Advanced 경량 설계 Tools for Professionals

Discover cutting-edge 경량 설계 tools built for intricate workflows. Perfect for experienced users and complex projects.

경량 설계

  • A lightweight C++ framework to build local AI agents using llama.cpp, featuring plugins and conversation memory.
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    What is llama-cpp-agent?
    llama-cpp-agent is an open-source C++ framework for running AI agents entirely offline. It leverages the llama.cpp inference engine to provide fast, low-latency interactions and supports a modular plugin system, configurable memory, and task execution. Developers can integrate custom tools, switch between different local LLM models, and build privacy-focused conversational assistants without external dependencies.
  • An open-source CLI tool that echoes and processes user prompts with Ollama LLMs for local AI agent workflows.
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    What is echoOLlama?
    echoOLlama leverages the Ollama ecosystem to provide a minimal agent framework: it reads user input from the terminal, sends it to a configured local LLM, and streams back responses in real time. Users can script sequences of interactions, chain prompts, and experiment with prompt engineering without modifying underlying model code. This makes echoOLlama ideal for testing conversational patterns, building simple command-driven tools, and handling iterative agent tasks while preserving data privacy.
  • A Java-based multi-agent communication demo using JADE, showcasing two-way interaction, message parsing, and agent coordination.
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    What is Two-Way Agent Communication using JADE?
    This repository provides a hands-on demonstration of two-way communication between agents built on the JADE framework. It includes example Java classes showing agent setup, FIPA-ACL compliant message creation, and asynchronous behavior handling. Developers can study Agent A sending a REQUEST, Agent B processing the request, and returning an INFORM message. The code illustrates registering agents with the Directory Facilitator, using cyclic and one-shot behaviors, applying message templates to filter messages, and logging conversation sequences. It’s an ideal starting point for prototyping multi-agent exchanges, custom protocols, or integrating JADE agents into larger distributed AI systems.
  • Agents-Flex: A versatile Java framework for LLM applications.
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    What is Agents-Flex?
    Agents-Flex is a lightweight and elegant Java framework for Large Language Model (LLM) applications. It allows developers to define, parse and execute local methods efficiently. The framework supports local function definitions, parsing capabilities, callbacks through LLMs, and the execution of methods returning results. With minimal code, developers can harness the power of LLMs and integrate sophisticated functionalities into their applications.
  • Cyrano is a lightweight Python AI agent framework for building modular, function-calling chatbots with tool integration.
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    What is Cyrano?
    Cyrano is an open-source Python framework and CLI for creating AI agents that orchestrate large language models and external tools through natural language prompts. Users can define custom tools (functions), configure memory and token limits, and handle callbacks. Cyrano handles parsing JSON responses from LLMs and executes specified tools in sequence. It emphasizes simplicity, modularity, and zero external dependencies, enabling developers to prototype chatbots, build automated workflows, and integrate AI capabilities into applications quickly.
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