Search Results for "multi agent simulation"
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Showing 133 open source projects for "multi agent simulation"
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Sonic Agent
Agent of Sonic cloud real machine platform
Agent of Sonic Cloud Real Machine Platform. Sonic is a platform that integrates remote control debugging and automated testing of mobile devices, and strives to create a better use experience for global developers and test engineers. -
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Page Agent
JavaScript in-page GUI agent. Control web interfaces
Page Agent is an open-source in-page AI agent framework that allows developers to control and interact with web interfaces using natural language directly within the browser. Unlike traditional browser automation tools, it operates entirely through in-page JavaScript, eliminating the need for browser extensions, headless browsers, or external automation environments. The system enables users to manipulate the DOM through text-based commands, allowing complex workflows such as form filling,... -
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colleague-skill
Transform a cold separation into a warm Skill
...The system likely integrates with broader agent frameworks, enabling seamless inclusion in multi-agent environments. It emphasizes communication and coordination, ensuring that agents can contribute meaningfully to shared objectives. This makes it particularly useful for complex projects where multiple perspectives or iterative refinement are needed. -
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Grey Wolf Optimizer for Path Planning
Grey Wolf Optimizer (GWO) path planning/trajectory
...This project demonstrates applications in multi-agent and multi-UAV cooperative path planning, making it useful for research and educational purposes in the field of intelligent optimization and robotics. -
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Make It heavy
A Python framework that emulates Grok Heavy functionality
...The framework uses OpenRouter for model access and can also run in single-agent mode for simpler tasks. Overall, it is useful for users who want broader research coverage, richer reasoning diversity, and structured multi-agent responses from one command-line workflow. -
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OWL
Optimized Workforce Learning for General Multi-Agent Assistance
OWL (Optimized Workforce Learning) is a sophisticated open-source framework built on the CAMEL-AI ecosystem for orchestrating teams of AI agents to collaboratively solve complex, real-world tasks with dynamic planning and automation capabilities. Unlike single-agent systems, it treats task completion as a collaborative workforce where agents take on specialized roles (planning, execution, analysis) and coordinate via a modular multi-agent architecture that supports flexible teamwork across domains. OWL delivers state-of-the-art performance on benchmarks like GAIA and emphasizes real-time decision-making, web automation, rich search integration, document parsing, and multi-tool workflows, making it suitable for tasks ranging from information retrieval to interactive automation. -
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Neuro SAN Studio
A playground for neuro-san
Neuro SAN Studio is a development environment and playground for building, testing, and deploying multi-agent AI systems using the Neuro SAN framework. It provides a hands-on interface where users can design agent networks, run experiments, and observe how multiple agents collaborate to solve complex tasks. The platform is built around a data-driven approach, where entire agent systems can be defined using configuration files rather than extensive code, making it accessible to both developers and domain experts. ... -
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nature-skills
Skill that conforms to the academic expression and scientific research
...The repository is designed to support experimentation with multi-step reasoning, task automation, and contextual tool orchestration for agentic AI systems. By standardizing reusable capabilities, the project helps developers build more maintainable and extensible AI workflows. Overall, nature-skills contributes to the growing ecosystem of modular AI agent tooling and structured automation frameworks. -
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Antfarm
Build your agent team in OpenClaw with one command
Antfarm is an OpenClaw-focused agent team installer for creating coordinated AI development workflows. It gives users a predefined group of specialized agents such as planner, developer, verifier, tester, and reviewer. The project is designed to make multi-agent work more repeatable by giving each agent a clear role in the development process. It can be installed with a single command and does not require users to set up separate infrastructure. -
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BotSharp
AI Multi-Agent Framework in .NET
Conversation as a platform (CaaP) is the future, so it's perfect that we're already offering the whole toolkits to our .NET developers using the BotSharp AI BOT Platform Builder to build a CaaP. It opens up as much learning power as possible for your own robots and precisely control every step of the AI processing pipeline. BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding, computer vision and audio... -
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InkOS
Autonomous novel writing CLI AI Agent
InkOS is a multi-agent creative writing system designed to automate the production of long-form narrative content such as novels through coordinated AI workflows. The system organizes multiple specialized agents that collaborate in stages, including drafting, reviewing, editing, and refining text, with optional human checkpoints to ensure quality and coherence. -
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TradingAgents
Chinese Financial Trading Framework Based on Multi-Agent LLM
TradingAgents-CN is a Chinese-enhanced, multi-agent LLM framework aimed at building financial analysis and trading-oriented workflows, with an emphasis on collaboration between specialized agents rather than a single monolithic prompt. It organizes market-related tasks into roles and stages so different agents can contribute research, reasoning, aggregation, and decision support in a structured pipeline. -
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OpenAI Agents JS
A lightweight, powerful framework for multi-agent workflows
...The SDK is provider-agnostic, meaning while it’s deeply compatible with OpenAI’s APIs, you can also adapt it to use other backends or extension layers. At its core, it introduces primitives like Agents, Tools, Guardrails, and Handoffs, letting you define structured multi-agent systems in a modular way. The repo includes examples showing how to build agents that call local functions, chain between agents, validate input/output, stream responses, and interact in real time (e.g. voice agents via WebRTC). It also has tracing and debugging support so you can introspect how agents executed their workflows. ... -
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Obsidian Skills
Agent skills for Obsidian
Obsidian-Skills is a repository of agent skills tailored for use with Obsidian and any Claude-compatible agent that follows the standard Agent Skills specification, enabling AI assistants to better understand and interact with Obsidian content. These skills are markdown-driven specifications that teach Claude Code (or similar agents) how to perform context-aware tasks within Obsidian’s unique environment, such as interpreting different file types and workflows, automating workflows tied to notes, or enhancing agent responses with structured knowledge. ... -
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zeroshot CLI
Your autonomous engineering team in a CLI. Point Zeroshot at an issue
zeroshot CLI is an autonomous engineering agent orchestration CLI that lets developers point it at a software task or issue and have it plan, implement, validate, and iterate on code changes without direct manual intervention. It orchestrates a multi-agent workflow where separate roles such as planner, implementer, and independent validators collaborate in isolated environments, looping until the code meets acceptance criteria or produces reproducible failures. ... -
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Antigravity Awesome Skills
The Ultimate Collection of 700+ Agentic Skills for Claude Code
Antigravity Awesome Skills is a playful yet practical repository that curates a set of clever, expressive, and sometimes whimsical AI agent skill templates designed to help users bootstrap agent behavior quickly. Rather than focusing on production-grade systems, it provides creative and high-impact skills that demonstrate how agents can be used to automate tasks, generate content, assist with daily operations, or integrate into larger workflows with minimal configuration. ... -
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VT Code
VT Code - semantic AI coding agent
...VTCode operates as an agent rather than a simple autocomplete tool, meaning it can interpret user intent, navigate codebases, and assist with multi-step tasks. It is highly configurable, allowing developers to define behavior, prompts, and workflows tailored to their projects. The tool is especially useful for developers who prefer lightweight, local-first environments but still want advanced AI assistance comparable to modern IDE-based tools. -
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Agents.jl
Agent-based modeling framework in Julia
Agents.jl is a pure Julia framework for agent-based modeling (ABM): a computational simulation methodology where autonomous agents react to their environment (including other agents) given a predefined set of rules. The simplicity of Agents.jl is due to the intuitive space-agnostic modeling approach we have implemented: agent actions are specified using generically named functions (such as "move agent" or "find nearby agents") that do not depend on the actual space the agents exist in, nor on the properties of the agents themselves. ... -
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ACE-Step UI
The Ultimate Open Source Suno Alternative
...The project emphasizes clarity and usability, making it easier to understand multi-step logic. It is particularly valuable in agent-based or workflow-driven applications. Its modular design allows integration into larger systems. -
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AG-UI
The Agent-User Interaction Protocol
...The project is especially useful for teams building copilots, workflow assistants, multi-agent products, or custom AI interfaces. Overall, AG-UI provides a shared communication layer between autonomous systems and the interfaces where people actually use them. -
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pi-subagents
Pi extension for async subagent delegation with truncation
pi-subagents is an extension for the Pi coding agent that lets a parent session delegate focused work to independent child agents. Built-in specialists handle tasks such as codebase scouting, external research, planning, implementation, review, context gathering, and second-opinion analysis. Child sessions can run in the foreground with streamed results or continue asynchronously as background jobs. Agents can be composed into sequential chains, parallel groups, reusable workflows, and more... -
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Everything Claude Code
Complete Claude Code configuration collection
...The repository curates reproducible templates that demonstrate how Claude Code agents can participate in end-to-end developer journeys such as building new features, adhering to coding standards, generating documentation, and automating refactor patterns. It also includes example orchestrations that showcase multi-step agent workflows, letting developers see how to compose small, discrete agent capabilities into productive, cohesive sequences. Because it is designed to be modular, individuals and teams can pull in only the skills they need, combine them with custom logic, or share their own extensions with the community. -
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Motia
Multi-Language Backend Framework that unifies APIs
Motia is a large-scale, multi-language backend framework designed to unify and simplify core backend concerns — from APIs and background jobs to distributed workflows, streams, and advanced AI agent integration — through a single coherent primitive with built-in observability and state management. It provides a unified development philosophy that seamlessly bridges different backend patterns (queues, event streams, scheduled jobs) while offering integrated tools for telemetry, logging, metrics, and tracing to help teams maintain high visibility into production workloads. ... -
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Craft Agents
Work effectively with agents
Craft Agents project from lukilabs is an open-source desktop application and workflow environment built around agent interaction and document-centric tasks, designed to help users work with AI assistants more effectively across multiple information sources. This repository extends the idea of “agents” by providing a user-friendly interface that integrates APIs, multitasking workflows, and session sharing so that you can easily orchestrate multiple AI interactions and retrieve context from... -
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All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
...Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. A companion cheat sheet gives learners a quick reference for formulas, pseudocode, and key concepts.
