AI Agent에 대한 개인 지식 라이브러리는 Model Context Protocol (MCP)의 아케이드에 내장되어 있습니다.

Status. ** Agent Library는 Arcade.dev 엔지니어가 구축한 커뮤니티 프로젝트입니다. ** 공식 Arcade.dev 제품이 아니며 Arcade.dev 지원 또는 SLA가 포함되지 않습니다. 무엇을 기대하는지 아래 지원하십시오.

네이밍. 패키지는 Agent Library (agent-library]] PyPI, librarian] 파이썬 모듈). 내부 MCP 서버는 Librarian]]로 자신을 식별합니다. 즉, 에이전트가 이야기하는 역할을하고 노출 된 도구가 Librarian_SearchLibrary, Librarian_AddToLibrary], 등입니다.

개요

Agent Library는 텍스트, 문서 및 지식에 대한 지속적인 스토리지를 갖춘 AI 에이전트를 제공합니다. 에이전트는 정보를 저장하고 나중에 semantic 및 키워드 검색을 통해 검색 할 수 있습니다, 대화를 통해 컨텍스트 유지.

특징

• AI Agent에 대한 지속적인 지식 저장
• 벡터 검색에 대한 sqlite-vec와 SQLite 저장
• BM25 순위를 가진 FTS5를 사용하여 전체 텍스트 검색
• semantic 및 키워드 매칭 결합 하이브리드 검색
• 다양한 결과에 대한 Max Marginal 관련 (MMR)
• 구성 가능한 embedding 모델 (현지 또는 OpenAI 호환 API)

Agent Library

A personal knowledge library for AI agents, built on Arcade for the Model Context Protocol (MCP).

Status. Agent Library is a community project built by Arcade.dev engineers. It is not an official Arcade.dev product and is not covered by Arcade.dev support or SLAs. See Support below for what to expect.

Naming. The package is Agent Library (agent-library on PyPI, librarian as the Python module). The MCP server inside it identifies itself as Librarian — that’s the role your agent talks to, and it’s why the exposed tools are named Librarian_SearchLibrary, Librarian_AddToLibrary, and so on.

Overview

Agent Library provides AI agents with persistent storage for text, documents, and knowledge. Agents can store information and retrieve it later through semantic and keyword search, maintaining context across conversations.

graph LR
    A[Agent Stores Info] --> B[Parser]
    B --> C[Chunker]
    C --> D[Embedder]
    D --> E[(SQLite + vec)]
    F[Agent Queries] --> G[Hybrid Search]
    E --> G
    G --> H[Relevant Context]

Features

• Persistent knowledge storage for AI agents
• SQLite storage with sqlite-vec for vector search
• Full-text search using FTS5 with BM25 ranking
• Hybrid search combining semantic and keyword matching
• Max Marginal Relevance (MMR) for diverse results
• Configurable embedding models (local or OpenAI-compatible API)
• Header-aware text chunking with overlap
• Time-bounded search filters
• CLI and MCP server interfaces

Multi-Modal Support

Agent Library supports indexing and searching across multiple file types:

Installation

The recommended way to install Agent Library is as a uv tool, which gives you the libr CLI and the MCP server in an isolated environment:

uv tool install 'agent-library[all]'

The [all] extra pulls in optional support for PDFs, images, OCR, and code-aware embeddings. If you’d rather install only what you need:

uv tool install agent-library                # core only (text + code)
uv tool install 'agent-library[pdf]'         # add PDF support
uv tool install 'agent-library[vision]'      # add image support
uv tool install 'agent-library[ocr]'         # add OCR for image-based PDFs

Installing from source

If you want to contribute or run from a clone:

git clone https://github.com/arcadeai-labs/agent-library.git
cd agent-library
./setup.sh