Tencent / WeKnora
Tencent / WeKnora
WeKnora — Turn Documents into Living Knowledge with RAG, Agents and Auto-Wiki WeKnora — 通过 RAG、智能体和自动 Wiki 将文档转化为鲜活的知识
📌 Overview
📌 概述
WeKnora is an open-source, LLM-powered knowledge framework built for enterprise-grade document understanding, semantic retrieval, and autonomous reasoning. WeKnora 是一个基于大语言模型（LLM）的开源知识框架，专为企业级文档理解、语义检索和自主推理而构建。
It is organized around three core capabilities: RAG-based Quick Q&A for everyday lookups, a ReAct Agent that autonomously orchestrates retrieval, MCP tools, a tenant skill catalog, session-persistent Docker / E2B / Cube sandboxes 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, complete with manual editing, revision history and one-click rollback. 它围绕三大核心能力构建：用于日常查询的基于 RAG 的快速问答；能够自主编排检索、MCP 工具、租户技能目录、会话持久化 Docker/E2B/Cube 沙箱及网络搜索以处理复杂多步任务的 ReAct 智能体；以及全新的 Wiki 模式——智能体可将原始文档提炼为可自我维护、相互链接的 Markdown 知识库，并配有交互式知识图谱，支持手动编辑、修订历史和一键回滚。
Cross-session long-term memory remembers who you are and what you keep asking about. Knowledge curation is equally hands-on: a tree-structured folder view preserves the directory layout of uploads, and chunk editing with revision history lets retrieval chunks be edited, diffed and reverted like documents. 跨会话的长期记忆功能可以记住用户身份及持续关注的问题。知识管理同样注重实操：树状文件夹视图保留了上传时的目录结构，而带有修订历史的切片编辑功能允许像编辑文档一样对检索切片进行编辑、差异对比和回滚。
Combined with multi-source ingestion (Feishu wiki / Feishu Drive / GitLab / Tencent IMA / Notion / Yuque / DingTalk Docs / RSS, and growing), website embed widgets for publishing agents to external sites, scoped API keys with a principal model for programmatic integrations, multi-instance storage backends per workspace for flexible data placement, 20+ LLM provider integrations (including LiteLLM), full Langfuse observability plus a runtime task-queue dashboard with worker-pool governance, enterprise-ready multi-workspace RBAC (4-tier role matrix + per-resource ownership + per-workspace audit log), and a fully self-hostable modular architecture, WeKnora turns scattered documents into a queryable, reasoning-capable, continuously evolving knowledge asset. 结合多源数据摄入（飞书文档/飞书云文档/GitLab/腾讯 IMA/Notion/语雀/钉钉文档/RSS 等，且持续增加）、用于将智能体发布到外部网站的网页嵌入组件、支持程序化集成的带主体模型的范围限定 API Key、每个工作区支持多实例存储后端以实现灵活的数据部署、20 多种 LLM 提供商集成（包括 LiteLLM）、完整的 Langfuse 可观测性以及带有工作池治理的运行时任务队列仪表板、企业级多工作区 RBAC（4 层角色矩阵 + 资源所有权 + 工作区审计日志）以及完全可自托管的模块化架构，WeKnora 将零散的文档转化为可查询、具备推理能力且持续进化的知识资产。
The framework supports auto-syncing knowledge from Feishu, GitLab, Tencent IMA, Notion, and Yuque (more data sources coming soon), handles 10+ document formats including PDF, Word, images, Excel and XMind, 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, LiteLLM, and Ollama. Office files can be parsed in-process with anydoc. 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. 该框架支持从飞书、GitLab、腾讯 IMA、Notion 和语雀自动同步知识（更多数据源即将推出），处理包括 PDF、Word、图片、Excel 和 XMind 在内的 10 多种文档格式，并可通过企业微信、飞书、Slack 和 Telegram 等即时通讯渠道直接提供问答服务。它兼容主流 LLM 提供商，包括 OpenAI、DeepSeek、通义千问（阿里云）、智谱 AI、混元、Gemini、MiniMax、NVIDIA、LiteLLM 和 Ollama。Office 文件可通过 anydoc 进行进程内解析。其完全模块化的设计允许更换 LLM、向量数据库和存储后端，并支持本地和私有云部署，确保数据主权。WeKnora 还集成了 Langfuse，可对智能体推理、Token 使用量和流水线追踪进行全面观测。
✨ Latest Updates
✨ 最新更新
v0.8.0 — Skill sandbox runtime (session-persistent Docker / E2B / Cube backends with per-tenant network policy; Local host-process backend removed; Docker opt-in); tenant skill catalog (install from ClawHub / SkillHub / git / zip, per-sandbox snapshots, live progress, file browse/edit, personal and workspace env vars); cross-session long-term memory (profile / preference / fact / task / interest, auto-extract with confirm, search_memory); in-process anydoc office parser; official DeepSeek Harness plugin @wxg-prc-cpg/dsh-weknora; GitLab and Tencent IMA data sources; LiteLLM; Exa and Metaso web search; XMind parsing; chat artifacts, question outline and timestamps; context compaction and provider prompt-cache markers. Plus OIDC JWKS verification, optional complex passwords, document auto-tagging, and broad sandbox/security hardening. See CHANGELOG.md. v0.8.0 — 技能沙箱运行时（支持会话持久化的 Docker/E2B/Cube 后端，具备租户级网络策略；移除本地主机进程后端；Docker 需手动开启）；租户技能目录（支持从 ClawHub/SkillHub/git/zip 安装，支持沙箱快照、实时进度、文件浏览/编辑、个人及工作区环境变量）；跨会话长期记忆（个人资料/偏好/事实/任务/兴趣，支持自动提取确认及 search_memory）；进程内 anydoc Office 解析器；官方 DeepSeek Harness 插件 @wxg-prc-cpg/dsh-weknora；GitLab 和腾讯 IMA 数据源；LiteLLM；Exa 和秘塔搜索；XMind 解析；聊天工件、问题大纲和时间戳；上下文压缩及提供商 Prompt 缓存标记。此外还包括 OIDC JWKS 验证、可选复杂密码、文档自动打标签以及广泛的沙箱/安全加固。详见 CHANGELOG.md。