The safety checkpoint every AI action passes through — before it happens.
Your agent can send the email, issue the refund, update the record. SafeAct makes sure it does that safely, once, and on the record — so your risk team says yes instead of no.
Building MCP tools? One decorator makes any MCP tool exactly-once and auditable — no double-charges when your agent retries.
pip install safeact-mcp
$ pip install safeact
The agent isn't too dumb to deploy. It's too untrusted to let act.
Almost every company launched an AI agent this year. Almost none put one into production — not for lack of intelligence, but because no one could trust it to touch real money and real customers.
Four things, every single time your agent acts.
Enforced in code — underneath any agent framework. The model proposes; SafeAct decides whether the action is allowed to happen.
Checked
Every action is checked against your rules — in code, not a prompt the model can talk its way around.
Held when unsure
Low-confidence actions are routed to a human to approve or reject — not fired off blindly. The confidence signal comes from real checks (our companion library wobbly), not the model grading its own homework.
Exactly once
Crash, restart, retry — the refund still goes out once. And if it's genuinely unknowable, SafeAct says so instead of guessing.
Provable
A tamper-evident receipt for every action: what happened, why, which rule was in force, and who approved it.
It crashes. It retries. It still charges once.
Nothing staged — the open-source core doing its job. (Hold-for-approval & one-click "prove it" export arrive with SafeAct Control.)
Free core. Paid control.
Two free libraries get developers in the door — the action spine and the verifier. The control plane is what their risk and compliance leads pay to keep.
- Exactly-once, crash-safe execution
- Honest "in-doubt" state — never a blind retry
- Saga rollback for multi-step actions
- Python & TypeScript, ten lines to adopt
- Runs local — one file, no server
- safeact-mcp — one decorator makes any MCP tool exactly-once + auditable
- Ships with wobbly — label-free checks that flag unstable AI outputs before you act on them
- Human-approval inbox for held actions
- Rules-as-code policy engine, versioned
- Tamper-evident audit of every action
- One-click "prove it" regulator export
- Roles, multi-team, SSO
Let your AI act. Keep the proof.
SafeAct comes out of running real, action-taking agents in production — where we learned exactly how they break. That knowledge is the product.
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