k3r053n3/README.md

k3r053n3: Kimi K3 OpenAI-compatible proxy gateway

A standalone, high-performance, zero-dependency Go proxy gateway that exposes a standard OpenAI-compatible API (/v1/chat/completions and /v1/models) for the Kimi K3 model hosted on a demo Gradio space.

Features

• Zero external dependencies: pure Go standard library (net/http, encoding/json, bufio, bytes, crypto/rand, flag, strings, time).
• SOCKS5 proxy support: zero-dependency built-in RFC 1928 / RFC 1929 SOCKS5 client supporting domain name resolution (socks5h://), IPv4/IPv6, and username/password authentication (via -socks, -proxy, or ALL_PROXY/all_proxy/SOCKS5_PROXY/socks5_proxy environment variables).
• Reasoning effort support: defaults to "max" reasoning effort out of the box, with support for "max", "high", "low", and "default" (via reasoning_effort request field or CLI flag).
• Real-time token streaming: Server-Sent Events (SSE) streaming engine (stream: true) with separate token-by-token emission for delta.reasoning_content and delta.content.
• Reasoning extraction: clean separation of <think>...</think> internal thoughts into reasoning_content (streaming chunks and non-streaming messages) without leaking raw tags into content.
• OpenAI-compatible tool calling:
  • automatic tool definition formatting into system instructions.
  • multi-turn tool execution response formatting (role: "tool" / role: "function").
  • real-time stream interceptor (StreamToolInterceptor) that catches <tool_call> blocks on the fly and emits standard OpenAI delta.tool_calls chunks with finish_reason: "tool_calls".
  • non-streaming tool call parsing with structured tool_calls and finish_reason: "tool_calls".
• Backend & model routing: route between multiple upstream backends (direct:together, direct:fireworks, hf:together, hf:fireworks-ai, hf:featherless-ai, hf:baseten) dynamically or via model suffix (kimi-k3:together, kimi-k3:fireworks, etc.).
• Reliability & resilience: automatic Fibonacci exponential backoff retry mechanism (DoWithFibonacciRetry) on upstream network connections.
• Full CORS support: ready for direct browser integration, web frontends, and OpenAI-compatible client libraries.

Quick start

Installation

Install directly with go install:

go install code.luxferre.top/luxferre/k3r053n3@latest

Build from source

make k3r053n3

Or build manually with Go:

go build -trimpath -ldflags="-s -w" -o bin/k3r053n3 .

Run

./bin/k3r053n3

By default, the server starts on port 8080 pointing to https://cw-105-kimi-k3-gguf-demo.hf.space with default reasoning effort "max" and default backend "direct:together".

CLI options

| Flag | Default | Description | |||| | -port | 8080 | Port to listen on | | -endpoint | https://cw-105-kimi-k3-gguf-demo.hf.space | Root URL of the Kimi K3 Gradio space | | -model | kimi-k3 | Exposed default model name | | -backend | direct:together | Default Space backend (direct:together, direct:fireworks, hf:together, hf:fireworks-ai, hf:featherless-ai, hf:baseten) | | -reasoning | max | Default reasoning effort (max, high, low, default) | | -max-tokens | 8192 | Default max completion tokens (256 - 8192) | | -temperature | 0.7 | Default sampling temperature (0.0 - 1.5) | | -socks, -proxy | "" | SOCKS5 proxy URL (socks5://127.0.0.1:1080 or socks5://user:pass@host:port, also checks ALL_PROXY/all_proxy/SOCKS5_PROXY/socks5_proxy env vars) | | -user-agent, -ua | Firefox 153 on Linux | Custom User-Agent header for upstream requests |

Endpoints

• GET / - gateway health check and route overview
• GET /v1/models (or GET /models) - list of available models and backend mappings
• POST /v1/chat/completions (or POST /chat/completions) - OpenAI-compatible chat completions

Usage examples

1. Models list

curl -s http://localhost:8080/v1/models

2. Standard chat completion (non-streaming)

curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "user", "content": "What is 25 * 4? Show brief work."}
    ],
    "stream": false
  }'

Response includes separated reasoning_content and content:

{
  "id": "chatcmpl-88054d2e-2084-4bcd-b9fa-8e99e9267523",
  "object": "chat.completion",
  "created": 1788348384,
  "model": "kimi-k3",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "**25 × 4 = 100**\n\nQuick way: 25 × 4 = 25 × 2 × 2 = 50 × 2 = **100**\n\n(Think of it as 4 quarters = 1 dollar.)",
        "reasoning_content": "The user is asking a simple arithmetic question: 25 * 4..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 0,
    "completion_tokens": 0,
    "total_tokens": 0
  }
}

3. Real-time streaming with reasoning deltas

curl -s -N http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "user", "content": "Tell me a 1-sentence joke about computers."}
    ],
    "stream": true
  }'

Output delivers real-time delta.reasoning_content chunks during thinking, followed by delta.content chunks for the answer, ending with [DONE]:

data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1788348397,"model":"kimi-k3","choices":[{"index":0,"delta":{"reasoning_content":"Thinking..."}}]}
...
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1788348397,"model":"kimi-k3","choices":[{"index":0,"delta":{"content":"There are only 10 types of people in the world: those who understand binary and those who don't."}}]}
data: {"id":"chatcmpl-...","object":"chat.completion.chunk","created":1788348397,"model":"kimi-k3","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
data: [DONE]

4. Reasoning effort control

Control reasoning effort via the reasoning_effort field ("max", "high", "low", "default"):

curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "reasoning_effort": "low",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

5. Tool / function calling

curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "user", "content": "What is the weather in Seattle right now?"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get current weather for a given city",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {"type": "string", "description": "City name"}
            },
            "required": ["location"]
          }
        }
      }
    ],
    "stream": false
  }'

Response emits standard OpenAI tool_calls with finish_reason: "tool_calls":

{
  "id": "chatcmpl-cbe006c5-1b5d-42d3-ab89-254c7f012061",
  "object": "chat.completion",
  "created": 1788348416,
  "model": "kimi-k3",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": null,
        "reasoning_content": "The user is asking about the weather in Seattle...",
        "tool_calls": [
          {
            "index": 0,
            "id": "call_de6cdf1a_0",
            "type": "function",
            "function": {
              "name": "get_weather",
              "arguments": "{\"location\":\"Seattle\"}"
            }
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ]
}

6. Submitting tool results in follow-up turns

curl -s http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "kimi-k3",
    "messages": [
      {"role": "user", "content": "What is the weather in Seattle right now?"},
      {
        "role": "assistant",
        "tool_calls": [
          {
            "id": "call_de6cdf1a_0",
            "type": "function",
            "function": {"name": "get_weather", "arguments": "{\"location\":\"Seattle\"}"}
          }
        ]
      },
      {
        "role": "tool",
        "tool_call_id": "call_de6cdf1a_0",
        "name": "get_weather",
        "content": "{\"temperature\": \"16C\", \"conditions\": \"Partly cloudy with gentle breeze\"}"
      }
    ]
  }'

Python OpenAI client integration

from openai import OpenAI

client = OpenAI(
  base_url="http://localhost:8080/v1",
  api_key="not-needed",
)

# Streaming with reasoning
response = client.chat.completions.create(
  model="kimi-k3",
  messages=[
    {"role": "user", "content": "Explain quantum superposition in 2 sentences."}
  ],
  stream=True,
  extra_body={"reasoning_effort": "max"},
)

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

SOCKS5 proxy usage

Run the gateway through a SOCKS5 proxy (e.g. Tor or local tunnel):

# Using CLI flag
./bin/k3r053n3 -socks socks5://127.0.0.1:9050

# With authentication
./bin/k3r053n3 -socks socks5://user:pass@127.0.0.1:1080

# Using environment variable
export ALL_PROXY=socks5://127.0.0.1:1080
./bin/k3r053n3

Testing

Run unit and integration tests:

make test

Credits

Created by Luxferre in 2026, released into the public domain with no warranties.