ai-agent/README.md

# AgentDesk

English | [简体中文](README_ZH.md)

An open-source AI Agent customer support system with knowledge-based answers, human handoff, ticket workflows, and self-hosted deployment.

> 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.

## Product Preview

Customer chat, agent workspace, knowledge base, model configuration, and AI Agent orchestration are managed in one system.

### Customer Chat

![Customer Chat](screenshots/1.png)

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.

### Agent Workspace

![Agent Workspace](screenshots/2.png)

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.

### Knowledge Base and AI Agent Configuration

| Knowledge Base FAQ | AI Agent Configuration |
| --- | --- |
| ![Knowledge Base FAQ](screenshots/4.png) | ![AI Agent Configuration](screenshots/5.png) |

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.

### Model Configuration

![Model Configuration](screenshots/3.png)

Model configuration supports OpenAI-compatible providers. You can configure LLMs, embedding models, rerank models, context limits, output settings, timeout, retry behavior, and enablement state.

## Why Use It

- **AI-first support**: Let AI Agents handle common questions, standard procedures, and knowledge-base answers first.
- **Knowledge-constrained replies**: Use RAG and the Answerability Gate to decide whether retrieved knowledge is strong enough to answer, reducing unsupported responses.
- **Natural human handoff**: Move to human agents when knowledge is insufficient, the user asks for help, or a workflow requires human confirmation.
- **Conversation-to-ticket loop**: Online chat, support handling, ticket creation, status flow, and progress records stay in one system.
- **Built for extension**: The backend uses Go, the frontend uses Next.js, and the runtime supports Skills, MCP, and OpenAI-compatible model access.
- **Self-host friendly**: Supports SQLite / MySQL and Qdrant for local trials, intranet deployment, and enterprise self-hosting.

## Core Capabilities

- **AI Agent support**: AI replies first, with fallback, confirmation, tool calling, and human collaboration.
- **Online conversation system**: Visitor sessions, message send/receive, unread status, assignment, transfer, and close flows.
- **Agent workspace**: Agents can take over conversations, reply to users, transfer teammates, link customers, and create tickets.
- **Knowledge-base RAG**: Knowledge bases, documents, FAQs, chunking, vector retrieval, retrieval logs, and quality analysis.
- **Answerability Gate**: Checks whether retrieved content can support an answer; otherwise returns a fallback and recommends human support.
- **Ticket system**: Create tickets from conversations, categorize, assign, move through status flows, record progress, and close the loop.
- **Support organization management**: Agent profiles, teams, schedules, and automatic assignment.
- **AI extensibility**: Skills, MCP debugging, and external tool integration.
- **Multiple entry points**: Admin dashboard, agent workspace, customer-facing web pages, and embeddable SDK.

## Use Cases

- Website live support
- SaaS product support
- AI + human hybrid support
- Internal enterprise service desk
- After-sales service, incident reporting, complaints, and operations support
- Support teams that need knowledge-base Q&A with human collaboration

## Quick Start

The fastest way to try the full stack is Docker Compose:

```bash
docker compose up -d --build
```

For the full English setup guide, see [Docker Compose Quick Start](https://agent-desk.huabei.pro/docs/getting-started/docker-compose.html).

To embed customer support on your website, see [Web Widget Integration](https://agent-desk.huabei.pro/docs/integration/web-widget.html).

To connect OpenAI-compatible model providers, see [Model Provider Configuration](https://agent-desk.huabei.pro/docs/config/model-provider.html).

Compose starts:

- `agent-desk`: application service on port `8083`
- `mysql`: MySQL 8.4 with the `mysql-data` volume
- `qdrant`: vector database with the `qdrant-data` volume, ports `6333` / `6334`

After startup, open:

- Admin dashboard: `http://localhost:8083/dashboard`
- Agent workspace: `http://localhost:8083/dashboard/conversations`
- Customer web integration demo: `http://localhost:8083/support/demo`
- Customer chat page: `http://localhost:8083/support/chat`

Default administrator account:

- Username: `admin`
- Password: `ChangeMe123!`

> 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.

## Local Development

### Requirements

- Go `1.26+`
- Node.js `20+`
- `pnpm`
- Qdrant

### Prepare Configuration

```bash
cp config/config.example.yaml config/config.yaml
```

The default configuration uses:

- SQLite: `data/app.db`
- Backend: `http://127.0.0.1:8083`
- Qdrant gRPC: `127.0.0.1:6334`

If Qdrant is not running locally, start it with Docker:

```bash
docker run -p 6333:6333 -p 6334:6334 qdrant/qdrant
```

Install frontend dependencies:

```bash
cd web
pnpm install
cd ..
```

Start backend and frontend development servers together:

```bash
task dev
```

Default development URLs:

- Admin dashboard: `http://localhost:3000/dashboard`
- Agent workspace: `http://localhost:3000/dashboard/conversations`
- Customer web integration demo: `http://localhost:3000/support/demo`
- Customer chat page: `http://localhost:3000/support/chat`

## Tech Stack

- Backend: Golang + Gin + GORM + `github.com/mlogclub/simple`
- Frontend: Next.js 16 + React 19 + shadcn/ui + Tailwind CSS
- Database: SQLite / MySQL
- Vector DB: Qdrant
- AI: OpenAI-compatible LLM / Embedding + RAG + Skills + MCP

## Project Structure

```text
.
├── cmd/                    # server / migration / generator / testdata
├── internal/
│   ├── bootstrap/          # startup, routes, database, and migration initialization
│   ├── builders/           # model / aggregate result to response DTO mapping
│   ├── handlers/           # dashboard / api / third HTTP handlers
│   ├── middleware/         # Gin middleware
│   ├── migration/          # idempotent data migrations
│   ├── models/             # GORM models
│   ├── repositories/       # data access layer
│   ├── services/           # business orchestration and transaction boundaries
│   ├── ai/                 # LLM / RAG / Runtime / Skills / MCP
│   └── pkg/                # config / dto / enums / httpx / utils and shared packages
├── web/                    # Next.js frontend project
│   ├── app/dashboard/      # admin dashboard and agent workspace
│   ├── app/support/        # customer integration and chat pages
│   ├── components/         # React components
│   ├── lib/                # API client, SDK source, and utilities
│   └── public/sdk/         # built embeddable SDK
├── config/                 # configuration files
├── docker/                 # Docker configuration
└── docs/                   # documentation site
```

## Common Commands

```bash
task dev        # start backend and frontend development servers
task build      # build the frontend SPA and current-platform Go binary into dist/
task build:lancedb  # build the current-platform LanceDB binary into dist/
task release    # build linux/darwin/windows release binaries into dist/
task release:lancedb  # build LanceDB release binaries into dist/
task generator  # run code generation
task enums      # generate frontend enums
task --list     # show available tasks
```

## AI Agent Workflow

```mermaid
flowchart TD
    A[User starts a support request<br/>Web support entry / Open API] --> B[Create or match a conversation]
    B --> C[Customer sends a message]
    C --> D[Trigger AI Reply Runtime]
    D --> E[Load conversation history / AI configuration]
    E --> F[Retrieve from bound knowledge bases]
    F --> G{Are retrieved chunks enough to answer?}
    G -- No --> Z[Return knowledge fallback<br/>and recommend human support]
    G -- Yes --> H[Prepare Skills / MCP Tools]
    H --> I[Pass trusted knowledge context to the Agent]
    I --> J{Direct reply?}
    J -- Yes --> K[LLM generates a knowledge-grounded reply]
    J -- No --> N{Call Graph / MCP Tool?}
    N -- Yes --> O[Run Skill / Graph / MCP Tool]
    O --> P{Need user confirmation?}
    P -- No --> I
    P -- Yes --> Q[Ask the user to confirm]
    Q --> R{Confirmation result}
    R -- Confirm handoff --> S[Move conversation to human handoff pool]
    S --> T[Automatic or manual assignment]
    T --> U[Agent workspace takeover]
    U --> V{Need ticket tracking?}
    V -- Yes --> W[Create or link a ticket]
    V -- No --> X[Human agent continues handling]
    W --> X
    X --> Y[Resolve and close]
    R -- Confirm ticket --> AA[Create a ticket from the current conversation]
    AA --> I
    R -- Cancel --> K
    N -- No --> K
```

## Support Loop

```mermaid
flowchart LR
    A[Customer request] --> B[AI Agent handles first]
    B --> C{Can the knowledge base answer?}
    C -- Yes --> D[AI replies with trusted knowledge]
    C -- No --> E[Fallback / recommend human support]
    D --> F{Need a human?}
    E --> G[Human takeover]
    F -- No --> H[Conversation ends or data is retained]
    F -- Yes --> G
    G --> I[Agent workspace handles the case]
    I --> J{Need follow-up tracking?}
    J -- Yes --> K[Create / link a ticket]
    J -- No --> L[Resolve directly]
    K --> M[Ticket status flow and progress records]
    M --> N[Complete]
    L --> N
```

## Docker Image

If you only need to build the application image, prepare MySQL and Qdrant yourself and mount a configuration file:

```bash
docker build -t mlogclub/agent-desk .
docker run --rm -p 8083:8083 \
  -v $(pwd)/docker/agent-desk.yaml:/app/config/config.yaml:ro \
  -v agent-desk-data:/app/data \
  mlogclub/agent-desk
```

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.

## Open-source Positioning

`AgentDesk` is useful as an open-source foundation for:

- AI customer support systems
- AI Helpdesk / AI Support Platform projects
- RAG answerability + human handoff implementation references
- Enterprise AI Agent application frameworks

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.