Showing 134 open source projects for "multi-layer perceptron python"

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    LangChain Apps on Production with Jina

    LangChain Apps on Production with Jina

    Langchain Apps on Production with Jina & FastAPI

    Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own...
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  • 2
    Multi-Agent Particle Envs

    Multi-Agent Particle Envs

    Code for a multi-agent particle environment used in a paper

    Multiagent Particle Environments is a lightweight framework for simulating multi-agent reinforcement learning tasks in a continuous observation space with discrete action settings. It was originally developed by OpenAI and used in the influential paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The environment provides simple particle-based worlds with simulated physics, where agents can move, communicate, and interact with each other. Scenarios are designed to...
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  • 3
    AEA Framework

    AEA Framework

    A framework for autonomous economic agent (AEA) development

    agents-aea by Fetch.ai is a framework for building autonomous economic agents (AEAs) that can act independently, communicate, and transact on decentralized networks. It focuses on enabling AI-driven agents to participate in digital marketplaces and ecosystems.
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  • 4
    Multi-Agent path planning in Python

    Multi-Agent path planning in Python

    Python implementation of a bunch of multi-robot path-planning

    multi_agent_path_planning is a Python-based implementation of multi-agent pathfinding algorithms for coordinating multiple agents in shared environments without collisions. It is useful in robotics, warehouse automation, and gaming AI.
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  • 5
    Universe Starter Agent

    Universe Starter Agent

    A starter agent that can solve a number of universe environments

    The universe-starter-agent repository is an archived OpenAI codebase designed as a starter reinforcement-learning agent that can interact with and solve tasks in OpenAI’s Universe environment platform. Its purpose is to serve as a baseline or reference implementation so researchers or developers can see how to build agents that operate in real-time, visual environments (e.g., games, browser apps) via pixel observations and keyboard/mouse actions. Under the hood, this starter agent implements...
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  • 6
    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.
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  • 7
    This project simulates a multi-agent system (swarm) behavior both graphically and not. The purpose of this project is to research the properties suggested in "stability analysis of swarms" V.Gazi & K.M.Passino. Using the vpython library for 3D modeling
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  • 8
    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.
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  • 9

    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...
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