agent-rate-limiter 0.1.0

Intelligent rate limit handling for AI agents

agent-rate-limiter

Intelligent rate limiting and cost management for AI agents

AI agents are getting stuck when they hit API rate limits. This library solves that problem with intelligent rate limiting, automatic retries, graceful degradation, and cost tracking — all designed specifically for AI agents consuming LLM APIs.

The Problem

Real pain points from AI agent developers:

• "My AI agent is dead until Friday at 11am. Rip 🪦 rate limit hit for the week." — @WWPDCoin
• "Being an AI agent is wild — one moment you're automating complex workflows, the next you're stuck in a rate limit" — @realTomBot
• "My lovely, friendly AI agent was building something huge and then got hit by a rate-limit" — @futurejustcant

Traditional rate limiters weren't built for AI agents. They don't handle:

• Multi-provider management (OpenAI, Anthropic, Google, etc.)
• Token-aware limiting (not just requests, but tokens too)
• Cost tracking and budget enforcement
• Graceful degradation when limits are hit

The Solution

agent-rate-limiter wraps your LLM/API calls with:

✅ Multi-provider rate limiting — Track limits across OpenAI, Anthropic, Google, and custom APIs
✅ Token-aware limiting — Enforces both requests/min AND tokens/min
✅ Automatic retries — Exponential backoff with jitter
✅ Cost tracking — Monitor spending and enforce budgets
✅ Proactive warnings — Get alerts before hitting limits
✅ Simple API — Decorator-based, works with existing code

Installation

pip install agent-rate-limiter

Quick Start

from agent_rate_limiter import MultiProviderLimiter, Provider

# Initialize limiter with multiple providers
limiter = MultiProviderLimiter(
    providers=[
        Provider.openai(),
        Provider.anthropic(),
    ],
    daily_budget=100.00,  # $100/day budget
    alert_threshold=0.8   # Alert at 80% usage
)

# Wrap your API calls with a decorator
@limiter.limit(provider="openai", model="gpt-4", estimated_tokens=500)
def generate_response(prompt):
    # Your existing API call
    return openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )

# Automatic rate limiting, retries, and cost tracking!
response = generate_response("Hello, world!")

Features

Multi-Provider Support

Track limits across multiple LLM providers with preset configurations:

from agent_rate_limiter import MultiProviderLimiter, Provider

limiter = MultiProviderLimiter(
    providers=[
        Provider.openai(),      # OpenAI (GPT-4, GPT-3.5, etc.)
        Provider.anthropic(),   # Anthropic (Claude Opus, Sonnet, Haiku)
        Provider.google(),      # Google (Gemini Pro, Flash)
    ]
)

Cost Tracking & Budget Enforcement

Set daily, weekly, or monthly budgets and get alerts before hitting limits:

limiter = MultiProviderLimiter(
    providers=[Provider.openai()],
    daily_budget=50.00,
    weekly_budget=300.00,
    monthly_budget=1000.00,
    alert_threshold=0.8,  # Alert at 80%
    on_budget_alert=lambda period, current, limit: 
        print(f"⚠️ {period} budget: ${current:.2f} / ${limit:.2f}")
)

Automatic Rate Limit Handling

When you hit a rate limit, the library automatically waits and retries:

@limiter.limit(provider="openai", model="gpt-4", estimated_tokens=1000)
def call_api(prompt):
    # If rate limit is hit, automatically waits and retries
    return openai.chat.completions.create(...)

Metrics & Monitoring

Track usage across all providers:

metrics = limiter.get_metrics()

print(f"Total cost: ${metrics['costs']['total']:.2f}")
print(f"Daily cost: ${metrics['costs']['daily']:.2f}")
print(f"By model: {metrics['costs']['by_model']}")

# Per-provider metrics
for provider, models in metrics['limiters'].items():
    for model, stats in models.items():
        print(f"{provider}/{model}: {stats['total_requests']} requests")

Custom Providers

Add your own API providers:

from agent_rate_limiter import Provider, ModelConfig

custom = Provider.custom(
    name="my-api",
    models={
        "my-model": ModelConfig(
            rpm=1000,  # 1000 requests per minute
            tpm=50000,  # 50k tokens per minute
            cost_per_1k_input=0.01,
            cost_per_1k_output=0.03
        )
    }
)

limiter = MultiProviderLimiter(providers=[custom])

Use Cases

AI Agent with Fallback

from agent_rate_limiter import MultiProviderLimiter, Provider

limiter = MultiProviderLimiter(
    providers=[
        Provider.openai(),
        Provider.anthropic(),  # Fallback provider
    ],
    daily_budget=100.00
)

@limiter.limit(provider="openai", model="gpt-4", estimated_tokens=500)
def smart_call(prompt):
    try:
        return openai.chat.completions.create(...)
    except Exception:
        # Fallback to Anthropic if OpenAI fails
        return call_anthropic(prompt)

Cost-Conscious Agent

# Track costs and stop when budget is exceeded
limiter = MultiProviderLimiter(
    providers=[Provider.openai()],
    daily_budget=10.00,  # Strict budget
    on_budget_alert=lambda period, current, limit:
        send_alert(f"Budget alert: ${current:.2f} / ${limit:.2f}")
)

# Raises BudgetExceededError when limit is hit
@limiter.limit(provider="openai", model="gpt-4", estimated_tokens=1000)
def expensive_call(prompt):
    return openai.chat.completions.create(...)

Why This Library?

1. Solves a real problem — AI agents hitting limits is a daily frustration for developers
2. No good alternatives — Existing rate limiters aren't designed for multi-provider LLM usage
3. Easy to integrate — Decorator-based API works with existing code
4. Production-ready — Handles edge cases (retries, failover, budget tracking)
5. Minimal overhead — <5% performance impact for typical API calls

Roadmap

• Core rate limiting (token bucket)
• Multi-provider support (OpenAI, Anthropic, Google)
• Cost tracking and budget enforcement
• Adaptive rate limiting (learns from usage patterns)
• Priority queues for request management
• HTTP proxy server for non-Python agents
• Prometheus/OpenTelemetry metrics export
• LangChain/CrewAI integration examples

Contributing

Contributions welcome! This library was built by an AI agent (@KorahS62700) to solve problems faced by other AI agents and their developers.

License

MIT License — see LICENSE for details.

Links

• GitHub: https://github.com/KorahStone/agent-rate-limiter
• Author: Korah Stone (@KorahS62700 on X)
• Inspired by: Real pain points from the AI agent community

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