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Integrations Overview

AnyAPI seamlessly integrates with popular AI frameworks, development tools, and platforms to accelerate your AI development workflow. Our integrations provide native support, optimized configurations, and simplified authentication.

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Available Integrations

LiteLLM

Universal LLM interface with unified API

Langflow

Visual AI workflow builder and automation

Cline

AI-powered code editing and automation

Continue.dev

VS Code AI coding assistant extension

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Integration Benefits

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Unified API Access

  • Single endpoint for multiple AI models
  • Consistent authentication across all services
  • Standardized response formats for easy parsing
  • Rate limiting and quotas managed automatically

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Developer Experience

  • Native SDK support in popular programming languages
  • Framework-specific optimizations for better performance
  • Pre-built templates and starter projects
  • Comprehensive documentation and examples

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Enterprise Features

  • SSO integration with popular identity providers
  • Audit logging and compliance tracking
  • Custom deployment options including on-premises
  • Priority support and dedicated account management

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Quick Setup Guide

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1. Get Your API Key

First, obtain your AnyAPI key from the dashboard: 
export ANYAPI_KEY="your-api-key-here"

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2. Choose Your Integration

Select the integration that best fits your workflow:
  • LiteLLM
  • Langflow
  • VS Code
  • Cline
pip install litellm

from litellm import completion

response = completion(
    model="anyapi/gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
    api_key=os.environ["ANYAPI_KEY"]
)

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3. Test Your Connection

Verify the integration works: 
# Test API connection
import requests

response = requests.post(
    "https://api.anyapi.ai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {os.environ['ANYAPI_KEY']}",
        "Content-Type": "application/json"
    },
    json={
        "model": "gpt-4o",
        "messages": [{"role": "user", "content": "Test connection"}]
    }
)

print("✅ Connection successful!" if response.status_code == 200 else "❌ Connection failed")

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Integration Architecture

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OpenAI-Compatible API

AnyAPI implements the OpenAI API specification, ensuring compatibility with existing tools:

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Request Flow

  1. Authentication - API key validation and user identification
  2. Model Selection - Route request to appropriate model provider
  3. Processing - Execute request with optimized parameters
  4. Response - Return standardized response format
  5. Monitoring - Log usage and performance metrics

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Supported Frameworks

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LLM Frameworks

  • LangChain - Building applications with LLMs
  • LlamaIndex - Data framework for LLM applications
  • Haystack - End-to-end NLP framework
  • Semantic Kernel - Microsoft’s AI orchestration SDK

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Development Tools

  • VS Code - Code editor with AI extensions
  • JetBrains IDEs - IntelliJ, PyCharm, WebStorm
  • Cursor - AI-first code editor
  • GitHub Copilot - AI pair programmer

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Workflow Platforms

  • n8n - Workflow automation platform
  • Zapier - No-code automation
  • Jupyter - Interactive development environment
  • Streamlit - AI app deployment platform

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Authentication Methods

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API Key Authentication

Simple and secure API key authentication: 
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

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Environment Variables

Recommended for production deployments: 
# .env file
ANYAPI_KEY=your-api-key
ANYAPI_BASE_URL=https://api.anyapi.ai/v1

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SDK Authentication

Language-specific SDK configurations: 
from anyapi import AnyAPI

client = AnyAPI(api_key="your-key")
# or use environment variable
client = AnyAPI()  # reads ANYAPI_KEY

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Configuration Best Practices

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Environment Management

Use different configurations for different environments: 
# config/development.yml
anyapi:
  base_url: https://api.anyapi.ai/v1
  timeout: 30
  retries: 3
  
# config/production.yml  
anyapi:
  base_url: https://api.anyapi.ai/v1
  timeout: 60
  retries: 5
  rate_limit: 1000

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Error Handling

Implement robust error handling: 
import anyapi
from anyapi.exceptions import APIError, RateLimitError

try:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello"}]
    )
except RateLimitError:
    # Handle rate limiting
    time.sleep(60)
    retry_request()
except APIError as e:
    # Handle API errors
    logging.error(f"API error: {e.status_code} - {e.message}")
except Exception as e:
    # Handle unexpected errors
    logging.error(f"Unexpected error: {e}")

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Monitoring and Logging

Track usage and performance: 
import logging
from anyapi.middleware import LoggingMiddleware

# Configure logging
logging.basicConfig(level=logging.INFO)

# Add middleware for request/response logging
client = AnyAPI(
    api_key="your-key",
    middleware=[LoggingMiddleware()]
)

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Migration Guides

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From OpenAI

Migrate from OpenAI with minimal code changes: 
# Before (OpenAI)
from openai import OpenAI
client = OpenAI(api_key="openai-key")

# After (AnyAPI)
from openai import OpenAI
client = OpenAI(
    api_key="anyapi-key",
    base_url="https://api.anyapi.ai/v1"
)

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From Anthropic

Switch from Anthropic Claude: 
# Before (Anthropic)
from anthropic import Anthropic
client = Anthropic(api_key="anthropic-key")

# After (AnyAPI)  
from openai import OpenAI
client = OpenAI(
    api_key="anyapi-key", 
    base_url="https://api.anyapi.ai/v1"
)

# Use Claude models
response = client.chat.completions.create(
    model="claude-3-5-sonnet",
    messages=[{"role": "user", "content": "Hello"}]
)

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From Google AI

Migrate from Google’s AI services: 
# Before (Google AI)
import google.generativeai as genai
genai.configure(api_key="google-key")

# After (AnyAPI)
from openai import OpenAI
client = OpenAI(
    api_key="anyapi-key",
    base_url="https://api.anyapi.ai/v1"  
)

# Use Gemini models
response = client.chat.completions.create(
    model="gemini-pro",
    messages=[{"role": "user", "content": "Hello"}]
)

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Common Integration Patterns

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Retry Logic

Implement exponential backoff: 
import time
import random

def api_call_with_retry(func, max_retries=3):
    for attempt in range(max_retries):
        try:
            return func()
        except Exception as e:
            if attempt == max_retries - 1:
                raise e
            wait_time = (2 ** attempt) + random.uniform(0, 1)
            time.sleep(wait_time)

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Streaming Responses

Handle streaming data efficiently: 
def stream_response(messages):
    stream = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        stream=True
    )
    
    for chunk in stream:
        if chunk.choices[0].delta.content:
            yield chunk.choices[0].delta.content

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Batch Processing

Process multiple requests efficiently: 
async def batch_process(requests, batch_size=10):
    results = []
    
    for i in range(0, len(requests), batch_size):
        batch = requests[i:i + batch_size]
        batch_results = await asyncio.gather(
            *[process_single_request(req) for req in batch]
        )
        results.extend(batch_results)
    
    return results

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Troubleshooting

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Common Issues

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Authentication Errors

Error: Invalid API key
Solution: Verify your API key is correct and has the necessary permissions.

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Rate Limiting

Error: Rate limit exceeded
Solution: Implement exponential backoff and respect rate limits.

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Model Not Found

Error: Model 'invalid-model' not found
Solution: Check the available models list.

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Debug Mode

Enable debug logging for troubleshooting: 
import logging
logging.basicConfig(level=logging.DEBUG)

# This will log all HTTP requests/responses
client = AnyAPI(api_key="your-key", debug=True)

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Health Check

Verify service availability: 
def health_check():
    try:
        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=[{"role": "user", "content": "ping"}],
            max_tokens=1
        )
        return True
    except Exception:
        return False

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Next Steps

LiteLLM Integration

Universal LLM interface setup

Langflow Integration

Visual workflow automation

VS Code Setup

AI coding assistant configuration

API Reference

Complete API documentation

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Support

Need help with integrations? Our integration team is here to help you get up and running quickly with any supported platform or framework.
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