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RagLeap Core logo

RagLeap Core

自律型AIエージェント — RAGだけではない。

license MIT 46 AI Employees Autonomous Self-Hosted PyPI ragleap-rag Downloads

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RagLeap Coreは、RagLeapを支えるオープンソースエンジンです — ベンダーロックインなしに、自分のサーバー上の自分のドキュメントからビジネスを運営するセルフホスト型エージェントシステム。

46名のロールベースAI従業員: 9つのコアジェネラリストロール（AIマネージャー、秘書、CEO、営業、サポート、HR、財務、マーケティング、オペレーション）に加え、37の業種特化グローバルロール。

何をするか:

  • 自己学習型、成果加重メモリ
  • 自動ワークフロートリガーとエスカレーション
  • 完全自律 / 半自律モード
  • 思考-行動-決定ループ、単なる検索ではない

Quickstart · Docs · Website · Hosted Version · Packages

Not to be confused with install.ragleap.com — that's a separate, paid, license-gated self-hosted product. ragleap-core (this repo) is MIT-licensed, completely free, and never requires a license key.

Install

pip install ragleap-rag

⭐ 役に立ったら、リポジトリにスターを付けてください — より多くの人が見つけるのに本当に役立ちます。

Prefer Java? ragleap-rag is also on Maven Central:

<dependency>
    <groupId>io.github.antonyrag</groupId>
    <artifactId>ragleap-rag</artifactId>
    <version>0.5.0</version>
</dependency>

Add ragleap-graph for Neo4j-backed knowledge graph retrieval:

pip install ragleap-rag ragleap-graph

Add ragleap-vectorstores for pluggable vector backends:

pip install ragleap-rag ragleap-vectorstores[chroma]

機能
📄 Document ingestion Upload PDFs, text, and common document formats
🔍 RAG retrieval Vector search over your documents via pgvector
💬 Chat with citations Answers reference the source document, not a black box
🔌 Bring your own AI key OpenAI, Gemini, Anthropic, or any OpenAI-compatible endpoint
🌐 Web chat widget Embed a chat widget on any website
🐳 Docker-based setup One-command local deployment
🕸️ Knowledge Graph (Neo4j) Entity extraction and graph-boosted retrieval alongside vector search
🌍 Language detection Auto-detects document and query language, applied across every channel
🔗 Integrations Connect MySQL, PostgreSQL, MongoDB, REST APIs, Salesforce, HubSpot, Shopify, Google Sheets, Stripe
🔀 Hybrid search Combines dense (vector) and sparse (full-text) retrieval via Reciprocal Rank Fusion
⚡ Streaming responses Answers stream token-by-token instead of waiting for the full response
🔁 Provider fallback Automatically retries with a backup LLM provider if the primary fails
💰 Token usage reporting Real per-call token counts from the provider
🧑‍💼 AI Employees Role-based agents (46 default roles) with persistent business-context memory
🛠️ Build your own AI Employee Define a fully custom role via PATCH /employees/{role}
🔗 n8n workflow automation Fire a webhook after the AI replies on WhatsApp/Telegram/Discord

クイックスタート

最速の試し方 — 1つのコマンドでDockerを確認し、リポジトリをクローンして、.envをセットアップします:

curl -fsSL https://raw.githubusercontent.com/antonyrag/ragleap-core/main/install.sh | bash

(Windows users: run this in Git Bash, not Command Prompt or PowerShell.)

Or, the manual way:

git clone https://github.com/antonyrag/ragleap-core.git
cd ragleap-core
cp .env.example .env
# add your Gemini API key to .env
docker compose up --build -d

Requirements: Docker, Docker Compose, an API key from OpenAI, Google Gemini, or Anthropic.

Supported LLM Providers (BYOK)

LLM_PROVIDER value Required env vars Notes
gemini (default) GEMINI_API_KEY Get a key at aistudio.google.com/apikey
anthropic ANTHROPIC_API_KEY, ANTHROPIC_MODEL
openai OPENAI_API_KEY, OPENAI_MODEL
mistral MISTRAL_API_KEY, MISTRAL_MODEL
groq GROQ_API_KEY, GROQ_MODEL Free tier available
ollama OLLAMA_MODEL (no API key needed) Self-hosted
deepseek DEEPSEEK_API_KEY, DEEPSEEK_MODEL
custom CUSTOM_API_KEY, CUSTOM_MODEL, CUSTOM_BASE_URL Any OpenAI-compatible endpoint

Contributing

RagLeap Core is working, tested, and open for contributions now. See CONTRIBUTING.md for how to get started.

License

MIT © 2026 RagLeap