OpenAI Swarm is a versatile orchestration library enabling parallel execution and consensus-driven decision-making across multiple AI agents. It broadcasts tasks to independent model instances, aggregates their outputs, and applies configurable voting or ranking schemes to select the highest-scoring result. Developers can fine-tune agent counts, voting thresholds, and model combinations to enhance reliability, mitigate individual bias, and refine solution quality. Swarm supports chaining responses, iterative feedback loops, and detailed reasoning logs for auditability, elevating performance on summarization, classification, code generation, and complex reasoning tasks through collective intelligence.

Multi-Agent-Blueprint is a comprehensive open-source codebase for building and orchestrating multiple AI-driven agents that collaborate to address complex tasks. At its core, it offers a modular system for defining distinct agent roles—such as researchers, analysts, and executors—each with dedicated memory stores and prompt templates. The framework integrates seamlessly with large language models, external knowledge APIs, and custom tools, enabling dynamic task delegation and iterative feedback loops between agents. It also includes built-in logging and monitoring to track agent interactions and outputs. With customizable workflows and interchangeable components, developers and researchers can rapidly prototype multi-agent pipelines for applications like content generation, data analysis, product development, or automated customer support.