# agentcad vs Augment Code

agentcad records 0 monthly visits with 0% month-over-month growth and Free pricing. Augment Code records 326,215 monthly visits with +24.1% growth and Freemium, Free Trial, Contact for Pricing pricing.

| Metric | [agentcad](https://openaitools.app/tool/agentcad) | [Augment Code](https://openaitools.app/tool/augment-code) |
| :--- | :--- | :--- |
| Website URL | https://agentcad.dev | https://www.augmentcode.com |
| Categories | AI Agent, AI 3D Model Generator, AI Developer Tools | AI Agent, AI Code Assistant, AI Code Generator, AI Developer Tools |
| Pricing Model | Free | Freemium, Free Trial, Contact for Pricing |
| Monthly Visits | 0 | 326,215 |
| MoM Growth Rate | 0% | +24.1% |
| Primary Market | Global | United States |
| Direct Visits | 0 | 137,070 |
| Review Score | 4.4 | 4.5 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## agentcad

agentcad tackles a specific problem: AI agents write plausible-looking CAD scripts that produce bad geometry on the first try. Instead of leaving you to babysit the iteration loop, it gives the agent a tight feedback cycle so it converges on printable, valid geometry on its own. You install it with pip inside a Python 3.10–3.12 virtual environment, point your agent at it, and hand over a parametric part description.

The design decisions center on verification. Every run returns machine-readable metrics — dimensions, volume, face and edge counts, and an is_valid flag — alongside PNG renders from any angle. The agent reads both, spots its own errors, and revises before you ever see a broken part. A pre-execution validation layer catches problems before anything runs, an inspection mode produces a topology report for debugging, and version diffs track how a design evolved over iterations.

Integration is deliberately lightweight. Scripts need no boilerplate imports because build123d primitives, show_object, and agentcad edit helpers are pre-injected, so `box = Box(10, 20, 5)` followed by `show_object(box)` just works. Adding it to `.mcp.json` makes it available in Claude Code, Cursor, or Windsurf, and it can run through uvx without a prior install. `agentcad init` also writes a Claude Code skill and an AGENTS.md block into the project so the agent knows the workflow in later sessions. Examples on the site include a 234-part 1903 Wright Flyer reconstruction and single-prompt parts such as a phone stand, a chess rook, and a filleted electronics enclosure — each reporting valid geometry, face and edge counts, and volume.

**Pricing & Plans:** Free and open source. agentcad is Apache-2.0 licensed, installs via pip or uvx, requires no signup, and runs entirely on your own machine.

- **Verified geometry feedback loop:** Every run returns dimensions, volume, face/edge counts, and an is_valid flag plus PNG renders so the agent can iterate until the part is genuinely printable.
- **build123d-native, CadQuery-compatible:** Scripts are written with the build123d API and pre-injected primitives, while CadQuery-compatible workflows are supported for existing projects.
- **Multi-format export:** Produce versioned STEP files for CAD interchange and STL, GLB, or OBJ for 3D printing and web viewers.
- **Validation, inspection, and diffing:** Pre-execution checks catch errors before running, a topology report helps debug geometry issues, and version diffs track design iteration.
- **Zero-boilerplate agent setup:** Add to .mcp.json for Claude Code, Cursor, or Windsurf, run via uvx without installing, and let agentcad init write a skill plus AGENTS.md into the project.

**Strengths:** Free and open source under Apache-2.0 with no signup required; Returns metrics and renders so agents catch bad geometry before you do; Runs locally with a small, zero-boilerplate scripting surface; Works with Claude Code, Cursor, Windsurf, and any MCP-compatible agent via uvx

**Limitations:** Requires Python 3.10–3.12 and comfort with script-based parametric modeling; Best results depend on an agent that can read metrics and iterate; It complements rather than replaces a full GUI CAD application for manual edits

[Profile (Markdown)](https://openaitools.app/tool/agentcad.md)

## Augment Code

Augment Code is an AI coding platform aimed at engineering organisations rather than individual autocomplete users. Its foundation is the Context Engine, which builds a structured understanding of an existing repository — dependencies, architecture, conventions — so that AI suggestions and generated changes fit the codebase instead of reading like generic boilerplate. The product works inside common IDEs and also in the terminal via a CLI.

The platform's recent focus is Cosmos, an agent orchestration layer for the software development lifecycle. Instead of a single assistant answering prompts, Cosmos runs connected loops: a ticket becomes a reviewed pull request, every new PR gets a first-pass code review, failed CI runs get diagnosed, Dependabot and Snyk alerts get triaged into prioritised issues, and on-call engineers inherit a completed investigation. Each loop hands its outcome to the next, and quality gates pause work for human review rather than shipping automatically.

Agents can be deployed in managed cloud sandboxes, on your own machines for code that cannot leave the building, or a mix of both. Native triggers cover GitHub, GitLab, Slack, Linear, Jira, schedules and typed webhooks, with integrations for CI systems. There is a free tier for individuals to try the assistant, while team and enterprise adoption is quote-based; the company has announced it is joining Harness to push the autonomous SDLC from idea to production.

**Pricing & Plans:** Freemium with a free tier for individual developers to start, plus paid team plans and enterprise/contact-for-pricing options. Enterprise deployment includes self-hosted and hybrid agent execution; exact seat and usage pricing is quoted by sales.

- **Context Engine codebase understanding:** Indexes architecture, dependencies and conventions across large repositories so generated code and reviews match how your team actually writes software.
- **Cosmos agent orchestration:** Runs connected SDLC loops — code review, ticket-to-PR, incident triage, dependency upgrades and security remediation — where each agent hands its result to the next.
- **Human-in-the-loop quality gates:** Work pauses at defined checkpoints so engineers approve plans and consequential actions instead of letting agents ship unreviewed changes.
- **Deploy anywhere:** Execute agents in managed cloud sandboxes, on local machines and dev VMs for sensitive code, or in your own AWS and GCP environments.
- **Expert registry and shared memory:** Choose from shipped agents, fork them, or build your own; corrections and shared knowledge improve future runs for the whole team.

**Strengths:** Deep codebase context means suggestions fit existing architecture and conventions; Loops connect across the SDLC instead of solving one isolated task; Quality gates keep humans in control of consequential changes; Flexible deployment including local and self-hosted execution for sensitive code

**Limitations:** Enterprise pricing is quote-based, which makes budgeting harder before a trial; The full value depends on wiring up integrations and triggers across your toolchain; Small teams and solo developers may find the orchestration layer heavier than needed

[Profile (Markdown)](https://openaitools.app/tool/augment-code.md)

## Frequently Asked Questions

### How do agentcad and Augment Code compare in monthly traffic?

agentcad has 0 monthly visits, while Augment Code has 326,215. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

agentcad has a month-over-month growth rate of 0%; Augment Code has +24.1%.

### How does pricing compare?

agentcad: Free. Free and open source. agentcad is Apache-2.0 licensed, installs via pip or uvx, requires no signup, and runs entirely on your own machine. Augment Code: Freemium, Free Trial, Contact for Pricing. Freemium with a free tier for individual developers to start, plus paid team plans and enterprise/contact-for-pricing options. Enterprise deployment includes self-hosted and hybrid agent execution; exact seat and usage pricing is quoted by sales.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | The AI coding agent for building ambitious software | [.md](https://openaitools.app/tool/cursor.md) |
| 2 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Offers powerful parallel agentic workflows and dedicated IDE tools, but with less focus on proprietary enterprise codebase context. | [.md](https://openaitools.app/tool/google-antigravity.md) |
| 3 | [Hyper3D Rodin](https://openaitools.app/tool/rodin) | 10,751,371 | +11.2% | Freemium, Free Trial, Paid, Contact for Pricing | Generate production-ready 3D assets from text or a single image | [.md](https://openaitools.app/tool/rodin.md) |
| 4 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Streamlines cloud-based full-stack app building and deployment from natural language, but less tailored for large legacy enterprise repos. | [.md](https://openaitools.app/tool/replit.md) |
| 5 | [Cursor](https://openaitools.app/tool/cursor-sh) | 4,790,129 | +6.5% | Freemium | Integrates directly into VS Code as a daily coding editor, but lacks enterprise-wide multi-agent SDLC orchestration. | [.md](https://openaitools.app/tool/cursor-sh.md) |
| 6 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | A free, open-source personal AI agent that learns your projects and works across every messaging app. | [.md](https://openaitools.app/tool/hermes-agent-1.md) |
| 7 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | Focuses on spec-driven engineering and automated verification from AWS, whereas Augment targets broader multi-agent SDLC automation. | [.md](https://openaitools.app/tool/kiro.md) |
| 8 | [Devin](https://openaitools.app/tool/devin) | 2,549,346 | +1% | Paid | Operates as a fully autonomous software engineer tackling complex tickets independently, whereas Augment focuses on developer-in-the-loop workflows. | [.md](https://openaitools.app/tool/devin.md) |
| 9 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | Delivers a terminal-first workflow optimized for open-weight models, but offers less graphical enterprise management tooling. | [.md](https://openaitools.app/tool/command-code.md) |
| 10 | [Orca](https://openaitools.app/tool/orca) | 1,025,891 | +1% | Free, Open Source, Enterprise on request | Provides an open-source agent development environment running parallel CLI coding agents, but requires manual configuration and setup. | [.md](https://openaitools.app/tool/orca.md) |

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Source: https://openaitools.app/compare/agentcad-vs-augment-code

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