Deterministic reliability stack for LLM pipelines

BN — Sat, 09 May 2026 18:28:13 +0000

I have been spending the last few months wiring up a deterministic reliability stack for structured LLM pipelines.

Today, LLM Contract Check (locc) and Release Governor went live on PyPI. EGA went live last week.

The stack is straightforward:
LLM Contract Check - CI contract testing to catch schema regressions.
Release Governor - Blocks staging promotion if malformed outputs leak.
EGA - Runtime enforcement. Forces outputs to ground against source evidence before they move downstream.

The idea is simple:
don’t wait until production logs or human evals tell you something broke.

Try to catch:

unstable contracts in CI
leakage before deploy
unsupported outputs at runtime

Still early.
Not benchmarked.
Definitely not claiming this "solves AI safety."

I'm mainly looking for engineers building RAG or structured-output systems who are willing to plug pieces of this in and tell me where the assumptions break.

pip install llm-locc
pip install llm-release-governor
pip install ega

EGA: Runtime Enforcement for LLM Outputs (v1.0.0)

BN — Fri, 01 May 2026 01:36:39 +0000

I built EGA, a runtime enforcement layer for LLM outputs.

The problem: eval tools usually score after something already went wrong.

They do not stop bad outputs from going downstream.

EGA sits in the runtime path and checks the model output against the source before letting it pass through.

If something does not have support, it gets dropped or flagged.

v1.0.0 is live on PyPI today.

This is still early:

not benchmarked yet
not production-grade calibration yet
needs real RAG pipeline feedback

I am looking for engineers building RAG pipelines who are willing to plug this in and tell me where it breaks.

pip install ega
GitHub: https://github.com/bh3r1th/llm-evidence-gated-generation
PyPI: https://pypi.org/project/ega/1.0.0/

DEV Community: BN

Deterministic reliability stack for LLM pipelines

EGA: Runtime Enforcement for LLM Outputs (v1.0.0)