RAgent AI Platform

An enterprise AI agent platform (智能体中台) built with Next.js. It lets organizations build and operate AI agent applications on top of their own knowledge and business processes — from RAG-powered knowledge bases and intelligent Q&A to visual workflow orchestration, process management, and multi-tenant administration.

_{Screenshot from v0.4.5 — predates the Digital Employee rename and the Skills section in the sidebar.}

Core Features

• 🤖 Digital Employees — build, configure, and publish agent applications, including embeddable chatbots
• 📚 Knowledge Base Management — document upload, vector indexing, access control, RAG-powered Q&A with streaming responses
• 🔄 Workflow Orchestration — visual workflow editor for multi-step automation
• 📋 Process Management — business process trees, document review and revision flows, handbook generation
• 🧩 Skills — package instructions plus the scripts and files an agent needs, review them, and bind them to a digital employee; each user supplies their own credentials
• 🧰 Tools — MCP server registration and management. Native tools ship with the code and are authorized there, so there is nothing to bind for them
• 👥 Multi-tenant Support — organization management with a complete RBAC permission system
• 📄 Document Preview & Editing — Office file preview (kkFileView) and online editing (OnlyOffice)
• 📊 Monitoring — usage and license status dashboards
• 🌐 i18n — Simplified Chinese and English out of the box

Skills

A skill is a knowledge package: instructions (SKILL.md) plus the scripts and files needed to carry them out. The database is the single source of truth — nothing is read from the filesystem at runtime — and the UI covers authoring, review, binding, and per-user credentials.

• Draft and published are separate. What runs is always the published snapshot, so editing a live skill cannot change behaviour until it is published again.
• Executable skills run in a Docker sandbox managed by ragent-service: no network unless the skill declares it needs one, read-only filesystem, dropped capabilities, non-root user, and memory/CPU limits. The command line written in SKILL.md is the command that runs — there is no translation layer.
• Credentials belong to people, not the platform. A skill declares which environment variables it needs via a .env.example asset; each user fills in their own values, injected per execution. Values are never returned by any endpoint, including to administrators, who see only which keys are set.

⚠️ Deployment requirement. Skill execution needs the ragent-service process to be able to run docker. If the backend itself runs in a container, that container needs the Docker CLI and a mounted socket — otherwise skills can be authored, reviewed and bound, but every execution fails. Authoring works without it; running does not.

Agent prompts

A digital employee's system prompt lives in its Agent.md, edited on the employee's own page. The standalone prompt library is being retired and its navigation entry is disabled; existing prompts were copied into the corresponding Agent.md verbatim, and the original prompt_id is kept as a rollback anchor.

Architecture

This repository is the web frontend. A full deployment consists of:

Service	Role	Configured via
ragent (this repo)	Next.js web app + API routes	—
ragent-service	FastAPI backend (RAG pipeline, LLM calls, skill sandbox)	EXTERNAL_API_BASE_URL
PostgreSQL + pgvector	Data and vector storage	DATABASE_URL
OnlyOffice Document Server	Online editing, docx→PDF conversion	ONLYOFFICE_INTERNAL_URL, ONLYOFFICE_JWT_SECRET
kkFileView	File preview	KKFILEVIEW_BASE_URL
markdown-to-pdf	PDF report generation	PDF_SERVICE_URL

The auxiliary services are wired together in docker/docker-compose.yml (production) and docker/docker-compose.dev.yml (development).

Tech Stack

• Frontend: Next.js 15, React 19, Tailwind CSS, Radix UI
• Backend: Next.js API routes, PostgreSQL (pgvector)
• AI: OpenAI-compatible embeddings, multiple LLM providers via ragent-service

Quick Start

Prerequisites

• Node.js 20+ and pnpm
• PostgreSQL with the pgvector extension
• Docker (optional, for the auxiliary services)

Environment Setup

cp env.example .env
# Edit .env — see inline comments in env.example for every variable

Required variables (the app fails fast when they are missing instead of falling back to insecure defaults):

• DATABASE_URL — PostgreSQL connection string
• JWT_SECRET — session token signing key. Must be identical to JWT_SECRET_KEY in ragent-service, otherwise logins are rejected with 401
• ONLYOFFICE_JWT_SECRET — required by the OnlyOffice-related API routes; must match the JWT_SECRET passed to the OnlyOffice container

Local Development

pnpm install
pnpm dev

Tests & Checks

pnpm test        # node --test (needs Node >= 22.6 for type stripping)
pnpm check:ci    # biome lint + format check

Deployment

For detailed instructions see deploy/README.md.

# PM2 deployment
./deploy/start.sh

# Docker deployment (pulls image and starts the compose stack)
./deploy/start-docker.sh

# Pin the running image to a specific build tag (short git SHA)
./deploy/pull-tag.sh <tag>

# Roll back to a previous image
./deploy/rollback.sh

Project Structure

app/          # Next.js App Router pages and components
pages/api/    # API routes (auth, knowledge, chat, internal services)
lib/          # Shared server-side logic (db, auth, document versions…)
hooks/        # Client-side data hooks (one per resource: skills, tools, reviews…)
components/   # Shared UI components
messages/     # i18n resources (zh-CN, en)
docker/       # Compose files for the full service stack
deploy/       # Deployment scripts
scripts/      # Data import / maintenance scripts

Author

squarezw www.ragents.net