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How to Build an AI Agent With n8n on Your Own VPS

A practical guide to running an n8n AI agent on your own VPS: size the server, install Docker Compose, deploy PostgreSQL and HTTPS, protect the encryption key, connect a model and secure the workflow.
Length11 min Posted Quest giverVGSources Team

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The dependable way to run an n8n AI agent on your own VPS is a Docker Compose stack containing n8n and PostgreSQL, with a reverse proxy terminating HTTPS. Bind n8n to localhost rather than publishing its editor port, create and back up N8N_ENCRYPTION_KEY before storing any credentials, then add a model credential and assemble the agent workflow in n8n.

This guide uses Ubuntu as the example operating system. Package names and n8n screen labels can vary by release, but the deployment principles remain the same.

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What you are building

The finished installation has four layers:

  • A Linux VPS running Docker Engine and the Compose v2 plugin.
  • n8n, which stores workflow definitions and encrypted credentials.
  • PostgreSQL for durable application data.
  • An HTTPS reverse proxy such as Caddy, which accepts public traffic and forwards it to n8n on the private Docker network.

Users reach the editor and webhooks at your domain over HTTPS. The n8n container listens on port 5678, but that port is bound to 127.0.0.1; the proxy is the only service exposed to the internet. This arrangement avoids sending editor traffic or webhook requests directly to an unencrypted n8n port.

n8n creates a random encryption key in its data directory on first launch. That key encrypts credentials before they are written to PostgreSQL. Keep the key and the /home/node/.n8n data persistent. If the key is lost or changed, existing credentials can no longer be decrypted.

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Choose the VPS before installing

CPU and memory

Requirements depend on what the agent does. The official n8n Assistant sandbox documentation specifies at least 4 GB of RAM and 2 vCPUs. Third-party deployment guides describe a wider range, from 2 GB RAM up to 4 vCPUs and 8 GB RAM, with 4 GB RAM commonly recommended for AI workloads. Treat 4 GB RAM as a practical floor for a small production agent; reserve more memory if you run a local language model, embeddings, a browser tool, or several concurrent executions.

Hosted model APIs keep inference off the VPS, so the server mainly runs n8n, PostgreSQL and your tools. An Ollama model runs on the VPS itself and can consume substantially more memory and CPU than the n8n process. A machine that is adequate for API calls may be inadequate for local inference.

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Operating system and network

Ubuntu 20.04 and 22.04 are the versions most commonly named by installation guides. Use a supported 64-bit Linux image from your VPS provider, a static public IPv4 address, and a domain name whose DNS A (and, if used, AAAA) record points to that address.

Allow SSH during setup and plan to allow TCP ports 80 and 443 for the reverse proxy. Do not open 5678 to the public internet. If you use a provider firewall as well as the host firewall, apply the same rules in both places.

Install Docker and Compose v2

Log in over SSH as a user with sudo access. Docker’s convenience installer is used by many n8n guides; review any installation script before piping it to a shell, and use your distribution’s official Docker instructions when your security policy requires package pinning.

  1. Update the operating system:

    sudo apt update && sudo apt upgrade -y
  2. Install the prerequisites and Docker:

    sudo apt install -y ca-certificates curl openssl ufw
    curl -fsSL https://get.docker.com | sh
  3. Optionally allow your login user to run Docker without sudo, then start a new SSH session:

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    sudo usermod -aG docker "$USER"
  4. Confirm both Docker and the Compose plugin:

    docker --version
    docker compose version

Compose v2 is invoked as docker compose with a space. The older docker-compose command is a separate legacy program and is not required for this deployment.

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Create the project and protect the encryption key

Create a directory with permissions restricted to your administrative user:

sudo mkdir -p /opt/n8n
sudo chown "$USER":"$USER" /opt/n8n
cd /opt/n8n

Generate a key before the first run

Generate a long random value and put it in a root-readable environment file:

openssl rand -hex 32

Copy the output into /opt/n8n/.env. Do not commit this file to Git, paste it into a ticket, or place it in a public backup.

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POSTGRES_USER=n8n
POSTGRES_PASSWORD=replace-with-a-long-random-password
POSTGRES_DB=n8n
N8N_HOST=n8n.example.com
N8N_ENCRYPTION_KEY=paste-the-generated-value-here
WEBHOOK_URL=https://n8n.example.com/

Set restrictive permissions:

chmod 600 .env

Back up the value of N8N_ENCRYPTION_KEY in a password manager or an encrypted offline backup before creating credentials. In queue mode, every n8n worker must receive exactly the same key. Never replace an automatically generated key with a different value on an existing instance unless you have a deliberate credential-migration plan; a mismatch makes stored credentials unreadable and moving a generated key incorrectly has reset installations.

Define n8n, PostgreSQL and the reverse proxy

Create /opt/n8n/compose.yml:

services:
postgres:
image: postgres:16
restart: unless-stopped
environment:
POSTGRES_USER: ${POSTGRES_USER}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
POSTGRES_DB: ${POSTGRES_DB}
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER} -d ${POSTGRES_DB}"]
interval: 10s
timeout: 5s
retries: 10

n8n:
image: docker.n8n.io/n8nio/n8n:2.34.4
restart: unless-stopped
env_file:
- .env
environment:
DB_TYPE: postgresdb
DB_POSTGRESDB_HOST: postgres
DB_POSTGRESDB_PORT: 5432
DB_POSTGRESDB_DATABASE: ${POSTGRES_DB}
DB_POSTGRESDB_USER: ${POSTGRES_USER}
DB_POSTGRESDB_PASSWORD: ${POSTGRES_PASSWORD}
N8N_HOST: ${N8N_HOST}
N8N_PROTOCOL: https
N8N_PORT: 5678
WEBHOOK_URL: ${WEBHOOK_URL}
N8N_ENCRYPTION_KEY: ${N8N_ENCRYPTION_KEY}
ports:
- "127.0.0.1:5678:5678"
volumes:
- n8n_data:/home/node/.n8n
depends_on:
postgres:
condition: service_healthy

caddy:
image: caddy:2
restart: unless-stopped
ports:
- "80:80"
- "443:443"
volumes:
- ./Caddyfile:/etc/caddy/Caddyfile:ro
- caddy_data:/data
- caddy_config:/config
depends_on:
- n8n

volumes:
postgres_data:
n8n_data:
caddy_data:
caddy_config:

The 2.34.4 image tag is an example pin used by a third-party guide, not a statement that it is the current stable release. Check the current n8n release information before deploying and decide whether you will pin a tested version or deliberately track a moving tag.

The n8n data volume is required even with PostgreSQL. It contains the encryption key and operational files such as logs. Removing it can strand encrypted credentials even if the database volume survives.

Configure Caddy

Create /opt/n8n/Caddyfile:

n8n.example.com {
reverse_proxy n8n:5678
}

Replace the hostname with the same value used for N8N_HOST and WEBHOOK_URL. Caddy obtains and renews a certificate when DNS points to the VPS and ports 80 and 443 are reachable. If you use another reverse proxy, configure equivalent TLS termination, forwarding to the n8n container on port 5678, and WebSocket support.

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Open only the required firewall ports

On Ubuntu with UFW, allow SSH before enabling the firewall so you do not lock yourself out:

sudo ufw allow OpenSSH
sudo ufw allow 80/tcp
sudo ufw allow 443/tcp
sudo ufw enable
sudo ufw status verbose

There is no UFW rule for 5678. The Compose binding to 127.0.0.1:5678 provides a second layer of protection, but verify your provider-level firewall does not publish that port either.

Start the stack and complete the owner setup

  1. Validate the Compose file and interpolated variables:

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    docker compose config

    Inspect the output for an empty hostname, password or encryption key before starting.

  2. Pull images and start in the background:

    docker compose up -d
  3. Check container state and recent logs:

    docker compose ps
    docker compose logs --tail=100 n8n
    docker compose logs --tail=100 caddy
  4. Open https://n8n.example.com. Create the n8n owner account, use a unique password, and enable two-factor authentication in the account security settings.

The first page may not appear until DNS has propagated and the proxy has obtained its certificate. A healthy n8n container with a failed certificate usually indicates a DNS, firewall or hostname mismatch rather than an application failure.

Build the AI agent workflow

n8n’s exact AI Agent labels can change between releases, so use the current node names shown in your editor. The workflow pattern is stable:

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  1. Create a new workflow and add a trigger. A chat trigger is useful for interactive testing; a Webhook trigger is appropriate when another application will send JSON; a schedule trigger suits periodic jobs.

  2. Add the AI Agent node. Configure its system instructions with the agent’s role, allowed actions, response format and boundaries. Keep instructions specific: state which fields are required, what to do when data is missing, and when the agent must ask for clarification instead of guessing.

  3. Create a model credential from the credential picker and select the matching model node. Hosted choices commonly documented for n8n include API-key connections to OpenAI, Anthropic and Google AI. Store keys in n8n credentials, not in prompts or ordinary Set-node fields.

  4. Attach tools only for actions the agent genuinely needs. Examples include an HTTP Request tool for an approved API, a database query with restricted permissions, or another n8n workflow exposed as a callable tool. Give each tool a clear description and validate input before performing a write operation.

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  5. Add memory only when conversational context is required. Define how long context should persist and what information must not be retained. Memory increases database use and can expose sensitive conversation content.

  6. Connect the trigger to the AI Agent, then connect the agent’s output to a response node or the application that called the webhook. Execute the workflow manually with harmless test data and inspect every node’s input and output.

  7. Activate the workflow only after testing authentication failures, malformed input, model timeouts and tool errors. Use the production webhook URL generated by n8n, which depends on the correctly configured WEBHOOK_URL.

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Hosted model or local Ollama?

Choice Where inference runs VPS resources Data location Operational work Cost pattern
Hosted API Provider infrastructure Usually modest; n8n still needs memory for workflows and tools Requests leave the VPS and are processed by the selected provider Maintain API credentials and handle quotas and outages Typically metered per request or token
Ollama model Your VPS Higher RAM, CPU and disk requirements; 4 GB RAM may be insufficient for useful models Prompts and responses can remain on your server, subject to your own logging and backups Maintain Ollama, model files, updates and capacity No provider token charge, but you pay for VPS capacity and storage

A self-hosted starter stack can combine n8n, PostgreSQL, Ollama and Qdrant, a vector database used for retrieval-augmented generation. Every additional service adds memory consumption, backups, upgrades and failure points. Start with a hosted model when the VPS is small or you need the simplest reliable deployment; choose Ollama when local data handling or predictable inference control justifies the extra operations.

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Add retrieval without giving the agent unrestricted access

For a knowledge-base agent, ingest documents in a separate workflow, split them into appropriately sized chunks, create embeddings and store them in a vector database such as Qdrant. The runtime workflow retrieves only the top relevant chunks and passes them to the AI Agent as context.

  • Keep ingestion and question answering separate so a bad document cannot alter the live prompt unexpectedly.
  • Attach source identifiers and access-control metadata to each chunk.
  • Limit retrieval results and token size to prevent oversized prompts.
  • Tell the agent to state when the supplied context does not answer the question.

RAG does not make an agent authoritative by itself. It improves grounding only when the source documents are current, permissions are enforced and the workflow handles empty or conflicting results.

Backups, upgrades and day-to-day operations

Back up both database and n8n data

PostgreSQL contains workflow records and encrypted credentials; the n8n volume contains the encryption key and operational files. Back up both, and store the encryption key separately in an encrypted password manager or offline archive.

A logical database backup can be created with:

docker compose exec -T postgres pg_dump -U "$POSTGRES_USER" "$POSTGRES_DB" > n8n-$(date +%F).sql

Test restoration on a disposable stack rather than assuming a successful dump is usable. Include the Compose file, the environment-variable names (but not secrets in an unencrypted repository), and a documented record of the image versions you have deployed.

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Upgrade deliberately

  1. Read the release notes and check compatibility with your nodes and credentials.
  2. Take fresh PostgreSQL and n8n-volume backups.
  3. Record the currently running image digest or tag.
  4. Change the image tag in compose.yml, pull the image and recreate the stack:
docker compose pull
docker compose up -d

Keep the previous image reference until the workflows pass smoke tests. Never delete the data volumes as part of a routine upgrade.

Monitor the signals that matter

  • Disk space, especially PostgreSQL growth, execution data and local model files.
  • Memory pressure and swap activity during concurrent agent runs.
  • Repeated container restarts or database health-check failures.
  • HTTP 4xx and 5xx responses from webhook endpoints.
  • Unexpected credential or owner-account changes.

Troubleshooting common failures

Symptom Likely cause What to check
Credentials show as unreadable or n8n reports an encryption-key mismatch The running key differs from the key used to encrypt the credentials, or the n8n data volume was replaced Compare N8N_ENCRYPTION_KEY with the protected backup and verify that the n8n_data volume is mounted. Do not generate a new key as a first response.
The domain returns 502 Bad Gateway n8n is stopped, still starting, or the proxy cannot resolve the service Run docker compose ps and inspect docker compose logs n8n and docker compose logs caddy. Confirm the proxy target is n8n:5678 on the Compose network.
The editor loads but webhooks call an old or HTTP URL N8N_HOST, N8N_PROTOCOL or WEBHOOK_URL is wrong Use the public HTTPS hostname consistently, recreate the n8n container after changing environment values, and test a newly generated webhook URL.
Certificate issuance fails DNS does not point to the VPS, or ports 80/443 are blocked Check DNS records, provider firewall rules, UFW status and Caddy logs. Ensure another service is not occupying 80 or 443.
Agent runs terminate with out-of-memory errors Local inference, large prompts, parallel executions or too many services for the VPS Inspect memory usage, reduce concurrency and context size, move inference to a hosted API, or resize the VPS.
PostgreSQL keeps restarting Incorrect credentials, an unready volume, or insufficient disk space Read the PostgreSQL logs, verify the three database variables, check volume ownership and free disk space, then restart only after correcting the cause.

Security checklist before exposing the agent

  • Use HTTPS and a valid certificate; never publish the editor over plain HTTP.
  • Bind n8n to localhost and expose only the reverse proxy’s 80/443 ports.
  • Enable two-factor authentication on the owner account.
  • Use a dedicated PostgreSQL password and keep .env permissions at 600.
  • Back up N8N_ENCRYPTION_KEY before creating credentials, and keep it out of source control.
  • Run tools with least-privilege API keys. Separate read-only and write-capable credentials.
  • Validate webhook authentication and input schemas before allowing an agent to call tools.
  • Set execution-data retention deliberately so sensitive prompts do not remain forever in the database.
  • Patch the host and review n8n release notes before upgrades.

When this architecture is the right fit

A Docker Compose VPS is a good fit when you need control over the n8n instance, predictable networking for webhooks, and the ability to keep workflow data on infrastructure you administer. It is not maintenance-free: you are responsible for operating-system updates, TLS, backups, database health, secrets and capacity planning.

For a first deployment, use the smallest stack that meets the workload: n8n, PostgreSQL and an HTTPS proxy. Add Ollama, Qdrant, browser automation or other services only after measuring the base system and defining how each new component will be backed up and secured.

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