CSGLite
AI Skill Hub 推荐使用：CSGLite 是一款优质的Agent工作流。AI 综合评分 7.5 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
CSGLite 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 7.5 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
CSGLite是一款开源的AI工作流，专为CSGHub平台设计，提供轻量级的LLM运行功能，简化工作流管理。
CSGLite 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。
📖 中文文档
CSGLite是一款开源的AI工作流，专为CSGHub平台设计，提供轻量级的LLM运行功能，简化工作流管理。
CSGLite 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。
- 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
- 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
# 方式一：go install（推荐） go install github.com/OpenCSGs/csglite@latest # 方式二：从源码编译 git clone https://github.com/OpenCSGs/csglite cd csglite go build -o csglite . # 方式三：下载预编译二进制 # 访问 Releases 页面下载对应平台二进制文件 # https://github.com/OpenCSGs/csglite/releases
- 访问 GitHub 仓库获取工作流文件
- 在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
- 上传工作流文件
- 按照提示配置必要的环境变量和 API Key
- 运行测试确认流程正常后投入使用
# 查看帮助 csglite --help # 基本运行 csglite [options] <input> # 详细使用说明请查阅文档 # https://github.com/OpenCSGs/csglite
# csglite 配置说明 # 查看配置选项 csglite --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 CSGLITE_CONFIG="/path/to/config.yml"
简介
Features
Local Inference Capabilities
CSGLite can run several model families locally today, with more runtime types planned. The model detail page shows whether a downloaded model is currently supported for local inference.
| Capability | Status | Runtime | Typical models |
|---|---|---|---|
| Text generation / chat | Supported | llama.cpp | Qwen/Qwen3, GLM, Llama, Mistral, DeepSeek distilled GGUF models |
| Embeddings | Supported | llama.cpp | BGE, GTE, E5, Sentence Transformers-compatible embedding models |
| Vision-language chat | Supported | llama.cpp multimodal | Qwen2.5-VL, Gemma 3 / 4 vision, Idefics-style multimodal models |
| Text-to-image | Supported | Diffusers runtime; MLX on Apple Silicon | Qwen-Image, Qwen-Image-2.1 MLX (including Turbo), FLUX, Stable Diffusion, PixArt, Sana, CogView, Z-Image, Kolors, Hunyuan |
| Image-to-image | Supported | Diffusers runtime; MLX on Apple Silicon for one reference image | Qwen-Image-Edit, FLUX Kontext, inpaint, img2img, editing pipelines |
| Automatic speech recognition | Supported | Python ASR runtime | FunASR, Whisper, Wav2Vec2-family ASR models |
| Text-to-speech | Supported | Python TTS runtime | VoxCPM, Qwen3-TTS, Kokoro, MMS-TTS and other transformers-native TTS models |
| Realtime voice | Supported | Python ASR + TTS runtimes | Full-duplex over WebRTC or WebSocket, OpenAI Realtime-compatible |
| Decisions | Supported | llama.cpp (decision LoRA or native head) | JEV models such as autotrust/JEV-9B and its GGUF quantizations; native decision models such as convaiinnovations/laya; OpenAI Decisions API-compatible |
| Image-to-video | Coming soon | - | Stable Video Diffusion, SV3D |
| Text-to-video | Coming soon | - | Video generation Diffusers models |
Installation
Docker
For Docker installation, see the Docker guide.
Build for all platforms
make build-all
Quick Start
```bash
Set CSGHub access token (optional, for private models)
csghub-lite login ```
Note: The install script automatically installs llama-server (required for inference). If you installed from source, install it separately: brew install llama.cpp (macOS) or download from llama.cpp releases.
Configure Claude Code with GLM model
csghub-lite launch claude-code --model "glm-4-flash"
Configure Codex with local Qwen model
csghub-lite launch codex --model "Qwen/Qwen2.5-Coder-7B"
Configure Pi with Kimi model
csghub-lite launch pi --model "moonshot-v1-8k"
Configure Kimi Code with GLM model
csghub-lite launch kimi-code --model "glm-4-flash"
Configure DeepSeek Harness with GLM model
csghub-lite launch dsh --model "glm-4-flash" ```
Configuration
Configuration is stored at ~/.csghub-lite/config.json.
The CLI and Web UI expose a convenience storage_dir setting. When you set it, CSGLite expands it into the persisted model_dir and dataset_dir.
| Key | Default | Description |
|---|---|---|
storage_dir | ~/.csghub-lite | Shared local storage root for models and datasets |
server_url | https://hub.opencsg.com | CSGHub platform URL |
ai_gateway_url | https://ai.space.opencsg.com | AI Gateway URL for cloud inference models |
model_dir | ~/.csghub-lite/models | Effective local model storage directory |
dataset_dir | ~/.csghub-lite/datasets | Effective local dataset storage directory |
listen_addr | :11435 | API server listen address |
token | (none) | CSGHub access token |
Environment variables
See the Environment Variables Reference for all CSGLite runtime, llama.cpp/ROCm, installer, Python runtime, and AI Apps environment variables, including defaults and supported values.
Switch to a private CSGHub deployment:
csghub-lite config set server_url https://my-private-csghub.example.comCLI client
Linux / macOS
curl -fsSL https://hub.opencsg.com/csghub-lite/install.sh | shPin a release version:
curl -fsSL https://hub.opencsg.com/csghub-lite/install.sh | CSGHUB_LITE_VERSION=v0.8.55 shWindows PowerShell
irm https://hub.opencsg.com/csghub-lite/install.ps1 | iexCLI Commands
| Command | Description |
|---|---|
csghub-lite run <model> | Pull, start server, and chat (all automatic) |
csghub-lite chat <model> | Chat with a locally downloaded model |
csghub-lite ps | List currently running models and their keep-alive |
csghub-lite start | Start the API server in the background |
csghub-lite stop | Stop the background API server |
csghub-lite stop model [MODEL] | Stop/unload a running model |
csghub-lite serve | Start the API server in the foreground (auto-started by run) |
csghub-lite restart | Restart the background API server |
csghub-lite pull <model> | Download a model from CSGHub |
csghub-lite list / ls | List locally downloaded models |
csghub-lite show <model> | Show model details (format, size, files) |
csghub-lite rm <model> | Remove a locally downloaded model |
csghub-lite login | Set CSGHub access token |
csghub-lite search <query> | Search models on CSGHub |
csghub-lite config set <key> <value> | Set configuration |
csghub-lite config get <key> | Get a configuration value |
csghub-lite config show | Show current configuration |
csghub-lite uninstall | Remove CSGLite and llama-server, preserving local data unless --all is set |
csghub-lite --version | Show version information |
Model names use the format namespace/name, e.g. Qwen/Qwen3-0.6B-GGUF.
REST API
The server listens on localhost:11435 by default.
OpenAI-compatible endpoints (point any OpenAI SDK at http://localhost:11435/v1):
| Endpoint | Purpose |
|---|---|
POST /v1/chat/completions | Chat completions, streaming, tools, logprobs / top_logprobs, logit_bias, response_format |
POST /v1/responses | Responses API |
POST /v1/messages | Anthropic Messages API |
POST /v1/embeddings | Embeddings |
POST /v1/images/generations, POST /v1/images/edits | Image generation and editing |
POST /v1/audio/transcriptions, POST /v1/audio/speech | Speech recognition and synthesis |
POST /v1/realtime/calls, GET /v1/realtime | Realtime voice over WebRTC or WebSocket |
POST /v1/decisions | Decisions API: predicate, choice, and score questions |
GET /v1/models | List models |
For full endpoint details and examples, see the REST API Reference.
If the local server is running, you can also open the interactive API docs in your browser: http://localhost:11435/api-docs.html
Integrations
- Third-Party Providers — integrate OpenAI, DeepSeek, MiMo, Kimi, BigModel, Qianfan, MiniMax, OpenRouter, and any OpenAI-compatible API
- Coding Agents — one-click config for Claude Code, Codex, Pi, OpenCode, and Open Code Review
- Context compression — optionally shrinks the tool output inside agent requests before any model sees it (Settings → Context compression). Safe mode removes only redundancy and keeps every distinct line; aggressive mode also samples long logs, arrays and search results. File reads and source code are never changed. On captured Claude Code / Codex traffic tool output drops 6% (safe) to 17% (aggressive), search results 24–56%; savings per request show in Observability
- AI Applications — one-click setup for Claude Code, OpenCode, Open Code Review, Codex, Codex App, ZCode, Pi, OpenClaw, CSGClaw, Dify, and AnythingLLM
Integrations
run vs chat
run— Downloads the model if not present, auto-starts the background server, and opens a chat session. After you exit, the model stays loaded for 5 minutes by default so the nextrunis instant. Use--keep-alive -1to keep it loaded until you stop it manually.chat— Starts a chat session with a model that is already downloaded. Supports--systemflag for custom system prompts.
```bash
CSGLite是一款功能齐全的AI工作流，提供轻量级的LLM运行功能，适合需要快速构建和管理AI工作流的开发者。然而，项目的活跃度和维护情况需要进一步评估。
⚡ 核心功能
- 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
👥 适合人群
🎯 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
- +Apache-2.0 协议，可免费商用
- +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
- −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
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。
🔗 相关工具推荐
❓ 常见问题 FAQ
总体来看，CSGLite 是一款质量良好的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。
| 原始名称 | csglite |
| 原始描述 | 开源AI工作流：CSGLite is a lightweight local LLM runner for the CSGHub platform. One command d。⭐31 · Go |
| Topics | workflowai-gatewaycc-switchinferenceollama-apigo |
| GitHub | https://github.com/OpenCSGs/csglite |
| License | Apache-2.0 |
| 语言 | Go |
收录时间：2026-07-13 · 更新时间：2026-07-13 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。