WeKnora

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Overview · Quick Start · What's New · Features · Clients · Docs · Development

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 / 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: 27 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.

Optional services

Add --profile flags to enable additional components; multiple profiles can be combined.

docker compose --profile neo4j --profile minio pull
docker compose --profile neo4j --profile minio up -d
docker compose down     # Stop services

Upgrading

If you already have WeKnora running and downloaded a newer release:

# Set WEKNORA_VERSION in .env to the target release (e.g. 0.8.2), or keep latest
docker compose pull     # Pull images matching WEKNORA_VERSION
docker compose up -d    # Recreate containers with new images

[!NOTE] docker compose up -d alone reuses locally cached images and may leave the UI version out of sync with the release you downloaded. Read the upgrade notes before moving from v0.8.0.

Other ways to deploy

All options, hardware requirements and deployment topologies: Installation guide.

[!WARNING] WeKnora ships with login authentication, but for production deployments we strongly recommend that you:

• deploy it in an internal / private network rather than on the public internet;
• avoid exposing the service directly to public networks, to prevent information leakage;
• configure proper firewall rules and access controls for the deployment environment;
• regularly update to the latest version for security patches and improvements.

What's New

v0.8.2 _{· 2026-09-24 · release notes}

Agents can operate the browser on your computer, knowledge bases can be published to other AI tools over MCP, and a running conversation can be steered, forked or rewound.

• Local Browser (BrowserSkill): agents drive the user's own Chrome / Edge through the open-source BrowserSkill extension, with a live task preview, pause / resume and hand-off for logins and CAPTCHAs.
• Built-in MCP Server: per-workspace /mcp/<endpoint_id> endpoints over Streamable HTTP, each with its own token, knowledge-base scope, rate limit and tool groups. The Python mcp-server/ is deprecated.
• Conversation control: append requirements to a running turn, fork from any earlier question, rewind in place with sandbox checkpoints, and pick reasoning effort per session. Generated files are collected in a new artifacts library.
• Sandbox: interactive terminal and graphical desktop; macOS Lite host sandbox with project folders; a sidebar Toolbox for skills, MCP services and the browser connection.
• Models: rebuilt model catalog (27 built-in vendors with generated context-window, max-output, reasoning and vision metadata). Agent retrieval tools are consolidated into search_knowledge / read_document / list_documents.
• Knowledge and platform: Confluence and DingTalk Docs data sources; Bocha and Serply web search; Japanese UI; per-channel IM reply language; whitelist-only outbound mode.

[!IMPORTANT] Breaking: DingTalk channels are Stream-only, and sandbox commands run as root. See the upgrade notes.

v0.8.0 _{· release notes}

• Skill sandbox runtime: session-persistent Docker / E2B / Cube backends with per-tenant network policy; the Local host-process backend is removed and Docker is opt-in.
• Tenant skill catalog: install from ClawHub / SkillHub / git / zip, with per-sandbox snapshots, live progress, file browse/edit, and personal and workspace env vars.
• Cross-session long-term memory: profile / preference / fact / task / interest, auto-extracted with user confirmation, plus search_memory.
• 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.
• Security: OIDC JWKS verification, optional complex passwords, document auto-tagging, and broad sandbox/security hardening.

Product Tour

Quick Q&A and smart reasoning

Two ways to ask. Quick Q&A answers from the knowledge base with RAG and cites the sources it used. In smart reasoning the agent plans multi-step work, searching, reading documents and calling tools and skills, and shows each step in the conversation. Docs →

Local browser

Operate the browser on your computer. Through Tencent's open-source BrowserSkill extension, the agent opens pages and fills in forms in your own Chrome or Edge, and hands over to you for logins and CAPTCHAs. Docs →

Skills and sandbox

Run skills and produce files. Docker, E2B and Cube backends are supported. Turns in the same session share one workspace, and generated files can be previewed and downloaded. Open the graphical desktop or interactive terminal beside the chat to follow each step and take over when needed. Docs →

Toolbox: MCP services and skills

Tools the agent can use. Connect external MCP services and choose, tool by tool, which are enabled and which calls need approval. Install skills from ClawHub, SkillHub, Git or ZIP, manage them per workspace, and reuse them across sandboxes. Docs →

Automatic wiki

Documents organized into a browsable wiki. With Wiki enabled, WeKnora extracts people, products and concepts from knowledge-base documents into pages with source citations, organized by directory. The knowledge graph shows how pages relate; pages can be edited directly and every change can be rolled back. Docs →

Observability

Tracing and runtime monitoring. Langfuse traces the reasoning, tool calls and token usage of each agent step. The document parsing timeline shows progress stage by stage, and the task-queue dashboard lists queued and failed tasks. Docs →

Architecture

A modular pipeline from document parsing, vectorization and retrieval to LLM inference, in which every component can be replaced or extended. It runs locally or on a private cloud, and the Web UI needs no setup to get started. More: Architecture overview · RAG pipeline · Extension points.

Features

Supported backends

Clients and Integrations

	Client	What it does
	CLI weknora	Agent-first command line for the full API, with a curated MCP tool surface and bundled Agent Skills
	Built-in MCP Server	Publishes knowledge bases to Cursor, Claude and other MCP clients over Streamable HTTP; the Python mcp-server/ is deprecated
	Local Browser (BrowserSkill)	Lets agents operate the user's own Chrome / Edge
	Chrome Extension	Select text, images, or entire pages in the browser and save them as knowledge entries with one click, without copy-paste or file upload
	WeChat Mini Program	Lightweight mobile client: configure API access, select knowledge bases, import URLs, and ask knowledge chat from WeChat
	ClawHub Skill	A WeKnora skill on ClawHub for document import, hybrid search and knowledge management via the REST API
	DeepSeek Harness plugin	Gives dsh coding agents four read-only tools: search, read document, ask and list knowledge bases
	Website Embed Widget	Publishes agents on external sites
	Go SDK	CRUD for knowledge bases, documents and sessions, plus SSE streaming Q&A
	WeChat Dialog Open Platform	Hosted Q&A built on WeKnora: upload knowledge and publish a Q&A service in WeChat without writing code

Command-Line Interface

weknora is the official CLI for driving the API from a terminal or an AI agent. It is agent-first: every command emits a stable JSON envelope by default (with typed error codes mapped to exit codes), and --format text renders for humans. It also serves a curated MCP tool surface (weknora mcp serve) and ships bundled Agent Skills.

weknora profile add prod --host https://kb.example.com --use
weknora auth login
weknora kb list
weknora link --kb my-knowledge-base    # bind the current directory
weknora doc upload notes.md
weknora chat "summarise the design doc"

For headless / CI use, set WEKNORA_API_KEY + WEKNORA_HOST and skip auth login; no credentials are written to disk. See cli/README.md for install + 5-minute quickstart and cli/AGENTS.md for the operational contract AI agents rely on.

Documentation

The full product documentation lives at weknora.weixin.qq.com/docs (in Chinese), organized as Getting Started → Architecture → Features → API → Clients → Development and covering ~360 API endpoints and ~150 environment variables.

Development

If you need to frequently modify code, you don't need to rebuild Docker images every time. Use fast development mode:

make dev-start      # Start infrastructure
make dev-app        # Start backend (new terminal)
make dev-frontend   # Start frontend (new terminal)

• 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
• Supports IDE breakpoint debugging

See the Development guide for details.

The documentation site and product homepage are built from website-docs/. With Node.js 24, run cd website-docs && npm run setup && npm run build && npm run preview to preview both together; the unified static output serves the homepage at / and documentation at /docs/. See the directory's README for Nginx and Docker deployment.

Contributing

Welcome to submit Issues or Pull Requests.

• Process: Fork → Create branch → Commit changes → Open PR
• Standards: Format code with gofmt, follow Conventional Commits (feat: / fix: / docs: / test: / refactor:)

git fetch origin main
git diff --check origin/main...HEAD
golangci-lint run --new-from-rev=origin/main ./...
go test ./path/to/changed/package -count=1

make fmt
make lint
make test

Contributors

Thanks to everyone who has contributed:

License

This project is licensed under the MIT License. You are free to use, modify, and distribute the code with proper attribution.