Search Results for "deep learning with python"
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Showing 79 open source projects for "deep learning with python"
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Infor M3 ERPEfficiently executing the complex processes of enterprise manufacturers and distributors. Infor M3 is a cloud-based, manufacturing and distribution ERP system that leverages the latest technologies to provide an exceptional user experience and powerful analytics in a multicompany, multicountry, and multisite platform. Infor M3 and related CloudSuite™ industry solutions include industry-leading functionality for the chemical, distribution, equipment, fashion, food and beverage, and industrial manufacturing industries. Staying ahead of the competition means staying agile. Our new capabilities bring improved data-driven insights and streamlined workflows to help you make informed decisions and take quick action.
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ERP Software To Simplify Your ManufacturingGlobal Shop Solutions AI-integrated ERP software provides the applications needed to deliver a quality part on time, every time from quote to cash and everything in between, including shop management, scheduling, inventory, accounting, quality control, CRM and 25 more.
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AI Agent Deep Dive
AI Agent Source Code Deep Research Report
AI Agent Deep Dive is a comprehensive educational repository designed to provide a deep and structured understanding of how modern AI agents work, focusing on architecture, workflows, and real-world implementation patterns. It breaks down complex concepts such as planning, tool usage, memory management, and multi-step reasoning into digestible explanations and practical examples. -
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Deep Search Agent
Implement a concise and clear Deep Search Agent from 0
Deep Search Agent is an experimental demonstration project that showcases an autonomous AI agent designed to perform multi-step research and information gathering tasks. The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses. It typically combines reasoning, retrieval, and iterative refinement so the agent can break down questions, gather evidence, and synthesize structured... -
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Cybergod
A program that can do anything to earn money without human operators
AGI Computer Control is an experimental autonomous software system designed to operate independently and generate income without human intervention. It aims to simulate artificial general intelligence (AGI) by leveraging evolutionary algorithms, deep active inference, and other advanced AI techniques. The project explores the boundaries of machine autonomy and self-directed behavior in computational environments. -
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Zeta
Build high-performance AI models with modular building blocks
zeta is a deep learning library focused on providing cutting-edge AI and neural network models with a strong emphasis on research-grade architectures. It includes state-of-the-art implementations for rapid experimentation and model building. -
Effortlessly manage macOS, iOS, iPadOS and tvOS devices across multiple clients and locations.Addigy solutions accelerate Apple adoption in any environment.
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Agent Reinforcement Trainer
Train multi-step agents for real-world tasks using GRPO
...The framework is designed to integrate easily with Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ART also supports scalable training patterns, observability tools, and integration with hosted platforms like Weights & Biases, and it provides notebooks that demonstrate training on standard benchmarks and tasks. -
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CUDA Agent
Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
CUDA Agent is a research-driven agentic reinforcement learning system designed to automatically generate and optimize high-performance CUDA kernels for GPU workloads. The project addresses the long-standing challenge that efficient CUDA programming typically requires deep hardware expertise by training an autonomous coding agent capable of iterative improvement through execution feedback. Its architecture combines large-scale data synthesis, a skill-augmented CUDA development environment, and long-horizon reinforcement learning to build intrinsic optimization capability rather than relying on simple post-hoc tuning. ... -
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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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MiroThinker
MiroThinker is an open source deep research agent
MiroThinker is an open-source deep research AI agent designed to perform complex reasoning, information gathering, and predictive analysis tasks. The system focuses on enabling long-horizon research workflows by allowing the agent to interact repeatedly with external tools, search systems, and data sources while refining its reasoning through iterative steps. Rather than simply generating responses from a single prompt, the agent performs structured multi-step reasoning processes that... -
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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. -
Remote Network Monitoring and Management for an IoT WorldDomotz is the premier Remote Network Monitoring and Management platform for IoT. We offer powerful network management software for MSP's, Integrators, Security Professionals, and Business Owners. Domotz enables the complete solution to cost-effectively manage and monitor your customers’ networks with plug and play setup, a friendly UX, and a comprehensive feature set, accessible from any desktop browser or mobile device. Utilize one interface to manage multiple networks at multiple locations anywhere in the World. One person can deploy remote monitoring and management in less than 15 minutes.
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Diplomacy Cicero
Code for Cicero, an AI agent that plays the game of Diplomacy
...It is designed to play the board game Diplomacy by combining open-domain natural language negotiation with strategic planning. The repository includes training code, model checkpoints, and infrastructure for both language modelling (via the ParlAI framework) and reinforcement learning for strategy agents. It supports two variants: Cicero (which handles full “press” negotiation) and Diplodocus (a variant focused on no-press diplomacy) as described in the README. The codebase is implemented primarily in Python with performance-critical components in C++ (via pybind11 bindings) and is configured to run in a high‐GPU cluster environment. ... -
11
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. -
12
DeerFlow
Deep Research framework, combining language models with tools
DeerFlow is an open-source, community-driven “deep research” framework / multi-agent orchestration platform developed by ByteDance. It aims to combine the reasoning power of large language models (LLMs) with automated tool-use — such as web search, web crawling, Python execution, and data processing — to enable complex, end-to-end research workflows. Instead of a monolithic AI assistant, DeerFlow defines multiple specialized agents (e.g. -
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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
...It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. 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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Habitat-Lab
A modular high-level library to train embodied AI agents
Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks. Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and... -
15
MetaClaw
Just talk to your agent
MetaClaw is an AI or agent-oriented system that appears to focus on advanced control, coordination, or training of autonomous agents, potentially within reinforcement learning or tool-using environments. The project likely emphasizes meta-level reasoning, where agents are not only executing tasks but also adapting their strategies based on feedback and performance signals. It may incorporate mechanisms for learning from interactions, improving decision-making over time, and generalizing... -
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VectorizedMultiAgentSimulator (VMAS)
VMAS is a vectorized differentiable simulator
VectorizedMultiAgentSimulator is a high-performance, vectorized simulator for multi-agent systems, focusing on large-scale agent interactions in shared environments. It is designed for research in multi-agent reinforcement learning, robotics, and autonomous systems where thousands of agents need to be simulated efficiently. -
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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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Youtu-Agent
A simple yet powerful agent framework that delivers with models
Youtu-Agent is an open-source framework developed to simplify the creation, execution, and evaluation of autonomous AI agents. The system focuses on reducing the complexity traditionally involved in configuring large language model agents by providing a modular architecture that separates execution environments, tools, and context management. This structure allows developers to rapidly assemble agent systems capable of performing tasks such as research, file processing, and data analysis.... -
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OpenJarvis
Personal AI, On Personal Devices
OpenJarvis is an open-source framework designed to build personal AI agents that run primarily on local devices rather than relying on cloud infrastructure. Developed as part of the Intelligence Per Watt research initiative, it focuses on improving the efficiency and practicality of on-device AI systems. The framework provides shared primitives for building local-first agents, along with evaluation tools that measure performance using metrics such as energy consumption, latency, cost, and... -
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PokeeResearch-7B
Pokee Deep Research Model Open Source Repo
PokeeResearchOSS provides an open-source, agentic “deep research” model centered on a 7B backbone that can browse, read, and synthesize current information from the web. Instead of relying only on static training data, the agent performs searches, visits pages, and extracts evidence before forming answers to complex queries. It is built to operate end-to-end: planning a research strategy, gathering sources, reasoning over conflicting claims, and writing a grounded response. The repository... -
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Hermes Agent
The agent that grows with you
Hermes Agent is a fully open-source autonomous AI agent designed to run persistently on your own machine or server, becoming more capable the longer it operates by learning from experience and building reusable procedural skills. Rather than functioning as a stateless chatbot, it maintains long-term memory across sessions and can generate searchable “Skill Documents” that capture how it solved complex tasks so it doesn’t start from scratch each time. The agent interfaces with messaging... -
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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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rLLM
Democratizing Reinforcement Learning for LLMs
rLLM is an open-source framework for building and training post-training language agents via reinforcement learning — that is, using reinforcement signals to fine-tune or adapt language models (LLMs) into customizable agents for real-world tasks. With rLLM, developers can define custom “agents” and “environments,” and then train those agents via reinforcement learning workflows, possibly surpassing what vanilla fine-tuning or supervised learning might provide. The project is designed to... -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter... -
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PilottAI
Python framework for building scalable multi-agent systems
pilottai is an AI-based autonomous drone navigation system utilizing reinforcement learning for real-time decision-making. It is designed for simulating and training drones to fly safely through dynamic environments using AI-based controllers.
