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Manage your team's AI skills, rules, and MCP configs across Claude Code, Codex, Cursor, and other agents via a shared git-backed config layer.
TeamAI distributes CLAUDE.md fragments, skills, hooks, and MCP configs from a shared repo to every developer's local Claude Code or Codex environment. A SessionStart hook runs `teamai pull` automatically, so each session starts with the latest team standards without any manual steps.
If your team is not using any agentic AI coding tools or is on a single-user setup, TeamAI's sharing model has nothing to distribute.
Admins configure role-to-namespace mappings and tag-based subscriptions once, then any new skill or rule pushed to the team repo propagates to exactly the members who need it. The `teamai status` command shows each member's local vs. team repo diff, making it easy to audit alignment.
If you need real-time config enforcement rather than pull-based sync, note that TeamAI updates land on session start, not continuously.
The friction-based learning capture scores sessions for signals like AI retries and user interruptions. When a session involved real problem-solving, TeamAI suggests running `/teamai-share-learnings` to summarize and push a structured learning document to the team repo. Future sessions can search it via `teamai recall`.
If your sessions are mostly routine code generation with no significant debugging, the friction scoring will rarely trigger and the recall knowledge base will grow slowly.
TeamAI covers Claude Code, Codex, Cursor, CodeBuddy, OpenCode, and other agents from one shared repo. Skills and rules written once are distributed to whichever agents each team member uses, removing the need to maintain separate configuration files per tool.
If your team has standardized on a single agent that has a built-in team sync feature, TeamAI's multi-agent breadth adds setup overhead without benefit for that specific setup.
Engineering teams using AI coding agents accumulate configuration in isolation. Each developer has their own CLAUDE.md, their own Cursor rules file, their own MCP tools configured locally. When one person figures out an effective skill or a useful coding standard for the AI, they have no channel to distribute it. Other team members either repeat the same discovery process or never benefit from it.
The challenge compounds as teams adopt more agents. A team running Claude Code and Cursor simultaneously has two separate configuration systems with no shared layer. Skills written for one agent do not transfer to another. And when team standards evolve, updating everyone's local configs is a manual, error-prone process. Teams with a strong knowledge-sharing culture still find that AI agent configuration is the one area where everything stays siloed.
Stores skills, rules, docs, hooks, MCP configs, and environment variables in a shared git repo and distributes them to Claude Code, Codex, Cursor, CodeBuddy, WorkBuddy, OpenCode, and five other agents on pull. Each resource type has its own directory format: skills at `skills/<name>/SKILL.md`, rules at `rules/*.md`, and MCP at `mcp/mcp.yaml`.
When a session ends, a Stop hook scores it for friction signals: interruptions, denied tool calls, and AI retries. High-friction sessions trigger a prompt to run `/teamai-share-learnings`, which summarizes the session and opens a pull request to the team repo. Low-friction sessions generate no prompts, keeping noise low.
An opt-in subagent (`teamai-recall`) deploys to each AI tool's agents directory and searches accumulated team knowledge before a task using BM25 ranking plus a codebase graph boost. The agent runs a relevance precheck first and skips retrieval when the task is unrelated to stored team knowledge.
`teamai import` parses repositories into a structured graph under `teamwiki/` using a WASM tree-sitter parser for TypeScript, JavaScript, Python, and Go, falling back to regex-based heuristics for other languages. The graph stores components, interfaces, configs, and cross-repo import edges, which recall uses for graph-boosted re-ranking.
Admins configure role-to-namespace mappings so each team member syncs only the skills relevant to their role. Tags let members subscribe to a subset of skills and rules. A `teamai source` command subscribes to skills from other teams' or shared repos, which sync automatically on pull.
GitHub Copilot Enterprise distributes coding instructions across an organization via repository-level instruction files and its organization settings UI. Both tools aim for consistent AI behavior across a team. The key difference is scope and vendor dependency.
| Feature | TeamAI CLI | GitHub Copilot Enterprise |
|---|---|---|
| License | MIT | Proprietary |
| Config hosting | Any git provider | GitHub only |
| Agent coverage | Claude Code, Codex, Cursor, 8+ others | Copilot only |
| Self-hosting | Yes (your git repo) | No |
TeamAI is the better choice when your team uses multiple AI coding agents or wants to avoid committing to a single vendor. The git-native config model means changes to team AI standards go through the same approval process as code changes. GitHub Copilot Enterprise is the better fit when your team is already standardized on GitHub and Copilot, since the native integration requires no additional tooling or onboarding.
Cursor Teams syncs rules and project-level AI context across a team within Cursor. TeamAI serves a similar need but covers a broader set of agents and adds knowledge recall and session learning capture.
| Feature | TeamAI CLI | Cursor Teams |
|---|---|---|
| License | MIT | Proprietary |
| Config hosting | Any git provider | Cursor-managed |
| Agent coverage | Claude Code, Codex, Cursor, 8+ others | Cursor only |
| Knowledge recall | Yes (opt-in) | No |
| Self-hosting | Yes (your git repo) | No |
For teams using only Cursor, the native Cursor Teams sync is simpler to set up. TeamAI adds value when teams run Cursor alongside Claude Code or Codex, or when teams want to capture and search accumulated session learnings across the codebase.
Install globally with npm, then initialize against your team's shared git repository.
```bash
npm install -g teamai-cli
teamai init https://github.com/yourorg/yourrepo
```TeamAI uses a shared git repository as the source of truth. Admins push skills, rules, hooks, and MCP configs to the repo; members run teamai pull (or let the automatic SessionStart hook do it) to sync the latest resources to their local AI tools. Changes go through the standard push/merge-request/merge flow before reaching members.
The README lists full support for Claude Code, Codex, Cursor, CodeBuddy, and Qoder. OpenCode, WorkBuddy, OpenClaw, Hermes, ZCode, and the DeepSeek integration have partial support, with some feature categories unavailable. The feature matrix in the README documents which capabilities apply to each agent.
When enabled, recall deploys a teamai-recall subagent to each AI tool's agents directory. Before a task, the agent searches accumulated team knowledge using BM25 ranking plus a codebase graph boost, then returns a structured summary of relevant learnings. It is disabled by default and must be enabled explicitly with teamai recall enable.
TeamAI is a CLI that runs locally; your team's configuration lives in any git repository you choose (GitHub, GitLab, private Git). There is no cloud service to subscribe to. The CLI is MIT licensed and free to use.
teamai import parses source repositories using a WASM tree-sitter parser for TypeScript, JavaScript, Python, and Go, extracting import edges, call sites, and interface implementations into a graph under teamwiki/. For other languages, a regex-based heuristic track runs instead. The graph improves recall results by boosting hits that are architecturally related to the query.
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