Sagi is a production-ready, open-source LLM agent framework that combines advanced technologies to create powerful AI agents. It features:
- 🧠 Deep-research workflow architecture, more details refers to the doc
- 🛠️ MCP (Model Context Protocol) integration, the guide of the usage of MCP refers to the doc
- 📊 GraphRAG-powered retrieval system, HiRAG mcp server refers to the repo
- [2025/04] 🔥 Sagi is publicly released!
- Interactive Web UI
- Advanced file chunking & indexing supports
- Improved documentation with more demos
- Chat history & workflow state management
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System Requirements
- Docker and Docker Compose (Installation Guide)
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Clone the Repository
git clone https://github.com/Kasma-Inc/Sagi.git cd Sagi git submodule update --init --recursive # Download MCP servers and Markify(MinerU)
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Environment Setup
cp .env.example .env
Configure the following in your
.envfile:OPENAI_API_KEY- Your OpenAI API keyBRAVE_API_KEY- Your Brave Search API keyOPENAI_BASE_URL- (Optional) Custom endpoint URL
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Build the Docker Container
chmod +x dev/setup_dc.sh ./dev/setup_dc.sh
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Access the Container (Choose one method)
- Option A: Use VSCode Remote Container (Recommended). You can run
bash dev/install_vscode_extensions.shto install the basic plugins for the development. - Option B: Access via terminal:
docker exec -it "$(whoami)_chatbot_python" /bin/bash
- Option A: Use VSCode Remote Container (Recommended). You can run
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Install Dependencies
- Option A: pip install
pip install -e .- Option B: uv install
uv venv source .venv/bin/activate uv pip install -e .
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Start CLI
python cli.py
We welcome contributions! Please feel free to submit a pull request. We also welcome any questions via Github Issues or via the community group, please join us.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.