技能潮 SkillTide 提交收录
官方说明3 个来源MCP 服务3 天前
Harbor

Harbor

A containerized toolkit for running local LLM backends, UIs, and supporting services with one command
在 GitHub 查看 ↗直链 · 无中转页
来源明细
收录自 steven2358/awesome-generative-ai 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-14 · 最近见到 2026-10-16 · 共见到 4 次 · 被 3 个来源列出
评分构成
综合分 303 · 3 个来源 · mcp
常见问题(答案都来自我们的记录)
怎么安装?
我们【没有】从该仓库取到安装命令(我们的 install 覆盖率只有约 4%)。请看它的 README 或 releases 页。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
我们收录于 2026-10-14;最近见到 2026-10-16。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。
第三方质量标记

https://github.com/user-attachments/assets/e4897391-c5a8-4391-93c3-9f8b76155f11

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

Setup your local LLM stack effortlessly.
Harbor is a CLI and companion app that lets you spin up a complete local LLM stack—backends like Ollama, llama.cpp, or vLLM, frontends like Open WebUI, plus supporting services like SearXNG for web search, Speaches for voice chat, and ComfyUI for image generation—all pre-wired to work together with a single harbor up command. No manual setup: just pick the services you want and Harbor handles the Docker Compose orchestration, configuration, and cross-service connectivity so you can focus on actually using your models.
News
- **v0.5.14** - Eight new services (Latitude, AutoGen Studio, LobeHub, R2R, Caddy, RAGFlow, ClaraVerse, Qwen Code), a Dockerized Harbor CLI runner, external Ollama support for Boost and Open WebUI, opt-in CLI command wrappers, and fixes for ROCm detection, Perplexica on LAN, AnythingLLM settings persistence and the Harbor App on dot-prefixed homes
- **v0.5.13** - harbor models pull no longer reports success when a model download actually failed, and the PrismML backend documents its two Bonsai 2 quants with measured speed and accuracy
- **v0.5.12** - New PrismML llama.cpp backend that runs Ternary Bonsai 2 GGUFs on CPU, NVIDIA, and ROCm with the same model workflow and cross-service integrations as llama.cpp
- **v0.5.11** - Halogen stays up through long Open WebUI generations with image 0.6.3, a 65536-token chat budget and a 900-second engine watchdog
- **v0.5.10** - n8n first-time setup includes the Assistant code sandbox so new instances can finish the GUI wizard
- **v0.5.9** - Hugging Face downloads run as your host user and recover root-owned caches automatically after upgrading
- **v0.5.8** - New Halogen Flash Server backend for AMD Strix Halo and a fix for Hugging Face downloads failing on root-owned cache directories
Documentation
- Installing Harbor
Guides to install Harbor CLI and App, including the agent install prompt for Claude Code, Codex, and similar tools
- Harbor User Guide
High-level overview of working with Harbor
- Harbor App
Overview and manual for the Harbor companion application
- Harbor Services
Catalog of services available in Harbor
- Harbor CLI Reference
Read more about Harbor CLI commands and options.
Read about supported services and the ways to configure them.
- Harbor Guides
Practical guides for local LLM stack setup, local web RAG, coding agents, OpenAI-compatible backends, and self-hosted AI stack planning.
- Join our Discord
Get help, share your experience, and contribute to the project.
What can Harbor do?
✦ Local LLMs
Run LLMs and related services locally, with no or minimal configuration, typically in a single command or click.
Local Coding Tools
Use Harbor backends from installed coding and agent CLIs without hand-editing each tool's provider config. harbor launch starts or detects a Harbor OpenAI-compatible backend, wires the selected model into the host tool, and leaves the tool running in your current project directory.
Supported host tools include claude, codex, copilot, droid, grok, hermes, mi, openclaw, opencode, pi, pool, and vscode.
Agentic coding with Boost
Harbor Boost chains agentic modules—web research, deliverable audits, scope checks, and style passes—into custom workflows. harbor launch --workflow routes coding agents through a single Boost module instead of the raw backend model.
Modules quickhop and deephop auto-start SearXNG. Define multi-step workflows with HARBOR_BOOST_WORKFLOWS or workflows.yaml, and mount your project with boost.workspace so agents can verify real file paths—see Boost configuration and the modules reference.
来源
awesome · https://github.com/av/harbor
按领域浏览
MCP 服务
收录于
昨天

更多MCP 服务

查看MCP 服务全部 5,525 条 →

所属领域与标签

每个标签都能点进去看同类的全部条目。数字是当前库里的真实条数。

浏览全部领域 →

← 返回索引