Overview

Atlas revolutionizes agent-based development by providing robust source control for coding agents. It empowers you to run multiple coding agents like Claude Code, Codex, and others from the ACP registry side-by-side on the same codebase. Atlas meticulously tracks every agent's actions, capturing prompts, tool calls, and reasoning, creating immutable checkpoints that link directly to the session that produced them. This ensures complete traceability and auditability of agent-driven changes, allowing you to query exactly what an agent did and why, even months later.

Key Features

• Comprehensive Agent History: Every commit is linked to the agent session that created it, preserving prompts, tool calls, and reasoning. Query past actions with full context.
• Multi-Agent Synergy: Run diverse agents (Claude Code, Codex, ACP registry agents) concurrently. Shared memory ensures seamless transitions and continuous context, even when switching agents mid-task.
• Persistent, Shared Memory: Agents benefit from a unified memory store, where decisions, plans, file changes, and failures are accessible to all agents. This enables smarter collaboration and reduces redundant work.
• Integrated Knowledge Base: Leverage your existing Markdown notes, CLAUDE.md, and AGENTS.md as direct context for your agents.
• Flexible Prompting with @ Mentions: Effortlessly reference files, folders, symbols, branches, commits, notes, and past sessions directly within prompts, with local resolution before sending.
• Privacy-Focused & Local-First: By default, your code, notes, and sessions remain on your machine. Account creation and organization syncing are opt-in features.
• Seamless Git Integration: Track agent-generated changes with a real commit graph, lane assignment, and file-level diffs. Checkpoints survive history rewrites.
• Rich Workspace Environment: Includes a powerful editor, interactive terminal, Git interface, knowledge base management, research tools, and a native web browser.

Typical Use Cases

• Auditing and Debugging AI-Generated Code: Understand the precise steps and reasoning an agent took to produce a specific code change, facilitating easier debugging and review.
• Collaborative Agent Development: Enable multiple AI agents to work together on a project, sharing context and learning from each other's actions.
• Experimenting with Different Agents: Easily swap between different coding agents for the same task, comparing their outputs and understanding their strengths without losing session context.
• Maintaining a Clear History of AI Contributions: Keep a structured and queryable record of all code modifications made by agents, enhancing project transparency and maintainability.
• Building Complex Agent Workflows: Orchestrate multi-step agent processes where each step builds upon the knowledge and actions of previous ones, even across different agent types.
• Enhancing Developer Productivity: Integrate AI assistance directly into your development workflow, providing agents with rich context from your codebase and documentation.