How Squad runs coordinated AI agents inside your repository

Squad is an open-source GitHub Copilot project that places a preconfigured team of AI agents directly inside a repository. Rather than relying on a single chatbot or complex orchestration infrastructure, it coordinates specialized agents for design, implementation, testing, documentation, and review. Its core argument is that repository-native, versioned context makes multi-agent development more accessible, inspectable, and resilient.

Coordinating Specialized Agents

• Install Squad with npm install -g @bradygaster/squad-cli, then run squad init in a repository.
• The setup creates roles such as lead, frontend developer, backend developer, tester, and documentation specialist.
• A coordinator interprets natural-language requests, loads repository context, and assigns work to specialists.
• Agents can work in parallel, create files and branches, write tests, and open pull requests.
• They use shared decisions and project history rather than requiring every detail to be repeated in prompts.
• Testing and review happen within the workflow:
  • Testers evaluate implementations and reject failing code.
  • A rejected author is prevented from revising its own work.
  • Another agent must address the problems, providing a more independent review.
• Developers still answer questions, correct assumptions, and review and merge pull requests; Squad is collaborative orchestration rather than full autonomy.

Repository-Based Shared Memory

• Squad uses a “drop-box” model instead of depending on live chat synchronization or complex vector databases.
• Architectural decisions, library choices, and conventions are appended to a versioned decisions.md file.
• This creates:
  • Persistent shared knowledge
  • An understandable audit trail
  • Recovery after disconnects or restarts
  • Memory that can be reviewed and changed like code

Replicating Context Across Agents

• The coordinator remains a thin router instead of attempting to manage all implementation work.
• Each specialist runs in its own inference call with an independent context window.
• This replicates relevant repository context across agents rather than splitting one limited context among multiple roles.
• Parallel, independent contexts reduce the risk that project-management instructions and other agents’ reasoning crowd out the actual coding task.
• Supported models may provide context windows of up to 200,000 tokens.

Versioned Agent Identities and History

• Each agent’s behavior is primarily defined by repository files:
  • A charter describing its role and responsibilities
  • A history recording previous work
  • Shared team decisions
• These files live in .squad/ alongside the application code.
• Cloning a repository also restores the team’s accumulated knowledge, making the agents effectively pre-onboarded.
• Keeping memory in plain text makes it inspectable, versioned, and independent of hidden model state.

Lowering the Barrier to Multi-Agent Development

Squad’s main goal is to make agentic workflows practical without requiring users to build orchestration layers, configure databases, or master advanced prompt engineering. Its repository-native design favors simple setup, transparent memory, independent review, and recoverable project context. Developers interested in this approach can install Squad and experiment with it directly in the project repository.