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MaxKB

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Basic Information

MaxKB is an open-source platform for building enterprise-grade agents and knowledge-driven applications. It combines retrieval-augmented generation (RAG) pipelines, an agentic workflow engine, and multi-component tool use (MCP) to enable robust, production-ready AI workflows. The project targets scenarios such as intelligent customer service, corporate internal knowledge bases, academic research, and education. MaxKB is model-agnostic and supports both private models (examples listed in the README) and public cloud models. It provides native multi-modal support for text, images, audio and video and offers on-premise deployment options for enterprise data control. The repository includes a Docker quickstart to run the web interface and shows technical stack components like Vue.js frontend, Python/Django backend, LangChain for LLM orchestration, and PostgreSQL with pgvector for vector storage. The project is licensed under GPLv3.

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App URL
https://github.com/1Panel-dev/MaxKB
Github Repository
https://github.com/1Panel-dev/MaxKB/blob/main/README.md

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App Details

Features
MaxKB ships a RAG engine with document upload and automatic web crawling, automatic text splitting and vectorization to improve retrieval and reduce hallucinations. It includes an agentic workflow engine, a function library and MCP tool-use capabilities to orchestrate multi-step AI processes. The platform advertises model-agnostic support for a range of private and public LLMs, native multi-modal input/output for text, image, audio and video, and features for rapid zero-code integration into third-party business systems. Enterprise-oriented features noted in the README include observability, on-premise deployment, and optional SSO/access control in the Pro tier. The technical stack uses Vue.js frontend, Python/Django backend, LangChain for LLM integration, and PostgreSQL plus pgvector for vector embeddings. A Docker image and default web credentials are provided for quick startup.
Use Cases
MaxKB helps organizations build and deploy knowledge-driven agents that provide more accurate and context-aware answers by combining vector retrieval with generative models. The RAG pipeline and vectorization reduce model hallucination when answering questions against uploaded or crawled documents. The workflow engine and MCP tool-use enable automation of complex, multi-step business logic and integrations without extensive custom coding. Model-agnostic design allows teams to use private on-premise models or public APIs depending on privacy and cost constraints. Native multi-modal capabilities expand use cases beyond text. On-premise deployment, observability and enterprise access controls support governance and operational needs. A provided Docker quickstart and a web interface with default admin credentials make it straightforward to evaluate and integrate the platform into existing systems.

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