2 Repos
Tools for executing asynchronous, high-volume tasks against AI model APIs.
Distinguishing note: Specifically for AI model batching, distinct from general data processing pipelines.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Batch Processors. Refine with filters or upvote what's useful.
LiteLLM is a unified gateway and proxy server designed to centralize access to over one hundred language model providers. It provides a standardized API interface that abstracts vendor-specific schemas, allowing developers to interact with diverse models through a single, consistent format. By acting as a central traffic management layer, it enables organizations to route, secure, and govern model interactions across multiple deployments. The platform distinguishes itself through its policy-driven architecture, which uses configuration-based routing to manage traffic distribution, load balanc
Executes batch processing tasks on supported providers via standard compatible API interfaces.
This is a Python SDK for interacting with large language models via API. It serves as a client library to generate text, process messages, and manage conversational states, while providing a specialized interface for connecting to models hosted across different cloud infrastructure providers. The SDK includes a tool-calling framework that maps Python functions to JSON schemas, allowing models to execute external tools. It also features a built-in token counting utility to estimate input size before transmission and a server-sent events client for receiving model tokens in real time. The libr
Provides a pipeline for submitting large arrays of requests for asynchronous background processing against model APIs.