ai-api-integration

Integration recipes for accessing GPT-5, Claude Opus 4.7, Gemini 3.1, Sora 2, Suno and other major models through a single OpenAI-compatible endpoint.

🇨🇳 中文

Background

Accessing major LLM APIs from regions where some providers require workarounds usually means dealing with three things per vendor: account creation, payment, and network reachability. Direct integration also means juggling different SDKs, parameters, and billing surfaces.

A cleaner pattern in practice is to route everything through an OpenAI-compatible gateway: same base_url, same OpenAI protocol, model selection via the model parameter. Most AI tools (Cursor, Cline, ChatBox, etc.) support custom OpenAI endpoints natively, so a one-time configuration unlocks all backend models.

This repository documents the full integration flow:

• 10 popular AI tools (IDE, CLI, desktop, web UI, low-code platforms, application frameworks)
• 7 modalities (text, code, image, video, audio, vision, embeddings)
• 12+ models with context limits and selection guidance
• 3 languages of runnable code samples (Python / Node.js / curl) covering chat, streaming, function calling, image generation, vision input

The example endpoint is 产灵 API, but the code is plain OpenAI-protocol — pointing base_url at any compatible service (including OpenAI's official endpoint) works the same way.

Quick start

Switch the model value to access a different backend (gpt-5, gemini-3.1-pro, deepseek-v3.2, etc.). The rest of the code is unchanged.

Python

from openai import OpenAI

client = OpenAI(
    api_key="sk-xxx",
    base_url="http://xdhdancer.top/v1",
)

resp = client.chat.completions.create(
    model="claude-opus-4-7",
    messages=[{"role": "user", "content": "Hello"}],
)
print(resp.choices[0].message.content)

Node.js

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-xxx",
  baseURL: "http://xdhdancer.top/v1",
});

const resp = await client.chat.completions.create({
  model: "claude-opus-4-7",
  messages: [{ role: "user", content: "Hello" }],
});

console.log(resp.choices[0].message.content);

curl

curl http://xdhdancer.top/v1/chat/completions \
  -H "Authorization: Bearer sk-xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-4-7",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

More examples (streaming, function calling, image generation, vision) in examples/.

How it works

your code ──┐
            │ standard OpenAI SDK
            ▼
┌──────────────────────┐
│  OpenAI-compatible   │     ┌─ OpenAI    (GPT-5, o3-pro, gpt-image-2 ...)
│  gateway             │ ──▶ │─ Anthropic (Claude Opus / Sonnet / Haiku)
│  base_url + key      │     │─ Google    (Gemini 3.1 ...)
└──────────────────────┘     │─ DeepSeek / Moonshot / Zhipu ...
                             └─ Sora / Suno / image / video providers ...

Your code only sees one endpoint. The model parameter decides which vendor handles the request.

Models

Selected entries; full list in docs/modalities/text-models.md.

Model id	Vendor	Context	Notes
gpt-5	OpenAI	128k	General reasoning, code
gpt-5-mini	OpenAI	128k	Faster, cheaper
gpt-5-codex	OpenAI	128k	Code-specialized
o3-pro	OpenAI	200k	Deep reasoning
claude-opus-4-7	Anthropic	200k	Long-context coding
claude-sonnet-4-6	Anthropic	200k	Balanced
claude-haiku-4-5	Anthropic	200k	Lowest latency
gemini-3.1-pro	Google	2M	Long documents, video
gemini-3.1-flash	Google	1M	Fast
deepseek-v3.2	DeepSeek	128k	Strong Chinese, low cost
kimi-k2	Moonshot	200k	Long Chinese documents
glm-4.6	Zhipu	128k	Chinese general-purpose
gpt-image-2	OpenAI	—	Image generation
sora-2	OpenAI	—	Video generation
suno-v4	Suno	—	Music generation
text-embedding-3-large	OpenAI	—	Embeddings

Tool setup

Tool	Type	Guide
Cursor	AI-first IDE	cursor-setup.md
Cline	VS Code agent	cline-setup.md
Claude Code	Anthropic CLI	claude-code-setup.md
ChatBox	Cross-platform desktop	chatbox-setup.md
Dify	Low-code LLM workflow	dify-setup.md
LobeChat	Self-hosted chat UI	lobechat-setup.md
Open WebUI	Self-hosted web UI	openwebui-setup.md
Continue	VS Code coding assistant	continue-setup.md
LangChain	LLM app framework	langchain-setup.md
LlamaIndex	RAG / data framework	llamaindex-setup.md

Modality guides

Text · Code · Image · Video · Audio · Vision · Embeddings

Full demo

src/ contains a multi-model CLI chat tool implemented in Python, Node.js, and Go — same functionality, three languages:

• Type a message → streamed reply
• /model gpt-5 to switch models on the fly
• /image a sunset over mountains to generate via gpt-image-2
• Multi-turn history

See src/README.md for details.

Code examples

Standalone snippets (chat, streaming, function calling, image generation, vision):

Language	Coverage	Path
Python	chat / streaming / image / function-calling / vision	examples/python/
Node.js	chat / streaming / image	examples/node/
curl	chat / streaming / image / embeddings	examples/curl/

Direct vs gateway

Aspect	Direct (per vendor)	Unified OpenAI-compatible gateway
Sign-up	One per vendor	Once
Billing	Multiple invoices	Single
Switching models	Different SDK + params + request shape	Change model string
Failover	Custom logic per vendor	Change model to switch backend
Network	Some vendors require workarounds	Handled by the gateway
New models	Each vendor's SDK update	Usually available immediately
Rate limits	Per-vendor quotas	Combined quota

When direct is better: single-model, heavy long-term use — unit cost may be lower with the original vendor. When a gateway is better: multi-model comparisons, runtime fallback, low ops overhead.

FAQ

Why not call the OpenAI API directly? You can — the code is fully compatible. Replace base_url with https://api.openai.com/v1. The gateway approach is one option, not a requirement.

Can I use my own gateway? Yes. Any OpenAI-compatible endpoint (LiteLLM, One-API, new-api, etc., self-hosted) works without code changes.

Does this work without an account at the example gateway? Yes. Use any OpenAI-compatible endpoint where you have a key.

Which model should I start with? - General chat and coding: claude-sonnet-4-6 - Heavy code, multi-file refactors: claude-opus-4-7 - Long-document analysis: gemini-3.1-pro (2M context) - Chinese workloads, low budget: deepseek-v3.2 or kimi-k2

How do I tell which vendor a model id belongs to? By prefix: gpt-* / o* are OpenAI, claude-* is Anthropic, gemini-* is Google, deepseek-* / kimi-* / glm-* are Chinese providers.

Contributing

Setup guides for additional tools or examples for new models are welcome. Open a PR or an issue.

License

MIT