Overview

Atlas is the groundbreaking source control system for AI coding agents. It meticulously tracks every action taken by your agents, from the initial prompts and tool calls to the final code commits. With Atlas, you gain unparalleled visibility into the "why" and "how" behind every code change, empowering you to understand, review, and debug agent-generated code like never before.

Key Features

• Agent-Centric Version Control: Every agent run generates checkpoints that link commits directly to the session that created them, including prompts, tool usage, and reasoning.
• Multi-Agent Collaboration: Run diverse coding agents (Claude Code, Codex, Atlas's native agent, and others from the ACP registry) side-by-side on the same codebase.
• Unified Agent Memory: Agents share a persistent, on-device memory, allowing for seamless context transfer when switching between agents mid-task.
• Contextual Prompts: Leverage @ mentions to easily reference files, folders, symbols, branches, commits, notes, and even past sessions directly within prompts.
• Local-First Design: Your code, notes, and agent sessions remain on your machine by default, with optional cloud synchronization for teams.
• Transparent Reasoning: Understand the decision-making process behind code changes by querying agent sessions, not just viewing the code diffs.
• Integrated Workspace: Includes a code editor, Git integration, terminal, knowledge base, and research tools, all designed to work harmoniously with your agents.

Typical Use Cases

• Agent Debugging & Auditing: Trace the exact steps an agent took to produce a bug, understand its reasoning, and identify problematic prompts or tool calls.
• Collaborative AI Development: Foster teamwork by allowing multiple agents and developers to contribute to a project, with clear lineage and context for all changes.
• Codebase Understanding: Gain deep insights into how your codebase evolved, particularly when leveraging AI for development, by seeing the "thought process" behind each commit.
• AI Experimentation: Easily compare the performance and outputs of different AI agents on the same task, with a robust system for tracking and analyzing their contributions.
• Knowledge Management for Agents: Integrate your existing documentation and notes directly into agent workflows, providing them with rich context for better decision-making.