---
title: "WeKnora"
type: "tool"
slug: "tencent-weknora"
canonical_url: "https://www.graphcanon.com/tools/tencent-weknora"
github_url: "https://github.com/Tencent/WeKnora"
homepage_url: "https://weknora.weixin.qq.com"
stars: 17914
forks: 2430
primary_language: "Go"
license: "Other"
categories: ["llm-frameworks"]
tags: ["evaluation", "embeddings", "ai", "generative-ai", "agentic", "chatbot", "agent", "golang"]
updated_at: "2026-07-07T19:44:42.437661+00:00"
---

# WeKnora

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

WeKnora is an open-source large language model knowledge platform that allows users to convert raw documents into a queryable retrieval and generation (RAG) system, develop autonomous reasoning agents, and create self-updating Wiki-like systems.

## Facts

- Repository: https://github.com/Tencent/WeKnora
- Homepage: https://weknora.weixin.qq.com
- Stars: 17,914 · Forks: 2,430 · Open issues: 341 · Watchers: 89
- Primary language: Go
- License: Other
- Last pushed: 2026-07-07T16:39:36+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)

## Tags

evaluation, embeddings, ai, generative-ai, agentic, chatbot, agent, golang

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## README (excerpt)

```text
<p align="center">
  <picture>
    <img src="./docs/images/logo.png" alt="WeKnora Logo" height="120"/>
  </picture>
</p>

<p align="center">
  <picture>
    <a href="https://trendshift.io/repositories/15289" target="_blank">
      <img src="https://trendshift.io/api/badge/repositories/15289" alt="Tencent%2FWeKnora | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/>
    </a>
  </picture>
</p>
<p align="center">
    <a href="https://weknora.weixin.qq.com" target="_blank">
        <img alt="Official Website" src="https://img.shields.io/badge/Official Website-WeKnora-4e6b99">
    </a>
    <a href="https://chatbot.weixin.qq.com" target="_blank">
        <img alt="WeChat Dialog Open Platform" src="https://img.shields.io/badge/WeChat Dialog Open Platform-5ac725">
    </a>
    <a href="https://chromewebstore.google.com/detail/jpemjbopikggjlmikmclgbmkhhopjdgd" target="_blank">
        <img alt="Chrome Extension" src="https://img.shields.io/badge/Chrome Extension-WeKnora-4285F4">
    </a>
    <a href="https://clawhub.ai/lyingbug/weknora" target="_blank">
        <img alt="ClawHub Skill" src="https://img.shields.io/badge/ClawHub Skill-WeKnora-ff6b35">
    </a>
    <a href="https://github.com/Tencent/WeKnora/blob/main/LICENSE">
        <img src="https://img.shields.io/badge/License-MIT-ffffff?labelColor=d4eaf7&color=2e6cc4" alt="License">
    </a>
    <a href="./CHANGELOG.md">
        <img alt="Version" src="https://img.shields.io/badge/version-0.6.3-2e6cc4?labelColor=d4eaf7">
    </a>
</p>

<p align="center">
| <b>English</b> | <a href="./README_CN.md"><b>简体中文</b></a> | <a href="./README_JA.md"><b>日本語</b></a> | <a href="./README_KO.md"><b>한국어</b></a> |
</p>

<p align="center">
  <h4 align="center">

  [Overview](#-overview) • [Architecture](#-architecture) • [Key Features](#-key-features) • [Getting Started](#-getting-started) • [API Reference](#-api-reference) • [Developer Guide](#-developer-guide)
  
  </h4>
</p>

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

## 📌 Overview

[**WeKnora**](https://weknora.weixin.qq.com) is an open-source, LLM-powered knowledge framework built for enterprise-grade document understanding, semantic retrieval, and autonomous reasoning.

It is organized around three core capabilities: **RAG-based Quick Q&A** for everyday lookups, a **ReAct Agent** that autonomously orchestrates retrieval, MCP tools and web search to handle complex multi-step tasks, and a brand-new **Wiki Mode** in which agents distill raw documents into a self-maintaining, interlinked markdown knowledge base with an interactive knowledge graph. Combined with multi-source ingestion (Feishu / Notion / Yuque / RSS, and growing), **website embed widgets** for publishing agents to external sites, 20+ LLM provider integrations, full Langfuse observability, **enterprise-ready multi-tenant RBAC** (4-tier role matrix + per-resource ownership + per-tenant audit log), and a fully self-hostable modular architecture, WeKnora turns scattered documents into a queryable, reasoning-capable, continuously evolving knowledge asset.

The framework supports auto-syncing knowledge from Feishu, Notion, and Yuque (more data sources coming soon), handles 10+ document formats including PDF, Word, images, and Excel, and can serve Q&A directly through IM channels like WeCom, Feishu, Slack, and Telegram. It is compatible with major LLM providers including OpenAI, DeepSeek, Qwen (Alibaba Cloud), Zhipu, Hunyuan, Gemini, MiniMax, NVIDIA, and Ollama. Its fully modular design allows swapping LLMs, vector databases, and storage backends, with support for local and private cloud deployment ensuring complete data sovereignty. WeKnora also integrates with **Langfuse** for comprehensive observability into agent reasoning, token usage, and pipeline tracing.


## ✨ Latest Updates

- **v0.6.3** — Website embed widget & Integrations Center (secure-mode token exchange + rate limits); chat experience overhaul (citation popove
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/tencent-weknora`](/api/graphcanon/tools/tencent-weknora)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_