Find Us on X: @ainativelang AI-led co-development project, human-initiated by Steven Hooley (x.com/sbhooley, stevenhooley.com, linkedin.com/in/sbhooley).
$ npx -y skills add sbhooley/ainativelang --agent claude-code
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.mdandtooling/project_provenance.json. -- This project was named by AI :)
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.
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.
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.
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 →
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.
Here for the programming language itself?
View @sbhooley’s profileGraph-native memory and learning for Claude Code — every interaction remembered, every pattern learned, every agent connected. → Install in 30 seconds AINativeLang: Website · X · PyPI · GitHub · Docs · Developer: Steven Hooley | @sbhooley
@sbhooleyainativelang is a Claude Code plugin with 3 hand-picked skills, indexed on Flowy in the Agent Orchestration category. Install it with the command on its page. It includes ainl, hermes, openclaw. Its skills do not fire on their own yet. Request auto-invocation to have Flowy route them as you prompt. Free and open source.
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