# Deploy AI agents in 5 lines of code.

> Source: <https://dev.to/custodianlabs/deploy-ai-agents-in-5-lines-of-code-3fji>
> Published: 2026-06-29 23:28:53+00:00

Build AI-agents in 5 lines of code. Skip the set up & infrastructure. Live and running.

``` python
from custodian_labs import Custodian

app = Custodian(
    model="gpt-4o",
    system_prompt="You are a concise, helpful assistant.",
).deploy()

print(app.chat("Hello!").response)
print(app.chat_url)  # ← live, shareable URL
```

Runnable [Colab notebook here](https://colab.research.google.com/gist/SherryCodes123/065d3b67eab16bdca416836e0d39475a/simple-ai-agents-rag-multi-agents.ipynb).

DIYing this: FastAPI behind a reverse proxy + Redis for sessions + a small frontend + an auth layer + a vector DB and ingestion pipeline. All of it is annoying. Most teams burn weeks on plumbing before the interesting work even starts.

Grab a free API key from the [dashboard](https://dashboard.custodianlabs.io), then:

```
!pip install -q custodian-labs
python
import os
from getpass import getpass
os.environ["CUSTODIAN_SDK_API_KEY"] = getpass("API key: ")
python
from custodian_labs import Custodian

app = Custodian(
    model="gpt-4o",
    system_prompt="You are a concise, helpful assistant.",
).deploy()

reply = app.chat("In one sentence, what can you help me with?")
print(reply.response)
print("\nShareable chat URL:", app.chat_url)
```

What you get back:

`app`

— handle to a live hosted agent.`reply.response`

— the text.`reply.session_id`

— pass it to the next `.chat()`

to continue the conversation.`app.chat_url`

— live URL to a hosted chat UI. Paste in Slack, share with PMs, done.

``` python
from custodian_labs import Custodian

agent = Custodian(
    model="gpt-4o",
    system_prompt=(
        "You answer questions about the car dataset you've been given. "
        "Be specific and cite the numbers."
    ),
)
agent.add_data_source_file("data_examples/car_info.csv")
app = agent.deploy()

reply = app.chat(
    "Which SUV has the best city MPG, and what is its annual maintenance cost?"
)
print(reply.response)
print("\nShareable chat URL:", app.chat_url)
```

`add_data_source_file`

handles chunking, embedding, and retrieval at chat time. Works on CSVs, PDFs, and the usual suspects — calling code doesn't change whether you point it at a CSV or a folder of contracts.

Same shape for all of them: write a system prompt, attach docs, deploy, share the URL.

The [Colab notebook](https://colab.research.google.com/gist/SherryCodes123/065d3b67eab16bdca416836e0d39475a/simple-ai-agents-rag-multi-agents.ipynb) runs end-to-end with sample data — bring no files, just a free key from the [dashboard](https://dashboard.custodianlabs.io). Should take ~1 min.

I'd genuinely love feedback — what's missing, what's confusing, what would make you actually use it. Feature requests very welcome. Drop them in the comments, or email ** sherry@custodianlabs.io** if you build something with it (or break it in an interesting way).

Next posts coming on multi-agent teams with topic routing, and the PII / proprietary-data masking layer for legal and healthcare agents.