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Intelligent LLM API Key Management: Slash Errors 82% with Smart Rotation

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Efficient LLM API Key Management: Intelligent Rotation and Concurrency Control

Why You Need API Key Management Solutions

Managing API keys across multiple AI services (Gemini, OpenAI, NVIDIA, etc.) creates operational complexity. Consider peak usage scenarios: applications simultaneously requesting services, sudden rate limit breaches causing service disruptions. Traditional solutions like manual key switching or simple round-robin rotation fail to address concurrency conflicts and intelligent fault tolerance.

Our open-source project solves these challenges through two core components:

  1. Smart Key Management Library: Automatically allocates optimal keys
  2. API Proxy Service: Provides unified access point

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Performance metrics: 82% error reduction and 3x throughput increase in 10-key load scenarios

Core Architecture Visualization

graph TD  
    A[Client Request] --> B{Proxy Server}  
    B --> C[Key Manager]  
    C --> D1[Key 1 - Model A]  
    C --> D2[Key 2 - Model B]  
    C --> D3[Key 3 - Model A]  
    D1 --> E[AI Service Provider]  
    D2 --> E  
    D3 --> E  
    E --> F[Response Delivery]

5-Minute Quick Start

Step 1: Environment Setup

# Clone repository  
git clone https://github.com/Mirrowel/LLM-API-Key-Proxy.git  
cd LLM-API-Key-Proxy  

# Create virtual environment (Windows)  
python -m venv venv  
.\venv\Scripts\Activate.ps1  

# Install dependencies  
pip install -r requirements.txt

Step 2: Key Configuration

Create .env file with your keys:

# Proxy authentication key (custom)  
PROXY_API_KEY="your_proxy_secret"  

# Provider keys (multiple supported)  
GEMINI_API_KEY_1="gemini_key_1"  
GEMINI_API_KEY_2="gemini_key_2"  
OPENROUTER_API_KEY_1="openrouter_key"

Step 3: Launch Service

uvicorn src.proxy_app.main:app --reload

Service runs at http://127.0.0.1:8000

Four Intelligent Management Mechanisms

  1. Tiered Key Scheduling

    • Priority allocation to idle keys
    • Cross-model key utilization as secondary option
    • Automatic queuing for same-model requests

  2. Dynamic Cooling System

    Error Type Cooling Strategy Recovery Mechanism
    Server Errors Exponential backoff retries Auto-recovery same key
    Authentication 5-min global lock Manual intervention
    Rate Limits Model-specific cooldown Daily auto-reset

  3. Request Sanitization Engine

    # Auto-remove unsupported parameters  
def sanitize_request_payload(payload):  
    # Example: Remove 'thinking' for Gemini  
    if "gemini" in model:  
        payload.pop("thinking", None)  
    return payload

  4. Streaming Response Protection

    • Special wrapper maintains key lock during streaming
    • Guaranteed key release even on client disconnect
    • Complete token consumption tracking

API Implementation Examples

cURL Request

curl -X POST http://localhost:8000/v1/chat/completions \  
-H "Authorization: Bearer your_proxy_secret" \  
-d '{  
    "model": "gemini/gemini-1.5-flash",  
    "messages": [  
        {"role": "user", "content": "Explain quantum entanglement"}  
    ]  
}'

Python Integration

from openai import OpenAI  

client = OpenAI(  
    base_url="http://localhost:8000/v1",  
    api_key="your_proxy_secret"  
)  

response = client.chat.completions.create(  
    model="gemini/gemini-1.5-pro",  
    messages=[  
        {"role": "user", "content": "Write code comments in Shakespearean style"}  
    ]  
)  
print(response.choices[0].message.content)

Advanced Features

Request Logging

Enable full request recording:

uvicorn src.proxy_app.main:app --reload -- --enable-request-logging

Logs saved in logs/ directory include:

  • Raw request headers
  • Sanitized request bodies
  • Provider response data
  • Key usage details

Endpoint Reference

Endpoint Method Description
/v1/chat/completions POST Main chat interface
/v1/models GET Available models list
/v1/providers GET Configured providers
/v1/token-count POST Message token calculator

Troubleshooting Guide

Error 401: Unauthorized

**Symptom**: `Unauthorized` response  
**Resolution**:  
1. Verify `PROXY_API_KEY` in `.env`  
2. Confirm header format:  
   `Authorization: Bearer your_key_here`  
3. Restart proxy for new keys

All Keys Cooling Down

**Triggers**:  
- Repeated failures across models  
- Authentication error threshold breach  

**Recovery**:  
- Daily UTC 00:00 auto-reset  
- Manual fix: Delete `key_usage.json`

Streaming Interruptions

**Safeguards**:  
1. `_safe_streaming_wrapper` encapsulation  
2. `finally` block ensures:  
   - Usage recording  
   - Key lock release  
3. Cleanup executes on client abort

Technical Deep Dive

Key Manager Workflow

sequenceDiagram  
    participant Client  
    participant RotatingClient  
    participant UsageManager  
    participant AIProvider  
    
    Client->>RotatingClient: Request Model A  
    RotatingClient->>UsageManager: Get best key  
    UsageManager-->>RotatingClient: Return Key X  
    RotatingClient->>AIProvider: Send request  
    alt Success  
        AIProvider-->>RotatingClient: Return data  
        RotatingClient->>UsageManager: Record success  
        RotatingClient->>Client: Deliver response  
    else Failure  
        AIProvider-->>RotatingClient: Return error  
        RotatingClient->>UsageManager: Record failure  
        RotatingClient->>UsageManager: Request new key  
    end

Key State Data Structure

{  
  "api_key_hash": {  
    "daily": {  
      "models": {  
        "gemini-1.5-pro": {  
          "success_count": 42,  
          "prompt_tokens": 15000,  
          "approx_cost": 0.12  
        }  
      }  
    },  
    "model_cooldowns": {  
      "gemini-1.5-flash": 1720000000.0  
    },  
    "failures": {  
      "gemini-1.5-pro": {  
        "consecutive_failures": 1  
      }  
    }  
  }  
}

Ideal Use Cases

  1. AI Application Developers

    • Prevent service outages from single-key limits
    • Seamless multi-provider integration

  2. Research Teams

    • Granular model cost control
    • Automatic usage metrics collection

  3. Enterprise IT Departments

    • Centralized API access management
    • Detailed usage auditing

Key Advantages

  • Zero Single Point of Failure: Automatic key failover
  • Precision Traffic Control: Model-level concurrency
  • Cost Transparency: Real-time expenditure calculation
  • Enterprise Resilience: Automatic error isolation
  • Seamless Integration: OpenAI ecosystem compatible

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GitHub Repository: LLM-API-Key-Proxy
Windows executable available for immediate deployment

This system liberates developers from infrastructure concerns. Every API call executes with optimal resource allocation – an intelligent dispatcher for your AI resource pool.

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