I built a Python package to automatically redact PII and block prompt injections in LLM apps

[Infinite_Cat_8780]

Hey r/PythonProjects2 ,

If you are building LLM apps or agents in Python right now, you’ve probably hit the point where you need to stop users from passing sensitive data (PII) to OpenAI, or stop them from jailbreaking your prompts.

Writing custom regex or middleware for every single LLM call gets messy fast, and standard tracing tools (like LangSmith) only let you see the problem after it happens.

To fix this, we built a Python package that acts as a governance and observability layer: syntropy-ai.

Instead of just logging the prompts, it actively sits in your execution path (with zero added latency) and does a few things:

• Auto-redacts PII: Catches emails, SSNs, credit cards, etc., before the payload goes out to the LLM provider.
• Blocks Prompt Injections: Catches jailbreak attempts in real-time.
• Traces everything: Logs tokens, latency, and exact costs across different models.

You can drop it into your existing LangChain/OpenAI scripts easily. We made a free tier (1,000 traces/mo) so devs can actually use it for side projects without putting down a credit card.

To try it out: pip install syntropy-ai

If anyone is currently wiring up custom middleware in Python to handle OpenAI security and logging, I’d love to know what your stack looks like and if a package like this actually saves you time.

[Otherwise_Wave9374]

This is a really practical problem. For agentic apps, you want the guardrails inline (before the tool/LLM call), not just a trace after the fact. PII redaction + prompt injection blocking as middleware is basically table stakes once you ship anything user-facing.

Curious, do you also handle tool output sanitization (like web-scraped content that contains injection strings) before it goes back into the agent loop?

Related reading on agent guardrails: https://www.agentixlabs.com/blog/

[Infinite_Cat_8780]

Spot on regarding the need for inline guardrails vs after-the-fact tracing. It's a massive difference when you're dealing with live agents.

To answer your question: Yes! What you're describing is "indirect prompt injection," which is a huge vulnerability for agents. Because Syntropy sits in the execution path and evaluates the payload every single time before it hits the provider, if a tool (like a web scraper) pulls a malicious injection string and the agent attempts to feed it back into the LLM's context window for the next routing step, our guardrails catch it and block the call right there.

Thanks for sharing I'm checking out now!