Standalone, performant, zero-dependency Go OpenAI proxy gateway for the Qwen3.8-Flash-Next Hugging Face Gradio space (https://halvo78-qwen3-8-flash-next-playground.hf.space).

Created by Luxferre in 2026, released into the public domain.

Features

Zero External Dependencies: Built entirely with Go standard library packages (net/http, encoding/json, bufio, etc.).
OpenAI-Compatible API: Implements standard /v1/chat/completions (streaming & non-streaming) and /v1/models.
Real-Time Token Streaming: Streams SSE chunks with incremental token delivery directly to clients.
Deep Reasoning Separation:
- Automatically isolates thinking traces from both standard <think>...</think> tags and the playground's blockquote thinking blocks (> 💭 **Thinking Process...**).
- Emits pure thought traces to delta.reasoning_content (streaming) and message.reasoning_content (non-streaming).
- Keeps delta.content and message.content clean.
Stateful Streaming Tool Call Interception:
- Injects tool schemas into system instructions.
- Intercepts <tool_call> blocks in real time via StreamToolCallFilter without leaking raw XML or JSON into delta.content.
- Emits structured delta.tool_calls chunks and sets finish_reason: "tool_calls".
Reasoning Effort Control: Respects standard reasoning_effort: "none" to switch dynamically into high-speed Instruct Mode.
Zero-Dependency SOCKS5 Proxy Client:
- RFC 1928 and RFC 1929 compliant client with domain resolution (socks5h://), IPv4, IPv6, and authentication.
- Wireable via -socks CLI flag or ALL_PROXY / SOCKS5_PROXY environment variables.
Bring Your Own Key (BYOK) Pass-through:
- Passes client API keys or custom base URLs directly to upstream inference engines when provided.

Architecture & Model Aliases

The gateway serves the following models under /v1/models:

Model ID	Target Model	Description
`Qwen/Qwen3.8-Flash-Next`	`Qwen/Qwen3.8-Flash-Next`	Primary playground model (125B MoE, 6B activated)
`qwen3.8-flash-next`	`Qwen/Qwen3.8-Flash-Next`	Standard lowercase alias
`qwen-flash-next`	`Qwen/Qwen3.8-Flash-Next`	Shorthand alias
`qwen-flash`	`Qwen/Qwen3.8-Flash-Next`	Quick convenience alias

Any unlisted custom model name requested by the client is passed through directly.

Build Instructions

Build binary with Go:

make qflash

Or run test suite:

make test

The resulting binary will be placed at bin/qflash.

Configuration Flags & Environment Variables

Flag	Shorthand	Environment Variable	Default	Description
`-port`		`PORT`	`8080`	Port to bind the HTTP server
`-endpoint`			`https://halvo78-qwen3-8-flash-next-playground.hf.space`	Upstream Gradio space base URL
`-model`			`Qwen/Qwen3.8-Flash-Next`	Default model ID
`-thinking`	`-enable-thinking`		`true`	Enable chain-of-thought reasoning by default
`-hf-token`		`HF_TOKEN`	`""`	Hugging Face user access token
`-api-key`		`OPENAI_API_KEY` / `QWEN_API_KEY`	`""`	Upstream inference engine API key
`-base-url`		`OPENAI_BASE_URL` / `QWEN_BASE_URL`	`""`	Upstream inference engine base URL
`-user-agent`	`-ua`	`USER_AGENT`	Firefox 153 on Linux	Custom User-Agent header
`-socks`	`-proxy`, `-socks5`	`ALL_PROXY`, `SOCKS5_PROXY`	`""`	SOCKS5 proxy URL (`socks5://127.0.0.1:1080`)

Usage Examples

1. Launch Gateway

./bin/qflash -port 8080

2. List Models

curl http://127.0.0.1:8080/v1/models

3. Non-Streaming Chat Completion

curl -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.8-flash-next",
    "messages": [
      {"role": "user", "content": "Explain QSA micro-blocks in one sentence."}
    ],
    "stream": false
  }'

4. Streaming Chat Completion (Real-Time SSE)

curl -N -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen-flash",
    "messages": [
      {"role": "user", "content": "Write a quick Python countdown loop."}
    ],
    "stream": true
  }'

5. Instruct Mode (Disable Thinking)

curl -X POST http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen-flash",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ],
    "reasoning_effort": "none"
  }'

6. Python OpenAI SDK Integration

from openai import OpenAI

client = OpenAI(
    base_url="http://127.0.0.1:8080/v1",
    api_key="sk-dummy"
)

stream = client.chat.completions.create(
    model="qwen3.8-flash-next",
    messages=[
        {"role": "user", "content": "Prove that the sum of the first n odd numbers is n^2."}
    ],
    stream=True
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content:
        print(f"[THINK] {delta.reasoning_content}", end="", flush=True)
    if delta.content:
        print(delta.content, end="", flush=True)
print()