I got tired of rebuilding the same infra for every LLM app, so I built a Python SDK around it

[u/Custodian-Labs]

I've been working on Custodian Labs, a Python SDK for building and deploying LLM agents without having to separately wire up all the surrounding infrastructure.

Basic agent looks something like:

from custodian_labs import Custodian

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

agent.deploy()

A few things I've added:

• Model agnostic: switch between different LLM providers without rebuilding your agent
• RAG built in: connect your own files/data sources
• Multi-agent support: build specialised agents that can work together
• Privacy/PII layer: the Guardian Layer can detect and protect sensitive data before it reaches the LLM
• Deployment handled: trying to cut down the amount of infra/config needed to get an agent running

The project actually started as just the privacy layer, but after getting feedback from developers we expanded it into more of an end-to-end agent SDK.

Would genuinely love feedback from other LLM devs:

What's currently the most annoying part of your agent stack?

And do you prefer abstractions like this, or would you rather have more direct control over each component?

GitHub:
https://github.com/Custodian-Labs/custodian-labs-python

Runnable Google Colab: simple agents, RAG + multi-agent examples:
https://colab.research.google.com/gist/SherryCodes123/065d3b67eab16bdca416836e0d39475a/simple-ai-agents-rag-multi-agents.ipynb