a real SLURM scheduler in Docker -- see development guide.

License

MIT