통합 지식 베이스와 customizable 기술로 AI 채팅 에이전트. 포함 된 모든 것을 가진 단일 독립 바이너리 - 설치에 의존하지 않고 설치가 필요하지 않습니다. OpenAI 호환 LLM, 색인 문서 및 웹 페이지를 로컬 벡터 데이터베이스로 다운로드, 실행 및 채팅을 시작하고, 자신의 콘텐츠에 기반을 둔 답변을 얻을.

무엇 그것

-Chat - 멀티턴 대화 및 자동 컨텍스트 압축을 사용한 대화식 REPL

• Index — /index )는 RAG 지식 베이스에 문서를 추가합니다. -Fetch — /fetch 검색 및 Markdown으로 웹 콘텐츠를 표시
• Skills - 전문 작업을위한 재사용 가능한 기술 파일로 에이전트 행동을 사용자 정의 -Integrations — Confluence, JIRA, GitHub, GitLab (기본적으로 사용 가능)

지원 파일 형식 : .md, .txt], .csv], .tsv], .docx]], .odt, .pptx, .xlsx], .html, .eml.

요구 사항

• Go 1.27.3 (또는 사전 제작 된 바이너리를 다운로드)
• OpenAI 호환 공급자 (OpenAI, Azure OpenAI, Ollama 등)에 대한 API 키

주의: Qdrant는 ** 더 이상 필요하지 않습니다 **. 기본 벡터 저장소는 Chromem (in, in-memory 옵션 persistence)입니다.

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
• Subagents — spawn specialized agents for complex tasks (exploration, research, parallel work)
• Integrations — Confluence, JIRA, GitHub, GitLab (all optional, disabled by default)

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

For a complete list of supported formats and their available modes, see docs/file_codecs.md.

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