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1panel-dev/maxkb

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TLDR

Open-source platform for building AI agents with document knowledge bases, visual workflows, and support for multiple AI models, deployable in minutes via Docker.

Mindmap

mindmap
  root((MaxKB))
    What it does
      RAG pipeline
      Document upload
      AI answering
      Workflow engine
    Key features
      Multi-model support
      Tool use via MCP
      Multimodal I/O
      No-code embedding
    Tech stack
      Python Django
      Vue.js frontend
      LangChain
      PostgreSQL pgvector
    Use cases
      Customer service bots
      Knowledge bases
      Research assistants
      Educational tools
    Deployment
      Docker ready
      Self-hosted models
      Cloud model support

Things people build with this

USE CASE 1

Build a customer service chatbot that answers questions from your company's documentation and FAQs.

USE CASE 2

Create an internal knowledge base assistant that employees can query to find policies, procedures, and past decisions.

USE CASE 3

Set up a research or educational tool that lets students ask questions about uploaded textbooks, papers, or course materials.

USE CASE 4

Embed an AI agent into your existing business software without writing custom code.

Tech stack

PythonDjangoVue.jsLangChainPostgreSQLpgvectorDocker

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Docker and PostgreSQL with pgvector extension; initial setup involves pulling images and database initialization.

Use freely for any purpose under GPLv3; you must share modifications and keep the license notice, but commercial use is allowed.

In plain English

MaxKB (short for Max Knowledge Brain) is an open-source platform for building enterprise-grade AI agents. It is primarily used to create intelligent customer service bots, internal company knowledge bases, and research or educational assistants. The project is built with Python and Django on the backend, Vue.js on the frontend, LangChain as its AI framework, and PostgreSQL with the pgvector extension for storing and searching document embeddings. The platform's core capability is a Retrieval-Augmented Generation (RAG) pipeline, which lets you upload documents or automatically crawl online content, then have an AI answer questions based on that knowledge. This approach reduces the likelihood of the AI making things up, since it answers from your actual documents. MaxKB also includes a visual workflow engine for orchestrating multi-step AI processes, support for tool use via the MCP protocol, and native handling of text, images, audio, and video inputs and outputs. It works with a wide range of AI models including private self-hosted models like DeepSeek, Llama, and Qwen, as well as cloud models like OpenAI, Claude, and Gemini. Existing business systems can embed MaxKB without writing code. It can be started in minutes using a single Docker command, licensed under GPLv3.

Copy-paste prompts

Prompt 1
How do I set up MaxKB with my own documents to create a customer support chatbot?
Prompt 2
Show me how to use the visual workflow engine in MaxKB to chain multiple AI steps together.
Prompt 3
What's the difference between using a self-hosted model like Llama versus OpenAI in MaxKB, and how do I switch between them?
Prompt 4
How do I deploy MaxKB using Docker and connect it to PostgreSQL with pgvector for document search?
Prompt 5
Can I embed MaxKB into my existing web app without modifying my backend code?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.