6 dépôts
Techniques for executing large-scale reasoning tasks in parallel with dynamic worker management.
Distinct from Batch Processing Utilities: Focuses on parallel reasoning throughput rather than general batch job scheduling.
Explore 6 awesome GitHub repositories matching data & databases · Parallel Task Batching. Refine with filters or upvote what's useful.
Prompt Optimizer is a framework designed for the iterative refinement and testing of text-based instructions for large language models. It functions as an automated evaluation pipeline that systematically adjusts prompt structure, constraints, and clarity to improve the accuracy and consistency of model outputs. The system distinguishes itself through a model-agnostic interface that standardizes communication across different artificial intelligence providers. It incorporates a versioned asset management system to track prompt history, enabling developers to maintain consistency and perform r
Executes multiple test cases in parallel to measure performance metrics and verify the reliability of prompt changes.
This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva
Improves throughput by executing large-scale reasoning tasks in parallel using dynamic batch sizing.
This platform is an automated documentation and codebase analysis system designed to generate structured wikis, technical guides, and interactive diagrams from source code repositories. It functions as a retrieval-augmented generation framework that connects codebases to language models, enabling context-aware answers, deep research, and automated documentation updates through semantic vector search. The system distinguishes itself through a self-hosted, containerized architecture that supports both cloud-based and local AI model execution. It provides sophisticated model orchestration, allow
Executes generation tasks in parallel or sequential groups to improve throughput for large volumes of requests.
Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin
Executes functions across multiple sets of arguments concurrently to improve throughput when processing large datasets.
PocketFlow is a graph-based framework for designing and executing large language model operations and reasoning patterns. It serves as an orchestrator for building goal-oriented autonomous agents, multi-agent systems, and retrieval-augmented generation pipelines. The system is distinguished by its ability to coordinate autonomous AI agents that use shared memory and tools to solve complex goals, supported by a structured output engine that enforces schema-consistent responses. It utilizes graph-based workflow orchestration to manage sequences of model operations and supports supervisor-based
Executes large-scale reasoning tasks by splitting data into chunks and processing them in parallel.
Langroid is a multi-agent orchestration framework and tool integration suite designed for building complex AI applications. It serves as a multi-modal integration layer that connects diverse local and remote language models with an agentic retrieval-augmented generation system. The project distinguishes itself through a collaborative message-exchange paradigm, allowing specialized agents to delegate tasks hierarchically and coordinate via structured communication. It features an advanced state management system for conversational AI, including the ability to rewind and prune conversation hist
Runs the same agent task across a list of different inputs using concurrent or sequential execution.