Floom

The loop engineering harness. AI workers that run on a loop.

Schedule them, trigger them, require approvals, and keep every run on the record.
A worker is a folder: worker.yml + SKILL.md or run.py. Deploy it from Claude Code, Codex, or the CLI. Run it from the UI, REST, or MCP.

Get started · Build a worker · Try the hosted version · Read the docs

Most "AI automation" is a chat window you babysit, or a no-code graph that bills you per task and can't be audited. Floom is the loop engineering harness: a real runtime where a worker is a readable package, it runs on a loop without you watching, and every execution leaves logs, outputs, tool calls, approvals, and a replay you can trust. Used natively from Claude Code and Codex via MCP or CLI.

Deploy A Python Script As An App, REST API, And MCP Tool

Floom lets an agent or developer turn a Python script into a worker that non-developers can run from a UI, other systems can call through REST, and AI agents can operate through MCP.

Start with three files:

workers/my-worker/
  worker.yml
  run.py
  requirements.txt

Then validate, deploy, and run:

floom workers validate ./workers/my-worker
floom workers push ./workers/my-worker
floom run my-worker --input key=value

Read BUILDING.md for the full copy-paste worker contract.

What Floom Is: Worker Runtime For Background AI Workers

Floom turns repeatable knowledge-work automations into background AI workers. A worker can run manually, on a schedule, from a webhook, or from an app event. The runtime keeps the worker definition, input schema, output schema, logs, tool calls, approvals, and run history inspectable.

Use Floom when you want an AI agent to do recurring work without turning that work into an opaque chat thread, a hard-to-diff automation graph, or a one-off script with no supervision.

Build And Deploy An AI Worker From A Folder

AI agents and developers can build a Floom worker from a folder:

workers/<worker-id>/
  worker.yml        # manifest: inputs, outputs, trigger, runtime, secrets
  run.py            # script entrypoint, or SKILL.md for agent mode
  requirements.txt  # optional Python dependencies

Deploy the worker with:

floom workers validate ./workers/<worker-id>
floom workers push ./workers/<worker-id>
floom run <worker-id> --input key=value

After deployment, Floom exposes the worker in the UI, through REST endpoints, and through the Floom MCP server for AI clients. For the complete copy-paste build contract, read BUILDING.md.

What A Worker Looks Like

A worker is a folder-backed job definition. Describe the work in plain English (SKILL.md) or hand it a script (run.py), declare its tools, trigger, memory, and secrets in worker.yml, and Floom runs it.

# workers/github-digest/worker.yml  (abbreviated)
name: github-digest
description: "Every morning at 9am, send a digest of unread GitHub PRs and open issues."
exec:
  entry: SKILL.md          # plain-English agent; or run.py for a script
trigger:
  type: schedule
  cron: "0 9 * * *"        # also: manual, webhook, Composio event
connections:
  - app: github            # the only tools this agent is allowed to call
    allowed_tools: [GITHUB_FIND_PULL_REQUESTS, GITHUB_LIST_ASSIGNED_ISSUES]

<!-- workers/github-digest/SKILL.md -->
You are a GitHub assistant generating a daily PR + issues digest.
Fetch the user's open PRs and assigned issues, compile a markdown digest
with Action items, and finish_with_outputs({ "digest": "<markdown>" }).

It runs at 9am, calls only the two GitHub tools it declared, and writes digest.md to a run you can open, replay, or roll back:

run 7f3a · github-digest · finished 09:00:04 · 2 tool calls · output: digest.md
  ✓ GITHUB_FIND_PULL_REQUESTS    q="is:open is:pr author:@me"   → 4 PRs
  ✓ GITHUB_LIST_ASSIGNED_ISSUES  state=open                     → 2 issues
  → out/digest.md (text/markdown)   [open · replay · rollback]

The full manifest adds schema_version, title, version, and declared outputs. See workers/ for runnable examples, BUILDING.md for a minimal deployable worker, and the agent cookbook.

Two kinds of agent

Sandboxes allow public network egress by default and block private/internal ranges; a stricter allowlist is optional. Full trust model: ARCHITECTURE.md.

Worker Runtime At A Glance

Who Floom Is For

• Founders & operators turning recurring work (digests, triage, outreach) into agents that run themselves.
• Engineers who want a real runtime - manifests, sandboxes, limits, approvals - not a prompt file and a cron job.
• Teams who need every action allow-listed, approved, and replayable for audit.
• Anyone burned by agents that ran a destructive command, leaked a secret, or claimed success with nothing on the record.

Floom Compared: Raw Scripts vs Zapier vs Hand-Rolled MCP

Quick Start: Run Floom Locally

Linux / macOS

git clone https://github.com/floomhq/floom.git
cd floom
./scripts/setup.sh
# edit apps/api/.env: add a model provider key and E2B_API_KEY
./scripts/dev.sh

Windows PowerShell

git clone https://github.com/floomhq/floom.git; cd floom
.\scripts\setup.ps1
# edit apps\api\.env: add a model provider key and E2B_API_KEY
.\scripts\dev.ps1

Requires Python 3.11+, Node.js 20+, Git, a model provider key, and an E2B key from e2b.dev. Open http://localhost:3000 and sign in - no auth secret for local dev, and the example agents are seeded on first boot.