AI Dev Tool: Multi-Provider LLM Fallback Router with Token Budget Enforcement

1. The Production Bottleneck

We engineered this zero-bloat Python 3.12+ / AsyncIO blueprint to enforce bounded concurrency, deterministic exponential backoff with jitter, strict schema validation at the ingestion boundary, and structured JSON telemetry—ready to drop into production without weeks of plumbing.

2. Request & Execution Architecture

3. Production Benchmarks

"""AI Dev Tool: Multi-Provider LLM Fallback Router with Token Budget Enforcement.

This module implements a multi-provider large language model (LLM) fallback router with token budget enforcement.
It utilizes bounded concurrency to manage concurrent requests and exponential backoff with jitter to handle provider failures.
The module is designed to be used as a command-line interface (CLI) tool, with usage:
    python -m ai_dev_tool --providers <provider1,provider2,...> --token-budget <budget> --concurrency <concurrency>

The architecture of this module consists of the following components:
- ProviderManager: responsible for managing the list of available providers and their corresponding LLMs.
- TokenBudgetEnforcer: responsible for enforcing the token budget across all providers.
- FallbackRouter: responsible for routing requests to the next available provider in case of failure.
- ExponentialBackoff: responsible for implementing exponential backoff with jitter.

The module uses dataclasses and typed configuration to ensure strict validation of input parameters.
It also implements structured JSON logging and deterministic error handling to ensure reliable and debuggable operation.
"""

import dataclasses
import json
import logging
import random
import sys
from typing import List

@dataclasses.dataclass
class ProviderConfig:
    """Configuration

5. Choose Your Access Tier

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6. Frequently Asked Engineering Questions

7. Related Production Blueprints