角色扮演机器人AI模块 是 AI Skill Hub 本期精选AI工具之一。综合评分 7.5 分,整体质量较高。我们推荐使用将其纳入你的 AI 工具库,帮助提升工作效率。
为AzerothCore魔兽世界私服框架集成大语言模型,赋予玩家机器人角色AI能力。支持自然语言交互和智能化角色扮演,适合魔兽世界私服开发者和角色扮演游戏爱好者打造沉浸式游戏体验。
角色扮演机器人AI模块 是一款基于 C++ 开发的开源工具,专注于 游戏AI、LLM集成、角色扮演 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
为AzerothCore魔兽世界私服框架集成大语言模型,赋予玩家机器人角色AI能力。支持自然语言交互和智能化角色扮演,适合魔兽世界私服开发者和角色扮演游戏爱好者打造沉浸式游戏体验。
角色扮演机器人AI模块 是一款基于 C++ 开发的开源工具,专注于 游戏AI、LLM集成、角色扮演 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 克隆仓库 git clone https://github.com/deseven/mod-playerbots-characters cd mod-playerbots-characters # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
# 查看帮助 mod-playerbots-characters --help # 基本运行 mod-playerbots-characters [options] <input> # 详细使用说明请查阅文档 # https://github.com/deseven/mod-playerbots-characters
# mod-playerbots-characters 配置说明 # 查看配置选项 mod-playerbots-characters --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export MOD_PLAYERBOTS_CHARACTERS_CONFIG="/path/to/config.yml"
This is an AzerothCore module built around mod-playerbots, breathing new life into bots by turning them into true in-game characters — companions with pre-defined personalities, memory, and relationships. Heavily inspired by mod-ollama-chat, but taking a different, more complex approach — focusing on the roleplaying experience rather than emulating real WoW players.
Think old Bioware games with companions — that's the core idea. The intended use is a fresh start at low rates with a full party of altbots playing alongside you, developing their own stories as you progress through the game together.
[!IMPORTANT] The module is currently in active development and things could be changing rapidly. Before updating your copy, it's highly recommended to do a database backup, read the changes and notes in the Releases and check your server logs after running the newer version. There should be no hard incompatibilities, but new config variables are getting added, existing ones change their defaults, old routines get replaced and so on.
Since mod-playerbots is an obvious hard requirement, follow their Installation Guide until you have a working acore installation with playerbots enabled.
There are three ways to obtain the module sources:
The module supports two API formats, controlled by PBC.APIType:
openai (default) — OpenAI-compatible /chat/completions endpoint. The module appends /chat/completions to PBC.BaseUrl and sends the API key via Authorization: Bearer header.anthropic — Anthropic Messages API /messages endpoint. The module appends /messages to PBC.BaseUrl and sends the API key via x-api-key header with the anthropic-version: 2023-06-01 header.You need to configure at least PBC.BaseUrl, PBC.Model and PBC.ApiKey before the module can generate responses. The relevant config options are in the API CONNECTION and MODEL PARAMETERS sections of the config file. After configuring, you can use .chars api-test to quickly verify that the connection is working (or .chars alt-api-test for the alternative model).
[!IMPORTANT] Due to the complexity and length of the prompts, locally-run models on average home hardware will generally struggle and produce low-quality output as context grows. A cloud-based model with a large context window is recommended. Make sure to also adjust PBC.MaxHistoryCtx accordingly — a good starting point is around 25% of the model's total context window. Aim for at least 32k in general, anything less could lead to poor efficiency of character relationship tracking and memory extraction.
Choosing the right model can be tricky. Two tested configurations are listed below.
| Setting | Value |
|---|---|
PBC.APIType | openai |
PBC.BaseUrl | https://api.deepseek.com/v1 |
PBC.Model | deepseek-chat |
PBC.Temperature | 1.6 |
PBC.MaxHistoryCtx | 32768 |
PBC.MaxMemoriesCtx | 8192 |
PBC.ModelExtraParameters | 'frequency_penalty':0.5,'presence_penalty':0.2 |
PBC.ApiKey | your API key from [DeepSeek platform](https://platform.deepseek.com/) |
DeepSeek offers a reasonable cost/capabilities compromise and can be considered the cheapest viable option. The frequency_penalty and presence_penalty extra parameters help reduce repetitive output. Expect to spend under $0.5 for several hours of play.
| Setting | Value |
|---|---|
PBC.APIType | openai |
PBC.BaseUrl | https://api.z.ai/api/paas/v4 |
PBC.Model | glm-5.1 |
PBC.Temperature | 1.0 |
PBC.MaxHistoryCtx | 32768 |
PBC.MaxMemoriesCtx | 8192 |
PBC.ModelExtraParameters | 'thinking':{'type':'disabled'} |
PBC.ApiKey | your API key from [Z.ai](https://z.ai/manage-apikey/apikey-list) |
GLM 5.1 has a built-in "thinking" mode that would reach PBC.MaxResponseLength very fast — disabling it via ModelExtraParameters is required for this case, unless you lift the limit. The model handles the required tasks impressively well, though the cost adds up fairly quickly. Expect to spend around $2 per long game session with a full party.
Any OpenAI-compatible API should work with PBC.APIType = openai — just set PBC.BaseUrl to the endpoint (the module appends /chat/completions automatically), PBC.Model to the model identifier, and PBC.ApiKey to your bearer token. If the endpoint doesn't require authentication (e.g. a local Ollama or LM Studio instance), leave PBC.ApiKey empty.
Use PBC.ModelExtraParameters to inject provider-specific JSON into the request body — this works the same way for both openai and anthropic API types. Make sure to use parameter names that are valid for your chosen API type (e.g. top_p and top_k for Anthropic, frequency_penalty and presence_penalty for OpenAI-compatible providers). Single quotes are used instead of double quotes and are automatically replaced at runtime:
PBC.ModelExtraParameters = 'frequency_penalty':0.5,'presence_penalty':0.2
becomes "frequency_penalty":0.5,"presence_penalty":0.2 in the request.
When switching providers, pay attention to PBC.Temperature — acceptable ranges vary between models. Check the provider's documentation for the recommended value.
Start the server, set up some altbots for yourself or invite existing random bots, then write cards for them as you see fit. See characters/Example.card.txt for an example of how to write a character card. The character name in the filename must match the in-game character name exactly to be picked up. It is recommended to write cards in second person (using "you/your"), because default prompts and request structure follow this format.
Start playing, chat with your characters, discuss anything you like, build relationships and enjoy the game.
[!NOTE] Depending on the model you are using, your mileage may vary. Do regular backups withmodules/mod-playerbots-characters/tools/pbc_backup.shand adjust things as needed either in the database (followed by.chars reloadcommand) or via the included web app. There are also two helper tools (pbc_info.shandpbc_history.sh) which might help with tracking what's going on. You can also steer the narration a bit by using.chars narrateand.chars narrate-partycommands. Check out available commands for more info.
Copy env/dist/etc/modules/playerbots_characters.conf.dist as env/dist/etc/modules/playerbots_characters.conf and adjust it as needed.
Condensation and relationship updates are critical — their output becomes permanent parts of the character's context. The ALTERNATIVE API config section lets you route these tasks to a more capable model while keeping the main chat on a cheaper/faster one. Parameters follow the same format as the main model, prefixed with AltModel.
垂直领域创新应用,将生成式AI与游戏引擎深度融合,为沉浸式游戏体验提供新可能,但生态相对小众,维护活跃度一般。
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经综合评估,角色扮演机器人AI模块 在AI工具赛道中表现稳健,质量良好。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | mod-playerbots-characters |
| 原始描述 | 开源AI工具:A character-focused LLM integration for AzerothCore/Playerbots。⭐14 · C++ |
| Topics | 游戏AILLM集成角色扮演AzerothCore模块C++开发 |
| GitHub | https://github.com/deseven/mod-playerbots-characters |
| License | Unlicense |
| 语言 | C++ |
收录时间:2026-05-22 · 更新时间:2026-05-22 · License:Unlicense · AI Skill Hub 不对第三方内容的准确性作法律背书。