Search Results for "algorithms framework"
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Showing 12 open source projects for "algorithms framework"
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1
AgentUniverse
agentUniverse is a LLM multi-agent framework
AgentUniverse is a multi-agent AI framework that enables coordination between multiple intelligent agents for complex task execution and automation. -
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Multi-Agent Orchestrator
Flexible and powerful framework for managing multiple AI agents
Multi-Agent Orchestrator is an AI coordination framework that enables multiple intelligent agents to work together to complete complex, multi-step workflows. -
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DSPy
DSPy: The framework for programming—not prompting—language models
Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs. -
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Agno
Lightweight framework for building Agents with memory, knowledge, etc.
Agno is a modular, open-source artificial general intelligence (AGI) research platform that allows developers to build, evaluate, and experiment with cognitive architectures in a composable way. It provides a flexible framework for modeling reasoning, memory, decision-making, and planning, aimed at long-term AI research beyond narrow learning. Agno embraces multi-agent environments and symbolic reasoning as part of its core design, enabling experiments with structured knowledge,... -
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5
RWARE
MuA multi-agent reinforcement learning environment
robotic-warehouse is a simulation environment and framework for robotic warehouse automation, enabling research and development of AI and robotic agents to manage warehouse logistics, such as item picking and transport. -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
...Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. The framework also tends to include automation layers for deployment, enabling trained models to operate in live or simulated environments with scheduled re-training and risk controls in place. -
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LiteMultiAgent
The Library for LLM-based multi-agent applications
LiteMultiAgent is a lightweight and extensible multi-agent reinforcement learning (MARL) platform designed for rapid experimentation. It allows researchers to design and test coordination, competition, and collaboration scenarios in simulated environments. -
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Agent Lightning
The absolute trainer to light up AI agents
...Agent-Lightning introduces a lightweight training pipeline that observes agents’ execution traces, converts them into structured data, and feeds them into training algorithms, enabling users to improve agent behaviors systematically. The project emphasizes minimalist integration, so you can drop this into existing systems without extensive rewrites, focusing instead on iterative performance improvement. -
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TaskWeaver
A code-first agent framework for seamlessly planning analytics tasks
TaskWeaver is a multi-agent AI framework designed for orchestrating autonomous agents that collaborate to complete complex tasks. -
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10
FRODO 2
Open-Source Framework for Distributed Constraint Optimization (DCOP)
FRODO is a Java platform to solve Distributed Constraint Satisfaction Problems (DisCSPs) and Optimization Problems (DCOPs). It provides implementations for a variety of algorithms, including DPOP (and its variants), ADOPT, SynchBB, DSA... -
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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. -
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This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.
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