Write production code with AI.
Spatial is built for technical developers who care about clean diffs, strong review loops, and catching bugs or AI slop before it lands. It stays strict enough for serious repos, while still being approachable for lighter app-building and prototype work.
Flexible stack, stricter workflow
Use the model that fits the job, then keep the guardrails on.
Spatial does not force one provider or one workflow. Pair your preferred models with review, approvals, and integration surfaces that help you close the loop.
Strongest when you care about production diffs, bug catching, branch hygiene, and keeping AI output under control.
The same workflow can stay relaxed for prototypes, casual app development, or less technical builders who still want clearer visibility than raw prompt-to-code tools.
Workflow integrations currently include GitHub, Vercel, Railway, Linear.
Why Spatial?
Built for real codebases,
not unchecked AI output.
The product is optimized for production work: review every diff, control risky operations, keep workflow context close, and maintain clean boundaries while the agent moves fast.
Review-first by default
Inspect every file diff before it lands, keep approvals explicit when needed, and use dedicated review flows to catch quality regressions early.
Guardrails that match the task
Use Build, Plan, or Agentic mode depending on the risk. Keep confirmations on when quality matters, then loosen autonomy when speed matters more.
Parallel work without branch chaos
Run isolated git worktree sessions for bigger tasks so risky refactors, hotfixes, and feature work do not collide in one working tree.
Subagents for focused discovery
Delegate research and exploration in parallel, then keep the main session cleaner for implementation and final review.
Real workflow integrations
Keep GitHub reviews and checks, plus Vercel, Railway, and Linear context, close to the session instead of bouncing between tabs.
Model choice without lock-in
Bring your own providers and pick heavy models for reviews, faster models for iteration, and cheaper models for throughput work.
Use AI when you want speed.
Keep Spatial when you need trust.
The core workflow is designed for technical teams shipping real software, but it remains approachable for smaller apps, prototypes, and less technical builders who still want visibility into what the agent is changing.
Bring your own API keys and track usage transparently.
macOS is the primary support target, Windows is supported, and Linux is currently alpha/experimental.