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  • Open-source Python framework orchestrating multiple AI agents for retrieval and generation in RAG workflows.
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    What is Multi-Agent-RAG?
    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-RAG Core Features
    • Modular multi-agent orchestration
    • Retrieval agent for vector database document fetching
    • Reasoning agent for chain-of-thought analysis
    • Generation agent for final answer synthesis
    • Plugin-based extension system
    • Configurable prompts and agent pipelines
    • Support for OpenAI and Hugging Face models
    • Logging and tracing of agent interactions
  • A Python-based personal AI assistant for conversational chat, memory storage, task automation, and plugin integration.
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    What is Personal AI Assistant?
    Personal AI Assistant is a modular AI agent built in Python to deliver conversational chat, context-aware memory, and automated task execution. It features a plugin system for web browsing, file management, email sending, and calendar scheduling. Backed by OpenAI or local language models and SQLite-based memory storage, it preserves conversation history and adapts responses over time. Developers can extend capabilities with custom modules, creating a tailored assistant for productivity, research, or home automation.
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