# ThumbGate vs Rein

5 min read · For teams evaluating AI-agent runtime governance options

TL;DR: Both Rein and ThumbGate intercept agent actions before they fire — that's the shared category. The honest differences: Rein is a generic in-process decorator aimed at production apps in regulated domains (trading, healthcare, legal). ThumbGate is the coding-agent specialist for Claude Code, Cursor, Codex, Gemini, Amp, Cline, OpenCode, with a feedback loop that learns from thumbs-down corrections, plus MIT licensing on the core CLI + hook layer. Note: Rein's exact license terms are documented at reinai.io; the source we read summarized them as "AGPL" but we have not independently verified — check the primary source before making a distribution decision.

## The shared thesis

Both projects start from the same observation: most AI agent frameworks optimize for task completion, very few optimize for what happens when the agent does the wrong task. Both insert a gate before the action fires rather than trying to clean up after it.

Below is an honest side-by-side. We're not going to claim ThumbGate "wins" on every dimension — Rein is well-designed software with a legitimate niche. Pick by what you're actually building.

## Side-by-side

Dimension | Rein | ThumbGate
--- | --- | ---
Integration layer | In-process Python decorator wrapping the agent's tool functions. | Out-of-process PreToolUse hook intercepting tool calls at the agent runtime boundary (Claude Code / Cursor / Codex / Gemini / Amp / Cline / OpenCode).
Target domain | Production apps in regulated domains: trading, healthcare, legal. High-stakes per-action. | AI coding agents. High-volume, high-frequency tool calls (file write, shell exec, git push, package install).
Target user | Engineering teams building production agent apps where each decision is consequential. | Developers and engineering teams running AI coding assistants who hit the same repeat mistake more than once.
Core mechanisms | Policy checks. Audit trails. Circuit breakers. | Pre-action checks. Thumbs-up/down feedback ledger. Auto-generated prevention rules. Lesson DB with semantic recall. Thompson Sampling for gate-policy bandits.
Learning loop | Not in the public framing. Policies are authored. | Yes. Thumbs-down feedback in one session auto-promotes to a prevention rule that blocks the same action in future sessions across machines.
Domain skill packs | Not in the public framing. | Ships Stripe, Railway, database-migration packs. Each pack is a set of pre-action rules specific to the domain's failure modes.
License | Open-source license per reinai.io — verify the exact terms at the source before making a distribution decision. (Our secondary source summarized it as AGPL; we have not independently confirmed.) | MIT for the CLI and hook layer. Commercial license for Team / Pro features. Open-core, not viral.
Installation | Python decorator import. | `npx thumbgate init` — auto-detects the agent, wires the hooks, zero workflow change.
Cost when the agent is wrong | Caught at the decorated function boundary. | Caught at the PreToolUse hook boundary, before the agent's process even attempts the call.

## When to pick which

### Use Rein if

- You're building a production application (not a coding agent) in a regulated domain
- You write your agent in Python and want decorator-level integration
- Your team has policy-authoring resources to write rules upfront
- Rein's licensing terms (verify at reinai.io) are acceptable for your distribution model
- Per-action stakes are high enough that a decorator wrapper is the right tradeoff

### Use ThumbGate if

- Your agent is Claude Code, Cursor, Codex, Gemini, Amp, Cline, or OpenCode
- You want the gate to learn from your corrections instead of being authored by hand
- Your team writes code (vs. authoring policy) and wants thumbs-down feedback as the primary signal
- You need an MIT-licensed core layer that can be embedded in commercial products
- You ship Domain Skill Packs as a feature (Stripe, Railway, database migrations)

## Both, side by side, in the same stack

The two projects don't conflict. Rein governs your production app's agent at the function boundary. ThumbGate governs your engineering team's coding agents at the PreToolUse boundary. The pre-action gate pattern is the shared category; the specialization differs. A team operating both an AI coding workflow and a regulated production agent can use ThumbGate for the first and Rein for the second.

Honest framing: Rein and ThumbGate are not direct alternatives. We share a thesis. We do not share a target user.

## What the category gets right

Both projects are correct that the dominant failure mode of agent frameworks today is "you can't undo an action after the model already took it." Prompt-level rules in `CLAUDE.md` or `.cursorrules` are eviction-prone under context pressure. Decorator-level wrappers are tighter. Hook-level intercepts are tighter still. Sandbox isolation is the strongest. Pick the layer that matches your operating model.

## Adoption in two minutes (ThumbGate)

1. Install: `npx thumbgate init` — the CLI detects your agent and wires PreToolUse hooks. No workflow change.
2. Give feedback when the agent is wrong: a thumbs-down with context becomes a structured failure record.
3. Prevention rules generate themselves: after repeated failures in the same domain, ThumbGate auto-promotes a prevention rule that fires the next time the agent tries the same pattern.
4. Pre-action checks block the mistake: the PreToolUse hook intercepts the call. The agent gets a structured error and a path back to the spec.

## Pick the layer that matches your stack

Building a Python production agent in a regulated domain? Look at Rein. Running AI coding agents and want them to stop repeating the same mistake? Start with ThumbGate.

$ npx thumbgate init

### Related comparisons

ThumbGate vs Heidi (Meterian supply-chain) → ThumbGate vs Mem0 (memory layer) → All comparisons →
Try it now: `npx thumbgate init` GitHub →