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

A developer built higi, a self-healing structural middleware layer that sits between raw LLM strings and strict business logic to prevent production crashes caused by malformed JSON or other structural errors. Using a single decorator, higi heals malformed strings in microseconds, adding only 0.0015% latency overhead to LLM calls. The tool is available via pip install higi.

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. python from higi import shield 1. Define schema blueprint = { "status code": int, "message": str, "is active": bool } 2. Define safe fallback fallback = { "status code": 500, "message": "Fallback operational state", "is active": False } @shield blueprint=blueprint, fallback=fallback def process data clean data : Guaranteed to never receive malformed keys or wrong types 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.