Open Dots: Open-Source Alternative to Paid AI Agents

Open Dots is an open-source, self-hosted alternative for people exploring paid AI agent products such as OpenAI Dots, Meta Muse, Grok Bot, Instinct, Manus Cue, Claude Cowork, and ChatGPT agent. The current prototype focuses on coding-agent workflows: run Claude Code tasks from Telegram or the web, review risky actions, connect GitHub, and schedule recurring prompts. Its features differ from those services; it is not a feature-for-feature replacement.

Both interfaces use the same API, sandbox, and credentials for each user.

▶ Watch the Open Dots demo on YouTube

Status: Prototype / active development. See Known limitations before running it for others.

Contents

• What it does
• Why Open Dots
• Quick start
• Using Open Dots
• Scheduling tasks
• Permission prompts
• Architecture
• Project layout
• Known limitations
• Contributing

What it does

• Run coding-agent tasks in disposable sandboxes from Telegram or a browser.
• Connect Claude through its OAuth login flow and optionally connect a user's GitHub account.
• Ask for approval before risky actions such as file writes, shell commands, and Git pushes.
• Continue completed tasks in the same Claude conversation from the web UI.
• Schedule recurring prompts and receive run results in Telegram.
• Store user credentials encrypted and manage sandbox, task, permission, and schedule records in PostgreSQL.

Why Open Dots

Open Dots gives individuals and developers an inspectable workspace they can run themselves, with coding tasks, visible approval steps, and local control over the app and its data. The web UI supports guest use without Telegram and can also link to a Telegram identity.

Quick start

Prerequisites

• uv for Python dependency management
• Node.js (18+) and npm, for the web UI
• Docker (for Postgres)
• Bash (Git Bash on Windows works fine) to use scripts/dev.sh
• A Boat API key for sandbox creation. If it belongs to a personal account that also has an org/team on a paid plan, you also need that org's id (see BOAT_ORG_ID below) — otherwise sandbox creation bills the personal account and returns 402 Payment Required.
• A Telegram bot token from @BotFather (only needed if you want to run the bot, or let people link the web UI to Telegram — the web UI's guest mode works without it)
• A public URL the sandbox can reach to call back into your backend for permission decisions (see Permission prompts) — in local dev this means tunneling your backend with ngrok or similar; not needed to run the app, only for any task that triggers a risky action (writing files, shell commands, etc.) to actually get a decision instead of hanging until it times out.

Configure the environment

cp backend/.env.example backend/.env

Fill in backend/.env:

Var	Required	Notes
BOAT_API_KEY	yes	From the Boat dashboard.
BOAT_BASE_URL	no	Defaults to https://boat.dev/api/v1.
BOAT_ORG_ID	see note above	Team/org id (e.g. team_...) to attach to sandbox creation so it bills the org instead of your personal account.
TOKEN_ENCRYPTION_KEYS	yes	Fernet key used to encrypt stored credentials. Generate with uv run python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())". Comma-separate multiple keys to support rotation.
TELEGRAM_BOT_TOKEN	only for the bot / Telegram linking	From @BotFather.
TELEGRAM_BOT_USERNAME	no	Your bot's @username (no @), shown as a clickable link in the web UI. Cosmetic only.
FRONTEND_ORIGINS	no	Comma-separated origins allowed to call the API from a browser. Defaults to http://localhost:3000.
HOOK_TOKEN	yes	Shared secret the permission hook's caller (the sandbox) must present, so only your backend's own sandboxes can submit permission decisions. Generate with python -c "import secrets; print(secrets.token_urlsafe(32))".
PERMISSION_HOOK_BASE_URL	yes	The public URL from the ngrok-or-similar prerequisite above, no trailing slash — e.g. https://your-tunnel.ngrok-free.app. Baked into each sandbox's .claude/settings.json at task start.
POSTGRES_*	no	Defaults match backend/docker-compose.yml.

The web UI (frontend/) has its own .env.example — scripts/dev.sh copies it to .env.local automatically on first run. Copy it yourself if you're running the frontend separately, and point NEXT_PUBLIC_API_BASE_URL at the backend if you're not using the defaults.

Start the app

./scripts/dev.sh

From the repo root, this starts Postgres via Docker, waits for it to be healthy, installs backend and frontend dependencies, runs migrations, and starts the FastAPI app (http://127.0.0.1:8000, docs at /docs), the Telegram bot, and the web UI (http://127.0.0.1:3000). Ctrl+C stops everything it started (Postgres keeps running — docker compose -f backend/docker-compose.yml down to stop it too).

Skip pieces you don't need:

./scripts/dev.sh --no-bot        # API + web UI only
./scripts/dev.sh --no-frontend   # API + bot only

Run the pieces manually

cd backend
docker compose up -d                      # Postgres
uv sync                                   # install deps
uv run alembic upgrade head               # migrations
uv run fastapi dev app/main.py            # API (terminal 1)
uv run python -m app.telegram_bot         # bot (terminal 2)

cd ../frontend
npm install && npm run dev                # web UI (terminal 3)

Using Open Dots

The Telegram bot walks a user through:

1. /connect — creates a sandbox and starts an OAuth login for the agent provider (currently Claude) inside it.
2. Pasting the OAuth code — the bot reads the resulting credential file out of the sandbox, encrypts it, and stores it.
3. /connect_github (optional) — links the user's own GitHub account via device-flow login, so tasks that git push do so as that user rather than a shared/anonymous identity. No token is ever typed into the chat: the bot posts a one-time code and a github.com/login/device link, the user approves it in their browser, and the bot picks up the result on its own.
4. Any plain text message — runs it as a task in the user's sandbox and replies with the result once it finishes. If the user has connected GitHub, that credential is restored into the sandbox before the task runs too.
5. /schedule — repeats a prompt on a cron schedule (hourly/daily/weekdays/ weekly, always in UTC) instead of running it once. /schedules lists them, /pause//resume//unschedule manage one. Each run is reported back in the chat — the result on success, the error on failure — and a schedule that can't succeed (e.g. an expired login) is paused automatically instead of retrying forever. See Scheduling tasks below.

The web UI covers the same ground from a browser:

1. Get started — either continue as a guest (a local id, no Telegram needed) or link Telegram: the page generates a one-time code, you send /link <code> to the bot, and that links the browser to your real Telegram user id instead.
2. Connect Claude — same OAuth flow as /connect, driven by REST endpoints instead of chat messages (get-or-create the sandbox, start login, paste the code back into the page).
3. Connect GitHub (optional) — same device-flow login as /connect_github: the page shows a one-time code and a github.com/login/device link, and picks up the result on its own once you approve it — no code to paste back.
4. Submit a task — a prompt box that submits to the same POST /api/v1/tasks endpoint, polls for the result, and lists past tasks for that user. Finished tasks can be replied to in place to continue that same Claude conversation, instead of starting a new unrelated one. Checking "Repeat this on a schedule" turns the same prompt into a recurring schedule instead (POST /api/v1/schedules) — pick a frequency (hourly/daily/weekdays/weekly) and a time, in the browser's own timezone. Schedules are listed below the composer with pause/resume/delete controls. See Scheduling tasks.

In Telegram:

1. /start
2. /connect — creates a sandbox (can take a bit) and replies with a login URL. Open it, complete the OAuth flow, and paste the code back into the chat.
3. /connect_github (optional) — reply has a code and a link; open the link, enter the code, approve it. No need to send anything back in the chat.
4. Send any message, e.g. create a webpage that says hello — the bot runs it in your sandbox and replies with the result (or the error).
5. /schedule daily 08:00 generate my report — repeats that prompt every day at 08:00 UTC. /schedules shows it; /pause 1, /resume 1, /unschedule 1 manage it by the id the bot gave you. The API process must be running for it to actually fire — see Scheduling tasks.

In the web UI (http://localhost:3000):

1. Get started — click "Continue without Telegram" for a standalone guest session, or "Link Telegram instead" and send /link <code> to the bot (the page picks this up automatically).
2. Connect Claude — click "Start login", open the link, and paste the code back into the page.
3. Connect GitHub (optional) — click "Connect GitHub", open the link, enter the code shown. No need to come back to the page — it updates once you approve it.
4. Submit a task — type a prompt and submit; the page polls for the result and lists past tasks below. Click a finished task to expand it and reply in that same conversation.
5. Check "Repeat this on a schedule", pick Daily and a time a couple of minutes out, and click "Create schedule". It appears in the schedules list below with its next run time; pause/resume/delete it from there.

Scheduling tasks

A schedule (schedule table) is a saved prompt plus a cron expression and timezone. Three background loops in the API process — not the bot process — drive it (app/core/background.py, app/services/ schedule_service.py, all on a 30s interval):

• poll_due_schedules — claims schedules whose next_run_at has passed and creates a Tasks row for each occurrence (FOR UPDATE SKIP LOCKED, safe with more than one API instance), then starts it.
• sync_active_tasks — finishes tasks that have no one polling them, the way a scheduled run does (nobody's watching a GET /tasks/{id} for it). Also recovers a task orphaned by a crash mid-launch.
• report_finished_runs — for each finished scheduled run, reported exactly once: sends the result on success, the error on failure, and pauses the schedule if it can't succeed on its own (an expired login, a missing credential) or after 3 failures in a row. Delivered over Telegram, to users identified by a numeric (Telegram-linked) user_id only — a web-only guest sees the outcome in the schedules list instead.

Because these loops live in the API process, the API must be running for a schedule to actually fire — creating one from Telegram doesn't require the bot process to also run the schedule (it only writes the row), but nothing fires without uv run fastapi dev app/main.py (or ./scripts/ dev.sh) up.

A schedule created from the web UI uses the browser's own timezone, captured at creation time. One created from Telegram is always UTC, since Telegram gives the bot no way to read the user's timezone — the bot's replies always spell out times in UTC.

Permission prompts

Every risky action the AI attempts inside a sandbox — writing a file, running a shell command, a git push, etc. — is intercepted by a PermissionRequest hook before it runs, and checked against a per-user rule table (permission_rule). No matching rule means the task pauses (status waiting_approval) and you're asked, in whichever channel you're using:

• Telegram: a message with Allow once / Always allow / Deny buttons.
• Web UI: the same three choices appear inline on the task once it's expanded, no page navigation needed.

"Always allow" writes a standing rule so the same action doesn't ask again; "Allow once"/"Deny" apply only to that one attempt. An unanswered prompt times out (110s) to a safe default of deny rather than hanging forever.

Shell (Bash) commands get a further pass: a small classifier (app/services/github_classifier.py) recognizes git subcommands and scopes the rule to the specific operation (git status read-only, git commit a local change, git push a publish) instead of treating every shell command the same — so "always allow committing" doesn't also silently allow pushing. Everything else run via Bash, and any tool without its own classifier, falls back to one coarse bucket per tool.

Extending classification to other connectors (beyond git) is still open; contributions welcome.

Architecture

Telegram bot ─┐
              ├── FastAPI backend ── PostgreSQL
Next.js web UI┘          ├── disposable sandboxes (Boat provider)
                         ├── permission prompts and rules
                         └── task and schedule services

Project layout

• backend/ — FastAPI app, Telegram bot, sandbox/auth/task services, Alembic migrations. See backend/README.md for backend-specific details (REST endpoints, env vars, DB models).
• frontend/ — Next.js web UI. See frontend/README.md for frontend-specific notes.
• scripts/dev.sh — one-command dev environment for both.

Known limitations

• Sandboxes are never automatically stopped/deleted — repeated /connect testing will accumulate them on the Boat account.
• Link codes expire after 10 minutes; there's no rate limiting on generating them.
• The web UI has no real authentication — a browser's identity is whatever id (guest or Telegram) it last linked, persisted in localStorage. This also means the permission engine's accumulated rules are only as safe as that identity.
• Automated tests exist for the permission engine (backend/tests/services/) but not yet for the rest of the app (sandbox/task/schedule services, routers).
• The git command classifier only covers the verbs it explicitly knows about (see github_classifier.py); an unrecognized verb defaults to the safer "mutation" bucket rather than being assumed read-only.
• Schedules poll on a 30s interval, so a run can start up to ~30s after its exact due time.
• A schedule missed by more than 15 minutes (e.g. the API was down) is skipped rather than run late; the schedule's last_error records that it happened.
• Scheduled-run notifications only reach Telegram-linked users (a numeric user_id) and only if TELEGRAM_BOT_TOKEN is set — a web-only guest has no chat to message and only sees outcomes in the UI's schedules list.

Contributing

Issues and pull requests are welcome. Keep the README aligned with the backend and frontend behavior, and include focused tests when changing services or API behavior.