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Version: v0.22.0

Docker Deployment

Deploy Obot using Docker for local development, testing, and proof-of-concept scenarios.

Overview​

Docker deployment is the fastest way to get Obot running. It's ideal for:

  • Local development and testing
  • Single-machine deployments
  • Proof-of-concept and evaluation
  • Small team usage

For production deployments, see Kubernetes Deployment.

Prerequisites​

  • Docker installed
  • 2+ CPU cores and 4GB RAM available
  • 10GB disk space

Quick Start​

Basic Deployment (Built-in PostgreSQL)​

Run Obot with the built-in PostgreSQL instance (suitable for development and testing):

docker run -d \
  --name obot \
  -v obot-data:/data \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -p 8080:8080 \
  -e OPENAI_API_KEY=your-openai-key \
  ghcr.io/obot-platform/obot:latest
important

Running Obot without authentication is not recommended unless you are behind a secure firewall, as unauthenticated users could launch MCP servers by sending requests to port 8080 on your system.

Host access and trusted users

This deployment bind-mounts the host Docker socket (-v /var/run/docker.sock:/var/run/docker.sock) so Obot can run MCP servers as sibling containers. Any MCP server Obot launches — including a containerized server, which runs an arbitrary container image with a user-supplied command — therefore runs on the host Docker daemon, which is equivalent to host-level access on that machine.

Users with the Power User or Power User+ role can deploy MCP servers, so on this deployment they can effectively run code on the host. Use Docker deployment only for development, evaluation, or single-tenant use where all such users are trusted. For multi-tenant deployments or untrusted users, use the Kubernetes deployment, which runs each MCP server in its own pod and supports stronger isolation controls you configure (restricted Pod Security Admission, a NetworkPolicy, and sandboxed runtimes such as gVisor or Kata Containers). See User Roles — Security Model.

docker run -d \
  --name obot \
  -v obot-data:/data \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -p 8080:8080 \
  -e OPENAI_API_KEY=your-openai-key \
  -e OBOT_SERVER_ENABLE_AUTHENTICATION=true \
  -e OBOT_BOOTSTRAP_TOKEN=your-bootstrap-token \
  ghcr.io/obot-platform/obot:latest

Using a Custom Port​

If you want to expose Obot on a different port (e.g., -p 9999:8080), you must also set OBOT_SERVER_HOSTNAME so that authentication redirects and MCP server connection URLs work correctly:

docker run -d \
  --name obot \
  -v obot-data:/data \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -p 9999:8080 \
  -e OPENAI_API_KEY=your-openai-key \
  -e OBOT_SERVER_HOSTNAME=localhost:9999 \
  -e OBOT_SERVER_ENABLE_AUTHENTICATION=true \
  -e OBOT_BOOTSTRAP_TOKEN=your-bootstrap-token \
  ghcr.io/obot-platform/obot:latest

Accessing Obot​

Once started, access Obot at http://localhost:8080.

If you enabled authentication, use your bootstrap token to log in as the owner and set up an authentication provider. If you didn't supply a bootstrap token, a random one will be generated and can be found in the container's logs by searching for "Bootstrap token".

Workflow Sharing Storage​

For local Docker deployments:

  • The default local published-workflow store is usually sufficient
  • Keep the Obot data volume mounted if you want published workflows to survive container replacement

If you want published workflows in Docker to use external object storage instead of local disk, configure the published workflow storage environment variables described in Server Configuration and Workflow Sharing.

Next Steps​

  1. Configure Authentication: Set up auth providers for secure access
  2. Configure Model Providers: Configure model providers (OpenAI, Anthropic, etc.)
  3. Set Up MCP Servers: Configure MCP servers for extended functionality