Comprehensive guide to deploying the full Bytebot stack on Railway using the official 1-click template
TL;DR – Click the button below, add your AI API key (Anthropic, OpenAI, or Google), and your personal Bytebot instance will be live in ~2 minutes.
edge
branch.
Service | Container Image (edge) | Port | Exposed? | Purpose |
---|---|---|---|---|
bytebot-ui | ghcr.io/bytebot-ai/bytebot-ui:edge | 9992 | Yes | Next.js web UI rendered to the world |
bytebot-agent | ghcr.io/bytebot-ai/bytebot-agent:edge | 9991 | No | Task orchestration & LLM calls |
bytebot-desktop | ghcr.io/bytebot-ai/bytebot-desktop:edge | 9990 | No | Containerised Ubuntu + XFCE desktop |
postgres | postgres:14-alpine | 5432 | No | Persistence layer |
bytebot-ui
is assigned a public domain.
1. Open the Template
2. Configure Environment
ANTHROPIC_API_KEY
for Claude modelsOPENAI_API_KEY
for GPT modelsGOOGLE_API_KEY
for Gemini models3. Kick off the Deployment
4. Launch Bytebot
https://bytebot-ui-prod.up.railway.app
). You should see the task interface. Create a task and watch the desktop stream!Dockerfile
references.Symptom | Likely Cause | Fix |
---|---|---|
Web UI shows “connecting…” | Desktop not ready or private networking mis-config | Wait for bytebot-desktop container to finish starting, or restart service |
Agent errors 401 or 403 | Missing/invalid API key | Re-enter your AI provider’s API key in Railway variables |
Slow desktop video | Free Railway plan throttling | Upgrade plan or reduce screen resolution in desktop settings |