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MaxKB

Open-source enterprise agent platform: RAG pipelines (upload or crawl docs), a visual workflow engine with MCP tool-use, and zero-code embedding into existing business systems.

22,109 3,005 Python GPL-3.0updated today

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Curator's take

The self-hosted answer to 'we need an internal AI assistant this quarter': upload or crawl your docs, get a RAG-grounded Q&A agent with a real workflow engine and MCP tool-use, then embed it into existing systems without code. Model-agnostic including fully private deployments. Battle-tested at 22k stars, mostly in enterprise support/knowledge-base roles. NOT a developer framework — you orchestrate in its UI, not your codebase (LangGraph territory); GPL-3.0 matters if you redistribute; and the project's center of gravity is the Chinese enterprise ecosystem — English docs and community trail the code.

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README.md

MaxKB

Open-source platform for building enterprise-grade agents

强大易用的企业级智能体平台

[中文(简体)] | [English]

MaxKB = Max Knowledge Brain, it is an open-source platform for building enterprise-grade agents. MaxKB integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities. MaxKB is widely applied in scenarios such as intelligent customer service, corporate internal knowledge bases, academic research, and education.

RAG Pipeline: Supports direct uploading of documents / automatic crawling of online documents, with features for automatic text splitting, vectorization. This effectively reduces hallucinations in large models, providing a superior smart Q&A interaction experience.
Agentic Workflow: Equipped with a powerful workflow engine, function library and MCP tool-use, enabling the orchestration of AI processes to meet the needs of complex business scenarios.
Seamless Integration: Facilitates zero-coding rapid integration into third-party business systems, quickly equipping existing systems with intelligent Q&A capabilities to enhance user satisfaction.
Model-Agnostic: Supports various large models, including private models (such as DeepSeek, Llama, Qwen, etc.) and public models (like OpenAI, Claude, Gemini, MiniMax, etc.).
Multi Modal: Native support for input and output text, image, audio and video.

Quick start

Execute the script below to start a MaxKB container using Docker:

docker run -d --name=maxkb --restart=always -p 8080:8080 -v ~/.maxkb:/opt/maxkb 1panel/maxkb

Access MaxKB web interface at http://your_server_ip:8080 with default admin credentials:

username: admin
password: MaxKB@123..

中国用户如遇到 Docker 镜像 Pull 失败问题，请参照该离线安装文档进行安装。

Screenshots

Technical stack

Frontend：Vue.js
Backend：Python / Django
LLM Framework：[LangChain](https:

Continue your stack

What teams reach for next — and why each earns a place beside MaxKB. Ranked by curator confidence.

Pairs well with

Ollama

Run Llama, Mistral and other open models locally with a single command and a clean API.

Why it fitsMaxKB's private-model story runs through Ollama — point the platform at a local DeepSeek/Llama/Qwen and the whole RAG + agent stack stays on-prem.

Alternative to See all + compare →

supavec

Open-source RAG-as-a-service (the Carbon.ai alternative): upload any data source, get vector search and a chat API in minutes — Supabase-based, multi-tenant with RLS, streaming responses.

Why it fitsBoth are open platforms for shipping RAG: MaxKB is the batteries-included enterprise product — visual workflows, UI, zero-code embedding into business systems; Supavec is the developer primitive — a clean REST API for ingestion and chat you build your own product on.

bytechef

Open-source platform unifying AI agent orchestration with classic workflow automation — visual builder, 200+ integration components, self-hosted via Docker. Apache 2.0 + EE split.

Why it fitsBoth are self-hosted no-code enterprise agent platforms with different centers of gravity: MaxKB starts from the knowledge base and RAG, ByteChef from integrations and workflow automation.