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claude_codex_bridge Agent工作流
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Agent工作流

claude_codex_bridge Agent工作流

基于 Python · 无代码搭建完整 AI 自动化流程
英文名:claude_codex_bridge
⭐ 2.7k Stars 🍴 263 Forks 💻 Python 📄 NOASSERTION 🏷 AI 8.2分
8.2AI 综合评分
多智能体工作流CLI工具AI协作跨模型
✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐:claude_codex_bridge Agent工作流 是一款优质的Agent工作流。已获得 2.7k 颗 GitHub Star,AI 综合评分 8.2 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。

📚 深度解析

claude_codex_bridge Agent工作流 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升,基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化(模拟鼠标键盘操作),AI Agent 工作流通过理解任务意图、动态规划执行路径,能够处理更复杂的非结构化任务。

claude_codex_bridge Agent工作流 工作流的设计遵循"最小配置,最大复用"原则:核心逻辑已经封装好,用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制,在网络波动或 API 限速等情况下仍能稳定运行,适合作为生产环境的自动化基础设施。

在实际部署时,建议先在测试环境中运行 3-5 次,验证各个环节的输出结果符合预期,再部署到生产环境。AI Skill Hub 评分 8.2 分,是同类 Agent 工作流中的精选推荐。

📋 工具概览

支持Claude、Codex、Gemini等多个AI模型的开源工作流框架。提供可视化多智能体协作CLI环境,适合需要多模型协同完成复杂任务的开发者和AI应用构建者。

claude_codex_bridge Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

GitHub Stars
⭐ 2.7k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
持续维护,定期更新
开源协议
NOASSERTION
AI 综合评分
8.2 分
工具类型
Agent工作流
Forks
263

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

支持Claude、Codex、Gemini等多个AI模型的开源工作流框架。提供可视化多智能体协作CLI环境,适合需要多模型协同完成复杂任务的开发者和AI应用构建者。

claude_codex_bridge Agent工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

📌 核心特色
  • 可视化 Agent 工作流编排,无需编写复杂代码
  • 支持多步骤自动化任务链,实现全流程无人值守
  • 与外部 API、数据库和第三方服务无缝集成
  • 内置错误处理与自动重试机制,保障稳定运行
  • 提供可复用的自动化模板,快速在同类场景部署
🎯 主要使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:pip 安装(推荐)
pip install claude_codex_bridge

# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install claude_codex_bridge

# 方式三:从源码安装(获取最新功能)
git clone https://github.com/SeemSeam/claude_codex_bridge
cd claude_codex_bridge
pip install -e .

# 验证安装
python -c "import claude_codex_bridge; print('安装成功')"
📋 安装步骤说明
  1. 访问 GitHub 仓库获取工作流文件
  2. 在对应平台(Dify / Flowise / Make 等)中找到「导入工作流」功能
  3. 上传工作流文件
  4. 按照提示配置必要的环境变量和 API Key
  5. 运行测试确认流程正常后投入使用
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
claude_codex_bridge --help

# 基本用法
claude_codex_bridge input_file -o output_file

# Python 代码中调用
import claude_codex_bridge

# 示例
result = claude_codex_bridge.process("input")
print(result)
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# claude_codex_bridge 配置文件示例(config.yml)
app:
  name: "claude_codex_bridge"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
claude_codex_bridge --config config.yml

# 或通过环境变量配置
export CLAUDE_CODEX_BRIDGE_API_KEY="your-key"
export CLAUDE_CODEX_BRIDGE_OUTPUT_DIR="./output"
📑 README 深度解析 真实文档 完整度 60/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

1. Install or update

New installs should use the npm package:

npm install -g @seemseam/ccb

After CCB is installed, use CCB's updater:

ccb update

Install or refresh the optional mobile controller setup:

ccb update mobile

This command checks the local mobile gateway prerequisites, guides Tailscale login/Serve setup when needed, keeps the gateway loopback-only, and prints the current Android APK download link plus pairing steps.

Install or refresh the optional rich media workbench; it bundles verified binaries where possible and installs only the required terminal/media/font dependencies through the platform package manager:

ccb update rich

After rich is enabled, plain ccb opens the rich WezTerm launcher unless it is already running inside a CCB-managed rich WezTerm; use ccb uninstall rich to return to the normal terminal startup.

<details> <summary><b>GitHub release package and source install fallbacks</b></summary>

If npm is not available in your environment, download the matching package from Releases:

tar -xzf ccb-*.tar.gz
cd ccb-*
./install.sh install

Source install is for development or temporary fallback use:

git clone https://github.com/SeemSeam/claude_codex_bridge.git
cd claude_codex_bridge
./install.sh install

Source installs link global ccb / ask back to the checkout. Regular users should prefer the npm package.

</details>

Out of the box, run ccb from your project directory. If startup reports that .ccb cannot be created automatically or that the project anchor is missing, create .ccb manually:

mkdir -p .ccb

<a id="create-project-config"></a>

Install And Update

Requirements

  • Node.js and npm for the recommended npm install path
  • Python 3.10+
  • tmux
  • At least one agent CLI you plan to use, such as Codex, Claude, Gemini, Kimi, MiMo, Qwen, Cursor, Copilot, Crush, Kiro, Z.ai CLI, OpenCode, Droid, or Antigravity
  • Linux, macOS, or WSL

Current v7 / newer versions do not claim native Windows support. Native Windows support only applies to the v5 line. If you are on Windows and want current versions, use WSL and keep both ccb and agent CLIs inside WSL.

npm first

For first install, prefer npm:

npm install -g @seemseam/ccb

For later updates:

ccb update

GitHub Releases remain available for environments where npm is unavailable. Source checkout install is for development, fix validation, or temporary fallback.

Uninstall

```bash ccb uninstall ccb reinstall

Quick Start

2. Create project config

Create .ccb/ccb.config in your project root. For v7, it is better to understand config from multi-window topology first: [windows] defines tmux windows and agent groups, agent:provider defines which CLI each agent uses, and (worktree) gives an agent its own git worktree.

version = 2
entry_window = "main"

[windows]
main = "main:codex"
work = "worker1:codex(worktree), worker2:claude(worktree)"
review = "reviewer:claude, qa:gemini"

[ui.sidebar]
mode = "every_window"
width = "15%"
bottom_height = 20
agents_height = "50%"
comms_height = "15%"
tips_height = "35%"
comms_limit = 3

If you are not sure how to group windows, how many workers you need, which agents should use worktrees, or which agents need separate models or API routes, ask ccb_self. It is CCB's built-in self-agent: it understands CCB commands, config authority, roles, windows, reload behavior, and common recovery paths, and can design the config with its private ccb-config skill. Blank projects include ccb_self; existing custom configs can add it with ccb roles add agentroles.ccb_self:codex.

Validate the config:

ccb config validate

Start the workspace:

ccb

Configure Your Agent Team

CCB resolves config in three layers, from lowest to highest priority:

  1. Built-in default config.
  2. User config at ~/.ccb/ccb.config.
  3. Project config at .ccb/ccb.config.

Higher layers replace lower layers as a whole; they are not merged. The project authority file is .ccb/ccb.config. The old .ccb_config/ccb.config path is legacy migration evidence only. The built-in default is a v2 [windows] config with agent1, agent2, agent3, and ccb_self. The optional rich workbench can be installed with ccb update rich; once enabled, normal ccb startup uses the rich launcher unless you run ccb uninstall rich. The default ccb_self agent uses codex and is bound to agentroles.ccb_self.

.ccb/ccb.config mainly controls:

Config areaSyntax or locationNotes
Window grouping[windows]Group agents into tmux windows such as main, work, review, or research.
Agent name and providermain:codex, reviewer:claudeNames are used by the UI, ask routing, and memory files; provider decides which CLI starts.
Workspace isolationworker1:codex(worktree)Gives implementation agents isolated git worktrees to reduce accidental overlap.
Sidebar behavior[ui.sidebar]Controls whether the sidebar appears in every window, its left/right position, width, and Comms height.
Tool windows[tool_windows.<name>]Add managed non-agent windows such as the rich workbench; they appear as one sidebar row and are not ask targets.
Per-agent model/API[agents.<name>]Configure model, key, url, and related agent-local overrides.
Role Pack bindingagentroles.archi:codexBind a reusable role package through a window leaf; role assets are installed once and projected into the derived agent.
Role description[agents.<name>] description = "..."Give an agent a short responsibility note; longer workflow rules belong in memory.

After editing .ccb/ccb.config in a mounted project, run ccb reload --dry-run to preview the plan and ccb reload to apply it. The explicit reload path can dynamically add agents, add windows, add/remove managed tool windows, unload idle agents, and remove idle windows while keeping unrelated agents and panes running. It does not run as a background file watcher, and unsafe changes such as busy unloads, provider replacement, agent moves, tool command replacement, and arbitrary reshapes are rejected without killing existing panes.

If you want to discuss the configuration before writing it by hand, ask ccb_self to describe the target team. Blank projects include this route by default; projects with a user or project config should add agentroles.ccb_self if they have overridden the built-in default. Its built-in ccb-config skill proposes a complete config first, then writes .ccb/ccb.config only after confirmation.

Role Packs

Role Packs define reusable agent roles through the Agent Roles Spec. The spec is a host-neutral package format for specialist agents: a Role carries a stable identity, responsibilities, non-goals, memory, skills, prompts, references, tools, plugin content, validation notes, and host adapter metadata as one reviewable unit.

The practical value is separation. Role source stays portable and versioned; project bindings decide where that Role is mounted; runtime provider state, credentials, task progress, and generated projection output stay outside the Role. That makes specialist agents easier to install, update, audit, migrate, and remove without copying long prompts into every project or mutating user global configuration.

The recommended default catalog roles are agentroles.ccb_self, the CCB self-maintenance role, and agentroles.archi, an architecture reviewer role from agent-roles-spec backed by Architec. install.sh install automatically attempts to install or refresh these recommended roles by default; ccb update refreshes installed roles and installs missing recommended roles in the user environment. You can also refresh manually:

ccb roles list
ccb roles show agentroles.archi
ccb roles install agentroles.archi
ccb roles update agentroles.ccb_self
ccb roles update agentroles.archi

Project role bindings stay pinned by .ccb/role-lock.json. ccb update does not rewrite project locks. When you run ccb inside a project, CCB checks bound role locks against the current installed roles; interactive starts ask whether to refresh stale project locks in place, and non-interactive starts print a warning only.

ccb_self is strongly recommended for CCB projects because it is the built-in CCB expert agent. It carries CCB-specific knowledge about project config, command usage, role binding, reload boundaries, runtime diagnostics, guarded recovery, workflow repair, and single-agent restart assistance without taking over product work. Blank projects include it in the built-in default. Existing projects, and projects with user or project config that replace the built-in default, should add it explicitly where they want that maintenance agent:

ccb roles add agentroles.ccb_self:codex
ccb reload

To use agentroles.archi in a project, add it as a window leaf:

ccb roles add agentroles.archi:codex
ccb reload

This writes the compact form agentroles.archi:codex. At runtime CCB resolves it to the project-local agent archi, then projects the role memory and skills into that agent's managed provider home.

<details> <summary><b>Config format examples: single window, multi-window, per-agent model/API</b></summary>

Single-window compact config

cmd; main:codex, worker1:codex(worktree); reviewer:claude

Meaning:

  • cmd is a shell pane, not an agent.
  • main, worker1, and reviewer are agent names.
  • codex and claude are providers.
  • ; splits left-to-right; , stacks top-to-bottom.
  • (worktree) means that agent uses an isolated git worktree.

Multi-window topology

When you want planning, implementation, review, and research in different tmux windows, use version = 2 and [windows]:

version = 2
entry_window = "main"

[windows]
main = "main:codex"
work = "worker1:codex(worktree), worker2:claude(worktree)"
review = "reviewer:claude, qa:gemini"

[ui.sidebar]
mode = "every_window"
width = "15%"
bottom_height = 20
agents_height = "50%"
comms_height = "15%"
tips_height = "35%"
comms_limit = 3

Note: cmd belongs to compact/hybrid single-window layouts. Do not put cmd inside [windows].

Rich workbench tool window

Tool windows are tmux windows managed by CCB, but they are not agents. They do not appear in ccb ask targets and do not create provider runtime records.

version = 2
entry_window = "main"

[windows]
main = "main:codex"

[tool_windows.rich]
command = "CCB_WORKBENCH_PROFILE=rich CCB_WORKBENCH_FORCE_RICH=1 ccb-workbench files"
label = "rich"

ccb update rich prepares the optional workbench bundle under CCB-owned XDG paths, downloads and validates bundled binaries where available, and uses the platform package manager only for required rich dependencies such as WezTerm, Markdown/PDF/image/video helpers, and recommended fonts. Under WSL, CCB can launch Windows-native wezterm.exe while running the rich tools inside the current Linux distro. Normal ccb update keeps this bundle untouched; rerun ccb update rich to install, repair, or refresh it. Run ccb uninstall rich to remove the bundle and return plain ccb to normal terminal startup. Set CCB_RICH_DOWNLOAD_BINARIES=0 to skip bundled binary downloads, or CCB_RICH_INSTALL_DEPS=0 to skip system package installation.

Per-agent model, API key, or base URL

Use compact format when layout is enough. If some agents need separate models or API routes, keep the compact header and add TOML overlays:

cmd; fast:codex, deep:codex; reviewer:claude

[agents.fast]
model = "gpt-5-mini"

[agents.deep]
key = "sk-..."
url = "https://api.example.com/v1"
model = "gpt-5"

[agents.reviewer]
model = "sonnet"

Do not commit real API keys to a public repository. key / url are agent-local shortcuts; advanced provider environment variables belong in provider profile or agent env fields.

</details>

Use ccb_self For CCB Config

The full ccb-config skill belongs to the agentroles.ccb_self role. It is not a globally inherited skill for every agent. CCB installs or refreshes this Role Pack by default, and blank projects include ccb_self in the built-in default config. Existing projects, or projects with a user/project config that replaces the built-in default, should bind it where they want the maintenance assistant.

ccb_self is more than a config helper: it is designed as CCB's self-understanding agent. Use it when you need help using CCB, explaining the active layout, choosing an agent topology, migrating .ccb/ccb.config, diagnosing project runtime state, or repairing a CCB workflow.

If you do not want to hand-write .ccb/ccb.config, ask ccb_self and describe your project goal, parallelism, window grouping, worktree isolation, provider/model/API preferences. ccb_self uses its built-in ccb-config skill to discuss the shape with you and propose a complete config.

Example:

ccb ask ccb_self "Design a team for a Python library: main coordinates work, three workers implement in worktrees, and one reviewer checks regressions and risks. Recommend whether this should stay single-window or become main/work/review windows."

For an existing project that does not already configure ccb_self, run ccb roles add agentroles.ccb_self:codex and ccb reload first.

<details> <summary><b>ccb-config write flow and boundaries</b></summary>

  1. Describe the project and team goal in natural language.
  2. ccb_self's built-in ccb-config reads the current config authority and decides whether this is a new config, an edit, or a migration.
  3. It proposes one complete config before writing.
  4. You confirm the proposal, then it edits only .ccb/ccb.config.
  5. It validates the config and tells you to use ccb reload --dry-run / ccb reload when the change can be applied dynamically.

By default, ccb-config does not edit .ccb/ccb_memory.md or .ccb/agents/<agent>/memory.md. It should touch those memory files only when you explicitly ask ccb_self for workflow memory or role memory design.

</details>

</details>

<details> <summary><b>Collaboration and maintenance</b></summary>

Fallback from the package or source directory:

./install.sh uninstall ```

FAQ

<details> <summary><b>The expected agents did not appear</b></summary>

Run ccb config validate and check that config_source_kind is the layer you expected. Project config .ccb/ccb.config has highest priority; if it is missing, CCB uses ~/.ccb/ccb.config or the built-in default.

</details>

<details> <summary><b>Copy/paste is awkward</b></summary>

First try mouse-drag copy and Ctrl+Shift+V / Cmd+V paste. If tmux captures the drag, use function key [ after <prefix> to enter copy / scroll mode. If you only want native terminal selection, many terminals support Shift + mouse drag.

</details>

<details> <summary><b>I want to migrate an old compact config to multi-window</b></summary>

Ask ccb_self to use its built-in ccb-config and describe your target window groups, such as main/work/review. Migration should preserve old agent names, providers, worktree markers, model/key/url fields, and write [windows] only after confirmation.

</details>

<details> <summary><b>The sidebar helper is unavailable</b></summary>

Prefer a release package because it carries or handles the sidebar helper. Source installs may need a local Rust toolchain if no compatible prebuilt helper is available.

</details>

🇨🇳 中文文档镜像 AI 翻译 2026-05-29
英文原文章节由系统翻译为中文摘要,便于快速理解。完整原文见上方 "📑 README 深度解析"。
📌 简介

claude_codex_bridge (CCB) 是一个专为 Agent 设计的桥接工具,旨在通过统一的接口实现不同 AI Agent 之间的协作与通信。它不仅能让开发者在终端中高效调度多个 Agent,还能通过灵活的指令机制,让 Agent 之间实现自主的任务委派与协同工作,构建高效的 AI 协作工作流。

⚡ 功能介绍

最新版本已实现重大更新:macOS 侧边栏现已支持原生 Universal Binary,完美兼容 Intel 与 Apple Silicon 芯片;同时,Release CI 流程已集成自动化验证,确保 macOS 辅助工具在上传前通过 smoke-test 校验。此外,Linux 侧边栏功能也得到了持续优化,提升了跨平台的交互体验。

📋 环境依赖

在使用本项目之前,请确保您的开发环境已安装 Python 3.10 或更高版本。此外,由于项目涉及终端交互与会话管理,建议在 Unix-like 环境下配合 tmux 使用,以获得最佳的终端操作体验。

🛠 安装步骤(Docker/pip/源码)

本项目支持多种环境的安装。对于 Unix-like 系统(包括 Linux、macOS 及 WSL),请克隆仓库后运行 `./install.sh install` 进行安装;若在 Windows 原生环境下运行 Agent CLI,请使用 PowerShell 进行相关配置。如需移除或重新安装,可直接使用 `ccb uninstall` 或 `ccb reinstall` 命令。

🚀 使用教程

CCB 采用“Agent 优先”的设计理念。用户可以通过显式指令(如使用 `/ask` 或 `$ask` 前缀)来指定特定的 Agent 执行任务,例如 `/ask reviewer review the changes`。同时,CCB 也支持隐式委派模式,允许 Agent 根据上下文自主决定是否调用其他 Agent 来完成复杂任务。

⚙️ 配置说明(含 MCP / env)

CCB 的配置遵循严格的分层优先级机制:项目级配置 `.ccb/ccb.config` 优先级最高,其次是用户级配置 `~/.ccb/ccb.config`,最后是内置默认配置。这种设计确保了项目特定的行为不会污染全局环境。此外,项目还支持通过 `.ccb/ccb_memory.md` 实现项目范围内的共享记忆机制。

🔌 API 说明

CCB 作为 Agent CLI Hub,致力于实现“每一次交互皆可见”与“每一个模型皆可控”的核心理念。它为开发者提供了一套强大的接口能力,支持在团队协作中灵活调度不同的 AI 模型,确保在复杂的 Agent 协作流中,开发者能够精准掌控每一个交互环节与模型输出。

🔄 工作流/模块

CCB 深度集成了现代编辑器(如 Neovim),实现了代码编写与 AI 评审的无缝衔接。开发者可以在 Neovim 中进行代码编写,而后台的 Agents 则可以并行地进行代码审查、迭代与测试,构建起一套“编写-评审-迭代”自动化循环的高效开发工作流。

🎯 aiskill88 AI 点评 A 级 2026-05-20

架构设计完善,支持多模型协作是核心优势。社区活跃度高(2.7k星),文档充分,适合企业级AI应用构建。

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
claude_codex_bridge 中文教程claude_codex_bridge 安装报错怎么办claude_codex_bridge MCP 配置claude_codex_bridge Agent 工作流claude_codex_bridge 与同类工具对比claude_codex_bridge 最佳实践claude_codex_bridge 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • Python 依赖冲突:建议用 venv / uv 隔离环境

👥 适合人群

自动化工程师和运维人员项目经理和业务分析师希望减少重复性工作的专业人士数字化转型团队

🎯 使用场景

  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同

⚖️ 优点与不足

✅ 优点
  • +大幅减少重复性人工操作
  • +可视化流程,清晰直观
  • +可扩展性强,支持复杂场景
⚠️ 不足
  • 初始配置和调试需投入一定时间
  • 强依赖外部服务的稳定性
  • 复杂场景需具备一定技术基础
⚠️ 使用须知

该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。

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🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

支持Claude、OpenAI Codex、Google Gemini、OpenCode和Droid等主流模型。
💡 AI Skill Hub 点评

总体来看,claude_codex_bridge Agent工作流 是一款质量优秀的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 获取与下载
📚 深入学习 claude_codex_bridge Agent工作流
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 claude_codex_bridge
原始描述 开源AI工作流:Visible multi-agent CLI teams for Claude, Codex, Gemini, OpenCode, and Droid wit。⭐2.7k · Python
Topics 多智能体工作流CLI工具AI协作跨模型
GitHub https://github.com/SeemSeam/claude_codex_bridge
License NOASSERTION
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/SeemSeam/claude_codex_bridge

收录时间:2026-05-17 · 更新时间:2026-05-19 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。

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