Translations: 简体中文 · 日本語 · 한국어 · Español · Português · Deutsch · Français · Русский · हिन्दी · Türkçe · Tiếng Việt · Italiano · العربية · עברית
Observability and enforcement for every harness your agents run in. Wherever your agents run, we see it — and we can say no. Failproof hooks 12 agent harnesses — coding CLIs like Claude Code and Codex, chat gateways like Hermes, self-hosted assistants like OpenClaw — capturing every run and blocking dangerous tool calls before they execute. 40 built-in policies. Zero latency. Runs locally.
Supported harnesses
Twelve harnesses in two classes — ten coding CLIs, and two chat and assistant gateways (Hermes, OpenClaw). Same events, same policies, same session history, whichever one your agent runs in.
Agents that run in none of them report through the Python SDK, which gives you tracing, sessions and audits. Enforcement there needs a hook in your own runtime — talk to us and we'll map it.
Install
npm install -g failproofai
failproofai policies --install # or just run `failproofai` and accept the first-run prompt
failproofai
40 built-in policies activate immediately. Dashboard at localhost:8020. Disable the first-run prompt with FAILPROOFAI_NO_FIRST_RUN=1.
What it stops
| Policy | What it blocks |
|---|---|
sanitize-api-keys |
API keys leaking into the agent's context |
block-env-files |
Reads of .env and other secret files |
warn-repeated-tool-calls |
The agent looping on the same call |
block-sudo |
Privilege escalation |
warn-destructive-sql |
DROP, TRUNCATE, unbounded DELETE |
block-terraform / block-kubectl |
Unreviewed changes to live infrastructure |
block-rm-rf |
Recursive file deletion |
block-force-push / block-push-master |
git push --force, direct pushes to main |
The first five apply to any agent that can call a tool. The last three are the developer favourites — coding CLIs are the harness class we cover deepest.
Your own policies
Drop a file into .failproofai/policies/ — it loads automatically, no flags needed.
Commit it and the whole team gets it on next pull.
import { customPolicies, deny, allow } from "failproofai";
customPolicies.add({
name: "no-production-writes",
match: { events: ["PreToolUse"] },
fn: async (ctx) => {
if (ctx.toolInput?.file_path?.includes("production"))
return deny("Writes to production paths are blocked.");
return allow();
},
});
Three decisions available to every policy:
| Decision | Effect |
|---|---|
allow() |
Permit the operation |
deny(message) |
Block it — message goes back to the agent |
instruct(message) |
Let it through, but add context to the agent's next prompt |
Observability
Enforcement is one half. The other half is seeing what the agent actually did.
Run failproofai with no arguments and it serves a dashboard on localhost:8020
reading the run history already on your machine — no account, no signup, nothing
leaving the box. You get the session list, the sequence of model calls, tool calls
and hook decisions inside each run, what was blocked and what the policy told the
agent, and an offline audit (failproofai audit) that scans your history for risky
patterns and suggests policies to stop them.
→ Local dashboard · Read a trace · Local audit
Failproof AI Observability is the hosted side of the same data model, for teams running agents across a fleet: every run from every harness in one place, an execution graph with parallel sub-agents on their own lanes, p50/p95/p99 latency for models, tools and hooks, per-model cost and context-window track