ÆTHERYA Core – deterministic action-governance kernel for LLM agents
Fail-closed policy layer blocks LLM tool calls before execution, no LLM in decision path.
Deterministic capture + replay for LLM agents is a practical, under-served problem and this repo actually ships a 'golden run' zip with cold‑run verification hashes — that’s the kind of evidence chain auditors want. The focus on portable evidence bundles and stress verification suggests useful forensics and load testing of agent logic, but the release page looks early-stage; I'd like to see integrations (tooling for popular agent frameworks), richer docs, and example pipelines before I'd evangelize it.
LLM/agent developers, security auditors, SREs/DevOps, and ML researchers who need reproducible forensic evidence for agent behavior
Fail-closed policy layer blocks LLM tool calls before execution, no LLM in decision path.
Replay-first architecture beats LangSmith's static traces for debugging non-deterministic agents.
VCR for LLM calls—eliminates API costs and non-determinism in agent testing.
Git for agent cognition—clever framework, but no working implementation yet.
BEAM kernel with deterministic replay solves agent state durability problems.
Replays agent traces step-by-step to pinpoint exact failure turns automatically.