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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.
Twelve harnesses in two classes — ten coding CLIs, and two chat and assistant gateways (Hermes, OpenClaw). One policy API and one session history across all of them. What a policy can block is per-harness: stopping a tool call before it runs is verified on all twelve, turn-end gates on eight. The per-harness matrix lists the events each one honours.
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.
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npm install -g failproofai
failproofai config # wire up your agents and the daemon
failproofai policies add FailproofAI/policies # choose what to enforce
failproofai # dashboard on localhost:8020Setup wires the hooks and picks no policies — that second command is what
puts guardrails on the machine, and any pack is typed the same way
(failproofai policies add <owner>/<repo>; policies show <owner>/<repo> reads
one first). Run failproofai config with no terminal — CI, a container, an
agent driving it — and it applies rather than asking. On a machine that has
never been set up, any other command runs the same wizard first; disable that
with FAILPROOFAI_NO_FIRST_RUN=1.
Until a pack arrives, the only thing enforcing is block-failproofai-commands,
which is always on and cannot be switched off or paused: an agent that can pause
enforcement can switch off every other policy.
| Policy | What it blocks |
|---|---|
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 |
Every one of these gates the call before it runs, so they hold on all twelve
harnesses. The first four 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. The sanitize-* family is separate: it runs after a tool returns, so
it reports a secret in tool output rather than keeping it out of the context.
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 |
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 tracking, error tracking, SQL over your own traces with shareable dashboards, evaluations scored by your own service, scheduled audits that turn recurring failures into evidence-backed findings, and alerts routed to Slack, email or a signed webhook. Self-hosting in your own cluster is available on the Enterprise plan.
→ Sessions · Audits · Book a demo
| Start | |
|---|---|
| Quickstart | Install, connect a harness, see the first run |
| Concepts | How the hook system works |
| Supported harnesses | All 12, and what each one can enforce |
| Observe | |
|---|---|
| Sessions | Follow a run: models, tools, errors, latency |
| Read a trace | What the execution graph is telling you |
| Audits | Find failure patterns across many sessions |
| Local dashboard | localhost:8020, no account needed |
| Enforce | |
|---|---|
| Policy packs | The Failproof AI policies, and packs from the policy hub |
| Write a policy | From an audit, or in code |
| Configuration | Config scopes, merge rules and policy parameters |
| Instrument your own agent | |
|---|---|
| Python SDK | Report runs from an agent with no harness |
| Policy SDK | allow / deny / instruct reference |
MIT with Commons Clause — free for internal and personal use; commercial resale of failproofai itself requires a separate agreement. See LICENSE for the full text.
See CONTRIBUTING.md. New policies, edge cases, and translations all welcome.
Built with ❤️ by befailproof.ai in SF and Bengaluru.