Command Code Proxy

中文文档

A reverse proxy that converts Command Code API to OpenAI / Anthropic compatible endpoints. Single file, zero external dependencies.

Built by analyzing official CLI v0.32.3 network traffic to accurately replicate the Command Code API request protocol.

Features: OpenAI Chat Completions + Anthropic Messages API | Streaming & non-streaming | Tool calling (tool_use) | Multimodal image input | Reasoning effort | Dynamic model list | Cache hit metrics | 30s/90s stream timeout

Community: Linux.do — a friendly Chinese tech community.

Quick Start

npm start        # Start (default http://0.0.0.0:3000)
npm run dev      # Watch mode (auto-reload on file changes)

API Key is passed via the Authorization request header — no need to store it in config files. Key must start with user_ (automatically matched with any prefix, e.g. Bearer token_user_xxx):

curl http://127.0.0.1:3050/v1/chat/completions \
  -H "Authorization: Bearer user_xxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek/deepseek-v4-flash","messages":[{"role":"user","content":"hi"}]}'

File Structure

commandcode/
├── config.json     # Port / log path etc.
├── LICENSE         # MIT License
├── package.json    # npm start / npm run dev
├── proxy.mjs       # Single-file proxy core (~1400 lines)
├── AGENTS.md       # AI dev instructions
├── README.md       # This document (English)
└── README_zh.md    # Chinese documentation

Configuration

config.json

Field	Default	Description
port	3000	Listen port
host	0.0.0.0	Listen address
apiBase	https://api.commandcode.ai	CC API base URL
projectSlug	cc-proxy	x-project-slug header
logFile	""	Log file path (empty = console only)
logLevel	info	Log level
useProviderModels	true	Dynamically fetch model list from Provider API
modelRefreshIntervalMs	300000	Model list cache refresh interval (5 min)

Environment Variables

Variable	Overrides
PORT	port
HOST	host
CC_API_BASE	apiBase
PROJECT_SLUG	projectSlug
LOG_FILE	logFile
CC_USE_PROVIDER_MODELS	useProviderModels

API Endpoints

POST /v1/chat/completions

OpenAI Chat Completions compatible. Supports streaming, non-streaming, tool calling, multimodal image input, and reasoning effort.

Request parameters:

Parameter	Required	Description
model	Yes	Model ID (see model list)
messages	Yes	Conversation messages, supports system/user/assistant/tool roles
max_tokens	No	Max tokens to generate (default 64000)
stream	No	SSE streaming (default false)
temperature	No	Sampling temperature (0-2)
reasoning_effort	No	Reasoning intensity: low/medium/high/max
tools	No	Tool definitions (OpenAI function calling format)
tool_choice	No	Tool selection strategy
parallel_tool_calls	No	Allow parallel tool calls

Simple request:

{
  "model": "deepseek/deepseek-v4-flash",
  "messages": [{ "role": "user", "content": "hello" }],
  "stream": true
}

Multimodal image input (vision model required):

{
  "model": "xiaomi/mimo-v2.5",
  "messages": [{
    "role": "user",
    "content": [
      { "type": "text", "text": "Describe this image" },
      { "type": "image_url", "image_url": { "url": "data:image/jpeg;base64,..." } }
    ]
  }]
}

Tool calling:

{
  "model": "deepseek/deepseek-v4-flash",
  "messages": [...],
  "tools": [{
    "type": "function",
    "function": { "name": "get_weather", "description": "...", "parameters": {...} }
  }],
  "tool_choice": "auto"
}

Streaming response (SSE):

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"thinking..."}}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"Hello"}}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":10,"completion_tokens":20,"total_tokens":30,"prompt_tokens_details":{"cached_tokens":8}}}

data: [DONE]

Non-streaming response (with cache hits):

{
  "id": "chatcmpl-xxx",
  "object": "chat.completion",
  "created": 1234567890,
  "model": "deepseek/deepseek-v4-flash",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "Hello!",
      "reasoning_content": "The user said hello, I should respond."
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 7558,
    "completion_tokens": 42,
    "total_tokens": 7600,
    "prompt_tokens_details": { "cached_tokens": 7552 }
  }
}

POST /v1/messages

Anthropic Messages API compatible endpoint. Supports streaming, non-streaming, and tool calling.

Request body:

{
  "model": "claude-sonnet-4-6",
  "max_tokens": 1000,
  "system": "You are a helpful assistant.",
  "messages": [
    { "role": "user", "content": "hello" }
  ],
  "stream": true
}

Anthropic protocol conversion (automatic):

Concept	Anthropic Format	Conversion
System prompt	Top-level system field	Auto-converted to OpenAI system message
Message content	content array (text/tool_use/tool_result)	Auto-mapped to corresponding roles
Tool results	tool_result blocks in user messages	Auto-converted to role: "tool"
Tool definitions	input_schema	Auto-mapped to parameters
tool_choice	{type:"auto"/"any"/"tool"}	any→required, tool→function object
Reasoning	thinking.budget_tokens	Auto-mapped to reasoning_effort (≥10000→high, ≥5000→medium, ≥2000→low)
Stop reason	end_turn/max_tokens/tool_use	Auto-mapped to stop/length/tool_calls
Token usage	input_tokens/output_tokens + cache	Passed through, cache fields mapped to Anthropic format

Streaming response (SSE, Anthropic format):

event: message_start
data: {"type":"message_start","message":{"id":"msg_xxx","type":"message","role":"assistant","content":[],"model":"...","usage":{"input_tokens":0,"output_tokens":0}}}

event: content_block_start
data: {"type":"content_block_start","index":0,"content_block":{"type":"text","text":""}}

event: content_block_delta
data: {"type":"content_block_delta","index":0,"delta":{"type":"text_delta","text":"Hello"}}

event: content_block_stop
data: {"type":"content_block_stop","index":0}

event: message_delta
data: {"type":"message_delta","delta":{"stop_reason":"end_turn"},"usage":{"output_tokens":10,"cache_read_input_tokens":0,"input_tokens":100}}

event: message_stop
data: {"type":"message_stop"}

Non-streaming response:

{
  "id": "msg_xxx",
  "type": "message",
  "role": "assistant",
  "model": "deepseek/deepseek-v4-flash",
  "content": [{ "type": "text", "text": "Hello!" }],
  "stop_reason": "end_turn",
  "stop_sequence": null,
  "usage": {
    "input_tokens": 7558,
    "output_tokens": 42,
    "cache_read_input_tokens": 7552,
    "cache_creation_input_tokens": null
  }
}

GET /v1/models

Returns available model list. Fetched dynamically from Provider API (5 min cache), falls back to hardcoded list on failure.

GET /health

Health check. Returns OK.

Error Codes

HTTP Status	Description
400	Invalid request format
401	API Key missing / invalid format / rejected (Key must start with user_)
429	Rate limited (includes retry_after field)
502	CC upstream error
503	Service temporarily unavailable

Model List

The proxy returns a live model list via GET /v1/models. Below are common models for reference; the actual list depends on the live API response — see Command Code Pricing for plan details.

Common Models

Model ID	Description	Features
deepseek/deepseek-v4-flash	DeepSeek V4 Flash	Fast, general-purpose
deepseek/deepseek-v4-pro	DeepSeek V4 Pro	High-precision reasoning
claude-sonnet-4-6	Claude Sonnet 4.6	Long context
claude-opus-4-8	Claude Opus 4.8	Best reasoning
moonshotai/Kimi-K2.5	Kimi K2.5	Multimodal / frontend
xiaomi/mimo-v2.5	MiMo V2.5	Image input supported
Qwen/Qwen3.7-Max	Qwen 3.7 Max	Large parameters
google/gemini-3.5-flash	Gemini 3.5 Flash	Reasoning model

⚠️ Some models (e.g. deepseek-v4-flash, claude-sonnet-4-6) do not support image input. Use xiaomi/mimo-v2.5, Kimi-K2.5, or other vision models for multimodal.

Integration Examples

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    api_key="user_xxxxxxxxx",
    base_url="http://127.0.0.1:3050/v1",
)

response = client.chat.completions.create(
    model="deepseek/deepseek-v4-flash",
    messages=[{"role": "user", "content": "hello"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

cURL

curl http://127.0.0.1:3050/v1/chat/completions \
  -H "Authorization: Bearer user_xxxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek/deepseek-v4-flash",
    "messages": [{"role": "user", "content": "hello"}],
    "stream": true
  }'

Cursor

Add a Custom Provider in Cursor settings:

• API Base URL: http://127.0.0.1:3050/v1
• API Key: user_xxxxxxxxx
• Model: Choose from the model list

Anthropic (Python SDK)

import anthropic

client = anthropic.Anthropic(
    api_key="user_xxxxxxxxx",
    base_url="http://127.0.0.1:3050",
)
message = client.messages.create(
    model="deepseek/deepseek-v4-flash",
    max_tokens=1000,
    system="You are helpful.",
    messages=[{"role": "user", "content": "hello"}],
)
print(message.content[0].text)

OpenCode

{
  "provider": "openai-compatible",
  "baseUrl": "http://127.0.0.1:3050/v1",
  "apiKey": "user_xxxxxxxxx"
}

Anti-Detection

Based on analysis of official CLI v0.32.3 traffic:

Mechanism	Implementation
Per-Key Session	One session per API key, 12h expiry + 1h random jitter
Version	x-command-code-version auto-fetched from npm registry (24h refresh)
CLI Envelope	config/memory/taste/permissionMode/params/threadId
OpenTelemetry	traceparent (W3C Trace Context)
Environment	x-cli-environment: production
Project Slug	Custom x-project-slug
Reasoning Effort	reasoning_effort pass-through (low/medium/high/max)
Key Validation	Regex user_[a-zA-Z0-9_-]+, auto-cleans extra paths/prefixes, rejects sk-xxx format
Stream Timeout	30s streaming / 90s non-streaming auto-abort on idle

Protocol Details

CC API Request Structure

{
  "config": {
    "workingDir": "C:\\project",
    "date": "2026-06-07",
    "environment": "win32-x64, Node.js v24.16.0",
    "structure": [],
    "isGitRepo": false,
    "currentBranch": "",
    "mainBranch": "",
    "gitStatus": "",
    "recentCommits": []
  },
  "memory": "",
  "taste": "",
  "skills": "",
  "permissionMode": "standard",
  "params": {
    "model": "deepseek/deepseek-v4-flash",
    "messages": [...],
    "max_tokens": 64000,
    "stream": true,
    "reasoning_effort": "max"
  },
  "threadId": "<uuid>"
}

CC API Image Message Format

The CLI sends images in this format:

{
  "role": "user",
  "content": [
    { "type": "image", "image": "data:image/jpeg;base64,..." },
    { "type": "text", "text": "What does this image say?" }
  ]
}

The proxy receives OpenAI image_url format and converts it to the above CC format transparently.

Disclaimer

This project is for educational and research purposes only.

• Unofficial: This project is not affiliated with Command Code in any way.
• Personal Use: Users assume all responsibility. Please comply with the Command Code Terms of Service.
• API Key: This project does not collect, upload, or leak your API Key. The key must be sent in every request via the Authorization: Bearer <key> header and is never stored in configuration.
• Compliance: The protocol is based on passive observation of local CLI network traffic. No unauthorized access, cracking, or tampering of the server has been performed.
• Account Risk: Keep usage frequency consistent with normal CLI usage. Extremely high concurrent calls may trigger risk controls.

---

Development

# Start with watch mode (auto-reload on file changes)
npm run dev