ironclad-ai 0.0.29

Reliability for LLM agents through enforcement, not model size — Agent-Contract-Kernel (ACK) + a fail-closed orchestration engine.

Ironclad

Your own agentic platform — self-hosted, model-agnostic, reliable by contract. No vendor lock-in, no surprise limits, no subscription. Run it on your hardware with any open model; reliability comes from enforced contracts, not model size.

Ironclad is a generic framework for building reliable agentic systems that you fully own and host yourself. It exists to keep you independent of proprietary providers — no sudden rate changes or feature removals, no forced subscription, no data leaving your infrastructure. It pairs an Agent-Contract-Kernel (ACK) — schema-as-single-source-of- truth, validate→reask→retry, a generator and a preflight doctor — with a lean orchestration engine that turns multi-step agent workflows into deterministic, fail-closed pipelines. The guiding principle: a small, fast, self-hosted model under hard schema enforcement beats a large proprietary one you merely trust to format its output — and it learns from its own runs and stays yours.

Why Ironclad

• Independent & self-hosted. Any OpenAI-compatible endpoint (vLLM, …); your box, your model, your data. No cloud dependency, no lock-in, no subscription — immune to a vendor's sudden limits or pricing changes.
• Model-agnostic & standalone. Swap the orchestrator model freely; reliability comes from the kernel, not the weights. No hidden dependency on any private deployment.
• Contract-first, fail-closed. One Pydantic schema drives the prompt, the validator, the docs and (where the hardware allows) constrained decoding; macro steps do the mechanical file work deterministically in code — fewer round-trips, no silent half-completions.
• Yours to extend. One open, versioned plugin contract, a bring-your-own coding-agent CLI, and a framework that can scaffold and maintain itself.

Quickstart — describe an idea, let the agents build it

git clone https://github.com/GrokBuildMJW/ironclad.git && cd ironclad
pip install -e ".[engine]"
export GX10_BASE_URL=http://localhost:8000/v1 GX10_MODEL=your-model   # your model endpoint

python engine/server.py &              # 1) the orchestrator (the agent + state)
python engine/client.py --codedir .    # 2) the client — drives it; type what you want
#   /project new demo --type software      ← create a workspace first (artefacts live under vault/<slug>/)
#   Read README.md and summarise it.   /   Build add(a,b) in calc.py with a pytest test, and run it.

The orchestrator (engine/server.py) runs the agent and holds state; the client drives it from your machine and keeps your code local — the orchestrator's file/command tools are passed through to the client and run on YOUR local files. The agents plan it, write the code, write the test, and run it — you describe, they build; no prior coding needed.

One step first: any work that produces artefacts (a task, a handover, an MPR run) needs an active project — /project new <name> (/initiative is a deprecated alias). It is fail-closed: without one the macros refuse rather than scatter state into your project root. Plain Q&A turns need no project. See docs/project-isolation.md and docs/state-and-initiative.md.

What you get

• Headless server + thin client — the orchestrator runs as a service; clients drive it over plain HTTP and keep code local. Recommended client is the TypeScript terminal client (clients/ink/) on a purpose-built renderer (ghost-free resize, smooth streaming, native-grade scrollback/selection/copy). Zero-dependency Python clients (line REPL + legacy full-screen TUI) ship alongside it.
• Reliable tool-calling — the ACK validates every tool call (validate→reask) and recovers for models without native tool-calls, so structured output doesn't depend on a specific model or parser.
• Loop Intelligence — an always-on, self-improving context loop (ACE). The engine learns across runs into an evolving, itemized playbook of bullets, refined by Generator→Reflector→Curator with incremental delta updates + grow-and-refine (Agentic Context Engineering). It is the always-on core mechanic — no enable flag — registered as the lesson provider and superseding the earlier string-lesson layer. A mark-only Verifier scores each handover and a Quality circuit-breaker trips on sustained degradation (both ride a dependency-inverted Hook-Bus over agent-loop events, never relaxing the fail-closed core). The loop also learns from the dev-process itself — the transition ledger both dev-processes emit is distilled into per-unit lessons and correlated to the bullets each handover used. And at an architecture fork it can (gated ace.fork_mpr.enabled, default off) run the bundled MPR multi-perspective panel off the hot path to attach a decision-matrix as a recommendation to the human ask, then learn the chosen outcome so the next comparable fork is pre-informed — always proposing, never deciding.
• Governed parallelism + provider routing — server-side reasoning fan-out (/fanout + the in-engine parallel_reason tool), GPU-safe via a concurrency cap and a token-budget envelope; a provider router/dispatcher on top routes work across substrates with fail-soft spill (off by default).
• Scalable-context memory — long-term vector(+graph) store plus a multi-tier context system (bounded model window + short-term summary/cache tier + long-term retrieval) with rolling summarization and per-turn RAG.
• Web search, trust-gated — a first-class web_search tool over a vendor-neutral adapter seam (a CLI delegate or native HTTP), kept out of the sealed profile by default (operator opt-in); results stream a web N · Xms chip to the client. See docs/web-search.md.
• Built-in prompt & skill library — reusable prompt items and typed skills ship in the box (including the bundled MPR multi-perspective reasoner as one example skill); discover them with /prompts / /skills and run a prompt directly as /<prompt-name> (e.g. /code-review), or scaffold your own with the paved-road generator.
• Secure, session-gated channel — selectable trust profiles (open / token / sealed) with an explicit session that seals on disconnect (single-operator).
• An open extension surface — a versioned plugin API (no core fork) and a bring-your-own code-agent CLI: extend the platform without forking the core.

It's a natural reliability layer for regional open models too — point it at Falcon, Jais or K2 Think via vLLM (running on other models) and get fail-closed pipelines and structured tool-calls without forking or retraining anything.

Status & honesty (pre-release)

Ironclad's engine comes from a proven, in-production orchestrator, now rebuilt into the server + client architecture above and wired and tested. It is still pre-release (0.0.x, alpha): single-tenant by design (no multi-user auth yet) and APIs/layout/config may change. Tagged releases ship on PyPI (ironclad-ai) and as GitHub Releases (currently v0.0.29) — treat them as early previews and main as a development snapshot. The internal DEV → Prod → Public promote pipeline that hardens our releases is in development (today a manual gated path).

Verified by 3134 Python tests (3125 offline + 9 live) plus 440 TypeScript client tests, and a full end-to-end run with a real code-agent. Read these before relying on anything:

• docs/status.md — honest per-component wiring status (what runs now) + load tests.
• docs/test-report.md — what was tested, results, and the issues found and fixed during the campaign.
• docs/roadmap.md — what's planned or in progress (future only).
• docs/command-ergonomics.md — aliases, did-you-mean, argument autocomplete, discovery, and confirm-before-destructive for the slash-command surface.
• docs/docs-guide.md — how the docs are organised (one responsibility per doc).

Reference environment & benchmarks

Developed and exercised on an NVIDIA DGX Spark (GB10, Blackwell sm_121, 128 GB unified memory) running a local vLLM server with Qwen3.6-35B-A3B-NVFP4. Nothing is hard-wired to that box — any OpenAI-compatible endpoint works — but the defaults (localhost:8000, qwen3.6-35b), the throughput numbers and the constrained-decoding findings reflect that hardware. See docs/dgx-spark.md for the full reference stack and a one-shot bootstrap (scripts/spark-bootstrap.sh).

Workload	Result
Reasoning fan-out, 8 independent prompts	5.8× faster than serial (1.2 s vs 7.1 s), ~118 tok/s aggregate
Conversational turn (single agent)	~55–68 tok/s, ~2.1 s mean latency
Structured emission (ACK, thinking-off)	100% schema-valid in measurement

Numbers scale with the model and GPU; reproduce with your own endpoint. Full method and the per-component wiring status live in docs/status.md.

Demo

The recommended TypeScript client streams a turn live into the terminal's own scrollback, with a pinned status bar (model · connection · memory · warm · watcher · autopilot · tasks · coder · web search · throughput):

 █▀▄▀█ Ironclad · Orchestrator Client
   Ironclad CLI 0.1.0 · code . · ≤3 agents
  /help · exit

 > what is 17 times 23?

 17 times 23 is 391.
 ──────────────────────────────────────────────────────────────────────
 ◆ Ironclad · qwen3.6-35b · ● conn · ○ watch ○ auto · 0P/0IP/0D · 64 tok/s

Reply language is a setting (GX10_LANGUAGE — en default, ar, fr, …): the model answers in the configured language regardless of input language. /command routing, find-in-buffer (Ctrl+F), native scrollback/selection/copy and Ctrl+V paste are built in.

Extend it — a starting point to build on

Ironclad is a foundation, not a finished product. You extend it over one open, versioned contract — the plugin API: drop a tool into a skills/ directory, point GX10_PLUGINS_DIR at it, and the agent picks it up without forking the core. Building in your own repo? pip install ironclad-ai and import the curated Extension SDK (ack.sdk) to validate and ship plugins against a versioned surface. Concrete use cases dock on as vessels (see examples/demo-vessel/; a generator scaffolds new ones), so you build domain agents on a reliable, self-hosted base instead of starting from scratch. The framework even maintains itself — its agents scaffold new plugins, and a dev container runs the full suite as a build+test gate.

Directions the architecture supports today:

• Edge & energy efficiency — a small, enforced model on local/edge hardware instead of a large cloud one. That efficiency bet is the premise of the project.
• Education · healthcare · logistics — build a vessel for your domain: reliable tool-using agents and retrieval/RAG assistants over your own data, kept on-prem.

New here, or not a developer? You describe what you want and Ironclad's own agents plan it, write the code, write a test, and run it. Start with docs/self-maintenance.md — extend Ironclad with a plugin, or repurpose the whole thing for your own project.

Setup

Requires Python 3.10+ and an OpenAI-compatible endpoint (e.g. vLLM).

Install the library from PyPI:

pip install ironclad-ai             # the ACK library (import ack)
pip install "ironclad-ai[engine]"  # + the orchestration engine deps

Or clone + one-shot install (recommended while pre-release) — builds the venv, engine, client, config and an ironclad command in a single command (cross-platform, secret-free; endpoints default to localhost):

git clone https://github.com/GrokBuildMJW/ironclad.git && cd ironclad
bash install/ironclad-install.sh        # Windows: install\ironclad-install.ps1   (override: --base-url/--model)
source ~/.bashrc && ironclad            # Windows: . $PROFILE ; ironclad

See install/README.md for flags + the doctor.

Or wire it manually:

git clone https://github.com/GrokBuildMJW/ironclad.git
cd ironclad
python -m venv .venv && . .venv/bin/activate     # Windows: .venv\Scripts\Activate.ps1
pip install -e ".[engine]"

# Point at your model endpoint (defaults: http://localhost:8000/v1, qwen3.6-35b):
export GX10_BASE_URL=http://localhost:8000/v1
export GX10_MODEL=your-served-model-name
export GX10_API_KEY=...                           # only if your endpoint needs one

# the orchestrator + the recommended TypeScript client (needs Node ≥ 22):
python engine/server.py &
( cd clients/ink && npm install && npm install -g . )    # global `ironclad`, like claude / kimi
ironclad --server http://localhost:8100                  # runs in the current folder (codedir = cwd)
# then, before the first build/task:  /project new myproject --type software
# zero-Node alternative: python engine/client.py --codedir .   (legacy TUI: engine/tui.py)

• Full walkthrough, the server/client split, and the reference vLLM launch: see SETUP.md — including copy-paste shell shortcuts for Windows PowerShell, macOS and Linux.
• Let an AI coding agent set it up for you (deterministic, verifiable runbook): see AGENTS.md.

A runnable demo vessel lives in examples/demo-vessel/ — a minimal workspace showing a contract spec, a pipeline, and the doctor preflight.

Layout

ack/                   # Agent-Contract-Kernel: case-spec, validated-emit, registry, doctor, generator
engine/                # orchestration engine: agent loop, task store, fail-closed macros
clients/ink/           # recommended TypeScript terminal client (build-from-source)
examples/demo-vessel   # runnable example workspace
docs/  LICENSE  NOTICE  # guides + Apache-2.0

At runtime, in your workdir, state stays out of the project root: engine machinery is hidden under .ironclad/ (session, warm-cache, the active-initiative marker) and every produced artefact lives under vault/<slug>/ — see docs/state-and-initiative.md.

Roadmap

What's planned or in progress lives in docs/roadmap.md (future only); what runs today, per component, is in docs/status.md. In short: today is single-tenant, home-LAN trust (one operator, code stays on the client); multi-tenant identity & authorization does not exist yet — until it lands, treat enterprise/government use as single-tenant on trusted infrastructure.

Issues and discussions are welcome — this is an early, openly-developed project.

License

Apache License 2.0 — see LICENSE and NOTICE. Copyright 2026 MJWC-AI-LAB and Ironclad contributors.

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🇦🇪 Built in the United Arab Emirates by MJWC-AI-LAB.