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‎README.md‎

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## ❯❯ Showcase
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Real runs, each captured from Raven's task graph. The graph shows the
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orchestration Raven chose for the task; below it is what the run produced.
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orchestration Raven generates for the task; below it is what the run produced.
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<table>
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<tr>
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<td width="50%" valign="top">
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<p align="center"><b>An FPS game, picked, built and judged</b></p>
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<a href="https://github.com/user-attachments/assets/951e35bd-ff9c-4456-b539-32dd192827e1"><img src="https://github.com/user-attachments/assets/951e35bd-ff9c-4456-b539-32dd192827e1" alt="Task graph: three Raven-Code nodes running in sequence across 42 game rounds" width="100%"></a>
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https://github.com/user-attachments/assets/44724e3e-6564-467a-b422-473aa8f474bd
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</td>
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<td width="50%" valign="top"></td>
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</tr>
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</table>
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‎README.zh-CN.md‎

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<p align="center"><em>在 AI4S 任务上，Raven-Oncall 的成功率与成本均显著优于 Claude Code</em></p>
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## ❯❯ 案例展示
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真实运行记录，每条都取自 Raven 的任务图。上方展示了 Raven 为该任务生成的多智能体编排图，
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下方是这次运行的产出。
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<table>
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<td width="50%" valign="top">
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<p align="center"><b>构建 FPS 游戏：选题、实现、评判</b></p>
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<a href="https://github.com/user-attachments/assets/951e35bd-ff9c-4456-b539-32dd192827e1"><img src="https://github.com/user-attachments/assets/951e35bd-ff9c-4456-b539-32dd192827e1" alt="Task graph: three Raven-Code nodes running in sequence across 42 game rounds" width="100%"></a>
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https://github.com/user-attachments/assets/44724e3e-6564-467a-b422-473aa8f474bd
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</td>
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<td width="50%" valign="top"></td>
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</tr>
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</table>
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<table>
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<tr>
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<td width="50%" valign="top">
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<p align="center"><b>宋代居家美学</b></p>
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<a href="https://github.com/user-attachments/assets/8fa50c96-3d2d-4107-8a8c-7d041d1ac8b4"><img src="https://github.com/user-attachments/assets/8fa50c96-3d2d-4107-8a8c-7d041d1ac8b4" alt="Task graph: three Raven-Research nodes running in parallel into a synthesis node, then one Raven-Design node" width="100%"></a>
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<a href="https://github.com/user-attachments/assets/379328ea-e3ee-430f-865d-68ed90dae69f"><img src="https://github.com/user-attachments/assets/379328ea-e3ee-430f-865d-68ed90dae69f" alt="Cover, slides and closing slide of the Song-dynasty aesthetics deck" width="100%"></a>
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</td>
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<td width="50%" valign="top">
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<p align="center"><b>古希腊如何被漂白</b></p>
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<a href="https://github.com/user-attachments/assets/c8e9ac77-0377-487e-9d07-fb4fe51a0257"><img src="https://github.com/user-attachments/assets/c8e9ac77-0377-487e-9d07-fb4fe51a0257" alt="Task graph: three Raven-Research nodes running in parallel into a synthesis node, then one Raven-Design node" width="100%"></a>
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<a href="https://github.com/user-attachments/assets/3c5a2a9a-d6d7-40ad-b3dc-70e9f5c095df"><img src="https://github.com/user-attachments/assets/3c5a2a9a-d6d7-40ad-b3dc-70e9f5c095df" alt="Cover, slides and closing slide of the Greek polychromy deck" width="100%"></a>
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</td>
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</tr>
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<td width="50%" valign="top">
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<p align="center"><b>流行音乐如何被制造出来</b></p>
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<a href="https://github.com/user-attachments/assets/032fdf6e-8b65-49a0-92ba-2ea3066a7451"><img src="https://github.com/user-attachments/assets/032fdf6e-8b65-49a0-92ba-2ea3066a7451" alt="Task graph: three Raven-Research nodes running in parallel into a synthesis node, then one Raven-Design node" width="100%"></a>
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<a href="https://github.com/user-attachments/assets/9ab75945-81ba-4437-aec0-9ef6a633ed55"><img src="https://github.com/user-attachments/assets/9ab75945-81ba-4437-aec0-9ef6a633ed55" alt="Cover, slides and closing slide of the pop music deck" width="100%"></a>
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</td>
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<td width="50%" valign="top">
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<p align="center"><b>抽象艺术的一百年</b></p>
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<a href="https://github.com/user-attachments/assets/4b606bae-667e-4f2f-ae7d-2880b47ddbf6"><img src="https://github.com/user-attachments/assets/4b606bae-667e-4f2f-ae7d-2880b47ddbf6" alt="Task graph: three Raven-Research nodes running in parallel into a synthesis node, then one Raven-Design node" width="100%"></a>
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<a href="https://github.com/user-attachments/assets/b3dfce6c-366d-4e31-9a57-7c98df054618"><img src="https://github.com/user-attachments/assets/b3dfce6c-366d-4e31-9a57-7c98df054618" alt="Cover, slides and closing slide of the abstract art deck" width="100%"></a>
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</td>
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</tr>
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</table>
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产出不是演示文稿的运行。
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<table>
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<td width="50%" valign="top">
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<p align="center"><b>六个编排框架横向对比</b></p>
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<a href="https://github.com/user-attachments/assets/92ff2c83-3b0f-49eb-9995-c1ad4c298452"><img src="https://github.com/user-attachments/assets/92ff2c83-3b0f-49eb-9995-c1ad4c298452" alt="Task graph: two Raven-Research nodes running in parallel into one Raven-Design node" width="100%"></a>
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<a href="https://github.com/user-attachments/assets/75beba36-a112-46f2-949d-015856dae588"><img src="https://github.com/user-attachments/assets/75beba36-a112-46f2-949d-015856dae588" alt="Comparison board: six orchestration frameworks against four dimensions, colour-coded for explicit-graph versus code-or-model orchestration" width="100%"></a>
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</td>
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<td width="50%" valign="top">
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<p align="center"><b>参数扫描：编写、运行、绘图</b></p>
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<a href="https://github.com/user-attachments/assets/e842677a-8b71-4f1b-ae02-0162a440a1e8"><img src="https://github.com/user-attachments/assets/e842677a-8b71-4f1b-ae02-0162a440a1e8" alt="Task graph: Raven-Code into Raven-Oncall into Raven-Design, run in sequence" width="100%"></a>
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<a href="https://github.com/user-attachments/assets/2b03cc53-e707-4728-8d68-327fcf7d112a"><img src="https://github.com/user-attachments/assets/2b03cc53-e707-4728-8d68-327fcf7d112a" alt="Retrieval sweep: recall@k is set by top_k alone, and latency stays flat across the grid" width="100%"></a>
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</td>
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<td width="50%" valign="top"><p align="center"><b>一组光污染主题海报</b></p></td>
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<td width="50%" valign="top"><p align="center"><b>一个可交互讲解页及其求解器</b></p></td>
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</tr>
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<td width="50%" valign="top"><p align="center"><a href="https://github.com/user-attachments/assets/d3e4c873-cf95-4011-8471-0fda72778cfd"><img src="https://github.com/user-attachments/assets/d3e4c873-cf95-4011-8471-0fda72778cfd" alt="Task graph: three Raven-Research nodes in parallel into a Raven-Code node; that node and a Raven-Design plate node running alongside them both feed the final Raven-Design node" width="83%"></a></p></td>
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<td width="50%" valign="top"><p align="center"><a href="https://github.com/user-attachments/assets/9754976b-11ff-4738-b53d-0621ec6ae7da"><img src="https://github.com/user-attachments/assets/9754976b-11ff-4738-b53d-0621ec6ae7da" alt="Task graph: two Raven-Code nodes in parallel into a Raven-Oncall cross-check; that node and a Raven-Design plate node running alongside them both feed the final Raven-Design node" width="100%"></a></p></td>
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<td width="50%" valign="top"><p align="center"><a href="https://github.com/user-attachments/assets/7767ac05-6785-4e6a-9e27-c1b123753766"><img src="https://github.com/user-attachments/assets/7767ac05-6785-4e6a-9e27-c1b123753766" alt="Key visual plus the derived series: street poster, data panel, social square and wide banner" width="100%"></a></p></td>
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<td width="50%" valign="top"><p align="center"><a href="https://github.com/user-attachments/assets/25c57220-c5fc-4f72-94fb-ea4b7e83f429"><img src="https://github.com/user-attachments/assets/25c57220-c5fc-4f72-94fb-ea4b7e83f429" alt="GPS trilateration explainer: the live page, and the fix at two, three and four satellites" width="97%"></a></p></td>
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</table>
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## ❯❯ 连接第三方 Agent
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Raven 可以通过 ACP、CLI 或兼容 OpenAI 的 API 连接并编排 Agent，并提供 13 个**第三方 Agent**的预设，简化设置、任务委派和共享工作流中的协作。

‎docker/README.md‎

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# Raven in Docker
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Build and run the current checkout:
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```bash
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cd docker
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docker compose up
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docker compose up --build
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```
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To run the configured image without rebuilding, use `docker compose up --no-build`.
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Plain `docker compose up` may reuse or pull an image rather than rebuild local changes.
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Then open <http://localhost:18793>. The local Compose default signs that browser
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## Settings
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`.env` is committed and holds defaults; `.env.local` sits beside it, is
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git-ignored, is loaded second and wins. Put every credential in `.env.local`.
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`.env` is committed and holds defaults. The service loads the optional,
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git-ignored `.env.local` second for container runtime variables such as provider
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credentials and `RAVEN_AUTO_LOGIN`. Put every credential in `.env.local`.
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Compose interpolation is separate: `RAVEN_IMAGE`, `RAVEN_WEB_PORT`,
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`RAVEN_EXTRAS`, `RAVEN_PLUGINS`, and `RAVEN_OFFICE` are resolved before the
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container starts. Setting them only in the service's `.env.local` does not
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override the image, published port, or build arguments. From this directory,
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use shell variables, for example:
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RAVEN_IMAGE=raven:local RAVEN_WEB_PORT=18893 docker compose up --build
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```
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Or, after creating `.env.local`, load it explicitly for interpolation too:
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```bash
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docker compose --env-file .env --env-file .env.local up --build
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```
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The later file wins; shell environment values override both files.
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‎docs-site/docs/docker.md‎

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## Start the container
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variables such as `RAVEN_PROVIDER`, `RAVEN_API_KEY`, and `RAVEN_AUTO_LOGIN`,
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the service loads the optional `docker/.env.local` file second as an
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published port, or build arguments before the container starts. Merely putting
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as well:
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‎docs-site/docs/docker.zh.md‎

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## 启动容器 { #start-the-container }
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并不保证重新构建本地改动。
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`docker/.env` 保存已提交的默认值。对于 `RAVEN_PROVIDER`、`RAVEN_API_KEY`、
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`env_file` 后加载。凭据应写入该文件，Git 会忽略它。
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仅将这些变量写入服务的 `.env.local`，不会覆盖这些设置。
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```
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也可以先创建 `.env.local`，再显式将其用于插值：
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```bash
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```
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插值时，后一个文件覆盖前一个文件；Shell 环境变量的优先级高于这两个文件。
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## 持久化数据 { #persistent-data }
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