Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.
Does WeKnora need an API key?
No required environment variable is listed on this record.
What transport does WeKnora use?
WeKnora runs locally over stdio.
When was WeKnora last checked?
Last checked 2026-11-10. English · 简体中文 · 日本語 · 한국어 Overview · Quick Start · What's New · Features · Clients · Docs · Development
From the publisher
Documentation
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.
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.
Key features
01. Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki
02. English · 简体中文 · 日本語 · 한국어 Overview · Quick Start · What's New · Features · Clients · Docs · Development
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.
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 --profile flags to enable additional components; multiple profiles can be combined.
If you already have WeKnora running and downloaded a newer release:
# Set WEKNORA_VERSION in .env to the target release (e.g. 0.9.0), 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 upgrading. Deployments older than v0.8.2 should also read the v0.8.2 notes.
Other ways to deploy
Option
When to use it
Docker Compose
The standard deployment above: all features, multiple services
WeKnora assistant: Ask about WeKnora releases, features and configuration, then work from the current page: create agents, schedule tasks and manage knowledge, acting as you within your permissions.
Multi-agent collaboration: Delegate independent subtasks, follow progress and bring the results together.
Automations: Run one-off or recurring tasks with execution history, results and approval notifications.
Execution permissions: Read-only, confirm-before-change and automatic-write modes, with scoped session and automation grants.
Sandboxes and background work: Optional per-user sandbox reuse, managed background shell jobs and skill rollout controls.
Parsing, sync and retrieval: Configurable image pipelines, OCR preprocessing, deeper Feishu/Lark sync, Confluence PAT and retrieval improvements.
Upgrade notes: Review database migrations, Feishu resync, the sandbox allocation gate and configuration changes.
Image gallery: browse document images and filter by source, caption, OCR and image attributes.
Multimodal Embedding: support for multimodal embedding models that encode text and images into a shared vector space, including Tencent WeMM-Embedding and Qwen3-VL-Embedding, with opt-in image-vector retrieval and compatible image-aware reranking.
Wiki graph: clearer group layouts and theme captions, with group focus, switching and return to the full graph.
Source citations: navigate from an answer to the cited passage in the original document.
Chat and tools: on-demand attachment reading without a sandbox, unread replies, @ mention search, Docker terminals, Lite host skills and MCP interoperability updates.
Administration and integrations: platform user management, invitation-only signup mode, administrator model catalog, WeMM embeddings, TEI/Pinecone/Cohere reranking, Seafile and Traditional Chinese.
Reliability: broad retrieval, parsing, datasource, Wiki, OAuth and CLI fixes. Review upgrade requirements for registration modes, empty tool lists, database migrations and reindexing.
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.
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.
v0.6.0 — Workspace RBAC (4-tier role matrix Owner / Admin / Contributor / Viewer + per-KB ownership + per-workspace audit log), workspace member management & multi-workspace UX, self-service workspaces; weknora CLI v0.4 GA with mcp serve; KB retrieval fan-out across vector stores; AES-256-GCM credential encryption + docreader gRPC TLS + Token; Zhipu embedder + Huawei OBS; server-side user preferences; Go 1.26.0. See Tenants & auth.
v0.5.2 — Wiki ingest scales to 40k-document KBs (task queue + DLQ); MCP human-in-the-loop tool approval; Anthropic / Apache Doris / Tencent VectorDB / KS3 / SearXNG backends; adaptive 3-tier chunking with live preview; global ⌘K command palette; Yuque connector + WeChat Mini Program; weknora CLI preview.
v0.5.1 — Knowledge-base batch management; workspace-wide IM channels overview; session search + user-scoped pinning; unified Model / Web Search / MCP settings cards; per-agent LLM timeout; desktop workspace switching.
v0.5.0 — Wiki Mode GA — agents auto-generate structured, interlinked Markdown wiki pages with a knowledge graph; wiki browser + visual graph in the UI.
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.
Quick Q&A answers from knowledge bases with citations; smart reasoning runs a ReAct agent over knowledge bases, web search, MCP tools, skills and the local browser. Steer, fork or rewind a running conversation, and keep long-term memory across sessions
Workspace RBAC with four roles and an audit log; scoped API keys; OIDC; AES-256-GCM credential encryption; SSRF-safe outbound requests with a whitelist-only mode
Langfuse tracing for agent steps, tokens and pipelines; document parsing timeline; task-queue dashboard with worker pools; automatic migrations on upgrade
Docker Compose / Kubernetes (Helm) / Lite single binary / desktop app; offline and private-cloud installs; UI in Chinese, English, Japanese, Korean and Russian
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.
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.
Standards: Format code with gofmt, follow Conventional Commits (feat: / fix: / docs: / test: / refactor:)
For a focused PR, validate the changed scope first:
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
Run gofmt on changed Go files before committing. For frontend changes, run the relevant tests from frontend/ and use npm run type-check when the change affects TypeScript or Vue components.
The full maintainer gate remains:
make fmt
make lint
make test
make fmt formats the entire Go repository, so run it only with a clean worktree and review the resulting diff. Some full-suite tests require local infrastructure or service configuration. If a full check fails for an unrelated baseline or environment reason, include the exact command and failure in the PR while still providing passing targeted tests for your change.
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.
