What it does
Core capabilities at a glance
- Agent
- Agentic
- Chatbot
- DSH Plugin
- Embeddings
- Evaluation
- Generative AI
- Golang
Deep dive
The full breakdown - performance, comparisons, and setup
WeKnora
WeKnora is a vector database - Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
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
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.
WeKnora is open-source, written primarily in Go, with 33,278 GitHub stars under the Other license. The latest release is v0.8.2 (2026-09-24).
Key capabilities
From the project's documentation:
- Parsing and sources: in-process anydoc office parser; GitLab and Tencent IMA data sources; XMind parsing.
- Ecosystem: official DeepSeek Harness plugin @wxg-prc-cpg/dsh-weknora; LiteLLM; Exa and Metaso web search.
- Chat: chat artifacts, question outline and timestamps; context compaction and provider prompt-cache markers.
- Frontend modifications auto hot-reload (no restart needed)
- Backend modifications quick restart (5-10 seconds, supports Air hot-reload)
- No need to rebuild Docker images
How it fits a local-AI stack
WeKnora runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related vector databases in the directory:
Sources
- Source code & docs: Tencent/WeKnora
- Official website: https://weknora.weixin.qq.com
Stats from GitHub, 2026-10-12.
Frequently asked
Quick answers to common questions
What is WeKnora?
WeKnora is a vector-db tool for local AI workloads. Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
Is WeKnora free and open source?
Yes, WeKnora has 33,278 GitHub stars and is licensed under Other. You can self-host it for free on web.
What platforms does WeKnora support?
WeKnora runs on web.
What hardware do I need for WeKnora?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. WeKnora has 33,278 GitHub stars and an active community.
Does WeKnora support GPU acceleration?
WeKnora's GPU support depends on your specific setup. Check the documentation for details. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to WeKnora?
Popular alternatives include other vector-db tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does WeKnora cost?
WeKnora is free-open-source. It is completely free and open source to self-host.
Pairs well with
Complementary tools, models, and hardware