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gitbook/content/en/integration/other-tools.md
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gitbook/content/en/integration/other-tools.md
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# Other Tools Integration
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9Router is compatible with any tool that supports the OpenAI API format. This guide covers generic integration patterns for various tools and custom applications.
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## Overview
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9Router provides an OpenAI-compatible API endpoint that works with:
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- Custom scripts and applications
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- API clients and testing tools
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- CLI tools and utilities
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- Third-party integrations
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- Development frameworks
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## Generic Setup Pattern
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Any OpenAI-compatible tool can connect to 9Router using these settings:
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**Local 9Router:**
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```
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Base URL: http://localhost:20128/v1
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API Key: your-api-key-from-dashboard
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Model: any 9Router model (cc/*, cx/*, glm/*, etc.)
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```
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**Cloud 9Router:**
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```
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Base URL: https://9router.com/v1
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API Key: your-api-key-from-dashboard
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Model: any 9Router model (cc/*, cx/*, glm/*, etc.)
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```
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## Available Models
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### Claude Models (Anthropic)
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- `cc/claude-opus-4-5-20251101`
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- `cc/claude-sonnet-4-20250514`
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- `cc/claude-haiku-4-20250514`
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### DeepSeek Models
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- `cx/deepseek-chat`
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- `cx/deepseek-reasoner`
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### GLM Models (Zhipu AI)
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- `glm/glm-4-plus`
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- `glm/glm-4-flash`
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## Integration Examples
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### Python with OpenAI SDK
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```python
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from openai import OpenAI
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client = OpenAI(
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api_key="your-api-key-from-dashboard",
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base_url="http://localhost:20128/v1"
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)
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response = client.chat.completions.create(
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model="cc/claude-sonnet-4-20250514",
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messages=[
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{"role": "user", "content": "Hello, how are you?"}
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]
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)
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print(response.choices[0].message.content)
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```
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### Node.js with OpenAI SDK
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```javascript
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import OpenAI from "openai";
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const client = new OpenAI({
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apiKey: "your-api-key-from-dashboard",
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baseURL: "http://localhost:20128/v1"
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});
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const response = await client.chat.completions.create({
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model: "cc/claude-sonnet-4-20250514",
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messages: [
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{ role: "user", content: "Hello, how are you?" }
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]
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});
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console.log(response.choices[0].message.content);
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```
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### cURL Command
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```bash
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curl http://localhost:20128/v1/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer your-api-key-from-dashboard" \
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-d '{
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"model": "cc/claude-sonnet-4-20250514",
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"messages": [
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{"role": "user", "content": "Hello, how are you?"}
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]
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}'
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```
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### HTTP Client (Postman, Insomnia)
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**Request:**
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```
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POST http://localhost:20128/v1/chat/completions
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```
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**Headers:**
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```
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Content-Type: application/json
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Authorization: Bearer your-api-key-from-dashboard
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```
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**Body:**
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```json
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{
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"model": "cc/claude-sonnet-4-20250514",
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"messages": [
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{"role": "user", "content": "Hello, how are you?"}
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],
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"temperature": 0.7,
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"max_tokens": 1000
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}
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```
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### LangChain Integration
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```python
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from langchain.chat_models import ChatOpenAI
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from langchain.schema import HumanMessage
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llm = ChatOpenAI(
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model_name="cc/claude-sonnet-4-20250514",
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openai_api_key="your-api-key-from-dashboard",
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openai_api_base="http://localhost:20128/v1",
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temperature=0.7
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)
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messages = [HumanMessage(content="Explain quantum computing")]
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response = llm(messages)
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print(response.content)
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```
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### LlamaIndex Integration
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```python
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from llama_index.llms import OpenAI
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llm = OpenAI(
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model="cc/claude-sonnet-4-20250514",
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api_key="your-api-key-from-dashboard",
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api_base="http://localhost:20128/v1"
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)
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response = llm.complete("What is machine learning?")
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print(response.text)
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```
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## Custom Script Examples
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### Batch Processing Script
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```python
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import openai
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import json
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openai.api_key = "your-api-key-from-dashboard"
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openai.api_base = "http://localhost:20128/v1"
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def process_batch(prompts, model="cx/deepseek-chat"):
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results = []
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for prompt in prompts:
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response = openai.ChatCompletion.create(
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model=model,
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messages=[{"role": "user", "content": prompt}]
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)
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results.append({
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"prompt": prompt,
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"response": response.choices[0].message.content
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})
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return results
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prompts = [
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"Explain AI in one sentence",
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"What is machine learning?",
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"Define neural networks"
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]
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results = process_batch(prompts)
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print(json.dumps(results, indent=2))
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```
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### Streaming Response Handler
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```javascript
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import OpenAI from "openai";
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const client = new OpenAI({
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apiKey: "your-api-key-from-dashboard",
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baseURL: "http://localhost:20128/v1"
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});
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async function streamResponse(prompt) {
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const stream = await client.chat.completions.create({
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model: "cc/claude-sonnet-4-20250514",
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messages: [{ role: "user", content: prompt }],
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stream: true
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});
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for await (const chunk of stream) {
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const content = chunk.choices[0]?.delta?.content || "";
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process.stdout.write(content);
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}
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}
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streamResponse("Write a short story about AI");
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```
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### Multi-Model Comparison
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```python
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from openai import OpenAI
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client = OpenAI(
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api_key="your-api-key-from-dashboard",
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base_url="http://localhost:20128/v1"
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)
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models = [
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"cc/claude-sonnet-4-20250514",
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"cx/deepseek-chat",
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"glm/glm-4-plus"
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]
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prompt = "Explain quantum computing in simple terms"
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for model in models:
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response = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": prompt}]
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)
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print(f"\n=== {model} ===")
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print(response.choices[0].message.content)
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```
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## Common Integration Patterns
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### Environment Variables
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Store credentials securely:
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```bash
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# .env file
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ROUTER_API_KEY=your-api-key-from-dashboard
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ROUTER_BASE_URL=http://localhost:20128/v1
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ROUTER_MODEL=cc/claude-sonnet-4-20250514
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```
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```python
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import os
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from openai import OpenAI
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client = OpenAI(
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api_key=os.getenv("ROUTER_API_KEY"),
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base_url=os.getenv("ROUTER_BASE_URL")
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)
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```
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### Error Handling
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```python
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from openai import OpenAI, OpenAIError
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client = OpenAI(
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api_key="your-api-key",
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base_url="http://localhost:20128/v1"
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)
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try:
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response = client.chat.completions.create(
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model="cc/claude-sonnet-4-20250514",
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messages=[{"role": "user", "content": "Hello"}]
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)
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print(response.choices[0].message.content)
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except OpenAIError as e:
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print(f"Error: {e}")
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```
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### Retry Logic
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```python
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import time
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from openai import OpenAI, RateLimitError
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client = OpenAI(
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api_key="your-api-key",
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base_url="http://localhost:20128/v1"
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)
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def chat_with_retry(prompt, max_retries=3):
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for attempt in range(max_retries):
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try:
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response = client.chat.completions.create(
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model="cc/claude-sonnet-4-20250514",
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messages=[{"role": "user", "content": prompt}]
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)
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return response.choices[0].message.content
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except RateLimitError:
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if attempt < max_retries - 1:
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time.sleep(2 ** attempt) # Exponential backoff
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else:
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raise
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```
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## Troubleshooting
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### Connection Issues
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**Problem:** Cannot connect to 9Router
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```bash
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# Check if 9Router is running
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curl http://localhost:20128/health
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# Expected response:
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{"status": "ok"}
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```
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**Solution:**
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- Verify 9Router is running
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- Check port 20128 is not blocked
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- Ensure correct base URL (include `/v1`)
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### Authentication Errors
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**Problem:** 401 Unauthorized
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```
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Error: Invalid API key
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```
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**Solution:**
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- Verify API key from dashboard
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- Check Authorization header format: `Bearer your-api-key`
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- Ensure no extra spaces or newlines in API key
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### Model Not Found
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**Problem:** 404 Model not found
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```
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Error: Model 'cc/claude-opus' not found
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```
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**Solution:**
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- Use exact model name (case-sensitive)
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- Check available models: `curl http://localhost:20128/v1/models`
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- Verify model is enabled in your plan
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### Timeout Issues
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**Problem:** Request timeout
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```
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Error: Request timed out after 30s
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```
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**Solution:**
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- Increase timeout in client configuration
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- Use faster models for time-sensitive tasks
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- Check network connection to 9Router
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### Rate Limiting
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**Problem:** 429 Too Many Requests
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```
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Error: Rate limit exceeded
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```
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**Solution:**
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- Implement exponential backoff
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- Reduce request frequency
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- Check rate limits in dashboard
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- Consider upgrading plan
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## Best Practices
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### Security
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- Store API keys in environment variables
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- Never commit API keys to version control
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- Use HTTPS for cloud deployments
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- Rotate API keys regularly
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### Performance
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- Use appropriate models for task complexity
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- Implement caching for repeated queries
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- Use streaming for long responses
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- Batch requests when possible
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### Error Handling
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- Always implement try-catch blocks
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- Add retry logic with exponential backoff
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- Log errors for debugging
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- Provide fallback mechanisms
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### Cost Optimization
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- Choose cost-effective models for simple tasks
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- Cache responses when appropriate
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- Monitor usage in dashboard
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- Set request limits in code
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## Next Steps
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- [Configure Cursor](cursor.md) for IDE integration
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- [Set up Continue](continue.md) for VSCode
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- [Explore CLI usage](../cli/basic-usage.md)
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- [Learn about model selection](../models/overview.md)
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- [API Reference](../api/reference.md)
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