Tencent / WeKnora

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Overview

WeKnora is an open-source, LLM-powered knowledge framework for enterprise document understanding, semantic retrieval and reasoning. It brings a team's documents together so they can be searched, reasoned over and kept up to date.

https://github.com/user-attachments/assets/5722b10d-d04d-49ed-a6cc-635a8c77d91f

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Use RAG to look things up, the agent for multi-step tasks, and the wiki to organize knowledge. All three work on the same knowledge bases.

The agent's toolbox. Skills installed from ClawHub / SkillHub / Git / ZIP run in session-persistent Docker / E2B / Cube sandboxes, with an interactive terminal and graphical desktop beside the chat. Through the BrowserSkill extension the agent operates the user's own Chrome or Edge, and external MCP services (OAuth included) can be connected and enabled tool by tool.

Beyond the three modes:

• Memory and curation: cross-session long-term memory keeps the profile, preferences and facts a user has confirmed. Folder uploads keep their directory tree, and retrieval chunks can be edited, diffed and rolled back.
• Data sources and formats: auto-sync from Feishu wiki / Feishu Drive / Confluence / GitLab / Tencent IMA / Notion / Yuque / DingTalk Docs / Seafile / RSS, with more on the way. 10+ document formats including PDF, Word, images, Excel and XMind; Office files are parsed in-process by anydoc.
• Channels and integrations: Q&A in WeCom, Feishu, Slack, Telegram and other IM apps; an embed widget for external websites; a built-in MCP Server for Cursor, Claude and other AI tools; scoped API keys with a principal model for programmatic access.
• Models: 29 built-in vendors with a generated model catalog, including OpenAI, DeepSeek, Qwen (Alibaba Cloud), Zhipu, Hunyuan, Gemini, MiniMax, NVIDIA, LiteLLM and Ollama.
• Permissions and operations: multi-workspace RBAC (four roles, per-resource ownership, per-workspace audit log), several storage instances per workspace, a runtime task-queue dashboard with worker-pool governance, and Langfuse tracing for agent steps, token usage and pipelines.
• Deployment: LLMs, vector databases and storage backends are all swappable. Deploy locally or on a private cloud and keep the data in your own environment.

Quick Start

ONLINE WeChat Dialog Open Platform Manage knowledge bases online and connect Q&A to Official Accounts, Mini Programs and other WeChat scenarios. Open the platform →

CLOUD Tencent Cloud Lighthouse Deploy WeKnora from an application template and run it on your own cloud server. Deploy on Tencent Cloud →

SELF-HOSTED Your own environment Deploy with Docker or Kubernetes and configure models, storage and networking yourself. Run with Docker Compose ↓

Run with Docker Compose

Requires Docker, Docker Compose and Git.

git clone https://github.com/Tencent/WeKnora.git
cd WeKnora
cp .env.example .env    # Edit .env as needed, see comments in the file
docker compose pull     # Pull the latest images
docker compose up -d    # Start core services

Then open http://localhost and follow the onboarding guide. A walkthrough with sample data is in the Quickstart.

[!TIP] To use a local Ollama model, run ollama serve > /dev/null 2>&1 & first. For the Ollama embedding model name, OLLAMA_BASE_URL, and RAM notes, see Configuration.

Service	URL
Web UI	http://localhost
Backend API	http://localhost:8080
Langfuse Tracing	http://localhost:3000

Optional services

Add `