LAYRA is an enterprise-ready, out-of-the-box solution that unlocks next-generation intelligent systems powered by visual RAG and limitless visual multi-step agent workflow orchestration. It sees documents like a human, preserves layout and graphical elements, and executes arbitrarily complex workflows with full Python control.
One-Minute Overview#
LAYRA is the world's first "visual-native" AI automation engine. It sees documents like a human, preserves layout and graphical elements, and executes arbitrarily complex workflows with full Python control. From vision-driven Retrieval-Augmented Generation (RAG) to multi-step agent workflow orchestration, LAYRA empowers you to build next-generation intelligent systems—no limits, no compromises.
Core Value: Through pure visual embedding technology, it achieves lossless document understanding combined with a powerful workflow engine, providing end-to-end vision-driven automation solutions.
Quick Start#
Installation Difficulty: Medium - Requires Docker and Docker Compose, with optional GPU configuration
# Clone the repository
git clone https://github.com/liweiphys/layra.git
cd layra
# Configure environment variables
vim .env
# Build and start service
docker compose up -d --build
Is this suitable for my scenario?
- ✅ Enterprise document understanding and processing: Scenarios requiring preservation of original document layout and structure
- ✅ Complex AI workflow construction: Need for multi-step, loop-nested, and conditional branch automation
- ✅ Visual RAG applications: Processing documents containing charts, tables and other non-text elements
- ❌ Simple text Q&A: Basic applications where document layout understanding is not critical
Core Capabilities#
1. Visual-Native Multimodal Document Understanding#
- Uses ColQwen 2.5/Jina-Embeddings-v4 to transform documents into semantic vectors stored in Milvus
- Completely preserves document layout structure, table integrity, and embedded visual elements Actual Value: AI can understand documents like humans, including tables, charts, hierarchical structures, etc., providing more accurate contextual understanding
2. Powerful Workflow Engine#
- Build complex, loop-nested, and debuggable workflows with full Python execution capabilities
- Supports human-in-the-loop integration for injecting manual approvals at critical nodes Actual Value: Build fully custom AI automation workflows to handle complex business logic while maintaining human intervention capabilities
3. Advanced Debugging and Monitoring#
- Node-level breakpoint debugging to inspect variables, pause/resume execution
- Real-time streaming execution results display Actual Value: When developing complex AI workflows, visualize and debug each step to improve reliability and efficiency
Tech Stack & Integration#
Development Languages: TypeScript (frontend), Python (backend) Key Dependencies: Next.js 15, TailwindCSS 4.0, FastAPI, Redis, MySQL, MongoDB, Kafka, MinIO Integration Method: Complete platform/service
Maintenance Status#
- Development Activity: Actively developed with regular feature updates
- Recent Updates: August 2025 added embedding model support and Chinese language support
- Community Response: Provides user discussion groups and official WeChat account support
Documentation & Learning Resources#
- Documentation Quality: Comprehensive, including detailed installation guides, tutorials, and system architecture explanations
- Official Documentation: Tutorial guide on GitHub Pages
- Sample Code: Provides complete workflow examples and configuration instructions