adclaw.app  /  deploy  /  railway

Deploy AdClaw on Railway

Multi-agent AI marketing team — 130+ built-in skills, 23 LLM providers, multi-channel chat (Telegram, Discord, Feishu, DingTalk, QQ, Web Console). One-click template, ~10 min to a live agent.

Deploy on Railway → Hobby plan, ≥ 2 GB RAM · ~10 min build · referral kickback supports the project

What you get

Prerequisites

Plan: Hobby or higher (≥ 2 GB RAM, 25 GB disk).

AdClaw bundles a Python agent runtime + skill registry + Chromium that need ≥ 600 MB on startup. The 30-day Trial 512 MB instance OOMs immediately. Hobby is $5/month + usage; idle AdClaw consumes about $0.50–$1/day so the included credits cover ~5 days of testing.

PlanWorks?Notes
Trial / 512 MBNoOOM at startup
Hobby ($5/mo, 2 GB)YesRecommended starter
Pro and higherYesFor multiple personas under load

You'll also need:

You do not need a Citedy account to use AdClaw — Citedy MCP is opt-in.

One-click deploy (recommended)

  1. Click Deploy on Railway at the top of this page.
  2. Choose Hobby plan when prompted.
  3. The template provisions one service with:
    • Image source: Citedy/adclaw (GitHub repo, builds from deploy/Dockerfile)
    • Two persistent volumes (/app/working 25 GB and /app/working.secret 1 GB)
    • Environment variables (see below)
    • Health check on /api/diagnostics/health with 60 s timeout
  4. Wait 8–12 minutes for the first build (Chromium + Python + skill registry). Subsequent deploys are cached.

Manual deploy (without template)

If you prefer to wire it up yourself:

  1. New Project → Deploy from GitHub repo → Citedy/adclaw. Or Deploy from Docker Image → nttylock/adclaw:latest.
  2. Settings → Networking → expose port 8088, generate the public domain.
  3. Settings → Build → confirm builder is Dockerfile with path deploy/Dockerfile (read from railway.json automatically).
  4. Settings → Deploy:
    • Health check path: /api/diagnostics/health
    • Health check timeout: 60
    • Restart policy: ON_FAILURE, max retries 10
  5. Settings → Volumes — attach two volumes (see "Persistent volumes" below).
  6. Variables — add the env vars from "Configuration" below.
  7. Settings → Resources — bump RAM to at least 2 GB, vCPU 1, disk 25 GB.

Configuration

Set these in Variables:

VariableDefaultNotes
ADCLAW_ENABLED_CHANNELSconsole,telegramComma-separated subset of console,telegram,discord,dingtalk,feishu,qq
LOG_LEVELINFODEBUG for troubleshooting cold starts
TELEGRAM_BOT_TOKEN(empty)Required if telegram is in ADCLAW_ENABLED_CHANNELS
DISCORD_TOKEN(empty)Required if discord enabled

LLM provider API keys are entered through the first-boot wizard at the public URL, not as env vars. They land on the /app/working.secret volume so they survive redeploys.

Persistent volumes

AdClaw needs two volumes — without them every redeploy resets the wizard and your provider keys disappear.

Mount pathSizeHolds
/app/working25 GBconfig.json, sessions, skills cache, sqlite database, agent memory
/app/working.secret1 GBproviders.json (LLM API keys), envs.json

If you only attach one, pick /app/working and accept that LLM keys live inside it (less isolation but functional).

Variants (Docker image tags)

TagImage sizeIdle RAMBest for
nttylock/adclaw:latest (default)~4.2 GB600–800 MBFull feature set — Chromium for browser/scraping/social skills
nttylock/adclaw:1.0.2-browser~4.1 GBsimilarBrowser-only, no Feishu/DingTalk channels
nttylock/adclaw:1.0.2-core~2.7 GB250–400 MBLightweight — no Chromium, no desktop tools

Switch by editing Settings → Source to use a Docker Image source instead of GitHub repo, then set the image to nttylock/adclaw:1.0.2-core. Build is skipped — deploy is ~30 s.

Post-deploy workflow

  1. Open the public URL Railway gave you (e.g. https://adclaw-production.up.railway.app).
  2. The welcome wizard appears:
    • Pick an LLM provider from the dropdown
    • Paste the API key
    • Click Continue — the agent loads with default personas
  3. Start chatting in the web console, or add TELEGRAM_BOT_TOKEN to Variables, restart, message your bot in Telegram.
  4. Customize (optional):
    • Personas tab → add specialists (e.g. "TikTok Strategist" with custom soul.md)
    • Skills tab → enable/disable specific skills, scan custom ones
    • Cron tab → schedule recurring tasks per persona
    • MCP tab → connect external MCP servers (Citedy, your own)

Health check

curl https://<your-railway-url>/api/diagnostics/health
# → 200 OK
# → {"status":"healthy","uptime_seconds":1234,"subsystems":{...}}

Returns the status of LLM, channels, memory, AOM, MCP, and watchdog. Safe to poll from external monitors.

Cost estimate

Idle (no traffic, default config): about $0.50–$1/day on Hobby. Active use (continuous chat, browser skills, frequent cron): up to $3–5/day.

Hobby includes $5/month — covers light usage. Set Railway Spending Limits to cap.

Troubleshooting

Build fails with "skipping Dockerfile"
The railway.json must be at the repo root (not deploy/). The public repo already has it correctly placed.
Deploy succeeds but health check fails after 10 s
Default Railway health timeout is 10 s; AdClaw cold-starts in 30–60 s. Set healthcheckTimeout: 60 in railway.json (already set in the default).
Service crashes with SIGKILL
Out of memory. Either upgrade plan, switch to core variant, or reduce ADCLAW_ENABLED_CHANNELS.
LLM keys disappear after redeploy
Volume /app/working.secret not attached. Check Settings → Volumes.
Telegram bot doesn't respond
Confirm TELEGRAM_BOT_TOKEN is set, telegram is in ADCLAW_ENABLED_CHANNELS, and you've talked to the bot at least once (Telegram requires the user to send /start first).

Source code & support

Deploy on Railway →