Aegis Latent Core
Governed LLM traffic with verifiable audit evidence
What it does
Aegis is an open-source, self-hosted AI governance and evidence gateway. It applies request controls, bounded streaming PII redaction, durable audit records, and client-verifiable MMR inclusion proofs to supported LLM routes. Python clients and TypeScript helpers ship as aegis-latent-sdk 4.0.0. GitHub’s Release label is v4.0.1; source and attached package assets remain 4.0.0.
AI governance and evidence gateway for multi-provider LLM applications. FastAPI + optional Rust core for policy, WAF, egress, rate limits, sessions, signed durable evidence, and fail-closed error paths. Self-hosted; no certification or SLO claim. - JuanLunaIA/aegis-latent-core
Does the same job
all alternatives →- AegisoraAug 2026 · aegisora-ai.vercel.app · ▲93
The narrow control plane for AI agent tool and API calls.

- AAAegis – A framework for AI-governed software development2025 · github.com · ▲5
Hey HN – I built a framework called Aegis to govern AI-assisted software development. The core idea is that AI-generated code should follow the same rules as human code: versioned, validated, observable. Aegis enforces this through blueprint-based development, drift detection, and runtime compliance systems. It’s designed for teams using tools like Copilot, Kilo, or Lovable to build production systems with confidence. This isn’t a library — it’s a way to architect AI-native engineering workflows. Would love feedback, questions, and critiques. Especially curious if others are facing similar…


AEGIS – Clinical AI OS for Leadership19d ago · aegis.humancatalystbeacon.com · ▲2From insight to behavior: a AI OS for leaders and founders
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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