Deploy an AI agent

Stand up an AI agent framework on a Quake AI instance using Docker Compose. An agent connects a language model to tools, memory, and external services. You control the model provider, data, and runtime on infrastructure you operate.

Prerequisites#

• A Quake AI account with an active project
• An SSH key pair added to your project
• An API key from an AI model provider (OpenAI, Anthropic, Google, or OpenRouter for multi-provider access with a single key), or a self-hosted Ollama instance

Choose a framework#

OpenClaw has a dedicated guide with a Console Apps deployment option (Compute > Apps). The steps below cover the general Docker Compose pattern and walk through Flowise as a concrete example.

Step 1: Create a VM#

Use m2a.xlarge if running a local model on the same VM.

Step 2: Install Docker#

ssh ubuntu@YOUR_FLOATING_IP

curl -fsSL https://get.docker.com | sh

sudo usermod -aG docker ubuntu
newgrp docker

docker run --rm hello-world

Step 3: Deploy Flowise#

Create a project directory:

mkdir -p ~/flowise && cd ~/flowise

Create a docker-compose.yml. The steps below were last verified against Flowise 3.1.2; pinning the tag keeps the bootstrap flow predictable, since Flowise has reshaped its auth surface across releases.

services:
  flowise:
    image: flowiseai/flowise:3.1.2
    restart: unless-stopped
    ports:
      - "3000:3000"
    volumes:
      - ./data:/root/.flowise
    environment:
      - PORT=3000

Start the service:

docker compose up -d

Verify it is running:

docker compose ps

Once docker compose ps shows the flowise container as Up, open http://YOUR_FLOATING_IP:3000 in your browser.

First-time setup#

Flowise 3.x bootstraps an admin account from the browser UI on the first visit. When you open http://YOUR_FLOATING_IP:3000, Flowise redirects you to /signin. Click Register and create the first account using a valid email address. The first account becomes the workspace administrator.

If you need to follow the container output during boot, run docker compose logs -f flowise in a separate terminal.

Step 4: Connect a model provider#

In the Flowise UI, navigate to Credentials and add your AI provider API key. Flowise supports OpenAI, Anthropic, Google Gemini, Azure OpenAI, and others.

If you are running Ollama locally, configure the Ollama node to point to http://localhost:11434 (or the Ollama VM's private IP if it is on a separate instance).

Step 5: Build your first flow#

In Flowise, create a new Chatflow. The basic pattern:

1. Add a Chat Model node (e.g., ChatOpenAI, ChatOllama)
2. Add a Conversational Retrieval QA Chain or ReAct Agent node
3. Add a Buffer Memory node for conversation history
4. Connect them and save

Use the built-in chat interface to test your agent before integrating it with an external system.

Deploying n8n instead#

n8n suits automation-first use cases: connecting your agent to webhooks, databases, and external APIs without writing code.

services:
  n8n:
    image: n8nio/n8n
    restart: unless-stopped
    ports:
      - "5678:5678"
    volumes:
      - ./n8n-data:/home/node/.n8n
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=change-this-password
      - WEBHOOK_URL=http://YOUR_FLOATING_IP:5678

Access the UI at http://YOUR_FLOATING_IP:5678. n8n's AI nodes support OpenAI-compatible APIs, which means you can point them at Ollama for local inference.

Persisting data across restarts#

All three frameworks write state to a local directory inside the container. The volumes mapping in each compose file ensures that data survives container updates and restarts. Back up the volume directory before updating images:

docker compose down
cp -r ./data ./data-backup-$(date +%Y%m%d)
docker compose pull
docker compose up -d

Security considerations#

Restrict the UI port. The default security group in this guide restricts port 3000 to your IP. Avoid opening it to 0.0.0.0/0. Most frameworks rely on a username/password that is trivial to brute-force over the public internet.

Keep API keys out of the compose file for production. Use environment variable files (.env) and keep them out of version control, or reference secrets from a vault.

Run HTTPS in production. For production deployments, place the agent UI behind a reverse proxy (nginx or Caddy) with a TLS certificate. Use a DNS record pointed at your floating IP.

Next steps#

• OpenClaw guide: OpenClaw agent framework via Console Apps
• Run a local LLM: local inference instead of external model providers
• Deploy Qdrant: long-term memory and document retrieval for agents

Clean up#

Stop the compose stack and delete the instance when finished:

docker compose down