This note ties together AI Native Lang (AINL) tooling and ArmaraOS Ultra Cost-Efficient Mode so operators do not confuse three different layers.
| Layer | Where it runs | What it does |
|---|---|---|
| Input compression | Rust (openfang-runtime / prompt_compressor) |
Shortens the user message sent to the LLM before each agent turn. |
--efficient-mode / AINL_EFFICIENT_MODE |
Python CLI sets env only | Signal to the host (e.g. ArmaraOS kernel injecting manifest metadata). No compression in Python. |
modules/efficient_styles.ainl |
AINL graphs (optional) | Shapes model output (dense prose / JSON) via llm_query — complementary to input compression. |
- Sets
AINL_EFFICIENT_MODEwhen not already set (values such asbalanced,aggressive,off— host-defined). - Does not implement compression in the Python runtime.
human_dense_response— natural, concise technical prose from prior node output.terse_structured— JSON-only internal steps.
Include with the usual AINL include prelude pattern (see file header comments). Replace llm_query with your deployment’s LLM adapter when not under ArmaraOS.
Ground truth for retention rules, UI locations, API fields (compression_savings_pct, compressed_input), and logs: ArmaraOS repository:
docs/prompt-compression-efficient-mode.md- Root
README.md— Ultra Cost-Efficient Mode section
AGENTS.md(AINL repo) — HTTP adapter, MCPainl_run, and environment conventions.