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.env

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+TRAE_APP_ID = "6eefa01c-1036-4c7e-9ca5-d891f63bfcd8"
+TRAE_DEVICE_BRAND = "Modified by dsanke"
+TRAE_DEVICE_CPU = "Intel"
+TRAE_DEVICE_ID = "7472204647139558913"
+TRAE_DEVICE_TYPE = "windows"
+TRAE_IDE_VERSION = ""
+TRAE_IDE_VERSION_CODE = ""
+TRAE_IDE_VERSION_TYPE = ""
+TRAE_MACHINE_ID = "9f569ccf7d06c0c753133b06a0595ae27fabd4ab2d8f8e76fdf32b95b037085c"
+TRAE_OS_VERSION = "Microsoft Windows 10 רҵ��"

Dockerfile

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+# 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"]

README.md

@@ -1,10 +1,13 @@
----
-title: Trae
-emoji: 💻
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: trae
+emoji: 🌍
+colorFrom: red
+colorTo: red
+sdk: docker
+pinned: false
+app_port: 8000
+---
+
+
+
+

server.py

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

src/app.py

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+import json
+import time
+import uuid
+from datetime import datetime
+
+import httpx_sse
+from fastapi import FastAPI, Header
+from fastapi.responses import JSONResponse, 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")) -> JSONResponse:
+    ide_token = ide_token.removeprefix("Bearer ")
+    async with AsyncClient() as client:
+        response = await client.get(
+            "https://trae-api-us.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 JSONResponse(
+            {
+                "object": "list",
+                "data": [Model(created=0, id=model["name"]).model_dump() 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[:-1] 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-us.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(),
+                            "status": "success",
+                            "locale": "zh-cn",
+                        }
+                        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

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+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

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+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

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