deploy-content-writer

A content writing agent deployed with deepagents deploy. It writes blog posts, LinkedIn posts, and tweets — and remembers each user's preferences across sessions using per-user memory scoped by their identity.

This example also demonstrates custom auth: adding [auth] provider = "supabase" to deepagents.toml so that every user's memory is isolated to their account with zero custom code.

Prerequisites

Variable	Description
OPENAI_API_KEY	GPT-4.1 model access
LANGSMITH_API_KEY	Required for deploy
SUPABASE_URL	Your Supabase project URL (for auth)
SUPABASE_ANON_KEY	Your Supabase anon/public key (for auth)

Copy .env.example to .env and fill in your keys. The Supabase keys are only required if you keep the [auth] section in deepagents.toml. Remove it to deploy without authentication.

Deploy

deepagents deploy

On deploy, the [auth] section in deepagents.toml generates a Supabase token validator and wires it into the deployment automatically — no custom middleware needed.

How per-user memory works

Each authenticated user gets their own memory files at /memories/user/:

• preferences.md — the agent reads and updates this to remember tone, topics, and formatting choices
• context.md — static context about the user's company and product

Because auth scopes these files by user identity, one deployment serves many users without any bleed between accounts.

What to try

Once deployed, open the agent in LangSmith and send it prompts like:

• "Write a blog post about the benefits of AI agents for developer teams"
• "Turn this into a LinkedIn post: [paste your content]"
• "I prefer a more casual tone — remember that for future posts"
• "Draft three tweet variations for our new product launch"

Query via SDK

Pass your Supabase JWT in the Authorization header — the deployment validates it and infers the user identity automatically:

from langgraph_sdk import get_client

client = get_client(