# Mechanize

**Canonical:** https://apis.io/providers/mechanize-work/  
**Website:** https://www.mechanize.work/  
**APIs profiled:** 0

Mechanize, Inc. is a software company that builds reinforcement learning (RL) environments and evaluations for frontier coding agents and sells them to leading AI labs. In its environments, models carry out software engineering work such as building a feature, deploying an application or debugging an unfamiliar codebase, and a grader scores the result for use as RL training signal and evaluation. Its published work includes GBA Eval, a benchmark measuring whether coding agents can write a Game Boy Advance emulator from scratch in 24 hours. Mechanize publishes no public API, developer documentation or pricing on its website.

## Kin Score — 5.0 / 100 (minimal)

Scored 2026-10-31 under rubric 0.25.0.

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 7.1 |
| Access Clarity | 0.0 |

Regulatory layer — **Horizontal (data, software, accessibility, platform)**: 5.5 (matched via fallback).

## Agent readiness — 2.5 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | documented |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |
| Delegated Identity | no |
| Protected Resource Metadata | no |
| Dynamic Client Registration | no |
| Agentic Commerce | no |
| Pagination Ratio | na |
| Operation Distinctiveness | na |

## Security (1)

- **Mechanize Work Domain Security** — TLSv1.3 · DMARC

## Plans (1)

- **Mechanize Work Plans Pricing**

## Tags

Company, Artificial Intelligence, Reinforcement Learning, AI Evaluations, Benchmarks, Coding Agents, Software Engineering

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/mechanize-work/). Scores are computed from the provider's own public artifacts under a published rubric.