Failproof AI

A local policy layer that intercepts risky coding-agent actions before they become incidents.

Stars
★ 8.1k
Last updated
3d ago
License
NOASSERTION
Primary language
TypeScript

At a glance

How it runs
CLILibrary / SDK
Works with
Portable with changesCodex · Claude Code
Cost
Free tier plus a paid hosted plan
Setup effort
Low · running in minutes
You'll need
npmShell / CLILocal filesystem
Typical use
A developer using Claude Code or Codex on a production repository who wants direct pushes to main/master stopped before execution.
Not a fit if
  • Teams needing hosted fleet observability without an Enterprise plan
  • Teams whose agents run outside the twelve supported harnesses
  • Environments without npm, shell, or local filesystem access

What does this agent do, and when should you use it?

Failproof AI is a local runtime policy tool for coding-agent CLIs that operates through hooks around agent tool calls. The repository explicitly lists support for Claude Code, OpenAI Codex, GitHub Copilot CLI, Cursor Agent CLI, OpenCode, Pi, and other named CLIs. Its installation flow activates 30 built-in policies and exposes a local dashboard at localhost:8020. Documented protections include direct pushes to main/master, force pushes, commits or history operations on the primary branch, recursive deletion, and API-key exposure in agent context. Teams can add JavaScript policies under .failproofai/policies/ and commit them for teammates to load after pulling.

After failproofai runs, it hooks into supported agent CLIs and evaluates their tool calls. Built-in policies inspect actions such as git push --force, direct pushes to main/master, commits/merges/rebases on main/master, recursive file deletion, and API keys entering agent context; policies return allow(), deny(message), or instruct(message). Custom JavaScript policies are registered with customPolicies.add(), can match events such as PreToolUse, and can inspect tool input through ctx.toolInput, including file_path. Each agent tool call is logged locally, and the dashboard shows what ran, what was blocked, and the policy message returned to the agent.

  1. A developer using Claude Code or Codex on a production repository who wants direct pushes to main/master stopped before execution.
  2. An engineering team that permits agent-driven Git workflows but needs to block git push --force and primary-branch commits, merges, or rebases.
  3. A maintainer assigning cleanup work to a coding agent who wants recursive deletion intercepted by policy.
  4. A security-conscious team that wants sanitize-api-keys to prevent API keys from leaking into agent context.
  5. A repository owner who wants to block writes to production paths with a committed no-production-writes policy.

How do you install or deploy this agent?

No credentials are documented. Install globally with npm and install the policies:

npm install -g failproofai
failproofai policies --install

Then start it:

failproofai

The first run presents a prompt; set FAILPROOFAI_NO_FIRST_RUN=1 to disable it.

How do you use this agent?

Run failproofai after installation; the README states that 30 built-in policies activate immediately. Open localhost:8020 to inspect local session and policy activity. For repository-specific controls, add a JavaScript file under .failproofai/policies/ and register it with customPolicies.add({ name, match: { events: ["PreToolUse"] }, fn }); have fn return allow(), deny(message), or instruct(message). Commit the policy so teammates can load it on their next pull.

What are this agent's strengths and limitations?

Pros
  • Applies policy at the agent tool-call boundary, allowing it to block risky Git actions and recursive deletion before they execute.
  • Ships with 30 built-in policies, with explicitly documented coverage for primary-branch safety, force pushes, and API-key context exposure.
  • Supports repository-level JavaScript policies that autoload from .failproofai/policies/.
  • Keeps a local tool-call record and provides a localhost dashboard for executions, blocks, and policy feedback.
Limitations
  • The documented integrations are named agent CLIs; the supplied material does not describe a generic protocol or integration path for arbitrary agent runtimes.
  • Adoption requires shell usage, npm, and local filesystem access rather than a hosted service workflow.
  • Commercial resale of Failproof AI itself requires a separate agreement under its MIT plus Commons Clause licensing.
  • The supplied material links to, but does not detail, the full built-in-policy behavior, false-positive handling, or performance measurements.

How does this agent compare with similar options?

Failproof AI is not presented as a replacement for Claude Code or Codex; it adds a hook-based runtime policy layer to those coding agents and other listed CLIs, including GitHub Copilot CLI, Cursor Agent CLI, OpenCode, Pi, Hermes, OpenClaw, Factory Droid, Devin CLI, Antigravity CLI, and Goose.

Key facts side by side with the most closely related agents.

Agent Source review Form / cost Stars Updated Language Full support on
Failproof AI This agent 40 · Major gaps CLIFreemium ★ 8.1k 3d ago TypeScript Codex · Claude Code
Octogent 59 · Major gaps CLIFree + model costs ★ 1.4k 6mo ago TypeScript Claude Code
OrchestKit AI Development Toolkit 86 · Good Agent plugin / skillFree + model costs ★ 299 today TypeScript Claude Code
CC Safety Net — Safety Net for AI Coding Agents 63 · Some gaps CLIFree ★ 1.9k 1d ago TypeScript Codex · Claude Code

How does FollowAgents rate this agent?

FollowAgents source review · FARS-2.1
Major gaps
40/ 100 5-point scale 2.0 / 5
This review covers the source as of Aug 09, 2026. The project has shipped 8 stable release(s) since, which have not been re-reviewed. The score and verdict describe the reviewed version only. Recent releases ↓
Trust 10/29
Reliability 5/14
Adaptability 8/18
Convention 8/18
Effectiveness 6/13
Verifiability 3/8
Why each dimension lost points
Trust10 / 29 · 1.7/5

Evidence shows policy mechanisms (deny/allow) and local running, but least privilege is not explicit; user confirmation only mentioned for first-run prompt, not all dangerous operations; data flow transparency has local logs and dashboard but no data flow details; sensitive data handling has sanitize-api-keys policy but no storage/encryption details; dependency security has OSV-Scanner and Socket but no scan results; external effects include blocking dangerous git operations but no network access details; rollback mechanism not mentioned; source attribution has author info but publisher unverified.

Reliability5 / 14 · 1.8/5

Self-consistency: README claims zero latency but no performance data; dependency availability: CI and build processes exist but no availability guarantees; failure messages: deny messages returned to agent but no error handling details.

Adaptability8 / 18 · 2.2/5

Audience and scenarios: targets developers and multiple agent CLIs, scenarios clear; capability boundaries: 30 built-in policies but no limits on custom policies; trigger precision: event matching exists but no false positive rate; environment fit: supports multiple CLIs and local running but no system requirements.

Convention8 / 18 · 2.2/5

Information architecture: docs links and table of contents, clear structure; install notes: npm install command but no detailed steps; naming stability: beta version may be unstable; examples and FAQ: custom policy example but no FAQ; known limitations: not explicitly listed; license: MIT+Commons Clause but metadata NOASSERTION; versioning and changelog: version number but no changelog; maintenance responsibility: authors and contact but no maintenance plan.

Effectiveness6 / 13 · 2.3/5

Output usability: dashboard and logs but no output format; marginal value: failure prevention feature, unique value; cost benefit: local running but no performance data.

Verifiability3 / 8 · 1.9/5

Claim traceability: README has docs links but no specific evidence; cross-source corroboration: CI and scanning exist but no results; fact-inference separation: not explicitly distinguished.

Risks and how to mitigate them
  • Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
  • Publisher unverified, identity unknown.
  • License metadata NOASSERTION, actual MIT+Commons Clause, confirm compliance.
  • Beta version may be unstable.
  • No rollback mechanism provided.
  • No performance data, zero latency claim unverified.
Evidence confidence: Low Reviewed Aug 09, 2026 Reviewed revision 822022f758d5 New commits since this review; the score may not cover them
See the full review method →

Recent releases

Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.

  1. v1.0.13 FailproofAI v1.0.13
    Release notes excerpt
    failproofai 1.0.13 This release restores policy enforcement on OpenClaw 2026.9.6+ and on Antigravity's current file tools, and makes daemon setup work on Fedora, RHEL, Rocky and Alma. Fixes Antigravity - Path policies enforce again. Antigravity's read_file, write_file and edit_file tools were not recognized, so path-based policies and the guard protecting failproofai's own config saw no path and allowed the call. An agent could read and overwrite .env, create id_rsa, and overwrite .failproofai/policies-config.json. These calls are now denied. (#961) …
    View on GitHub ↗
  2. fp-cloud-cli-v0.0.2 fp-cloud-cli 0.0.2
    Release notes excerpt
    fp-cloud-cli 0.0.2 is on PyPI: https://pypi.org/project/fp-cloud-cli/0.0.2/ --- Stable release. The 0.0.2b0 line the bump job opened after 0.0.1 is cut stable here rather than published, because the server endpoints these commands drive are now deployed to FailproofAI Cloud and the CLI should not trail them as a pre-release. Nothing about the command surface changes at the cut: the entries below are the entire diff from 0.0.1, and the bump job opens 0.0.3b0 after the upload. Added …
    View on GitHub ↗
  3. v1.0.12 FailproofAI 1.0.12

FAQ

Does it replace my coding agent?
No. It hooks into supported agent CLIs and applies policies to the agents' tool calls.
What decisions can a policy make?
allow() permits the operation, deny(message) blocks it and sends a message back to the agent, and instruct(message) permits it while adding context to the agent's next prompt.
Are logs required to leave my machine?
The supplied material says every tool call is logged locally and the dashboard runs at localhost:8020; it does not state that log upload is required.
How can a team share custom controls?
Place policy files in .failproofai/policies/ and commit them. The README states that teammates receive them on their next pull.
Are there licensing limits?
The repository describes free internal and personal use, while commercial resale of Failproof AI itself requires a separate agreement.
View on GitHub ↗ Install ↓

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