GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
제작자가 쓴 설명(영어 원문)
Deploy Python functions as web apps with auto-generated UI, REST API, and shareable links.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
FLOOM_BASE_URL, FLOOM_API_KEY비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"floom": {
"command": "npx",
"args": [
"-y",
"@floomhq/mcp-server"
],
"env": {
"FLOOM_BASE_URL": "your-value",
"FLOOM_API_KEY": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add floom --env FLOOM_BASE_URL=your-value --env FLOOM_API_KEY=your-value -- npx -y @floomhq/mcp-server실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"floom": {
"command": "npx",
"args": [
"-y",
"@floomhq/mcp-server"
],
"env": {
"FLOOM_BASE_URL": "your-value",
"FLOOM_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"floom": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@floomhq/mcp-server"
],
"env": {
"FLOOM_BASE_URL": "your-value",
"FLOOM_API_KEY": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 floomhq 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: FLOOM_BASE_URL, FLOOM_API_KEY
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.1.6)은 7개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: GitHub, Desktop Commander, Cloudflare MCP
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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.
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.
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.
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/ floom run --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.
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:
<!-- 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": " " }).
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.
| runs in | host isolation | tools | side effects | |
|---|---|---|---|---|
Script (run.py / .sh / .js / .ts) | E2B sandbox microVM | isolated filesystem, env & process; platform secrets withheld | sandbox + declared connections | approval gate when declared |
Agent (SKILL.md) | AgentDriver in the API process (trusted bundles only) | not microVM-isolated by policy | declared connections, allow-listed | approval gate when declared |
Sandboxes allow public network egress by default and block private/internal ranges; a stricter allowlist is optional. Full trust model: ARCHITECTURE.md.
| What it is | Self-hosted runtime to create, run, and supervise background AI agents |
| Best for | Recurring agent work: inbox triage, digests, outreach drafting, enrichment, monitoring |
| Agent types | Script (run.py/.sh/.js/.ts) and plain-English agent (SKILL.md) |
| Isolation | Script workers run in E2B sandbox microVMs - isolated host filesystem, env & process; platform secrets withheld |
| Triggers | Manual, schedule (cron), webhook, Composio event |
| Safety | Human-in-the-loop approvals for side-effecting agents; tools allow-listed per agent |
| On the record | Every run records logs, outputs, tool calls, approval state, replay + rollback |
| Cost model | Floom adds no per-task fee - you pay E2B sandbox runtime (per second) plus your model/API provider usage |
| Stack | Next.js + Tailwind UI · FastAPI + SQLite API · MCP server + CLI |
| Runs on | Linux, macOS, Windows (Python 3.11+, Node 20+) |
| License | MIT · hosted version |
| raw scripts | Zapier / Make | hand-rolled MCP server | Floom | |
|---|---|---|---|---|
| Worker definition | ad-hoc prompt or code | visual graph, hard to diff | custom tool server code | a folder: `worker.yml` + `SKILL.md` or `run.py` |
| Runtime | cron, shell, or local process | vendor automation runtime | whatever the developer builds | agent runtime with triggers, runs, logs, outputs, approvals, replay, and rollback |
| Isolation | runs on your host | vendor cloud, opaque | depends on implementation | E2B sandbox microVM for script workers |
| Tool access | whatever the script can reach | per-connector | MCP tools only | declared and allow-listed per worker |
| Side effects | fire immediately | workflow-dependent | custom approval logic | human approval gate when declared |
| Observability | scrollback, if any | per-step logs | custom logs | logs, tool calls, outputs, artifacts, approval state, replay, and rollback |
| Agent access | custom wrapper needed | not an agent-native build target | agent can call tools, but app runtime is separate | MCP tools let agents create, run, watch, and inspect workers |
| Cost model | model tokens and infrastructure | per-task or per-execution fees | model tokens and infrastructure | no per-task fee; you pay runtime and provider usage |
| Hosting | your host | vendor only | your host | self-host, or [hosted](https://floom.dev) |
Linux / macOS
git clone https://github.com/floomhq/floom.git
cd floom ./scripts/setup.sh
./scripts/dev.sh
Windows PowerShell
git clone https://github.com/floomhq/floom.git; cd floom
.\scripts\setup.ps1
.\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.
Full setup, model providers, optional integrations, and the safe self-hosting checklist: docs/GETTING-STARTED.md. Common issues: docs/TROUBLESHOOTING.md.
Not ready to self-host? floom.dev is the hosted version - hire AI agents with no setup. Hosted users can start from an MCP client with:
npx -y @floomhq/floom mcp install --target claude
The .env, model-provider, and E2B keys above are for running your own local or self-hosted runtime, not for using Floom Cloud.
workers/ / with worker.yml plus a script entrypoint (run.py) or an agent prompt (SKILL.md).Write your first agent in docs/GETTING-STARTED.md, then docs/AUTHORING.md for the full manifest and runtime contract.
Script workers (.py/.sh/.js/.ts) run in an E2B sandbox microVM by default: isolated dependencies, no host process access, contained resources. TypeScript workers use the Node runtime with tsx (npx --yes tsx run.ts) and the same inputs.json/result.json contract as Python workers. A bundle that dumps os.environ inside the sandbox sees only sandbox metadata - FLOOM_SECRET, provider keys, and E2B_API_KEY are all absent. Agent workers (SKILL.md) run through the API-hosted AgentDriver tool loop and are governed by their declared connections an
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요