Why Your LLM Applications Crash in Production (and How to Fix It Under 15 Microseconds)

If you're building applications with OpenAI, Gemini, or LangChain agents, you already know the pain: Large Language Models are unreliable.

You ask for a JSON response. You set up a strict parser like Pydantic or Marshmallow. But then:

}

.'id'

) or True

instead of standard double quotes and true

.And just like that, your production API crashes. 💥

Pydantic is fantastic for validation, but it is designed to fail. If something is slightly off, it raises a ValidationError

and terminates the flow.

To prevent crashes, developers write endless, messy try/except

wrappers and heuristic cleanup codes.

That is why I built ** higi**—a self-healing structural middleware layer that sits directly between raw, volatile LLM strings and your strict business logic.

higi

Works With a single decorator, @shield

, you define:

When a malformed string enters your function, higi

heals it before it reaches your core logic.

from higi import shield

blueprint = {
    "status_code": int,
    "message": str,
    "is_active": bool
}

fallback = {
    "status_code": 500,
    "message": "Fallback operational state",
    "is_active": False
}

@shield(blueprint=blueprint, fallback=fallback)
def process_data(clean_data):
    print(f"Executing with: {clean_data}")

If an LLM returns this truncated string:

"{'status_code': '200', 'message': 'LLM output got cut off mid-se

Here is what higi

does in microseconds:

True

to JSON true

."

, and a brace {

are left open. It automatically closes them in correct reverse order: {"status_code": 200, "message": "LLM output got cut off mid-se"}

."200"

into an integer 200

.Resilience shouldn't compromise performance. I ran benchmarks using Python's timeit

over 50,000 iterations. Here are the results:

0.56 μs

per call.9.26 μs

per call.15.14 μs

To put this in perspective, an LLM call takes 1,000,000 μs

(1 second). Running higi

adds a negligible 0.0015% latency overhead to your app, but gives you 100% resilience.

Help build the self-healing Python runtime engine!

pip install higi

If you find it useful, leave a ⭐ on GitHub! Let's make production crashes a thing of the past.