feat: Add Rate Limit Manager with exponential backoff

[aygp-dr]

Rate Limit Manager Implementation

This PR implements the Rate Limit Manager as proposed in the RFC. It provides a robust solution for handling GitHub API rate limits with intelligent retry logic and exponential backoff.

Features

• Intelligent rate limit detection and monitoring
• Exponential backoff with configurable jitter
• Pre-emptive throttling based on remaining limits
• Comprehensive logging
• Full test coverage

Implementation Details

• Added RateLimitManager class for centralized rate limit handling
• Implemented exponential backoff with jitter
• Added comprehensive test suite
• Included detailed documentation

Usage Example

from utils.rate_limit_manager import RateLimitManager

manager = RateLimitManager()
result = manager.execute_with_retry(github_api_call, "operation_id")

Testing

All tests pass and provide 100% coverage of the core functionality.

Relates to #ISSUE_NUMBER

[jwalsh]

Strengths:

1. Robust Implementation

   • Well-structured with separate classes for Config, Strategy, and Manager
   • Comprehensive error handling and logging
   • Thread-safe where needed
   • Good use of type hints and docstrings

2. Testing

   • Strong test coverage including unit tests for individual components
   • Tests cover happy paths and error cases
   • Mock objects used appropriately
   • Edge cases like header parsing failures are tested

3. Documentation

   • Clear README with usage examples
   • Well-documented configuration options
   • Implementation details explained
   • Code comments are meaningful and useful

4. Best Practices

   • Uses dataclasses for configuration
   • Implements exponential backoff with jitter
   • Proper logging throughout
   • Clean separation of concerns

Areas for Improvement:

1. Observability

   • Could benefit from metrics collection for monitoring
   • Missing structured logging format
   • No tracing implementation
   • No health check endpoints

2. Flow Control

   • No circuit breaker pattern implementation
   • Missing bulk operation handling
   • Could use more sophisticated queuing/throttling
   • No concurrent request handling strategy

3. Recovery

   • Limited failure recovery options
   • No fallback mechanisms
   • Missing retry budgets
   • No graceful degradation strategy

Overall Rating: 8/10

This is a solid, production-ready implementation of rate limiting with good testing and documentation. It follows best practices and handles core functionality well. The main missing pieces are around observability and advanced flow control patterns, but these could be added in future iterations.