3 Repos
Capabilities for invoking multiple functions or extraction tasks in a single model request to improve efficiency.
Distinguishing note: None of the candidates relate to LLM tool calling; this is specific to AI agentic workflows.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Parallel Tool Execution. Refine with filters or upvote what's useful.
Ruflo is an AI agent orchestration platform and workflow automation tool designed to decompose high-level goals into executable action plans. It functions as a manager for multi-agent swarms, organizing autonomous entities into collaborative topologies that utilize shared consensus to complete complex tasks. The framework distinguishes itself through a retrieval-augmented generation layer and knowledge graphs for reasoning over linked data. It incorporates a trajectory-based learning loop that analyzes previous execution paths to refine cognitive patterns and improve future reasoning accuracy
Enables the concurrent invocation of multiple functions or tool calls in a single model request to accelerate workflows.
This project is a retrieval-augmented generation pipeline designed for building custom ChatGPT plugins that allow language models to query private or professional documents. It implements a full retrieval workflow, from processing and indexing document chunks to retrieving relevant context for natural language queries. The system distinguishes itself through a hybrid retrieval approach that combines dense vector embeddings with sparse keyword matching, further refined by a two-stage semantic re-ranking process. It includes specialized data privacy tools for screening personally identifiable i
Enables executing multiple data operations simultaneously within a single request to improve system efficiency.
Instructor is a framework designed for structured data extraction, validation, and language model integration. It functions as a library that transforms unstructured text into validated, type-safe objects by leveraging schema definitions and model-specific tool-calling capabilities. By acting as a validation middleware, the project ensures that language model outputs strictly conform to defined data structures. The library distinguishes itself through a robust validation-based retry loop that automatically re-submits failed responses with error feedback to iteratively correct schema complianc
Invoke several functions in a single request to reduce latency by processing multiple structured data extraction tasks simultaneously.