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bibliothèque Java

  • Java Action Interpolate module provides LightJason agents with advanced interpolation for smooth behavior transitions during execution.
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    What is Java Action Interpolate for LightJason?
    Java Action Interpolate is a specialized Java library designed to integrate with the LightJason multi-agent framework. It provides a suite of interpolation algorithms, including linear, polynomial, and spline methods, enabling agents to transition between states and actions fluidly. The module offers configurable interpolation parameters, hooks into the LightJason action lifecycle, and supports custom data types. By incorporating Java Action Interpolate, developers can eliminate jarring behavior jumps, enhance simulation fidelity, and simplify the implementation of smooth agent movements and decision-driven behaviors within distributed or simulation environments.
  • autogen4j is a Java framework enabling autonomous AI agents to plan tasks, manage memory, and integrate LLMs with custom tools.
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    What is autogen4j?
    autogen4j is a lightweight Java library designed to abstract the complexity of building autonomous AI agents. It offers core modules for planning, memory storage, and action execution, letting agents decompose high-level goals into sequential sub-tasks. The framework integrates with LLM providers (e.g., OpenAI, Anthropic) and allows registration of custom tools (HTTP clients, database connectors, file I/O). Developers define agents through a fluent DSL or annotations, quickly assembling pipelines for data enrichment, automated reporting, and conversational bots. An extensible plugin system ensures flexibility, enabling fine-tuned behaviors across diverse applications.
  • A framework integrating LLM-driven dialogue into JaCaMo multi-agent systems to enable goal-oriented conversational agents.
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    What is Dial4JaCa?
    Dial4JaCa is a Java library plugin for the JaCaMo multi-agent platform that intercepts inter-agent messages, encodes agent intentions, and routes them through LLM backends (OpenAI, local models). It manages dialogue context, updates belief bases, and integrates response generation directly into AgentSpeak(L) reasoning cycles. Developers can customize prompts, define dialogue artifacts, and handle asynchronous calls, enabling agents to interpret user utterances, coordinate tasks, and retrieve external information in natural language. Its modular design supports error handling, logging, and multi-LLM selection, ideal for research, education, and rapid prototyping of conversational MAS.
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