crane-in-clear-sky/WorkWit-AI-Agent

English | WorkWit - Enterprise AI Office Assistant

An enterprise-oriented, privately deployable AI Agent platform. The backend is built on FastAPI + SQLite, and the frontend is a zero-dependency, vanilla-JS single-page application. The whole stack is deployed with one command via Docker Compose.

It is far more than a "contract-review gadget" - it is a unified office-agent foundation that integrates multi-model inference, extensible Skills, Tools, MCP connectors, sandboxed execution, multi-tenant access control, scheduled automation, and long-term memory. Office scenarios such as contract review and resume screening work out of the box, and new capabilities can be continuously extended through "skills + tools".

Why WorkWit

WorkWit is a self-hosted, enterprise-grade AI Agent platform built to make an organization's office work smarter - without giving up control of your data. · Fully open source and self-hosted (MIT).No subscription, no per-seat fees, no vendor lock-in.Run it on your own infrastructure;your data never leaves your servers. · Built for organizations, not just individuals.Real multi-tenant RBAC (Organization / Department / User) with a single source of truth for permission bits (FEATURE_REGISTRY). · Office scenarios out of the box.

Contract review (with Party A / Party B stance) and resume screening (job-profile matching score) ship as built-in modules - no assembly required. · Trivial to deploy and audit.One docker compose up, a single backend container, SQLite storage, and a zero-dependency frontend.No build step;easy to read, fork, and harden. · Unified capability catalog.Skills, tools, and MCP connectors are merged into one catalog and kept in sync automatically, so adding a capability is one registration, not three integrations. · Safe by default.User-supplied code runs in a restricted sandbox (AST scan + CPU/memory/process/timeout limits + privilege drop);

secrets stay in .env / data/ that are git-ignored. · Extensible by design. 40+ built-in skills, a Skills Marketplace, a Tool Library, and the MCP connector framework let you grow capabilities continuously.Core Features · General-purpose Agent core: a ReAct (plan-act) loop supporting tool calls, streaming SSE output, and automatic decomposition/execution of multi-step tasks.

The model proactively warns when it "claims a file was generated" but never actually invoked a tool (hallucination guard). · Skills Marketplace: file-based skills (SKILL.md), with 40+ built-in skills (PPT generation, Word typesetting, WeChat official-account / Xiaohongshu writing, contract/legal, research reports, map compliance, 3D/video, Tencent Docs, and more).

Supports versioning, rollback, visibility control, and install/uninstall. · Tool Library: built-in tools (web search, web fetch, charts, email, text-to-image/video, PPT/Word generation, contract risk analysis, and more) + user-defined tools (sandboxed execution) + external MCP connectors. · MCP Connector Framework: connect to standard MCP servers via data/mcp.json (supporting both streamable-http and stdio transports);

external tools are auto-synced into the unified capability catalog. · Sandboxed Execution: user-uploaded Python code runs in a restricted subprocess (AST static scanning + CPU/memory/process/timeout limits + privilege drop + module and file guards). · Multi-tenancy and RBAC: a three-tier structure of Organization / Department / User, with fine-grained feature permission bits (FEATURE_REGISTRY);admins can centrally manage models, navigation, SMTP, MCP, and more. · Multi-model Access: OpenAI-compatible interfaces, with four roles - chat / vision / embed / rerank.

Can connect directly to vLLM / Ollama, or use LiteLLM as a unified gateway (a reference litellm_config.yaml is included). · Automation: a background scheduler supports scheduled / recurring tasks. · Long-term Memory and Conversation History: user-level long-term memory and cross-session history retrieval. · Office scenarios out of the box: contract review (selectable stance), resume screening (job-profile matching score), and more.

Tech Stack

| Layer | Choice | | --- | --- | | Backend | FastAPI (async), SQLite, Uvicorn | | Frontend | Vanilla HTML/JS SPA (backend/static/index.html, zero build dependencies) | | Model Access | OpenAI-compatible API (direct or via LiteLLM gateway) | | Deployment | Docker Compose (single backend container, port 4001:8000) | | Execution Isolation | subprocess sandbox (resource limits + AST scanning) |

Directory Structure

Quick Start Prerequisites · A Linux server (Ubuntu 22.04 recommended) with Docker (including the docker compose plugin) installed. · 4 OpenAI-compatible model endpoints (address + key) for chat / vision / embed / rerank. These can be local inference services (vLLM / Ollama) or fronted by a LiteLLM gateway. Deployment Steps

After startup, open http:// :4001 in a browser. Default admin account: admin / admin123 (change it in production - entry: "System Management -> User Management"). If you want to aggregate multiple model backends with LiteLLM, you can spin up a LiteLLM container yourself referencing litellm_config.yaml and point the MODEL_*_BASE values in .env to it; docker-compose.yml currently only orchestrates the single backend service. Upgrade / Rebuild

After code changes:

Configuration (.env)

| Variable | Description | | --- | --- | | MODEL_30B_BASE / MODEL_30B_KEY | OpenAI-compatible address and key for the main inference model (chat role) | | MODEL_VLM_BASE / MODEL_VLM_KEY | Address and key for the multimodal model (vision role) | | MODEL_EMBED_BASE / MODEL_EMBED_KEY | Address and key for the embedding model (embed role) | | MODEL_RERANK_BASE / MODEL_RERANK_KEY | Address and key for the rerank model (rerank role) | On first start the 4 models are written to app.db as seed data from .env; afterwards the database is the source of truth and can be adjusted under "System Management -> Model Configuration".

Other overridable env vars (for ops tuning): DB_PATH, SKILL_CPU_SEC, SKILL_MEM_MB, SKILL_NPROC, SKILL_TIMEOUT, SKILL_ARTIFACT_ROOT, MCP_CONFIG_PATH.

Feature Modules · Contract Review: upload txt / docx / pdf, select your stance (Party A / Party B), and get structured risk points and revision suggestions (with truncated-JSON repair fallback when parsing fails). · Resume Screening: upload a resume + a job profile, and get the candidate's key fields, a match score, and a recommendation. · Agent: issue tasks in natural language;it auto-plans and invokes skills / tools to complete them.Supports sketch planning, user clarification, and sub-agent delegation. · Skills Marketplace / Tool Library: view, enable, and manage skills and tools;

supports creating user-private skills and tools (sandboxed execution). · Automation: configure scheduled / recurring tasks, executed by the background scheduler. · My Memory / Conversation History: user-level long-term memory and cross-session retrieval. · System Management: models, organizations, departments, users, permissions, logs, SMTP, MCP, navigation, and basic info.

Security Notes · Secret isolation: .env (contains model keys) and data/ (contains the runtime DB and MCP tokens) are git-ignored - do not commit them to the repo. · Sandbox: user-defined code runs in a restricted subprocess, with AST static scanning, CPU / memory / process / timeout limits, privilege drop to nobody, and module and file-access guards. · Permissions: feature-level RBAC;admins are allowed by default, regular users get least-privilege permission bits;adding a new nav/feature only requires registering one line in db.FEATURE_REGISTRY and it takes effect automatically. · Production recommendations: change the default admin password;enable HTTPS (reverse proxy);

keep sensitive config such as MCP tokens in data/mcp.json (not in the repo).

Contributing

Issues / PRs are welcome. Before submitting, please ensure: · The .gitignore rules are applied and .env / data/ are not accidentally committed; · If a new feature introduces a nav entry, register its permission bit in backend/db.py's FEATURE_REGISTRY; · New built-in skills go under backend/builtin_skills/ /SKILL.md, following the existing when_to_use semantic-field convention.

License

This project is licensed under the MIT License. For other licenses (e.g., Apache-2.0, or proprietary/closed-source), please specify separately.

Roadmap (optional directions) · Visualization and monitoring for sub-agent orchestration; · More built-in skills / tools (spreadsheets, databases, BI, and more); · Multi-instance horizontal scaling (currently single-DB; DB persistence and hot-cache extension points are already reserved); · Frontend build tooling (currently zero-de 中文版 | 企业 AI 办公智慧助手（WorkWit）

一个面向企业的、可私有化部署的 AI Agent（智能体）平台。后端基于 FastAPI + SQLite，前端是一个零依赖的原生 JS 单页应用，整体用 Docker Compose 一键部署。

它不只是一个「合同审核小工具」——而是一个把 多模型推理、可扩展技能（Skills）、工具（Tools）、MCP 连接器、沙箱执行、多租户权限、定时自动化、长期记忆 整合在一起的统一办公智能体底座。开箱内置合同审核、简历筛选等办公场景，也支持通过「技能 + 工具」不断扩展新能力。

为什么选 WorkWit

WorkWit 是一个私有化、企业级的 AI 智能体平台，目标是让组织的办公更高效，同时不交出数据的控制权。· 完全开源 + 私有化部署（MIT）。无订阅、无按席位收费、无厂商锁定。部署在你自己的服务器上，数据始终留在你自己的基础设施内。· 为组织而生，而非仅面向个人。真正的多租户 RBAC（组织 / 部门 / 用户），权限位集中登记于 FEATURE_REGISTRY 单一真相源。· 办公能力开箱即用。合同审核（支持甲方 / 乙方立场）与简历筛选（岗位画像匹配评分）作为内置模块随平台提供，无需自己拼装。· 部署与审计极简。一条 docker compose up、单个后端容器、SQLite 存储、零依赖前端 —— 无构建步骤，易于阅读、fork 与加固。· 统一能力目录。技能、工具与 MCP 连接器合并为一张目录并自动同步，新增一项能力只需一次登记，而非三次集成。· 默认安全。用户代码在受限沙箱中执行（静态扫描 + 资源限额 + 降权），密钥留在 .env / data/（已被 .gitignore 忽略）。· 为扩展而生。40+ 内置技能 + 技能广场 + 工具库 + MCP 连接器框架，让能力可持续生长。✨ 核心特性 · 通用 Agent 内核：ReAct（规划—执行）循环，支持工具调用、流式 SSE 输出、多步任务自动拆解与执行；模型「声称已生成文件」但实际未调用工具时会主动提示（防幻觉）。

· 技能广场（Skills）：文件化的技能（SKILL.md），内置 40+ 技能（PPT 生成、Word 排版、公众号/小红书写作、合同/法务、研报、地图合规、3D/视频、文档等）；支持版本管理、回滚、可见性控制、安装/卸载。· 工具库（Tools）：内置工具（联网搜索、网页抓取、图表、邮件、文生图/视频、PPT/Word 生成、合同风险分析等）+ 用户自定义工具（沙箱执行）+ 外部 MCP 连接器。· MCP 连接器框架：通过 data/mcp.json 接入标准 MCP 服务（支持 streamable-http 与 stdio 两种传输），外部工具自动同步进统一能力目录。· 沙箱执行：用户上传的 Python 代码在受限子进程中运行（AST 静态扫描 + CPU/内存/进程/超时限额 + 降权 + 模块与文件护栏）。· 多租户 & RBAC：组织 / 部门 / 用户三级结构，细粒度功能权限位（FEATURE_REGISTRY），管理员可集中管控模型、导航、SMTP、MCP 等。· 多模型接入：面向 OpenAI 兼容接口，区分 chat / vision / embed / rerank 四种角色；可直连 vLLM / Ollama，或用 LiteLLM 做统一网关（仓库附 litellm_config.yaml 参考配置）。· 自动化（Automations）：后台调度器支持定时 / 周期性任务。· 长期记忆 & 历史对话：用户级长期记忆、跨会话历史检索。· 办公场景开箱即用：合同审核（可选立场）、简历筛选（岗位画像匹配评分）等。

🧱 技术栈

| 层 | 选型 | | --- | --- | | 后端 | FastAPI（异步）、SQLite、Uvicorn | | 前端 | 原生 HTML/JS 单页（backend/static/index.html，零构建依赖） | | 模型接入 | OpenAI 兼容 API（直连或经 LiteLLM 网关） | | 部署 | Docker Compose（单后端容器，端口 4001:8000） | | 执行隔离 | subprocess 沙箱（资源限额 + AST 扫描） |

📂 目录结构

🚀 快速开始 前置条件 · 一台 Linux 服务器（推荐 Ubuntu 22.04）并已安装 Docker（含 docker compose 插件）。 · 4 个 OpenAI 兼容模型端点的 地址与密钥（chat / vision / embed / rerank）。可以是本地推理服务（vLLM / Ollama），也可以前置 LiteLLM 网关。 部署步骤

启动后浏览器访问 http://服务器IP:4001。默认管理员账号：admin / admin123（生产环境务必修改，入口在「系统管理 → 用户管理」）。 若想用 LiteLLM 统一聚合多个模型后端，可参考 litellm_config.yaml 自行起一个 LiteLLM 容器，并把 .env 里的 MODEL_*_BASE 指向它；docker-compose.yml 当前只编排 backend 一个服务。 升级 / 重建 代码改动后：

⚙️ 配置说明（.env）

| 变量 | 说明 | | --- | --- | | MODEL_30B_BASE / MODEL_30B_KEY | 主推理模型（chat 角色）的 OpenAI 兼容地址与密钥 | | MODEL_VLM_BASE / MODEL_VLM_KEY | 多模态模型（vision 角色）地址与密钥 | | MODEL_EMBED_BASE / MODEL_EMBED_KEY | 嵌入模型（embed 角色）地址与密钥 | | MODEL_RERANK_BASE / MODEL_RERANK_KEY | 重排序模型（rerank 角色）地址与密钥 | 首次启动会按 .env 把 4 个模型写入 app.db 作为种子；之后以数据库为准，可在「系统管理 → 模型配置」里调整。

其他可覆盖的环境变量（运维调参）：DB_PATH、SKILL_CPU_SEC、SKILL_MEM_MB、SKILL_NPROC、SKILL_TIMEOUT、SKILL_ARTIFACT_ROOT、MCP_CONFIG_PATH。

🧩 功能模块 · 合同审核：上传 txt / docx / pdf，选择我方立场（甲方 / 乙方），输出结构化的风险点与修改建议（JSON 解析失败时具备截断修复兜底）。 · 简历筛选：上传简历 + 岗位画像，输出候选人关键字段、匹配评分与推荐结论。 · 智能体（Agent）：自然语言下达任务，自动规划并调用技能 / 工具完成；支持草图规划、用户澄清、子 Agent 委派。 · 技能广场 / 工具库：查看、启用、管理技能与工具；支持创建用户私有技能与工具（沙箱执行）。 · 自动化：配置定时 / 周期任务，由后台调度器执行。 · 我的记忆 / 历史对话：用户级长期记忆与跨会话检索。 · 系统管理：模型、组织、部门、用户、权限、日志、SMTP、MCP、导航、基础信息。

🔒 安全说明 · 密钥隔离：.env（含模型密钥）与 data/（含运行库、MCP 令牌）已被 .gitignore 忽略，请勿提交到仓库。 · 沙箱：

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