Comprehensive 에이전트 설계 Tools for Every Need

Get access to 에이전트 설계 solutions that address multiple requirements. One-stop resources for streamlined workflows.

에이전트 설계

  • AI Library is a developer platform for building and deploying customizable AI agents using modular chains and tools.
    0
    0
    What is AI Library?
    AI Library offers a comprehensive framework for designing and running AI agents. It includes agent builders, chain orchestration, model interfaces, tool integration, and vector store support. The platform features an API-first approach, extensive documentation, and sample projects. Whether you’re creating chatbots, data retrieval agents, or automation assistants, AI Library’s modular architecture ensures each component—such as language models, memory stores, and external tools—can be easily configured, combined, and monitored in production environments.
    AI Library Core Features
    • Modular agent builder
    • Chain orchestration
    • Tool integration
    • Language model interfaces
    • Vector store support
    • Monitoring dashboard
    • RESTful API endpoints
    AI Library Pro & Cons

    The Cons

    No direct pricing information available on the documentation site
    No mention of mobile or desktop app availability
    No details on limitations or restrictions of the platform

    The Pros

    Supports creation of autonomous AI agents with custom training
    Provides utilities to enhance agents with special skills
    Supports integrations with multiple third-party platforms
    Organized API structure for Agents, Knowledge Base, and Utilities
  • MIDCA is an open-source cognitive architecture enabling AI agents with perception, planning, execution, metacognitive learning, and goal management.
    0
    0
    What is MIDCA?
    MIDCA is a modular cognitive architecture designed to support the full cognitive loop of intelligent agents. It processes sensory inputs through a perception module, interprets data to generate and prioritize goals, leverages a planner to create action sequences, executes tasks, and then evaluates outcomes through a metacognitive layer. The dual-cycle design separates fast reactive responses from slower deliberative reasoning, enabling agents to adapt dynamically. MIDCA’s extensible framework and open-source codebase make it ideal for researchers and developers exploring autonomous decision-making, learning, and self-reflection in AI agents.
Featured