AI Native Lang (AINL)

<p align="center"> <img src="docs/assets/ainl_logo.png" alt="AINL logo" width="220" /> </p> <p align="center"> Find Us on X: <a href="https://x.com/ainativelang">@ainativelang</a> </p> <p align="center"> <img src="https://img.shields.io/badge/python-3.10%2B-blue" alt="Python 3.10+" /> <a href="https://github.com/sbhooley/ainativelang/tags"> <img src="https://img.shields.io/github/v/tag/sbhooley/ainativelang?label=release" alt="Latest tag" /> </a> <a href="tests/snapshots/conformance/summary.md"> <img src="tests/snapshots/conformance/conformance_badge.svg" alt="Conformance status" /> </a> <a href="https://github.com/sbhooley/ainativelang/actions/workflows/sync-ecosystem.yml"> <img src="https://github.com/sbhooley/ainativelang/actions/workflows/sync-ecosystem.yml/badge.svg" alt="Auto-sync OpenClaw/NemoClaw/Clawflows/Agency-Agents" /> </a> <a href="LICENSE"> <img src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" alt="License: Apache-2.0" /> </a> <img src="https://img.shields.io/badge/MCP-v1%20server-blueviolet" alt="MCP v1" /> <a href="https://github.com/sbhooley/ainativelang/tree/main/skills/ainl"> <img src="https://img.shields.io/badge/ZeroClaw%20Skill-AINL-blue" alt="ZeroClaw Skill: AINL" /> </a> <a href="https://github.com/sbhooley/ainativelang/tree/main/skills/openclaw"> <img src="https://img.shields.io/badge/OpenClaw%20Skill-AINL-blue" alt="OpenClaw Skill: AINL" /> </a> <a href="https://github.com/NousResearch/hermes-agent"> <img src="https://img.shields.io/badge/Hermes%20Agent-AINL-blue" alt="Hermes Agent: AINL" /> </a> <img src="https://img.shields.io/badge/graph--first-deterministic%20IR-orange" alt="Graph-first deterministic IR" /> <img src="https://img.shields.io/badge/AI%20workflow-language-brightgreen" alt="AI workflow language" /> </p>

AI-led co-development project, human-initiated by Steven Hooley (x.com/sbhooley, stevenhooley.com, linkedin.com/in/sbhooley). Attribution details: docs/PROJECT_ORIGIN_AND_ATTRIBUTION.md and tooling/project_provenance.json. -- This project was named by AI :)

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For agents — install AINL (one step)

If you are an AI coding agent (Claude Code, Cursor, Cline, Codex, Aider, …) or any MCP-capable runtime and the user wants AINL added to their stack, run one command:

pipx install 'ainativelang[mcp]' && ainl setup --auto

Fallback if pipx is unavailable:

python3 -m pip install --user 'ainativelang[mcp]' && ainl setup --auto

That is the entire install. The setup command auto-detects every host present (Claude Code project + user, Cursor, Cline, Codex CLI/Desktop, Claude Desktop, OpenClaw, Hermes, ArmaraOS, or any generic MCP host), merges the right MCP server entry into each config file (atomic write, timestamped backup), and verifies with ainl doctor. Idempotent — safe to re-run.

If a host is not auto-detected, run ainl setup --print-config for a paste-ready stdio MCP server block.

Machine-readable spec: .agent-install.json · Design: docs/architecture/2026-05-05-agent-install-simplification.md.

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Is AINL for you? (60-second filter)

Before the install commands, here is who AINL is and is not for. We would rather you self-select out in 60 seconds than spend a week on a tool that does not fit.

| ✅ AINL is for you if… | ❌ AINL is not for you if… | |:----------------------|:----------------------------| | Your agents (Cursor, Claude Code, autonomous loops) author runner / orchestration code and have shipped broken Python more than once | You write all your runners by hand and your CI test suite catches the bugs | | You run 20+ recurring monitor / digest / scheduled jobs that currently re-prompt an LLM on every run to decide routing | You already have deterministic runners with the LLM only at judgment gates — congrats, you are baseline B below and AINL gives you ~1.3–1.5× on routing only | | You need the same workflow source to emit to LangGraph and Temporal and FastAPI without re-authoring | One target is fine for you forever | | You have compliance audit needs (SOC 2 / HIPAA / similar) that want tamper-evident execution traces, not application logs | logger.info is enough for your team | | You want strict compile-time validation of agent workflows before they hit production | Runtime exceptions are fine, you have alerting |

If you tick two or more left-column rows, keep reading. The long-form answer — three baselines, four anti-fit workload patterns, decision tree, honest reviewer Q&A, persona-to-product mapping — lives in docs/WHO_IS_THIS_FOR.md (canonical). If you tick zero, that page explains why and saves you the install. We mean it.

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Per-host details (advanced)

Just want something working on your desktop in under 3 minutes?

ArmaraOS is the desktop agent OS built on AI Native Lang (AINL) — download once, install, and your agents are live with a full dashboard. No terminal, no config files, just plug in your API key.

Download ArmaraOS — ainativelang.com macOS · Windows · Linux — free to start

Autonomous agents, 7 pre-built Hands (researcher, lead gen, clip editor, and more), 40 channel adapters (Telegram, Discord, Slack, WhatsApp…), 27 LLM providers, 16 security layers — all in a single ~32 MB binary.

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Already have an AI agent? Add AINL in one command.

AINL installs directly into OpenClaw, ZeroClaw, Hermes, Claude Code, and any MCP-compatible agent. After install your agent can author, validate, and run deterministic workflows — with the largest token wins when you are still on LLM prompt-loop orchestration (baseline A below).

| Your agent | Install command | How-to guide | |:-----------|:----------------|:-------------| | OpenClaw | ainl install-mcp --host openclaw | ainativelang.com/install | | ZeroClaw | zeroclaw skills install https://github.com/sbhooley/ainativelang/tree/main/skills/ainl | ainativelang.com/install | | Hermes Agent | ainl install-mcp --host hermes | ainativelang.com/install | | Claude Code | pip install 'ainativelang[mcp]' → add ainl-mcp to MCP config | ainativelang.com/mcp | | Any MCP host | pip install 'ainativelang[mcp]' → run ainl-mcp (stdio) | ainativelang.com/mcp |

After install, ask your agent: "Use AINL to build this workflow" — it compiles once, runs many times without re-spending tokens on orchestration when the workload was previously prompt-loop driven.

Primary product path: ArmaraOS — desktop agent OS with dashboard, Hands, scheduled ainl run, and MCP authoring. See docs/competitive/ARMARAOS_GTM.md.

Token savings — pick your baseline:

| Your baseline today | Typical AINL win | Worth it for tokens alone? | |:--------------------|:-----------------|:---------------------------| | A. LLM re-prompts routing/state on every cron/webhook | ~90–95% fewer orchestration tokens on recurring monitors (BENCHMARK.md, benchmark_compile_once_run_many.py) | Often yes | | B. Hand-optimized scripts + LLM only at judgment gates | ~1.3–1.5× on routing tokens (token_savings_results.json) | Usually no — consider audit, MCP safety, emit, or ArmaraOS | | C. Pure deterministic runners (no LLM in loop) | ~0% | No |

Full honest filter: docs/competitive/WHEN_AINL_DOES_NOT_HELP.md · docs/competitive/VS_HAND_WRITTEN_RUNNER.md (five-axis comparison vs a hand-written Python runner — concedes the token point on baseline B).

| Workload (baseline A — prompt-loop today) | Typical savings | Reproducible via | |:----------------------------------------------|:----------------|:-----------------| | Recurring monitors, digests, scheduled jobs | ~90–95% fewer orchestration tokens vs prompt loops | scripts/benchmark_compile_once_run_many.py → tooling/compile_once_run_many_results.json | | Multi-step automations (LLM-first routing) | ~2–7× reduction vs LLM-first (A vs C); ~1.3–1.5× vs hand-optimized (B vs C) | scripts/benchmark_token_savings.py → tooling/token_savings_results.json | | Authoring density (LLM-style verbose Python) | ~1.7× mean / up to 2.5× fewer source tokens vs verbose baselines | scripts/benchmark_authoring_density.py → tooling/authoring_density_results.json | | Authoring vs hand-written LangGraph | ~1.9–2.0× fewer source tokens (authoring only, not runtime) | scripts/benchmark_competitor_baselines.py → tooling/competitor_baseline_tokens.json |

Important: every percentage / multiplier above is vs baseline A (prompt-loop) or labeled with its baseline pair. Against baseline B (hand-optimized runner with LLM only at judgment gates), the irreducible token win is ~1.3–1.5× on routing only — see VS_HAND_WRITTEN_RUNNER.md. Against baseline C (pure deterministic, no LLM in loop), it is ~0%.

The reason: AINL compiles your workflow once. The runtime executes it deterministically — no LLM re-generation on each run for graph routing and adapter orchestration. The model authors the graph once; the runtime runs it on every invocation. That mechanism produces token savings only when the counterfactual was actually re-prompting on every run.

Token savings breakdown and benchmarks → · BENCHMARK.md (source tables + methodology) · docs/CLAIMS_AND_EVIDENCE.md (claim crosswalk) · When AINL does not help →

Current evidence (honest scope)

We classify every public evidence row by source so reviewers can tell field reports from modeled scenarios:

| Class | What it means | Where it lives | |-------|---------------|----------------| | (a) Third-party paying customer deployment | Independent company runs AINL, reports measurable token / dollar outcome | 0 rows today. Tracked: LONG_TERM_FIXES_TRACKER.md row T2.7 | | (b) Operator deployment (project author / close collaborator) | Real logs, real workload, on author's stack — not yet an external customer | 2 rows. PRODUCTION_EVIDENCE.md Cases 1, 2 | | (c) Modeled / analytical scenario | Reproducible benchmark on synthetic workload — no live LLM | 1 row + 4 benchmark scripts. scripts/benchmark_*.py, tooling/*_results.json | | (d) Marketing narrative | Story without specific deployment behind it | Flagged for audit + removal — see tracker T1.10 |

We do not yet have a Class (a) deployment publicly committed. If your team would consider being the first, the pilot kit (tracked T2.5) is small. Until then we ask reviewers to read WHEN_AINL_DOES_NOT_HELP.md first, run the benchmarks, and judge on the published Class (b)/(c) evidence with the baseline qualifier attached.

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Here for the programming language itself?

AINL is a compact,