Resource details · Virtual life
CAMEL: give two original roles a small shared scene
Keep role responsibilities, a shared task, and a stopping point together. Start by planning a short, bounded bookshop scene.
Titles, introductions and guides are translated. Copyable resources, examples and licences retain their original language. Code is displayed only; this page does not execute it or call AI.
Resource details
For people with Python experience who want to study multi-role conversations. Start with role and turn boundaries. This is a developer framework, not a ready-to-use WeChat plugin.
Before you start
- Platforms
- Windows · macOS · Linux
- Content updated
- The pinned pyproject.toml requires Python >=3.10,<3.15. Follow the fixed official installation guide.
- Actual execution needs your chosen model backend and its account or local service; model calls may cost money.
- Use an isolated practice folder, no real chat history, and no terminal, browsing, or trading tools.
python -m pip show camel-aiOnly inspects an already installed package. It does not start a role-play session. Check official setup before installing or connecting a model.
Common pitfalls
- The first exercise is scene planning and interface reading, not an implemented scene or WeChat integration.
- The pinned official example defaults to chat_turn_limit=50. That is not the short exercise recommended here; reduce it and enforce termination outside the model.
- Role and simulation support does not establish that a fictional character really ate, went out, or had a real-world experience.
- Review tools, models, and assets separately. Do not install all extras or grant unrestricted computer access just for a scene.
Complete resource
[Small goal] An original scene plan with two role responsibilities, one shared task, at most six utterances, and a final decision. Any later implementation must stop through code at the limit. It was not run here. [Before you start] The pinned pyproject.toml requires Python >=3.10,<3.15. Follow the fixed official installation guide. Actual execution needs your chosen model backend and its account or local service; model calls may cost money. Use an isolated practice folder, no real chat history, and no terminal, browsing, or trading tools. [Three steps] 1. Choose a small task: Create two original bookshop clerks whose only task is to assign tomorrow’s shelf-tidying work before closing. Give each one clear responsibility. 2. Read the official role interface: Inspect the two role-name fields and task_prompt in the pinned RolePlaying example to understand initialization and alternating turns. Its default task concerns stock trading; do not run that task for this exercise. 3. Define the stopping point: Plan three turns per role and stop when responsibilities are settled. In an implementation, enforce the turn limit in surrounding code and record the final decision rather than relying on a model promise. [Scope] The first exercise is scene planning and interface reading, not an implemented scene or WeChat integration. The pinned official example defaults to chat_turn_limit=50. That is not the short exercise recommended here; reduce it and enforce termination outside the model. Role and simulation support does not establish that a fictional character really ate, went out, or had a real-world experience. Review tools, models, and assets separately. Do not install all extras or grant unrestricted computer access just for a scene. [Sources and license] Official repository camel-ai/camel; pinned commit 797296a718ce856d9c9476e4bb69fedb71ab6382, dated 2026-10-19 (UTC). Reviewed on 2026-10-27; this entry is not presented as a daily Trending result. The upstream main code is under Apache-2.0, allowing commercial use, modification, and redistribution under its conditions. Include the complete license, retain applicable notices, and mark modified files. Trademark rights are not granted. This is an original site guide shared under CC BY 4.0. Only official documentation and complete licenses were reviewed. It includes no upstream source, installers, icons, images, character cards, or models. The upstream project was not installed or run, no model trial was performed, and no security audit was conducted. Review dependencies, assets, models, and hosted services separately.
Original guide (Chinese)
【小目标】一份包括两个角色职责、共同任务、最多六次发言和最后决定的原创小场景计划。若进一步运行,应在到达上限后由代码停止;本站尚未运行验证。
【开始前准备】固定 pyproject.toml 要求 Python >=3.10,<3.15;安装方法以固定官方指南为准。
真实运行需自己选择模型后端及相应账号或本地服务;模型调用可能收费。
使用独立练习目录,不向示例提供真实聊天记录,不启用终端、浏览器或交易工具。
【三步尝试】1. 先定一个小任务:设计两位原创书店店员,任务只是在打烊前安排明天整理书架的分工;每位角色各有一个清楚职责。
2. 对照官方角色接口:阅读固定版本 RolePlaying 示例中的两个角色名称和 task_prompt 字段,理解一次初始化和交替发言。官方示例默认是股票交易任务,不要直接运行;本站不提供或执行这个交易任务。
3. 给场景一个结束点:在自己的练习计划中限定各三轮、分工确定即停止。实际开发时由外层代码执行轮数上限,并记录最后决定,不能只依赖模型口头承诺停止。
【适用边界】本条第一步是场景设计和接口阅读,不表示已完成可运行小剧场或微信接入。
官方固定示例默认 chat_turn_limit=50,不能把它当本站建议的短轮次练习;应自行缩小并保留外层停止判断。
框架提供角色和模拟环境,不说明虚构角色真实吃饭、出门或发生了现实经历。
完整项目的工具、模型和示例素材仍需各自核对,不能为了小剧场安装 all 扩展或开放全部电脑权限。
【来源与许可】官方仓库 camel-ai/camel;固定提交 797296a718ce856d9c9476e4bb69fedb71ab6382,提交日期 2026-10-19(UTC)。本条在2026-10-27核对,不宣称为今日热榜项目。
上游主代码采用 Apache-2.0,允许按条款商用、修改与再分发;分发需附完整许可,保留适用声明,修改文件标明更改,商标不随许可授予。
本条是本站原创导览,按 CC BY 4.0 分享;仅核对官方资料和完整许可,不附上游源码、安装包、图标、图片、人物卡或模型。未安装运行上游项目,未调用模型实测,也未进行安全审计。依赖、素材、模型及云服务仍需分别核对。How to use it
For people with Python experience who want to study multi-role conversations. Start with role and turn boundaries. This is a developer framework, not a ready-to-use WeChat plugin.
Choose a small task
Create two original bookshop clerks whose only task is to assign tomorrow’s shelf-tidying work before closing. Give each one clear responsibility.
Read the official role interface
Inspect the two role-name fields and task_prompt in the pinned RolePlaying example to understand initialization and alternating turns. Its default task concerns stock trading; do not run that task for this exercise.
Define the stopping point
Plan three turns per role and stop when responsibilities are settled. In an implementation, enforce the turn limit in surrounding code and record the final decision rather than relying on a model promise.
Expected output
An original scene plan with two role responsibilities, one shared task, at most six utterances, and a final decision. Any later implementation must stop through code at the limit. It was not run here.
This is an original site guide shared under CC BY 4.0. Only official documentation and complete licenses were reviewed. It includes no upstream source, installers, icons, images, character cards, or models. The upstream project was not installed or run, no model trial was performed, and no security audit was conducted. Review dependencies, assets, models, and hosted services separately.
Suitable uses
- For people with Python experience who want to study multi-role conversations. Start with role and turn boundaries. This is a developer framework, not a ready-to-use WeChat plugin.
Before using it
- The first exercise is scene planning and interface reading, not an implemented scene or WeChat integration.
- The pinned official example defaults to chat_turn_limit=50. That is not the short exercise recommended here; reduce it and enforce termination outside the model.
- Role and simulation support does not establish that a fictional character really ate, went out, or had a real-world experience.
- Review tools, models, and assets separately. Do not install all extras or grant unrestricted computer access just for a scene.
Authors and sources
- Attribution
- Site guide
- Upstream author
- CAMEL-AI.org and CAMEL contributors; LICENSE copyright: Copyright 2023 @ CAMEL-AI.org.
- Source repository
- https://github.com/camel-ai/camel
- Source revision
797296a718ce856d9c9476e4bb69fedb71ab6382- Licence checked
- 2026-10-27
Notices and modifications
Official repository camel-ai/camel; pinned commit 797296a718ce856d9c9476e4bb69fedb71ab6382, dated 2026-10-19 (UTC). Reviewed on 2026-10-27; this entry is not presented as a daily Trending result.
Complete licence
CC BY 4.0
CC BY 4.0 · Read the complete licence ↗
Upstream licence · Apache-2.0
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Documentation and license reviewed
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2026-10-27