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 uses bounded concurrency to manage concurrent requests and exponential backoff with jitter for retrying failed requests.
The module provides a command-line interface (CLI) for benchmarking and self-testing.

Usage:
    python llm_fallback_router.py --config config.json --budget 1000 --concurrency 5

Architecture:
    - The module uses a dataclass to represent the configuration, which includes the list of LLM providers, token budget, and concurrency level.
    - The LLMProvider class represents an individual LLM provider, with methods for sending requests and handling responses.
    - The LLMFallbackRouter class manages the list of LLM providers, routes requests to the providers, and enforces the token budget.
    - The module uses exponential backoff with jitter for retrying failed requests.
    - The module uses structured JSON logging for logging requests, responses, and errors.

"""

import json
import logging
import random
import sys
from dataclasses import dataclass, field
from typing import List

@dataclass
class LLMProvider:

5. Choose Your Access Tier

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6. Frequently Asked Engineering Questions

7. Related Production Blueprints