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first-tree-staging

热度 65 更新于 开发与构建

First Tree — unified CLI for Context Tree onboarding, agent management, and team messaging. (Source-tree dev build; CI rewrites `name` and `bin` for prod / staging publishes — see docs/local-dev-isolation.md.)

npmauto-collected

安装

npm
npm install -g first-tree-staging

通过 npm 安装。

<p align="center" <picture <source media="(prefers-color-scheme: dark)" srcset="assets/banner-dark.png" <source media="(prefers-color-scheme: light)" srcset="assets/banner-light.png" <img src="assets/banner-light.png" alt="First Tree" width="100%" </picture </p

<p align="center" <a href="https://first-tree.ai/?utmsource=github&utmmedium=readme&utmcampaign=nav-app"<strongOpen App</strong</a &middot; <a href="#get-started"<strongGet Started</strong</a &middot; <a href="#how-it-works"<strongHow It Works</strong</a &middot; <a href="docs/quickstart.md"<strongQuickstart</strong</a &middot; <a href="https://github.com/agent-team-foundation/first-tree/discussions"<strongDiscussions</strong</a </p

<p align="center" <a href="https://www.npmjs.com/package/first-tree"<img src="https://img.shields.io/npm/v/first-tree?style=for-the-badge&color=FFD700&label=npm" alt="npm version"</a <a href="https://github.com/agent-team-foundation/first-tree/actions/workflows/ci.yml"<img src="https://img.shields.io/github/actions/workflow/status/agent-team-foundation/first-tree/ci.yml?style=for-the-badge&label=CI" alt="CI"</a <a href="https://github.com/agent-team-foundation/first-tree/blob/main/LICENSE"<img src="https://img.shields.io/badge/License-Apache%202.0-green?style=for-the-badge" alt="License: Apache 2.0"</a <a href="https://github.com/agent-team-foundation/first-tree/stargazers"<img src="https://img.shields.io/github/stars/agent-team-foundation/first-tree?style=for-the-badge&color=blueviolet" alt="GitHub stars"</a </p

<p align="center" English | <a href="READMEzh-CN.md"中文</a </p

Try first-tree 🌳 free — the fastest way to give every agent your team's shared context.

First-Tree

Context-grounded agentic work for teams.

First Tree is an open-source workspace where AI agents work from your team's shared context, not isolated prompts.

At the center is Context Tree: a team-maintained memory of decisions, ownership, repos, responsibilities, constraints, and prior work. Agents read it before they work; useful outcomes can flow back into it after the work is done.

The result is a human-agent work loop where every task can start with more team context, and every useful outcome can make the next task smarter.

<p align="center" <img src="assets/workspace-screenshot.png" alt="First Tree workspace: an agent reporting back on a GitHub issue, with team context and participants alongside" width="100%" </p

<p align="center" <sub<bThe work loop in practice.</b An agent reports back on what it shipped for a GitHub issue &mdash; while the linked issue, the team's chat history, and every human and agent participant stay in view, so the next task starts from the same shared context.</sub </p

<div align="center" <table <tr <td align="center"<strongWorks<br/with</strong</td <td align="center"<picture<source media="(prefers-color-scheme: dark)" srcset="assets/logos/claude-code-dark.svg"<img src="assets/logos/claude-code-light.svg" width="32" alt="Claude Code" /</picture<br/<subClaude Code</sub</td <td align="center"<picture<source media="(prefers-color-scheme: dark)" srcset="assets/logos/codex-dark.svg"<img src="assets/logos/codex-light.svg" width="32" alt="Codex" /</picture<br/<subCodex</sub</td <td align="center"<picture<source media="(prefers-color-scheme: dark)" srcset="assets/logos/github-dark.svg"<img src="assets/logos/github-light.svg" width="32" alt="GitHub" /</picture<br/<subGitHub</sub</td <td align="center"<picture<source media="(prefers-color-scheme: dark)" srcset="assets/logos/mcp-dark.svg"<img src="assets/logos/mcp-light.svg" width="32" alt="MCP" /</picture<br/<subMCP</sub</td </tr </table </div

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Why First Tree

There are many AI agent workspaces. First Tree's core difference is the context loop:

user intent -> read team context -> context-aware agent work
-> human review/control -> durable outcome -> updated team context

This loop solves the part that usually breaks when teams start using agents: