Building the open agentic stack.
Aroora AI Labs ships open infrastructure for trustworthy agentic AI — a token-efficient format (ISON), property graphs for context (ISONGraph), a multi-agent protocol engine (MAPLE), deterministic context shaping (Contexel) and local multi-model inference (SnapLLM). Repository first, product second.
The stack
Canonical serialization, byte-identical context shaping, replayable agent runs — every layer of the stack can be checked, diffed and reproduced.
Context windows are the scarce resource. ISON cuts payloads 30–70% versus JSON; ISONGraph fits 3× more graph into the same window.
MIT-licensed specs, reference implementations and reproducible benchmarks — private work becomes public the moment it is stable.
ISON
A minimal, token-efficient data format optimized for LLMs and agentic AI workflows.
Familiar tabular patterns models have seen billions of times. 30–70% fewer tokens than JSON, lossless round-trip, and byte-identical canonical output across all seven implementations.