ChaosEngineAI AI技能包
ChaosEngineAI AI技能包 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。
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
ChaosEngineAI AI技能包 依赖 Python 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Python 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 ChaosEngineAI AI技能包 的版本更新，及时通知重要功能变化。
本地AI工作站，支持发现、运行、聊天、benchmark和生成图片等功能。
ChaosEngineAI AI技能包 是一款基于 Python 开发的开源工具，专注于 AI、Python 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。
本地AI工作站，支持发现、运行、聊天、benchmark和生成图片等功能。
ChaosEngineAI AI技能包 是一款基于 Python 开发的开源工具，专注于 AI、Python 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。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 方式一：pip 安装（推荐）
pip install chaosengineai
# 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 chaosengineai
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/cryptopoly/ChaosEngineAI
cd ChaosEngineAI
pip install -e .
# 验证安装
python -c "import chaosengineai; print('安装成功')"
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 命令行使用
chaosengineai --help
# 基本用法
chaosengineai input_file -o output_file
# Python 代码中调用
import chaosengineai
# 示例
result = chaosengineai.process("input")
print(result)
# chaosengineai 配置文件示例（config.yml） app: name: "chaosengineai" debug: false log_level: "INFO" # 运行时指定配置文件 chaosengineai --config config.yml # 或通过环境变量配置 export CHAOSENGINEAI_API_KEY="your-key" export CHAOSENGINEAI_OUTPUT_DIR="./output"
简介
<p align="center"> <img src="./ChaosEngineAI_AppIcon.svg" alt="ChaosEngineAI" width="200" /> </p>
ChaosEngineAI
<p align="center"> <strong>The local AI model runner for serious tinkerers.</strong><br/> Discover, convert, serve, chat with, benchmark, and generate images and video from open-weight models — all on your own machine. </p>
<p align="center"> <img alt="Status" src="https://img.shields.io/badge/status-work%20in%20progress-f59e0b?style=flat-square" /> <img alt="Platform" src="https://img.shields.io/badge/platform-macOS%20%7C%20Linux%20%7C%20Windows-1f2937?style=flat-square" /> <img alt="Shell" src="https://img.shields.io/badge/shell-Tauri%202-24c8db?style=flat-square" /> <img alt="Backend" src="https://img.shields.io/badge/backend-Python%20%2B%20llama.cpp-3776ab?style=flat-square" /> <img alt="Acceleration" src="https://img.shields.io/badge/Apple%20Silicon-MLX-000000?style=flat-square" /> <img alt="License" src="https://img.shields.io/badge/license-Apache--2.0-4b5563?style=flat-square" /> </p>
⚠️ Work in progress. ChaosEngineAI is under active development. Expect rough edges, breaking changes between versions, and features that appear (and occasionally disappear) from one release to the next. Feedback and issue reports are very welcome.
<p align="center"> <img src="./docs/tour.gif" alt="ChaosEngineAI tour" width="900" /> </p>
---
Dashboard — *System overview*
The launchpad. Surfaces backend health, the engine in use, the currently loaded model, hardware (platform, arch, memory), and quick stats from the warm pool. Big colored badges tell you instantly whether the runtime is online and what cache mode it will use on the next launch.
Feature Highlights
- 🧭 Discover open-weight models from curated catalogs and pull them with one click
- 💾 My Models library with format, size, context, and modified-date sorting
- 🔁 Conversion pipeline that turns Hugging Face checkpoints into MLX (Apple Silicon)
- 🚀 Server mode exposing an OpenAI-compatible REST API for your other tools
- 💬 Chat with the loaded model, including document attachments with inline citations, image inputs for vision-capable models, tool calls, collapsible reasoning traces, conversation branching, in-thread compare, mid-thread model swap, and retry on failed turns
- ⚖️ Compare mode to stream the same prompt through two models side-by-side with separate runtime settings and metrics
- 🧪 HTML Challenge — side-by-side HTML generation across 2-4 models with per-slot thinking mode, reasoning effort, and seed; sandboxed live previews, automated HTML validation, persistent history, retry + repair flows
- 🧰 Sampler controls in the composer —
top_p,top_k,min_p,repeat_penalty,seed,mirostat,reasoning_effort, JSON-schema constrained output - 🧷 MCP client — stdio JSON-RPC tool adapter so any local MCP server is callable from chat
- 📚 Workspace knowledge stacks — shared RAG corpus across sessions via
llama-embedding+ cosine retrieval - 🗂️ Prompt Library with built-in starter templates plus searchable tags, categories, CRUD, fill-form variables, and one-click apply-to-chat
- 🎨 Image Discover curated catalog of Stable Diffusion models with one-click download from Hugging Face
- 🖼️ Image Models library showing installed image models ready for generation
- 🖌️ Image Studio for prompt-based image generation with aspect ratio, quality presets, and negative prompts
- 🏛️ Image Gallery to browse, filter, and reuse saved outputs — compare models and re-run with the same settings
- 🎬 Video Studio for prompt-based video generation across Wan 2.1/2.2, LTX-Video 2.0/2.3, HunyuanVideo, CogVideoX, and Mochi — with model picker, runtime status overlay, live progress, and cancellable generation
- 🍿 Video catalog curated set of diffusion DiT video models with one-click downloads and a separate Video Models library
- ⚡ First Block Cache + TaylorSeer / MagCache / PyramidAttentionBroadcast / FasterCache cross-platform diffusion cache hooks (≈1.8× FLUX.1-dev with imperceptible drift), plus TeaCache for FLUX / HunyuanVideo / LTX-Video / CogVideoX / Mochi
- 🖼️ Live denoise thumbnails in Image + Video Studio via TAESD/TAEHV preview VAE swap — see what's being generated in real time, not just a progress bar
- ✨ MLX prompt enhancer (Apple Silicon) — local Qwen2.5-0.5B rewrites your prompt into the active DiT's training distribution per family (FLUX / Wan / LTX / HunyuanVideo / SDXL / SD3)
- 🧬 Distill LoRA + transformer support — Hyper-SD-8step / Turbo-Alpha for FLUX.1-dev, CausVid for Wan 2.1, lightx2v 4-step distill for Wan 2.2 A14B I2V (bf16 + fp8_e4m3)
- 🎯 Wan 2.1 / 2.2 mlx-video runtime end-to-end on Apple Silicon — one-shot convert pipeline, GUI install panel, runtime routing for all
Wan-AI/Wan2.{1,2}-*repos - 🩺 Diagnostics panel in Settings — per-section error fallback, platform-aware repair actions, and a one-click reinstall for the Apple Silicon mlx-video runtime
- 📊 Benchmarks with throughput, perplexity, and task-accuracy modes plus a history view for A/B comparisons
- 📜 Logs streaming straight from the Python runtime
- ⚙️ Settings for data directories, Hugging Face tokens, remote providers, integrations, default launch preferences, and runtime tuning
- 🔍 UI scale — segmented 75% / 100% / 125% / 150% control in Settings → Display, applied app-wide via
useUiScale - ☁️ Remote providers for OpenAI-compatible APIs, with keys stored locally and masked in the UI
- 📁 Model directories for custom stores, Ollama, LM Studio, or shared model paths
- 🧠 Warm pool keeps recently-used models hot so subsequent loads are instant
- 🔄 In-app updates — signed, verified, cross-platform. Launch, prompt, relaunch.
- 🍎 Notarized macOS builds with a fully embedded, hardened-runtime Python runtime
- 🐧 AppImage + .deb for Linux, NSIS installer for Windows
- 🎚️ Adaptive runtime controls for balancing speed, memory footprint, and quality on local hardware
- 📈 Live telemetry on the dashboard: backend health, engine, loaded model, hardware, warm-pool state
- 🔮 DFlash speculative decoding for 3-5x faster generation with zero quality loss (Qwen3, Qwen3.5, LLaMA 3.1, gpt-oss, Kimi families)
- 🌳 DDTree tree-based speculative decoding — explores multiple draft paths in parallel for higher acceptance rates
- 🪄 MTPLX Multi-Token Prediction speculative decoding (Apple Silicon) — 1.8-2.2× speedup with zero quality loss for trained-with-MTP models (Qwen3.5/3.6, DeepSeek V3/R1, Qwen3-Coder-Next, Youssofal MTPLX-Optimized variants)
- 🔧 Agent tools — web search, calculator, code executor, and file reader for tool-augmented conversations
- 🎓 Fine-tuning with LoRA adapter support for MLX models
- 🧩 Plugin system with 5 extension types: cache strategies, inference engines, tools, model sources, and post-processors
- 🔌 Integrations with copy-paste connection snippets for Continue.dev, Goose, Cursor, and Claude Code via the local API
---
Complete Feature Map
1. Install JS dependencies
npm install
Image Models — *Installed image models*
Your installed image model library. See which Stable Diffusion models are ready for generation — each card shows size, diffusion pipeline, resolution, and a one-click Generate to jump straight into Image Studio.
Download & Install
Head to the Releases page for signed builds:
| Platform | File | Notes |
|---|---|---|
| **macOS** (Apple Silicon) | ChaosEngineAI_*_aarch64.dmg | Signed + notarized |
| **Linux** | ChaosEngineAI_*_amd64.AppImage | Portable, in-app updates supported |
| **Linux** (Debian/Ubuntu) | ChaosEngineAI_*_amd64.deb | Install via dpkg, update via apt |
| **Windows** | ChaosEngineAI_*_x64-setup.exe | Unsigned for now — SmartScreen will warn on first run |
From v0.4.21 onward, every install auto-updates from GitHub Releases on launch. Updates are cryptographically signed.
---
Building a Release
Releases are tag-driven. Push vX.Y.Z and the GitHub Actions release workflow builds signed bundles for macOS, Linux, and Windows in parallel, generates the latest.json updater manifest, and stages a draft release.
```bash
locally, if you want to cut a build by hand:
npm run stage:runtime:release npm run tauri:build
Release artifacts land in `src-tauri/target/release/bundle/`.
For an unsigned local macOS app + DMG without Apple signing/notarization or Tauri updater signing configured:
npm run release:macos -- --skip-sign --skip-notarize ```
That writes the local app + DMG to releases/macos/.
---
Quick Start (from source)
Prereqs: Rust toolchain, Node 20+, Python 3.11+, and (on macOS) Xcode command-line tools.
```bash
Image Gallery — *Browse and reuse generated images*
All your generated images in one place. Search by prompt, model, or runtime; filter by frame size and sort order. Each card shows the source model, generation settings, and quick actions to re-run with the same seed or open in Image Studio.
Settings & integrations
- Data-directory relocation with migration/copy support
- Remote-provider configs stored locally with masked keys and HTTPS validation
- Integration snippets for Continue.dev, Goose, Cursor, and Claude Code through the local OpenAI-compatible API
---
Model Selection — *Configure and launch*
The launch modal. Pick a variant, set context length, choose the engine, and tune runtime strategy knobs in one place, pre-populated from your defaults.
Settings — *Directories and defaults*
Configure model and cache directories, remote-provider fallbacks, Hugging Face tokens for gated models, data-directory migration, integration snippets for external tools, default launch preferences (cache strategy, FP16 layers, fused attention, context tokens, fit-in-memory toggle), and advanced runtime knobs. Every default in this panel is reused as the starting state for the launch modal.
---
Server — *OpenAI-compatible local API*
Start, stop, and inspect a local OpenAI-compatible HTTP server backed by the loaded model. Shows the bind address, warm-pool entries, request count, active connections, LAN exposure, preferred port, auto-start controls, and a remote-test panel with copyable curl commands for /health, /models, and /chat/completions.
Chat, prompting & agent workflows
- Multi-thread chat with pinned sessions, persistent history, inline thread renaming, and per-thread runtime memory
- Conversation branching — fork from any assistant message into a sibling thread
- In-thread compare — render sibling variants side-by-side under the assistant bubble
- Mid-thread model swap with one-turn override (try a different model for a single response, then revert)
- Retry failed assistant turns with one click, against the active runtime profile
- Document uploads with chunked retrieval, inline citations, and per-session document management
- Workspace knowledge stacks: shared RAG corpus across sessions via
llama-embedding+ cosine retrieval - Image attachments for vision-capable models, with composer auto-gating from typed capability declarations
- Optional thinking mode with collapsible reasoning traces and cleanup of raw reasoning tokens
- Logprobs visualization (advanced-mode gated): per-message confidence summary, MLX logprobs streaming passthrough
- Substrate routing inspector + per-turn host strip surface engine, binary, CPU / GPU / RAM / temperature alongside tok/s and TTFT
- KV strategy chip in composer for per-turn cache override (native / turboquant / triattention) without touching launch settings
- Delve mode — critic-pass on assistant messages
- Tool-augmented conversations with
web_search,calculator,code_executor,file_reader, plus stdio MCP client (JSON-RPC) so any local MCP server is callable from chat. Tool results render as table / code / markdown / image based on the returned shape. - Side-by-side compare mode that streams the same prompt through two models with independent runtime settings and metrics
- HTML Challenge — 2-4 model slots, each with own thinking mode + reasoning effort + seed, all answering the same prompt with sandboxed live HTML previews, automated
valid/partial/script-error/blank-render/no-htmlvalidation, persistent history, plus retry + repair flows - OpenAI-compatible local server with full sampler-chain + embeddings parity (
top_p,top_k,min_p,repeat_penalty,seed,mirostat,reasoning_effort,json_schema) - Prompt Library with built-in starter templates, search, tagging, categories, CRUD, fill-form variables, and one-click apply-to-chat
Plugins — *Extensions and plugin system*
Inspect built-in and external plugins across cache strategies, inference engines, tools, model sources, and post-processors. Plugins can be enabled or disabled from the UI, and external plugins are discovered from the app's plugin directory.
History — *Compare saved runs*
Every benchmark you've ever run, side-by-side. Pick two runs and the page diffs them across throughput, latency, and quality metrics — perfect for proving that your new quant actually pays its keep.
ChaosEngineAI 是一个本地 AI 模型运行器，专为严肃的探索者设计。它允许您发现、转换、服务、与、benchmark 和生成图像和视频，从开源权重模型中——所有在您的机器上。
- 发现开源权重模型的列表和一键拉取 - My Models 库，格式、大小、上下文和修改日期排序 - 转换管道，将 Hugging Face 检查点转换为 MLX (Apple Silicon) - 服务模式，暴露一个 OpenAI 兼容的 REST API - 与加载的模型进行聊天，包括文档附件
1. 安装 JS 依赖项 npm install
下载并安装：前往 [Releases](https://github.com/cryptopoly/ChaosEngineAI/releases/latest) 页面获取签名构建：| 平台 | 文件 | 备注 | |---|---|---| | macOS (Apple Silicon) | ChaosEngineAI_*_aarch64.dmg | 签名 + 记录 | | Linux | ChaosEngineAI_*_amd64.AppImage | 可移植，内嵌更新支持 | | Linux (Debian/Ubuntu) | ChaosEngineAI_*_amd64.deb | 安装 via dpkg，u
快速启动（从源代码）：前置条件：Rust 工具链，Node 20+，Python 3.11+，以及（在 macOS 上）Xcode 命令行工具。
- 数据目录重定位，迁移/复制支持 - 远程提供商配置，存储本地，密钥掩码和 HTTPS 验证 - 继续.dev、Goose、Cursor 和 Claude 代码的集成片段通过本地 OpenAI 兼容 API
服务 — *OpenAI 兼容的本地 API*：启动，停止和检查一个由加载的模型支持的本地 OpenAI 兼容 HTTP 服务器。显示绑定地址，暖池条目，请求计数，活动连接，LAN 暴露，首选端口，自动启动控制，和一个远程测试面板，复制
- 多线程聊天，固定会话，持久历史，内线程重命名，和每线程运行时内存 - 会话分支 - 从任何助手消息分叉到一个兄弟线程 - 内线程比较 - 在助手气泡下渲染兄弟变体 - 中线程模式
该工具提供了一个完整的本地AI工作站功能，支持多种AI应用，但其代码质量和文档不够完善。
- 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
- 构建企业知识库 / RAG 检索应用的团队
- 跨境业务、多语言内容运营团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- 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
- embedding 模型与查询模型不一致导致检索失效
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- Python 依赖冲突：建议用 venv / uv 隔离环境
- CLI：直接 npm install -g / pip install，命令行调用
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 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
- 构建企业知识库 / RAG 检索应用的团队
- 跨境业务、多语言内容运营团队
- 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC，避免暴露公网
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- 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
- embedding 模型与查询模型不一致导致检索失效
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
- +Apache-2.0 协议，可免费商用
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
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。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
经综合评估，ChaosEngineAI AI技能包 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 | ChaosEngineAI |
| 原始描述 | 开源AI工具：Local AI workstation — discover, run, chat, benchmark, and generate images from 。⭐20 · Python |
| Topics | AIPython |
| GitHub | https://github.com/cryptopoly/ChaosEngineAI |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-17 · 更新时间：2026-05-24 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。