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Custodian Labs
Custodian Labs

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Deploy AI agents in 5 lines of code.

#rag #ai #llm #python

TL;DR

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

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
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Runnable Colab notebook here.

Skip the infrastructure

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.

Setup

Grab a free API key from the dashboard, then:

!pip install -q custodian-labs
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import os
from getpass import getpass
os.environ["CUSTODIAN_SDK_API_KEY"] = getpass("API key: ")
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The basic deploy 

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)
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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.

Adding your own data (RAG) 

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)
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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.

Ideas worth stealing

  • Internal "ask the docs" bots — handbook / Notion export / product wiki → drop the chat URL in Slack.
  • Customer support assistants — RAG over help-center articles + past tickets, in front of a human escalation path.
  • Sales / RFP helpers — past proposals + contracts, so reps can ask "how did we answer security questions for X?"
  • Research assistants — folder of PDFs (papers, reports, filings), chat the corpus.
  • Onboarding helpers — runbooks + architecture docs so new hires can self-serve without bothering a senior.
  • Personal knowledge agents — your notes or saved articles, hosted at a URL you can hit from anywhere.

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

Try it

The Colab notebook runs end-to-end with sample data — bring no files, just a free key from the dashboard. 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).

Custodian Labs SDK

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

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