一个集成知识库和可定制技能的AI聊天代理。一个包含所有功能的单一独立二进制文件——无需依赖安装，无需安装。只需下载、运行并开始与任何兼容OpenAI的大型语言模型聊天，将文档和网页索引到本地矢量数据库，就能基于你自己的内容获得答案。

它的作用

• Chat — 具备多回合对话和自动上下文压缩的交互式REPL
• 索引 — /index 将文档添加到你的RAG知识库（可选）
• Fetch — /fetch 以 Markdown 格式检索并显示网页内容
• 技能 — 通过可重复使用的技能文件定制代理行为以适应专业任务
• 集成 — Confluence、JIRA、GitHub、GitLab（均为可选，默认禁用）

支持的文件格式：.md、.txt、.csv、.tsv、.docx、.pdf、.odt、.pptx、.xlsx、.html

要求

• 使用 1.27.2（或下载预构建的二进制文件）
• 任何兼容 OpenAI 的提供者（OpenAI、Azure OpenAI、Ollama 等）的 API 密钥

注意： Qdrant 不再需要。默认向量存储是 Chromem（内置内存，可选持久化）。

go-magnetar

An AI chat agent with an integrated knowledge base and customizable skills. A single standalone binary with everything included — no dependencies to install, no go install needed. Just download, run, and start chatting with any OpenAI-compatible LLM, indexing documents and web pages into a local vector database, and get answers grounded in your own content.

What it does

• Chat — interactive REPL with multi-turn conversation and automatic context compaction
• Index — /index adds documents to your RAG knowledge base (optional)
• Fetch — /fetch retrieves and displays web content as Markdown
• Skills — customize agent behavior with reusable skill files for specialized tasks
• Integrations — Confluence, JIRA, GitHub, GitLab (all optional, disabled by default)

Supported file formats: .md, .txt, .csv, .tsv, .docx, .pdf, .odt, .pptx, .xlsx, .html, .eml.

Requirements

• Go 1.27.3 (or download a pre-built binary)
• API key for any OpenAI-compatible provider (OpenAI, Azure OpenAI, Ollama, etc.)

Note: Qdrant is no longer required. The default vector store is Chromem (built-in, in-memory with optional persistence). Qdrant is available as an optional external store for production deployments.

Vector Stores

go-magnetar supports two vector storage backends:

Chromem (default)

• Built-in: No external dependencies required
• In-memory by default: Store exists only during session (when rag.chromem.data_dir is empty)
• Persistent mode: Set rag.chromem.data_dir to persist vectors to disk
• Use case: Local development, testing, edge deployments

Qdrant (external)

• External server: Requires running Qdrant container
• Full persistence: Automatic disk persistence with advanced filtering
• Use case: Production, large-scale deployments

Switch between stores via config:

rag:
  store:
    type: chromem  # or qdrant

See docs/architecture.md for vector store architecture details.

Quick start

1. Install

Option A — pre-built binary (recommended):

Download a single standalone binary from GitHub Releases and place it in your $PATH.

Why a single binary? go-magnetar is distributed as a single self-contained binary with all dependencies compiled in. No need to install Go, manage dependencies, or worry about version conflicts. Just download, make executable, and run — everything you need is already there.

Option B — go install:

go install github.com/wmentor/go-magnetar/cmd/go-magnetar@latest

Option C — build from source:

git clone https://github.com/wmentor/go-magnetar.git
cd go-magnetar
make build          # binary → bin/go-magnetar

2. Configure

On first run, go-magnetar creates ~/.go-magnetar/config.yml automatically.

Edit it to set your LLM API key and model: