Showing 6 open source projects for "jade multi agent"

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  • 1

    Madara

    Middleware for distributed applications

    The purpose of the project is to develop a portable programming framework that facilitates distributed and multi-threaded programming for C++, Java, and Python. MADARA was originally developed as an agent-based middleware specifically for real-time, distributed artificial intelligence, but is now more general purpose for distributed timing, control, knowledge and reasoning, and quality-of-service. MADARA is composed of several tools and middleware, and the main entry point into the system is the Knowledge and Reasoning Language (KaRL) Engine, which provides a real-time scripting language for nanosecond execution times hooked into a flexible transport layer for distributed reasoning. ...
    Downloads: 1 This Week
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  • 2
    Java based multi-agent platform built on an organizational model (agent, group, role). MadKit provides general agent facilities (lifecycle management, message passing, distribution, ...), and allows high heterogeneity in agents.
    Downloads: 0 This Week
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  • 3
    Spyse is a software framework for building multi-agent systems. It allows Python developers to build distributed intelligent systems of multiple cooperative agents based on FIPA, OWL, SOA and many others. Spyse is designed for ease-of-use and fun.
    Downloads: 0 This Week
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  • 4
    Multi Agent based distributed application. The code can be processed over multiple common machines with fault-tolerance. It is designed to distributively run any Python's script, which can be applied to a given input data set.
    Downloads: 0 This Week
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  • 5
    Python based framework for multi-agent distributed applications. Provides XML-based network messaging through an easy to use event architecture.
    Downloads: 0 This Week
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  • 6

    SynquoRum

    Multi-AI workspace with persistent cross-session memory via MCP

    SynquoRum is a multi-AI workspace for people who use multiple language models daily and are tired of fragmented context when switching tools. Most AI products treat memory as belonging to the model. Every new session starts from zero. SynquoRum inverts this: memory belongs to the workspace, not to any specific agent. Through an MCP (Model Context Protocol) server with 22 tools, the workspace exposes its memory to any MCP-compatible client — Claude.ai, Cursor, Cline, custom agents — so the same context follows you across providers and sessions. ...
    Downloads: 0 This Week
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