Overview

TeamAI is a powerful CLI tool designed to transform your development team into an AI-native powerhouse. It centralizes and distributes your team's AI skills, rules, documentation, and environment configurations across a variety of AI agents, including Claude Code, Codex, Cursor, CodeBuddy, and more. By managing these resources through a shared Git repository, TeamAI ensures every team member is equipped with the latest collective intelligence, driving consistent and efficient AI-assisted development.

Key Features

• Unified AI Management: Centralize and manage skills, rules, documentation, agents, and environment variables in a shared Git repository.
• Seamless Distribution: Automatically distribute updates to all team members' local AI tools with a simple teamai pull command, eliminating manual synchronization.
• AI Agent Agnostic: Integrates with a wide range of popular AI agents like Claude Code, Codex, Cursor, CodeBuddy, WorkBuddy, and OpenCode.
• Team Context & Recall (Beta): Enables AI agents to leverage accumulated team knowledge, codebase structure, and past session learnings for more context-aware assistance.
• Friction-Based Learning: Automatically captures insights from challenging AI interactions to suggest sharing learnings, turning problem-solving into team-wide knowledge.
• Codebase Knowledge Graph: Parses your source repositories into a structured graph, enabling AI to understand code relationships and dependencies for more precise retrieval.
• Distribution Controls: Administrators can define roles and tags to control which AI resources are distributed to specific team members, ensuring relevance and efficiency.
• Team Improvement Loop (Beta): Provides insights into AI usage through digests and dashboards, facilitating the continuous improvement of shared skills and knowledge.
• CI Integration: Supports CI workflows for extracting knowledge from Merge Requests and updating the knowledge base post-merge.

Typical Use Cases

• Onboarding New Team Members: Quickly equip new hires with the team's established AI workflows, coding standards, and best practices.
• Standardizing Code Quality: Enforce team-wide coding rules and stylistic preferences across all AI-generated code.
• Accelerating Complex Problem Solving: Enable AI agents to access and utilize the collective experience of the team to tackle challenging bugs and feature development.
• Knowledge Sharing and Reuse: Turn individual AI session insights into documented learnings that benefit the entire team, preventing repeated mistakes and promoting best practices.
• Maintaining Consistent Project Context: Ensure all team members' AI tools are aware of project-specific configurations, libraries, and architectural patterns.
• Streamlining AI Agent Configuration: Simplify the setup and management of multiple AI agents for developers, allowing them to focus on coding rather than tool configuration.
• Continuous Improvement of AI Assistance: Leverage analytics and feedback loops to refine AI skills and rules, making AI assistance increasingly effective over time.