Upload folder using huggingface_hub

.env.example

@@ -0,0 +1,11 @@
+TRAE_APP_ID = ""
+TRAE_DEVICE_BRAND = ""
+TRAE_DEVICE_CPU = ""
+TRAE_DEVICE_ID = ""
+TRAE_DEVICE_TYPE = ""
+TRAE_IDE_TOKEN = ""
+TRAE_IDE_VERSION = ""
+TRAE_IDE_VERSION_CODE = ""
+TRAE_IDE_VERSION_TYPE = ""
+TRAE_MACHINE_ID = ""
+TRAE_OS_VERSION = ""

.gitignore

@@ -0,0 +1,3 @@
+.env
+.venv
+__pycache__

.python-version

@@ -0,0 +1 @@
+3.13.2

Dockerfile

@@ -0,0 +1,32 @@
+# Use a Python image with uv pre-installed
+FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim
+
+# Set working dir to `/trae-api`
+WORKDIR /trae-api
+
+# Enable bytecode compilation
+ENV UV_COMPILE_BYTECODE=1
+
+# Copy from the cache instead of linking since it's a mounted volume
+ENV UV_LINK_MODE=copy
+
+# Install the project's dependencies using the lockfile and settings
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,source=uv.lock,target=uv.lock \
+    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
+    uv sync --frozen --no-install-project --no-dev
+
+# Then, add the rest of the project source code and install it
+# Installing separately from its dependencies allows optimal layer caching
+ADD . /trae-api
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --frozen --no-dev
+
+# Place executables in the environment at the front of the path
+ENV PATH="/trae-api/.venv/bin:$PATH"
+
+# Reset the entrypoint, don't invoke `uv`
+ENTRYPOINT []
+
+# Run server.py by default
+CMD ["python", "server.py"]

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 A23187
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,10 +1,75 @@
----
-title: Trae Api
-emoji: 🌍
-colorFrom: yellow
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Trae to OpenAI API
+
+为 Trae IDE 内置的大模型提供 OpenAI 兼容的 API
+
+## 项目简介
+
+本项目通过一个代理服务将 Trae IDE 内置的大模型暴露为 OpenAI 兼容的 API 接口，以在其他应用中使用
+
+## 使用前提
+
+1. 下载并安装 [Trae](https://www.trae.ai/download)，本项目目前仅支持 Trae 海外版
+2. 浏览器需要安装 [油猴 Tampermonkey](https://www.tampermonkey.net) 扩展，用于安装脚本来获取并保存 Trae 登录凭证
+
+## 快速开始
+
+### 获取登录凭证
+
+1. 安装油猴脚本 [auth.js](https://greasyfork.org/zh-CN/scripts/531978-%E8%8E%B7%E5%8F%96%E5%B9%B6%E4%BF%9D%E5%AD%98-trae-%E7%99%BB%E5%BD%95%E5%87%AD%E8%AF%81)
+2. 打开 Trae IDE 进行登录，若已登录则先退出登录
+3. 在自动打开的网页中，点击 `Login in and open Trae` 完成登录
+4. 回到刚才的网页，点击 `Save token to clipboard` 获取并保存登录凭证到剪贴板
+
+### 运行代理服务
+
+```shell
+# 克隆本项目仓库
+git clone https://github.com/A-23187/trae-api.git
+cd trae-api
+
+# 编辑环境变量，填入前面通过脚本获取的凭证信息
+cp .env.example .env
+vim .env
+
+# 运行服务，默认运行在 8000 端口
+uv run server.py
+```
+
+### 设置 chat 客户端
+
+> 以 [Cherry Studio](https://cherry-ai.com) 为例
+
+1. 设置 > 模型服务 > 添加提供商 > 提供商类型选择 OpenAI
+2. API 密钥填入 `TRAE_IDE_TOKEN`
+3. API 地址填入 `http://localhost:8000`
+4. 添加模型 > 模型 ID 填入 `gpt-4o`，支持的模型可以通过 `GET /v1/models` 获取
+
+## API 说明
+
+- `POST /v1/chat/completions` 创建聊天补全
+- `GET /v1/models` 列出可用模型
+
+## 其他
+
+### 直接使用 uvicorn 运行
+
+```shell
+uv sync
+source .venv/bin/activate
+
+uvicorn --host 0.0.0.0 --port 8000 --reload --log-level debug src.app:app
+```
+
+### 使用 docker 部署
+
+```shell
+docker run --rm -p 8000:8000 -v /path/to/.env:/trae-api/.env ghcr.io/a-23187/trae-api:latest
+```
+
+## 免责声明
+
+- 本项目仅供学习研究使用，请遵循 Trae 的使用条款
+
+## 许可证
+
+[MIT License](https://github.com/A-23187/trae-api/blob/main/LICENSE)

auth.js

@@ -0,0 +1,62 @@
+// ==UserScript==
+// @name         获取并保存 Trae 登录凭证
+// @namespace    a23187.cn
+// @version      0.1.0
+// @description  获取并保存 Trae 登录凭证
+// @author       A23187
+// @match        https://www.trae.ai/authorization?*
+// @match        https://www.trae.com.cn/authorization?*
+// @icon         https://www.google.com/s2/favicons?sz=64&domain=trae.ai
+// @grant        GM_setClipboard
+// @license      MIT
+// ==/UserScript==
+
+(function () {
+    'use strict';
+    async function getRefreshToken(clientId) {
+        const resp = await fetch('https://www.trae.ai/cloudide/api/v3/trae/oauth/GetRefreshToken', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({ clientID: clientId }),
+        }).then((resp) => resp.json());
+        return resp.Result.RefreshToken;
+    }
+    async function exchangeToken(clientId, refreshToken) {
+        const resp = await fetch('https://api-sg-central.trae.ai/cloudide/api/v3/trae/oauth/ExchangeToken', {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            body: JSON.stringify({
+                ClientID: clientId, RefreshToken: refreshToken, ClientSecret: '-', UserID: '',
+            }),
+        }).then((resp) => resp.json());
+        return resp.Result.Token;
+    }
+    const id = setInterval(() => {
+        const mainTitleDiv = document.getElementsByClassName('main-title')[0];
+        if (mainTitleDiv === undefined || mainTitleDiv?.innerText !== 'Login Successful') {
+            return;
+        }
+        const saveTokenBtn = document.createElement('button');
+        mainTitleDiv.parentElement.appendChild(saveTokenBtn);
+        saveTokenBtn.innerText = 'Save token to clipboard';
+        saveTokenBtn.onclick = async () => {
+            const params = new URLSearchParams(document.location.search);
+            const clientId = params.get('client_id');
+            const refreshToken = await getRefreshToken(clientId);
+            const token = await exchangeToken(clientId, refreshToken);
+            GM_setClipboard(`
+TRAE_APP_ID = "6eefa01c-1036-4c7e-9ca5-d891f63bfcd8"
+TRAE_DEVICE_BRAND = "${params.get('x_device_brand')}"
+TRAE_DEVICE_CPU = "Intel"
+TRAE_DEVICE_ID = "${params.get('x_device_id')}"
+TRAE_DEVICE_TYPE = "${params.get('x_device_type')}"
+TRAE_IDE_TOKEN = "${token}"
+TRAE_IDE_VERSION = ""
+TRAE_IDE_VERSION_CODE = ""
+TRAE_IDE_VERSION_TYPE = ""
+TRAE_MACHINE_ID = "${params.get('x_machine_id')}"
+TRAE_OS_VERSION = "${params.get('x_os_version')}"`, 'text', () => alert('已复制'));
+        };
+        clearInterval(id);
+    }, 1000);
+})();

pyproject.toml

@@ -0,0 +1,16 @@
+[project]
+name = "trae-api"
+version = "0.1.0"
+description = "Make trae's builtin models openai api compatible"
+readme = "README.md"
+requires-python = ">=3.13.2"
+dependencies = [
+    "fastapi>=0.115.12",
+    "httpx>=0.28.1",
+    "httpx-sse>=0.4.0",
+    "python-dotenv>=1.1.0",
+    "uvicorn>=0.34.0",
+]
+
+[tool.black]
+line-length = 120

server.py

@@ -0,0 +1,4 @@
+import uvicorn
+
+if __name__ == "__main__":
+    uvicorn.run("src.app:app", host="0.0.0.0", port=8000)

src/app.py

@@ -0,0 +1,151 @@
+import json
+import time
+import uuid
+from datetime import datetime
+
+import httpx_sse
+from fastapi import FastAPI, Header
+from fastapi.responses import StreamingResponse
+from httpx import AsyncClient
+
+from .env import (
+    TRAE_APP_ID,
+    TRAE_DEVICE_BRAND,
+    TRAE_DEVICE_CPU,
+    TRAE_DEVICE_ID,
+    TRAE_DEVICE_TYPE,
+    TRAE_IDE_TOKEN,
+    TRAE_IDE_VERSION,
+    TRAE_IDE_VERSION_CODE,
+    TRAE_IDE_VERSION_TYPE,
+    TRAE_MACHINE_ID,
+    TRAE_OS_VERSION,
+)
+from .types import (
+    ChatCompletionChunk,
+    ChatCompletionChunkChoice,
+    ChatCompletionRequest,
+    Model,
+)
+
+app = FastAPI(
+    title="Trae2OpenAI Proxy",
+    description="A api proxy to make trae's builtin models openai compatible",
+    version="0.1.0",
+)
+
+
+@app.get("/v1/models")
+async def list_models(ide_token: str = Header(TRAE_IDE_TOKEN, alias="Authorization")) -> list[Model]:
+    ide_token = ide_token.removeprefix("Bearer ")
+    async with AsyncClient() as client:
+        response = await client.get(
+            "https://trae-api-sg.mchost.guru/api/ide/v1/model_list",
+            params={"type": "llm_raw_chat"},
+            headers={
+                "x-app-id": TRAE_APP_ID,
+                "x-device-brand": TRAE_DEVICE_BRAND,
+                "x-device-cpu": TRAE_DEVICE_CPU,
+                "x-device-id": TRAE_DEVICE_ID,
+                "x-device-type": TRAE_DEVICE_TYPE,
+                "x-ide-token": ide_token,
+                "x-ide-version": TRAE_IDE_VERSION,
+                "x-ide-version-code": TRAE_IDE_VERSION_CODE,
+                "x-ide-version-type": TRAE_IDE_VERSION_TYPE,
+                "x-machine-id": TRAE_MACHINE_ID,
+                "x-os-version": TRAE_OS_VERSION,
+            },
+        )
+        return [Model(created=0, id=model["name"]) for model in response.json()["model_configs"]]
+
+
+@app.post("/v1/chat/completions")
+async def create_chat_completions(
+    request: ChatCompletionRequest, ide_token: str = Header(TRAE_IDE_TOKEN, alias="Authorization")
+) -> StreamingResponse:
+    ide_token = ide_token.removeprefix("Bearer ")
+    current_turn = sum(1 for msg in request.messages if msg.role == "user")
+    last_assistant_message = next(filter(lambda msg: msg.role == "assistant", reversed(request.messages)), None)
+
+    async def stream_response():
+        async with AsyncClient() as client:
+            async with httpx_sse.aconnect_sse(
+                client,
+                "POST",
+                "https://trae-api-sg.mchost.guru/api/ide/v1/chat",
+                headers={
+                    "x-app-id": TRAE_APP_ID,
+                    "x-device-brand": TRAE_DEVICE_BRAND,
+                    "x-device-cpu": TRAE_DEVICE_CPU,
+                    "x-device-id": TRAE_DEVICE_ID,
+                    "x-device-type": TRAE_DEVICE_TYPE,
+                    "x-ide-token": ide_token,
+                    "x-ide-version": TRAE_IDE_VERSION,
+                    "x-ide-version-code": TRAE_IDE_VERSION_CODE,
+                    "x-ide-version-type": TRAE_IDE_VERSION_TYPE,
+                    "x-machine-id": TRAE_MACHINE_ID,
+                    "x-os-version": TRAE_OS_VERSION,
+                },
+                json={
+                    "chat_history": [msg.model_dump() for msg in request.messages[:-1]],
+                    "context_resolvers": [],
+                    "conversation_id": str(uuid.uuid4()),
+                    "current_turn": current_turn,
+                    "generate_suggested_questions": False,
+                    "intent_name": "general_qa_intent",
+                    "is_preset": True,
+                    "last_llm_response_info": (
+                        {"turn": current_turn - 1, "is_error": False, "response": last_assistant_message.content}
+                        if last_assistant_message
+                        else {}
+                    ),
+                    "model_name": request.model,
+                    "multi_media": [],
+                    "provider": "",
+                    "session_id": str(uuid.uuid4()),
+                    "user_input": request.messages[-1].content,
+                    "valid_turns": list(range(current_turn)),
+                    "variables": json.dumps(
+                        {"locale": "zh-cn", "current_time": datetime.now().strftime("%Y%m%d %H:%M:%S %A")}
+                    ),
+                },
+            ) as response:
+                chunk = ChatCompletionChunk(
+                    choices=[],
+                    created=int(time.time()),
+                    id="",
+                    model=request.model,
+                )
+                async for sse in response.aiter_sse():
+                    sse_data = sse.json()
+                    if sse.event == "metadata":
+                        chunk.id = str(sse_data["prompt_completion_id"])
+                    elif sse.event == "output":
+                        content = sse_data["response"]
+                        reasoning_content = sse_data["reasoning_content"]
+                        chunk.choices = [
+                            ChatCompletionChunkChoice(
+                                delta={"role": "assistant", "content": content, "reasoning_content": reasoning_content}
+                            )
+                        ]
+                        yield f"data: {chunk.model_dump_json()}\n\n"
+                    elif sse.event == "token_usage":
+                        chunk.choices = []
+                        chunk.usage = {
+                            "completion_tokens": sse_data["completion_tokens"],
+                            "prompt_tokens": sse_data["prompt_tokens"],
+                            "total_tokens": sse_data["total_tokens"],
+                        }
+                        yield f"data: {chunk.model_dump_json()}\n\n"
+                    elif sse.event == "done":
+                        chunk.choices = [ChatCompletionChunkChoice(delta={}, finish_reason="stop")]
+                        yield f"data: {chunk.model_dump_json()}\n\ndata: [DONE]\n\n"
+                    elif sse.event == "error":
+                        chunk.choices = [
+                            ChatCompletionChunkChoice(
+                                delta={"role": "assistant", "content": sse.data}, finish_reason="error"
+                            )
+                        ]
+                        yield f"data: {chunk.model_dump_json()}\n\ndata: [DONE]\n\n"
+
+    return StreamingResponse(stream_response(), media_type="text/event-stream")

src/env.py

@@ -0,0 +1,17 @@
+import os
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+TRAE_APP_ID = str(os.getenv("TRAE_APP_ID"))
+TRAE_DEVICE_BRAND = str(os.getenv("TRAE_DEVICE_BRAND"))
+TRAE_DEVICE_CPU = str(os.getenv("TRAE_DEVICE_CPU"))
+TRAE_DEVICE_ID = str(os.getenv("TRAE_DEVICE_ID"))
+TRAE_DEVICE_TYPE = str(os.getenv("TRAE_DEVICE_TYPE"))
+TRAE_IDE_TOKEN = str(os.getenv("TRAE_IDE_TOKEN"))
+TRAE_IDE_VERSION = str(os.getenv("TRAE_IDE_VERSION"))
+TRAE_IDE_VERSION_CODE = str(os.getenv("TRAE_IDE_VERSION_CODE"))
+TRAE_IDE_VERSION_TYPE = str(os.getenv("TRAE_IDE_VERSION_TYPE"))
+TRAE_MACHINE_ID = str(os.getenv("TRAE_MACHINE_ID"))
+TRAE_OS_VERSION = str(os.getenv("TRAE_OS_VERSION"))

src/types.py

@@ -0,0 +1,52 @@
+from typing import Any, Literal
+
+from pydantic import BaseModel
+
+
+class ChatMessage(BaseModel):
+    content: str | list[dict[str, Any]]
+    name: str | None = None
+    role: Literal["system", "user", "assistant", "tool", "function"]
+
+
+class ChatCompletionRequest(BaseModel):
+    model: str
+    messages: list[ChatMessage]
+    max_completion_tokens: int | None = None
+    modalities: list[str] | None = None
+    response_format: dict[str, Any] | None = None
+    seed: int | None = None
+    stop: str | list[str] | None = None
+    stream: bool | None = False
+    stream_options: dict[str, Any] | None = None
+    temperature: float | None = 1
+    tool_choice: Literal["none", "auto", "required"] | dict[str, Any] | None = None
+    tools: list[dict[str, Any]] | None = None
+    top_k: int | None = 20
+    top_p: float | None = 1
+    web_search_options: dict[str, Any] | None = None
+
+
+class ChatCompletionChunkChoice(BaseModel):
+    delta: dict[str, Any]
+    finish_reason: str | None = None
+    index: int = 0
+    logprobs: dict[str, Any] | None = None
+
+
+class ChatCompletionChunk(BaseModel):
+    choices: list[ChatCompletionChunkChoice]
+    created: int
+    id: str
+    model: str
+    object: str = "chat.completion.chunk"
+    service_tier: str | None = None
+    system_fingerprint: str = ""
+    usage: dict[str, Any] | None = None
+
+
+class Model(BaseModel):
+    created: int
+    id: str
+    object: str = "model"
+    owned_by: str = "trae"

uv.lock

@@ -0,0 +1,252 @@
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+revision = 1
+requires-python = ">=3.13.2"
+
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+version = "0.7.0"
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