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WeKnora

Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

★ 18,190 GitHub MIT ragknowledge-basedocument-processingllmenterprisewikiopen-source RAG & Knowledge

Overview

WeKnora is an open-source, LLM-powered knowledge framework built for enterprise-grade document understanding, semantic retrieval, and autonomous reasoning. It has three core capabilities: RAG-based Quick Q&A for everyday lookups, a ReAct Agent that autonomously orchestrates retrieval and multi-step tasks, and a Wiki Mode that distills documents into a self-maintaining knowledge base with an interactive graph. WeKnora supports auto-syncing from Feishu, Notion, and Yuque, handles 10+ document formats (PDF, Word, images, Excel), and serves answers through IM channels like WeCom, Slack, Telegram. It integrates with 20+ LLM providers, Langfuse observability, and features enterprise RBAC with multi-tenant support.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Affiliate disclosure

Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  weknora:
    image: wechatopenai/weknora-app:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/weknora:/data

Best VPS for WeKnora →

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Guides & articles

WeKnora Quick Start — From Zero to RAG-Powered Q&A in 15 Minutes

Step-by-step tutorial to deploy WeKnora with Docker Compose, upload your first documents, try the ReAct agent mode, and connect IM channels like Slack.

Jul 13, 2026

WeKnora — Turn Documents into Living Knowledge with RAG, Agents and Auto-Wiki

A deep dive into WeKnora, Tencent's open-source LLM knowledge platform that combines RAG Q&A, autonomous ReAct agents, and a self-maintaining wiki with an interactive knowledge graph.

Jul 13, 2026