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failproofai vs statewright

Observability plus policy enforcement for agent harnesses: records every run across 12 harnesses (Claude Code, Codex, Cursor, Hermes…) and blocks dangerous tool calls with 39 built-in policies. — versus — State-machine guardrails for AI agents: define workflow phases once and only that phase's tools unlock — enforced across Claude Code, Codex, Cursor, opencode and Pi. Rust.

The curated verdict

Both gate what an agent may call. Statewright unlocks tools by workflow phase (state machine); Failproof AI blocks by policy on the call itself (what, where, with what). Phase guardrails vs content guardrails.

failproofaistatewright
Stars5.9k506
Forks50423
LanguageMDXRust
LicenseNOASSERTIONApache-2.0
Last activity3 days ago5 days ago
Topicssecurity, codingcoding, orchestration
Curated connections76

failproofai — the curator's take

The one to install the day an agent first runs `rm -rf` or force-pushes: hooks into ten coding CLIs plus the Hermes and OpenClaw gateways, records sessions to a local dashboard on :8020, and enforces policies *before* the tool call runs — sanitize API keys, block .env reads, sudo, terraform/kubectl, destructive SQL, direct pushes to main. Custom policies are a JS file in .failproofai/ that returns allow/deny/instruct; packs are versioned GitHub repos. NOT a tracing platform for agents you built from code (the Python SDK gives tracing but enforcement needs a hook in your runtime), and note the source-available licence and optional cloud — local works without an account.

statewright — the curator's take

'Agents are suggestions, states are laws' — gating the TOOL space per phase is the right enforcement layer, and their small-model results (2/10 → 10/10 on a 5-task SWE-bench subset) suggest constraints can substitute for scale. When NOT: it's SaaS-keyed — sign-up required, NOASSERTION license; the enforcement client is open but the service isn't. The benchmark is 5 tasks, not 2294, and hard-blocked tools also block legitimate improvisation.