qw-proxy

HTTP proxy bridge to AgentRouter API via qwen-code SDK.

Allows you to access AgentRouter models through a simple REST API with minimal token overhead (~21 input tokens vs 12k+ default).

Installation

``bash
npm install -g qw-proxy
`

Quick Start

`bash


Basic usage (12k+ input tokens)

AGENTROUTER_API_KEY=sk-xxx qw-proxy

Optimized usage (~21 input tokens)

mkdir -p .qwen && echo "You are a helpful assistant." > .qwen/system.md
QWEN_SYSTEM_MD=1 AGENTROUTER_API_KEY=sk-xxx qw-proxy
`

Server starts on http://localhost:3001

Supported Models

| Model | Status | Notes |
|-------|--------|-------|
| deepseek-v3.2 | ✅ | Default, recommended |
| gpt-5.2 | ✅ | GPT-5.2 via AgentRouter |
| claude-haiku-4-5-20251001 | ✅ | Fast Claude model |
| claude-sonnet-4-5-20250929 | ✅ | Claude Sonnet 4.5 |
| glm-4.5 | ✅ | GLM model |
| glm-4.6 | ✅ | GLM model |

Set model via AGENTROUTER_MODEL environment variable.

Environment Variables

| Variable | Required | Default | Description |
|----------|----------|---------|-------------|
| AGENTROUTER_API_KEY | Yes | - | Your AgentRouter API key |
| AGENTROUTER_MODEL | No | deepseek-v3.2 | Model to use |
| AGENTROUTER_BASE_URL | No | https://agentrouter.org/v1 | API base URL |
| PORT | No | 3001 | Server port |
| QWEN_SYSTEM_MD | No | - | Set to 1 for minimal tokens |

API Reference

$3

Send a message and get AI response.

Request:
`json
{
"user_id": "user123",
"message": "Hello!",
"images": ["/path/to/image.png"]
}
`

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| user_id | string | Yes | Unique user ID for conversation history |
| message | string | Yes | User message |
| images | string[] | No | Local image paths (vision models) |

Response:
`json
{
"success": true,
"response": "Hello! How can I help you?",
"history_length": 3
}
`

Example:
`bash
curl http://localhost:3001/chat \
-H "Content-Type: application/json" \
-d '{"user_id": "test", "message": "Hello!"}'
`

$3

Health check.

`json
{
"status": "ok",
"model": "deepseek-v3.2",
"active_users": 1,
"active_clients": 1
}
`

$3

Clear conversation history.

`bash
curl -X POST http://localhost:3001/clear \
-H "Content-Type: application/json" \
-d '{"user_id": "user123"}'
`

$3

Cancel ongoing generation.

`bash
curl -X POST http://localhost:3001/cancel/user123
`

$3

Get conversation compression statistics.

Features

- Minimal tokens: ~21 input tokens with QWEN_SYSTEM_MD=1 (vs 12k+ default)
- Per-user history: Isolated conversation contexts
- Auto-compression: LLM-based history compression when limit exceeded
- Vision support: Send images with messages
- Request cancellation: Cancel ongoing generations
- 30s timeout: Protection against hanging requests

How It Works

`
Your App → qw-proxy (localhost:3001) → qwen-code SDK → AgentRouter API → LLM
`

qwen-code SDK handles authentication with AgentRouter. This proxy exposes it as a simple REST API.

Token Optimization

Default qwen-code includes ~12k tokens of system prompts, tools, and environment context.

To reduce to ~21 tokens:

1. Create .qwen/system.md with minimal prompt
2. Set QWEN_SYSTEM_MD=1`

The package has patched qwen-code to disable tools and environment context injection.

License

MIT