refactor: split backend into standalone server project
Remove embedded frontend and workflow editor assets, add standalone API deployment configuration, and retain the current backend service updates.
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@@ -1,278 +1,42 @@
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# AgentDesk
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# Agent Desk Server
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English | [简体中文](README_ZH.md)
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Customer-service API server built with Go, Gin, GORM, and `github.com/mlogclub/simple`.
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An open-source AI Agent customer support system with knowledge-based answers, human handoff, ticket workflows, and self-hosted deployment.
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The frontend is an independent sibling project, normally located at `../agent-desk-web`.
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> Built for teams that need online support, knowledge-base Q&A, human collaboration, and service tracking in one system. It is not just an LLM inside a chat box; it is an AI Helpdesk foundation designed around real support operations.
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## Product Preview
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Customer chat, agent workspace, knowledge base, model configuration, and AI Agent orchestration are managed in one system.
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### Customer Chat
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Customers can start a conversation from the web chat page. The AI Agent responds first with knowledge-grounded answers. When the user explicitly asks for a human, the system can start a handoff confirmation flow.
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### Agent Workspace
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The support workspace includes conversation lists, message handling, AI-to-human handoff, agent replies, conversation tags, linked customers, and ticket context for daily support work.
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### Knowledge Base and AI Agent Configuration
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| Knowledge Base FAQ | AI Agent Configuration |
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| --- | --- |
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|  |  |
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The knowledge base stores FAQs, documents, and retrievable content. AI Agents can be bound to model configurations, knowledge bases, Skills, and tools to create support agents for specific scenarios.
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### Model Configuration
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Model configuration supports OpenAI-compatible providers. You can configure LLMs, embedding models, rerank models, context limits, output settings, timeout, retry behavior, and enablement state.
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## Why Use It
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- **AI-first support**: Let AI Agents handle common questions, standard procedures, and knowledge-base answers first.
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- **Knowledge-constrained replies**: Use RAG and the Answerability Gate to decide whether retrieved knowledge is strong enough to answer, reducing unsupported responses.
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- **Natural human handoff**: Move to human agents when knowledge is insufficient, the user asks for help, or a workflow requires human confirmation.
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- **Conversation-to-ticket loop**: Online chat, support handling, ticket creation, status flow, and progress records stay in one system.
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- **Built for extension**: The backend uses Go, the frontend uses Next.js, and the runtime supports Skills, MCP, and OpenAI-compatible model access.
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- **Self-host friendly**: Supports SQLite / MySQL and Qdrant for local trials, intranet deployment, and enterprise self-hosting.
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## Core Capabilities
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- **AI Agent support**: AI replies first, with fallback, confirmation, tool calling, and human collaboration.
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- **Online conversation system**: Visitor sessions, message send/receive, unread status, assignment, transfer, and close flows.
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- **Agent workspace**: Agents can take over conversations, reply to users, transfer teammates, link customers, and create tickets.
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- **Knowledge-base RAG**: Knowledge bases, documents, FAQs, chunking, vector retrieval, retrieval logs, and quality analysis.
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- **Answerability Gate**: Checks whether retrieved content can support an answer; otherwise returns a fallback and recommends human support.
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- **Ticket system**: Create tickets from conversations, categorize, assign, move through status flows, record progress, and close the loop.
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- **Support organization management**: Agent profiles, teams, schedules, and automatic assignment.
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- **AI extensibility**: Skills, MCP debugging, and external tool integration.
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- **Multiple entry points**: Admin dashboard, agent workspace, customer-facing web pages, and embeddable SDK.
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## Use Cases
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- Website live support
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- SaaS product support
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- AI + human hybrid support
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- Internal enterprise service desk
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- After-sales service, incident reporting, complaints, and operations support
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- Support teams that need knowledge-base Q&A with human collaboration
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## Quick Start
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The fastest way to try the full stack is Docker Compose:
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```bash
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docker compose up -d --build
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```
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For the full English setup guide, see [Docker Compose Quick Start](https://agent-desk.huabei.pro/docs/getting-started/docker-compose.html).
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To embed customer support on your website, see [Web Widget Integration](https://agent-desk.huabei.pro/docs/integration/web-widget.html).
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To connect OpenAI-compatible model providers, see [Model Provider Configuration](https://agent-desk.huabei.pro/docs/config/model-provider.html).
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Compose starts:
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- `agent-desk`: application service on port `8083`
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- `mysql`: MySQL 8.4 with the `mysql-data` volume
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- `qdrant`: vector database with the `qdrant-data` volume, ports `6333` / `6334`
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After startup, open:
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- Admin dashboard: `http://localhost:8083/dashboard`
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- Agent workspace: `http://localhost:8083/dashboard/conversations`
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- Customer web integration demo: `http://localhost:8083/support/demo`
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- Customer chat page: `http://localhost:8083/support/chat`
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Default administrator account:
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- Username: `admin`
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- Password: `ChangeMe123!`
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> Before exposing the system to the public internet or a team environment, change the default administrator password and configure independent authentication, session, and model secrets.
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## Local Development
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### Requirements
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- Go `1.26+`
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- Node.js `20+`
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- `pnpm`
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- Qdrant
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### Prepare Configuration
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## Development
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```bash
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cp config/config.example.yaml config/config.yaml
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go mod download
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make dev
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```
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The default configuration uses:
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- SQLite: `data/app.db`
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- Backend: `http://127.0.0.1:8083`
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- Qdrant gRPC: `127.0.0.1:6334`
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If Qdrant is not running locally, start it with Docker:
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```bash
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docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
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```
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Install frontend dependencies:
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```bash
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cd web
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pnpm install
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cd ..
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```
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Start backend and frontend development servers together:
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```bash
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task dev
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```
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Default development URLs:
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- Admin dashboard: `http://localhost:3000/dashboard`
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- Agent workspace: `http://localhost:3000/dashboard/conversations`
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- Customer web integration demo: `http://localhost:3000/support/demo`
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- Customer chat page: `http://localhost:3000/support/chat`
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## Tech Stack
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- Backend: Golang + Gin + GORM + `github.com/mlogclub/simple`
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- Frontend: Next.js 16 + React 19 + shadcn/ui + Tailwind CSS
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- Database: SQLite / MySQL
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- Vector DB: Qdrant
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- AI: OpenAI-compatible LLM / Embedding + RAG + Skills + MCP
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## Project Structure
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The API listens on `http://127.0.0.1:8083` by default. Health check:
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```text
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.
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├── cmd/ # server / migration / generator / testdata
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├── internal/
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│ ├── bootstrap/ # startup, routes, database, and migration initialization
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│ ├── builders/ # model / aggregate result to response DTO mapping
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│ ├── handlers/ # dashboard / api / third HTTP handlers
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│ ├── middleware/ # Gin middleware
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│ ├── migration/ # idempotent data migrations
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│ ├── models/ # GORM models
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│ ├── repositories/ # data access layer
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│ ├── services/ # business orchestration and transaction boundaries
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│ ├── ai/ # LLM / RAG / Runtime / Skills / MCP
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│ └── pkg/ # config / dto / enums / httpx / utils and shared packages
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├── web/ # Next.js frontend project
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│ ├── app/dashboard/ # admin dashboard and agent workspace
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│ ├── app/support/ # customer integration and chat pages
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│ ├── components/ # React components
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│ ├── lib/ # API client, SDK source, and utilities
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│ └── public/sdk/ # built embeddable SDK
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├── config/ # configuration files
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├── docker/ # Docker configuration
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└── docs/ # documentation site
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GET http://127.0.0.1:8083/api/health
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```
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## Common Commands
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Set `server.frontendUrl` and `server.cors.allowedOrigins` in `config/config.yaml` to the frontend origin. Local defaults use `http://127.0.0.1:3000`.
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## Commands
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```bash
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task dev # start backend and frontend development servers
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task build # build the frontend SPA and current-platform Go binary into dist/
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task build:lancedb # build the current-platform LanceDB binary into dist/
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task release # build linux/darwin/windows release binaries into dist/
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task release:lancedb # build LanceDB release binaries into dist/
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task generator # run code generation
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task enums # generate frontend enums
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task --list # show available tasks
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make dev
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make build
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make test
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make generator
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make enums FRONTEND_DIR=../agent-desk-web
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```
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## AI Agent Workflow
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`make enums` writes the backend-owned shared enums into the frontend project.
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```mermaid
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flowchart TD
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A[User starts a support request<br/>Web support entry / Open API] --> B[Create or match a conversation]
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B --> C[Customer sends a message]
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C --> D[Trigger AI Reply Runtime]
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D --> E[Load conversation history / AI configuration]
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E --> F[Retrieve from bound knowledge bases]
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F --> G{Are retrieved chunks enough to answer?}
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G -- No --> Z[Return knowledge fallback<br/>and recommend human support]
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G -- Yes --> H[Prepare Skills / MCP Tools]
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H --> I[Pass trusted knowledge context to the Agent]
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I --> J{Direct reply?}
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J -- Yes --> K[LLM generates a knowledge-grounded reply]
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J -- No --> N{Call Graph / MCP Tool?}
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N -- Yes --> O[Run Skill / Graph / MCP Tool]
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O --> P{Need user confirmation?}
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P -- No --> I
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P -- Yes --> Q[Ask the user to confirm]
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Q --> R{Confirmation result}
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R -- Confirm handoff --> S[Move conversation to human handoff pool]
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S --> T[Automatic or manual assignment]
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T --> U[Agent workspace takeover]
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U --> V{Need ticket tracking?}
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V -- Yes --> W[Create or link a ticket]
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V -- No --> X[Human agent continues handling]
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W --> X
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X --> Y[Resolve and close]
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R -- Confirm ticket --> AA[Create a ticket from the current conversation]
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AA --> I
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R -- Cancel --> K
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N -- No --> K
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```
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## Support Loop
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```mermaid
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flowchart LR
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A[Customer request] --> B[AI Agent handles first]
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B --> C{Can the knowledge base answer?}
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C -- Yes --> D[AI replies with trusted knowledge]
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C -- No --> E[Fallback / recommend human support]
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D --> F{Need a human?}
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E --> G[Human takeover]
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F -- No --> H[Conversation ends or data is retained]
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F -- Yes --> G
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G --> I[Agent workspace handles the case]
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I --> J{Need follow-up tracking?}
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J -- Yes --> K[Create / link a ticket]
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J -- No --> L[Resolve directly]
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K --> M[Ticket status flow and progress records]
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M --> N[Complete]
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L --> N
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```
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## Docker Image
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If you only need to build the application image, prepare MySQL and Qdrant yourself and mount a configuration file:
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## Docker
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```bash
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docker build -t mlogclub/agent-desk .
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docker run --rm -p 8083:8083 \
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-v $(pwd)/docker/agent-desk.yaml:/app/config/config.yaml:ro \
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-v agent-desk-data:/app/data \
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mlogclub/agent-desk
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docker build --target app -t agent-desk-server .
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docker run --rm -p 8083:8083 agent-desk-server
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```
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Compose uses [docker/agent-desk.yaml](docker/agent-desk.yaml) as the in-container configuration. The application reaches `mysql` and `qdrant` through Docker service names.
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## Open-source Positioning
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`AgentDesk` is useful as an open-source foundation for:
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- AI customer support systems
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- AI Helpdesk / AI Support Platform projects
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- RAG answerability + human handoff implementation references
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- Enterprise AI Agent application frameworks
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If you are looking for a customer support system centered on AI Agents rather than a simple LLM chat box, this project is designed for that purpose.
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The Docker image contains only the API service. Deploy the frontend separately.
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