atlas
by tryatlasSource control for agents. Use multiple coding agents, track their changes and query them in one place
Claude Code plugin for Elixir/Phoenix/LiveView — 26 specialist agents, Iron Laws enforcement, and Tidewave MCP integration. Plan features with parallel research agents, execute with automatic verification, review with 4-agent parallel audits, and capture learnings as reusable knowledge.
This is the project's own description from GitHub — we show it as-is rather than inventing details.
This project is listed under AI Coding Assistants in Artificial Intelligence, a category made for creators, businesses, students and developers who want to use AI without getting lost in research papers.
Do I need coding knowledge? Coding Knowledge Recommended. (Estimate based on project type: A developer tool/library — setup usually follows the README with basic coding skills. Always confirm from the repo's README.)
The source code is released under the MIT license. Open-source licenses like this usually allow free use, including commercial use — but always read the actual license file in the repository before relying on it. Note: the code being free does not mean hosted services, APIs or infrastructure around it are free.
Activity level: Very active · Last code update: 06 Nov 2026 · Created: 12 Feb 2026 · Open issues: 21
Official links: Official Website ↗ · Releases ↗ · Documentation (README) ↗
We never publish installation steps we have not verified — the README above is the source of truth.
⭐ 573 stars · 🍴 45 forks · 🐛 21 open issues · 💻 Language: Python · 📄 License: MIT · Default branch: main
Source control for agents. Use multiple coding agents, track their changes and query them in one place
A curated list of vibe coding references, collaborating with AI to write code.
企业级 AI 办公工作台
Experience-Driven Black-Box Cost Control for Coding Agents
Open-source AI coding assistant for VS Code. Coding agent, code completion, chat, edits and reviews with local or hosted models. Your models, your infrastructure.
A harness optimized to smaller LLMs