Mason Daugherty
f2d955ebc5
`README.md` guidance now points readers toward the shared LangChain learning and community resources across the repo. Python install snippets also use `uv add` consistently instead of `pip install` or `uv pip install`. ## Changes - Added LangChain Academy and Code of Conduct links across package and example READMEs, using existing Resources sections where available and adding a short Resources section where needed. - Updated partner package quick-install snippets for Daytona, Modal, and Runloop to use `uv add`. - Updated example setup instructions to use `uv add deepagents-cli` and `uv add --editable .` for local installs. - Refreshed the root README intro so the JavaScript/TypeScript library link sits in the main overview and the Deep Agents Code install callout appears before Quickstart.
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 choicescontext.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(
url="https://<your-deployment-url>",
headers={"Authorization": "Bearer <your-supabase-jwt>"},
)
thread = await client.threads.create()
async for chunk in client.runs.stream(
thread["thread_id"], "agent",
input={"messages": [{"role": "user", "content": "Write a tweet about AI agents"}]},
stream_mode="messages",
):
print(chunk.data, end="", flush=True)
Find your deployment URL in LangSmith under Deployments. See test_user_memory.py for a full example and the LangGraph SDK docs for more.
Structure
deploy-content-writer/
├── AGENTS.md # Agent instructions and memory workflow
├── deepagents.toml # Deploy config (model, auth)
└── skills/
├── blog-post/ # Long-form blog post skill
└── social-media/ # LinkedIn and tweet skill
Resources
- deepagents deploy docs
- Custom auth docs
- Per-user memory docs
- LangChain Academy — Comprehensive, free courses on LangChain libraries and products, made by the LangChain team.
- Code of Conduct — community guidelines and standards