Local-first autonomous coding CLI that plans, builds, verifies, and validates against a spec — entirely on your hardware.
spiral-coder is an open-source, local-first autonomous coding CLI available on PyPI. It operates in a fully offline loop: given a specification, it plans the implementation, writes the code, runs verification steps, and validates the output against the original spec — all without sending data to the cloud. Designed for developers who want full privacy, air-gap compatibility, and no per-token costs, spiral-coder runs entirely on your own hardware using locally available models.
Who it's for
developersopen-source contributorsprivacy-conscious engineersair-gapped or regulated environment teams
Pricing · free
checked Jul 19, 2026
Plan
Price
Includes
Free (Open Source)
$0 /mo
Completely free and open source · No API keys or subscriptions required · Runs fully on local hardware · No usage limits or rate limits
AI-researched pricing — verify on the official site before subscribing.
Use it for
— Autonomously generating code from a written specification
— Validating generated code against a defined spec without human review
— Building small-to-medium projects fully offline
— Iterative plan-build-verify loops on local hardware
— Prototyping in restricted or air-gapped development environments
Get the most out of it
01Write a detailed, unambiguous spec file before invoking spiral-coder — the quality of the generated code scales directly with spec clarity.
02Test with a lightweight local model first to confirm the loop works, then switch to a more capable local model for production-quality output.
03Use the verify and validate steps as checkpoints: review intermediate artifacts after the plan phase before letting it proceed to the build phase.
04Pin your model version in the config so that output is reproducible across runs.
05Pair spiral-coder with a local model server (e.g., Ollama or llama.cpp) to get the best performance-per-watt on your hardware.
A security-first development workflow for organizations deploying open-source AI models. Scans codebases for vulnerabilities, tests models for adversarial robustness, and maintains air-gapped deployment options to address export control concerns.
How the workflow runs
01spiral-coder — Build and test AI features entirely on local hardware, ensuring no code leakage to cloud providers
02vulnhunter — Hunt for exploitable vulnerabilities in the AI-generated code before deployment
03laguna-s-2-1 — Use the secure open-weight coding model for generating additional test cases and security patches
04tabnine — Enable privacy-first code completion that learns from your secure codebase without cloud transmission
05inspect-ai — Rigorously evaluate model outputs for safety, bias, and adversarial resistance
With open-source AI facing potential export controls and licensing requirements, this stack lets teams prove security due diligence. Spiral-coder's local-first approach combined with VulnHunter's exploit detection creates a defensible security posture. Perfect for defense contractors, government agencies, and enterprises in regulated industries who need open models but can't risk security breaches.
