This project provides a unified, multi-tenant workbench for building and querying enterprise knowledge bases using AI agents. It integrates RAG retrieval, Milvus knowledge graphs, and LangGraph multi-agent orchestration, accessible via a conversational web interface.
Project overview
This project provides a unified, multi-tenant workbench for building and querying enterprise knowledge bases using AI agents. It integrates RAG retrieval, Milvus knowledge graphs, and LangGraph multi-agent orchestration, accessible via a conversational web interface.
Project type
AI Agent · RAG · Workflow
Use cases
Knowledge Q&A
Deployment
Refer to project documentation
License
MIT
Best for
Enterprise teams needing a self-hosted, multi-tenant platform to deploy configurable AI agents with integrated RAG and knowledge graph reasoning.
Developers looking to orchestrate multi-agent workflows using LangGraph and interact with enterprise knowledge bases via a conversational interface.
Key capabilities
Provides a unified platform where administrators can configure knowledge bases, models, and permissions for different tenants.
Uses Retrieval-Augmented Generation to allow users to search and retrieve information from enterprise knowledge bases.
Uses LangGraph to coordinate multiple intelligent agents for complex task execution and reasoning.
Users interact with agents via a conversational interface, receiving answers with cited sources and knowledge graph reasoning.
Supports document parsing using MinerU, PaddleX, and RapidOCR for ingesting knowledge base content.
Agents can be configured with attachable Skills, MCP (Model Context Protocol), sub-agents, and sandbox tools.
Limitations and risks
The platform requires a paid external service in the form of an OpenAI-compatible LLM API to function, meaning it cannot operate fully offline without external dependencies.
GPU requirements, minimum hardware specifications, and telemetry practices are not documented.
Getting started
Ensure Docker and Docker Compose are installed. Clone the repository, run the initialization script, execute 'docker compose up --build', and open http://localhost:5173 in a browser.
Prepare at least one OpenAI-compatible LLM API to enable language model capabilities for the agents.
Alternatives and comparisons
Offers an out-of-the-box platform for data processing, RAG retrieval, and visual AI workflow orchestration for building AI applications.
Provides a platform to build enterprise-grade AI agents with RAG capabilities and zero-coding integration into third-party business systems.
A full-stack agent development platform that simplifies enterprise scenarios through low-code visual orchestration, built-in RAG, and ontology agents.