Deploy and Host Odysseus | Personal AI workspace on Railway

Odysseus is a self-hosted and privacy-focused AI workspace designed to be a personalized alternative to ChatGPT or Claude. It allows you to run AI models on your own hardware, interact with your own data, and access features like deep research, intelligent document editing, and automated email triage.

About Hosting Odysseus | Personal AI workspace

Deploying Odysseus is highly flexible, with Docker Compose being the recommended approach for easy setup. The Docker configuration bundles the Odysseus core app with ChromaDB for vector memory, SearXNG for web searches, and ntfy for push notifications. By default, the application binds to localhost for security, so deploying on a Railway requires tweaking environment variables (like APP_BIND=0.0.0.0) to expose it to the web. While the app itself is lightweight, local model serving can be resource-intensive, making API-based or remote server connections a great alternative if your hosting environment lacks dedicated GPU resources.

Common Use Cases

• Running an autonomous AI agent to perform multi-step deep research and synthesize visual reports from web sources.
• Acting as an AI-assisted text editor for Markdown, HTML, and CSV documents with syntax highlighting and smart suggestions.
• Triaging personal emails through IMAP/SMTP integration with built-in AI for urgency reminders, auto-tagging, and drafting replies.

Dependencies for Odysseus | Personal AI workspace Hosting

• Docker and Docker Compose (Recommended for containerized deployments)
• Python 3.11+ (For native or isolated environments)
• tmux (Required for background model downloads via the Cookbook feature)

Deployment Dependencies

https://github.com/pewdiepie-archdaemon/odysseus

Implementation Details

# Odysseus UI — Environment Configuration

# ============================================================
# LLM Configuration
# ============================================================

# Primary LLM host (default: localhost)
LLM_HOST=localhost

# Additional LLM hosts, comma-separated (for model discovery)
# Use hostnames/IPs only; Odysseus scans common serve ports, including Ollama's 11434.
# LLM_HOSTS=llm-host.local,backup-llm.local

# Optional Ollama base URL. In Docker, host Ollama is usually reachable here
# when started with OLLAMA_HOST=0.0.0.0:11434.
# OLLAMA_BASE_URL=http://host.docker.internal:11434/v1

# Optional LM Studio URL. In Docker, host LM Studio is reachable here
# when LM Studio is set to serve on all interfaces (0.0.0.0).
# LM_STUDIO_URL=http://host.docker.internal:1234

# OpenAI API key (only needed if using OpenAI models).
# Do not commit real keys. Keep this commented until needed.
# OPENAI_API_KEY=your_openai_api_key_here

# Research service LLM endpoint
# RESEARCH_LLM_ENDPOINT=http://localhost:8000/v1/chat/completions

# ============================================================
# Search & Web
# ============================================================

# SearXNG instance URL (self-hosted, for web search).
# Docker Compose overrides this to http://searxng:8080 for in-network access.
SEARXNG_INSTANCE=http://localhost:8080

# Optional SearXNG cookie/CSRF secret. If blank, Docker generates one on first boot
# and stores it in the searxng-data volume.
# SEARXNG_SECRET=

# ============================================================
# Database
# ============================================================

# SQLite database path (default: sqlite:///./data/app.db)
# DATABASE_URL=sqlite:///./data/app.db

# ============================================================
# Auth & Security
# ============================================================

# Enable authentication (default: true)
# AUTH_ENABLED=true

# Host bind address and port for the Odysseus web UI in Docker Compose.
# Keep APP_BIND on loopback unless you intentionally want LAN/reverse-proxy access.
# APP_BIND=127.0.0.1
# Change this if another local service already uses 7000 (macOS AirPlay often does).
# APP_PORT=7000

# Development-only auth bypass for loopback requests.
# Keep false for Docker, LAN, reverse proxy, and any shared deployment.
# LOCALHOST_BYPASS=false

# Mark session cookies Secure. Set true when Odysseus is served through HTTPS
# by a trusted reverse proxy or private access gateway.
# SECURE_COOKIES=true

# Optional: pre-seed the first admin password during setup.
# Do not commit a real password.
# ODYSSEUS_ADMIN_PASSWORD=change_me_before_first_boot

# CORS allowed origins (default: localhost-only; restrict to your public origin in production)
# ALLOWED_ORIGINS=http://localhost:7000,http://localhost:8000

# ============================================================
# ChromaDB (vector store)
# ============================================================

# ChromaDB service host.
# Manual host run: localhost:8100 when using `docker run -p 8100:8000 chromadb/chroma`.
# Docker Compose overrides these to chromadb:8000 for in-network access.
# CHROMADB_HOST=localhost
# CHROMADB_PORT=8100

# Docker Compose host-port bind addresses for bundled services.
# Defaults are loopback-only for safety. To expose ntfy only on Tailscale,
# set NTFY_BIND to your host's Tailscale IP and update NTFY_BASE_URL.
# CHROMADB_BIND=127.0.0.1
# NTFY_BIND=127.0.0.1
# NTFY_BASE_URL=http://localhost:8091
# Example:
# NTFY_BIND=100.x.y.z
# NTFY_BASE_URL=http://100.x.y.z:8091

# ============================================================
# RAG / Embeddings
# ============================================================

# Embedding API endpoint (OpenAI-compatible /v1/embeddings)
# Default: http://{LLM_HOST}:11434/v1/embeddings (ollama)
# EMBEDDING_URL=http://localhost:11434/v1/embeddings

# Embedding model name (must be available at the endpoint above)
# EMBEDDING_MODEL=all-minilm:l6-v2

# Local fallback embedding model (used when no HTTP embedding API is available)
# Uses fastembed (ONNX) — downloads model on first run (~50MB)
# FASTEMBED_MODEL=sentence-transformers/all-MiniLM-L6-v2
# FASTEMBED_CACHE_PATH=  # defaults to ~/.cache/fastembed

# ============================================================
# Misc
# ============================================================

# Cleanup interval in hours (default: 24)
# CLEANUP_INTERVAL_HOURS=24

# In-process email pollers (default: on). Set to 0 if you're driving
# polling from cron / systemd via `scripts/odysseus-mail poll-scheduled`
# and `scripts/odysseus-mail poll-summary`, otherwise both schedulers
# race on the same SQLite.
# ODYSSEUS_INPROCESS_POLLERS=1

# In-process scheduled-task runner (default: on). Set to 0 to let an
# external driver fire scheduled tasks. Calendar reminders are
# frontend-driven (polling /api/notes from the browser) so no gate is
# needed there.
# ODYSSEUS_INPROCESS_TASKS=1

# Host used by the built-in "run_script" scheduled-task action.
# Empty/local/localhost runs scripts on the app host. Set to an SSH host alias
# if you intentionally want scheduled scripts to run remotely.
# ODYSSEUS_SCRIPT_HOST=localhost

# ============================================================
# GPU support (Docker Compose)
# ============================================================
# Pass the host GPU into the odysseus container. Default (unset) = CPU.
# COMPOSE_FILE is a native `docker compose` feature: a colon-separated
# list of files merged left-to-right. Pick ONE GPU line below, or leave
# all commented for CPU.
#
# NVIDIA (requires nvidia-container-toolkit + `nvidia-ctk runtime
# configure --runtime=docker` on the host):
# COMPOSE_FILE=docker-compose.yml:docker/gpu.nvidia.yml
# COMPOSE_FILE=docker-compose.yml;docker/gpu.nvidia.yml    #(Windows)
#
# AMD ROCm (requires ROCm drivers on the host and the GID of the render group):
# COMPOSE_FILE=docker-compose.yml:docker/gpu.amd.yml
# Find the render GID with: getent group render | cut -d: -f3
# RENDER_GID=989
#
# These overlays only expose the GPU devices. The slim Odysseus image
# still needs CUDA/ROCm userspace via Cookbook -> Dependencies (vLLM,
# llama-cpp-python, etc.) before models can actually serve on GPU.

Why Deploy Odysseus | Personal AI workspace on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying Odysseus | Personal AI workspace on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.