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Comprehensive KI-Agenten-Integration Tools for Every Need

Get access to KI-Agenten-Integration solutions that address multiple requirements. One-stop resources for streamlined workflows.

KI-Agenten-Integration

  • 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.
    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

  • 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.
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