OpenAI Compatibility with GeminiAI

GeminiAI is a very developer-friendly large model. It not only has a beautiful interface and powerful functions, but also provides a considerable amount of free daily quota, which is enough to meet daily use needs.

However, it also has some inconveniences, such as the need to always use scientific internet access, and the API is not compatible with the OpenAI SDK.

In order to solve these problems and achieve compatibility with OpenAI, I wrote a piece of JavaScript code and deployed it to Cloudflare, binding my own domain name. In this way, I can use Gemini in China without scientific internet access, and also be compatible with OpenAI. In any tool that uses OpenAI, you only need to simply replace the API address and key (SK).

Create a Worker on Cloudflare

If you don't have a Cloudflare account yet, please register for one (free). The registration address is: https://dash.cloudflare.com/ After logging in, remember to bind your own domain name, otherwise you will not be able to achieve proxy-free access.

After logging in, find Compute (Workers) in the left sidebar and click, then click the Create button.

Click Create Worker on the page that appears.

Then click Deploy in the lower right corner to complete the creation of the Worker.

Edit Code

The following code is the key to achieving compatibility with OpenAI. Please copy it and replace the default generated code in the Worker.

On the page after the deployment is complete, click Edit Code.

Delete all the code on the left, then copy the following code and paste it, and finally click Deploy in the upper right corner.

Copy the following code

export default {
  async fetch (request) {
    if (request.method === "OPTIONS") {
      return handleOPTIONS();
    }
    const errHandler = (err) => {
      console.error(err);
      return new Response(err.message, fixCors({ status: err.status ?? 500 }));
    };
    try {
      const auth = request.headers.get("Authorization");
      const apiKey = auth?.split(" ")[1];
      const assert = (success) => {
        if (!success) {
          throw new HttpError("The specified HTTP method is not allowed for the requested resource", 400);
        }
      };
      const { pathname } = new URL(request.url);
	  if(!pathname.endsWith("/chat/completions")){
		  return new Response("hello")
	  }
        assert(request.method === "POST");
        return handleCompletions(await request.json(), apiKey).catch(errHandler);
    } catch (err) {
      return errHandler(err);
    }
  }
};

class HttpError extends Error {
  constructor(message, status) {
    super(message);
    this.name = this.constructor.name;
    this.status = status;
  }
}

const fixCors = ({ headers, status, statusText }) => {
  headers = new Headers(headers);
  headers.set("Access-Control-Allow-Origin", "*");
  return { headers, status, statusText };
};

const handleOPTIONS = async () => {
  return new Response(null, {
    headers: {
      "Access-Control-Allow-Origin": "*",
      "Access-Control-Allow-Methods": "*",
      "Access-Control-Allow-Headers": "*",
    }
  });
};

const BASE_URL = "https://generativelanguage.googleapis.com";
const API_VERSION = "v1beta";

// https://github.com/google-gemini/generative-ai-js/blob/cf223ff4a1ee5a2d944c53cddb8976136382bee6/src/requests/request.ts#L71
const API_CLIENT = "genai-js/0.21.0"; // npm view @google/generative-ai version
const makeHeaders = (apiKey, more) => ({
  "x-goog-api-client": API_CLIENT,
  ...(apiKey && { "x-goog-api-key": apiKey }),
  ...more
});

const DEFAULT_MODEL = "gemini-2.0-flash-exp";
async function handleCompletions (req, apiKey) {
  let model = DEFAULT_MODEL;
  if(req.model.startsWith("gemini-")) {
      model = req.model;
  }
  const TASK = "generateContent";
  let url = `${BASE_URL}/${API_VERSION}/models/${model}:${TASK}`;

  const response = await fetch(url, {
    method: "POST",
    headers: makeHeaders(apiKey, { "Content-Type": "application/json" }),
    body: JSON.stringify(await transformRequest(req)), // try
  });

  let body = response.body;
  if (response.ok) {
    let id = generateChatcmplId();
      body = await response.text();
      body = processCompletionsResponse(JSON.parse(body), model, id);
  }
  return new Response(body, fixCors(response));
}

const harmCategory = [
  "HARM_CATEGORY_HATE_SPEECH",
  "HARM_CATEGORY_SEXUALLY_EXPLICIT",
  "HARM_CATEGORY_DANGEROUS_CONTENT",
  "HARM_CATEGORY_HARASSMENT",
  "HARM_CATEGORY_CIVIC_INTEGRITY",
];
const safetySettings = harmCategory.map(category => ({
  category,
  threshold: "BLOCK_NONE",
}));
const fieldsMap = {
  stop: "stopSequences",
  n: "candidateCount", 
  max_tokens: "maxOutputTokens",
  max_completion_tokens: "maxOutputTokens",
  temperature: "temperature",
  top_p: "topP",
  top_k: "topK", 
  frequency_penalty: "frequencyPenalty",
  presence_penalty: "presencePenalty",
};
const transformConfig = (req) => {
  let cfg = {};

  for (let key in req) {
    const matchedKey = fieldsMap[key];
    if (matchedKey) {
      cfg[matchedKey] = req[key];
    }
  }
  cfg.responseMimeType = "text/plain";
  return cfg;
};


const transformMsg = async ({ role, content }) => {
  const parts = [];
  if (!Array.isArray(content)) {

    parts.push({ text: content });
    return { role, parts };
  }

  for (const item of content) {
    switch (item.type) {
      case "text":
        parts.push({ text: item.text });
        break;

      case "input_audio":
        parts.push({
          inlineData: {
            mimeType: "audio/" + item.input_audio.format,
            data: item.input_audio.data,
          }
        });
        break;
      default:
        throw new TypeError(`Unknown "content" item type: "${item.type}"`);
    }
  }
  if (content.every(item => item.type === "image_url")) {
    parts.push({ text: "" });	
  }
  return { role, parts };
};

const transformMessages = async (messages) => {
  if (!messages) { return; }
  const contents = [];
  let system_instruction;
  for (const item of messages) {
    if (item.role === "system") {
      delete item.role;
      system_instruction = await transformMsg(item);
    } else {
      item.role = item.role === "assistant" ? "model" : "user";
      contents.push(await transformMsg(item));
    }
  }
  if (system_instruction && contents.length === 0) {
    contents.push({ role: "model", parts: { text: " " } });
  }
  return { system_instruction, contents };
};

const transformRequest = async (req) => ({
  ...await transformMessages(req.messages),
  safetySettings,
  generationConfig: transformConfig(req),
});

const generateChatcmplId = () => {
  const characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789";
  const randomChar = () => characters[Math.floor(Math.random() * characters.length)];
  return "chatcmpl-" + Array.from({ length: 29 }, randomChar).join("");
};

const reasonsMap = { 
  "STOP": "stop",
  "MAX_TOKENS": "length",
  "SAFETY": "content_filter",
  "RECITATION": "content_filter"
};
const SEP = "\n\n|>";
const transformCandidates = (key, cand) => ({
  index: cand.index || 0,
  [key]: {
    role: "assistant",
    content: cand.content?.parts.map(p => p.text).join(SEP) },
  logprobs: null,
  finish_reason: reasonsMap[cand.finishReason] || cand.finishReason,
});
const transformCandidatesMessage = transformCandidates.bind(null, "message");
const transformCandidatesDelta = transformCandidates.bind(null, "delta");

const transformUsage = (data) => ({
  completion_tokens: data.candidatesTokenCount,
  prompt_tokens: data.promptTokenCount,
  total_tokens: data.totalTokenCount
});

const processCompletionsResponse = (data, model, id) => {
  return JSON.stringify({
    id,
    choices: data.candidates.map(transformCandidatesMessage),
    created: Math.floor(Date.now()/1000),
    model,
    object: "chat.completion",
    usage: transformUsage(data.usageMetadata),
  });
};

Bind Domain Name

After the deployment is complete, there will be a second-level subdomain provided by Cloudflare, but this domain name cannot be accessed normally in China, so you need to bind your own domain name to achieve proxy-free access.

After the deployment is complete, click Back on the left.

Then find Settings -- Domains and Routes, click Add.

As shown in the figure below, add the domain name you have already hosted on Cloudflare.

After completion, you can use this domain name to access Gemini.

Use OpenAI SDK to Access Gemini

from openai import OpenAI, APIConnectionError
model = OpenAI(api_key='Gemini API Key', base_url='https://your custom domain.com')
response = model.chat.completions.create(
        model='gemini-2.0-flash-exp',
        messages=[
            {
                'role': 'user',
                'content': 'Who are you'},
        ]
    )
    
print(response.choices[0])

The return is as follows:

Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='I am a large language model, trained by Google.\n', refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None))

Use in Other OpenAI-Compatible Tools

Find the location to configure OpenAI information in the tool, change the API address to the custom domain name you added in Cloudflare, change the SK to your Gemini API Key, and fill in the model as gemini-2.0-flash-exp.

Access Directly Using Requests

If you do not use the OpenAI SDK, you can also directly use the requests library to access it.

import requests

payload={
    "model":"gemini-1.5-flash",
    "messages":[{
        "role":"user",
        "content":[{"type":"text","text":"Who are you？"}]
    }]
}

res=requests.post('https://xxxx.com/chat/completions',headers={"Authorization":"Bearer Your Gemini API Key","Content-Type":"application:/json"},json=payload)

print(res.json())

Output as follows:

Related Resources

1. The source code is modified from the project PublicAffairs/openai-gemini
2. GeminiAI Documentation