{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"fhlmi provides a unified async Python interface to multiple large language models, handling authentication, rate limiting, cost tracking, and tool calling across different LLM providers.","skillfed_tags":["llm-abstraction","rate-limiting","cost-tracking"],"use_cases":["Build a multi-provider LLM application where you can switch between providers by changing config without rewriting code.","Enforce rate limits across concurrent requests to avoid hitting provider quotas or incurring unexpected costs.","Track token usage and cost per LLM call to monitor spending and optimize prompt efficiency.","Implement tool calling against multiple LLM providers using a single abstraction.","Embed LLM capabilities in async Python services that need retries, timeouts, and cross-process rate limiting."],"what_it_does":"fhlmi is a Python library that abstracts away the differences between multiple large language model providers, letting you write code once and swap providers without changing your application logic. It wraps providers under a common async interface, exposing methods like `call_single()` and `call()` that accept text or structured messages.\n\nThe library handles several operational concerns automatically: it tracks token usage and cost per request, enforces rate limits (tokens per minute and requests per minute) with in-memory or Redis-backed storage, retries failed requests, and supports tool calling and structured output schemas. You configure providers and limits through a config dictionary, then call the LLM through the unified interface\u2014no need to learn each provider's API separately.","worth_installing":"Yes, if you need a unified interface to multiple LLM providers with built-in rate limiting and cost tracking. The library is actively maintained, has low install friction, and is permissively licensed. However, verify that all runtime dependencies fit your environment before committing; the dependency footprint is substantial."},"id":"fhlmi","links":{"html":"https://skillfed.io/packages/fhlmi","md":"https://skillfed.io/packages/fhlmi.md","pypi":"https://pypi.org/project/fhlmi/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":null,"license_treatment":"permissive","name":"fhlmi","python_support":"supports_current","summary":"A client to provide LLM responses for FutureHouse applications."},"popularity":{"monthly_downloads":173969,"position":10294,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.0.5"}