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Comprehensive integración de agentes de IA Tools for Every Need

Get access to integración de agentes de IA solutions that address multiple requirements. One-stop resources for streamlined workflows.

integración de agentes de IA

  • Secure Agent Augmentation

    An open-source framework that secures LLM agent access to private data through encryption, authentication, and secure retrieval layers.
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    What is Secure Agent Augmentation?
    Secure Agent Augmentation provides a Python SDK and set of helper modules to wrap AI agent tool calls with security controls. It supports integration with popular LLM frameworks like LangChain and Semantic Kernel, and connects to secret vaults (e.g., HashiCorp Vault, AWS Secrets Manager). Encryption-at-rest and in-transit, role-based access control, and audit trails ensure that agents can augment their reasoning with internal knowledge bases and APIs without exposing sensitive data. Developers define secured tool endpoints, configure authentication policies, and initialize an augmented agent instance to run secure queries against private data sources.
    Secure Agent Augmentation Core Features
    • Encrypted data retrieval and storage
    • Authentication and role-based access control
    • Integration with secret vaults (HashiCorp, AWS, Azure)
    • Audit logging and compliance reporting
    • Wrappers for LangChain and Semantic Kernel

  • Multi-Agent-RAG

    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.
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