Multi-Agent-RAG provides a modular framework for constructing retrieval-augmented generation (RAG) applications by orchestrating multiple specialized AI agents. Developers configure individual agents: a retrieval agent connects to vector stores to fetch relevant documents; a reasoning agent performs chain-of-thought analysis; and a generation agent synthesizes final responses using large language models. The framework supports plugin extensions, configurable prompts, and comprehensive logging, enabling seamless integration with popular LLM APIs and vector databases to improve RAG accuracy, scalability, and development efficiency.

Multi-Agent Architecture provides a scalable, extensible platform to define, register, and coordinate multiple AI agents working together on a shared objective. It includes a message broker, lifecycle management, dynamic agent spawning, and customizable communication protocols. Developers can build specialized agents (e.g., data fetchers, NLP processors, decision-makers) and plug them into the core runtime to handle tasks ranging from data aggregation to autonomous decision workflows. The framework’s modular design supports plugin extensions and integrates with existing ML models or APIs.

AI Staff Dev Agent is a command-line AI agent designed for software engineering teams. It automatically generates code snippets, reviews pull requests for quality and style, writes unit tests to ensure coverage, and produces project documentation. Configurable via environment variables and prompt templates, it integrates directly with GitHub to create branches, commits, and pull requests. Teams can customize workflows, extend functionality through plugins, and run the agent locally or in CI pipelines to maintain consistent code standards and accelerate project delivery.

LLM Functions provides a simple framework to bridge large language model responses with real code execution. You define functions via JSON schemas, register them with the library, and the LLM will return structured function calls when appropriate. The library parses those responses, validates the parameters, and invokes the correct handler. It supports synchronous and asynchronous callbacks, custom error handling, and plugin extensions, making it ideal for applications that require dynamic data lookup, external API calls, or complex business logic within AI-driven conversations.