Search Results for "project"
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Showing 42 open source projects for "project"
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MetaGPT
The Multi-Agent Framework
...MetaGPT takes a one-line requirement as input and outputs user stories / competitive analysis/requirements/data structures / APIs / documents, etc. Internally, MetaGPT includes product managers/architects/project managers/engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. -
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Sandstorm
One API call, pull Claude agent, completely sandboxed
...This approach lowers the friction of building autonomous agents by removing the need to provision servers, orchestrate distributed agents, or manage persistent tooling; agents can be spun up in parallel without manual setup and shut down when complete. The sandbox environment isolates agent execution for security and predictability, and project updates continue to harden observability, fault handling, and configuration validation. -
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OpenSandbox
OpenSandbox is a general-purpose sandbox platform for AI applications
...OpenSandbox is particularly useful for AI agents, code execution services, and any scenario where untrusted code must be executed safely. Its architecture emphasizes flexibility, security boundaries, and operational consistency across environments. Overall, the project aims to standardize sandbox execution for modern AI and cloud native workflows. -
4
ex-skill
Distill your ex into an AI Skill
...It is designed to run within Claude Code environments, where users can generate, manage, and interact with these personalized AI entities through command-based interfaces. The project emphasizes emotional realism by reconstructing conversational tone, habits, and contextual memories, enabling interactions that feel consistent with the original person. -
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5
Get Physics Done (GPD)
The first open-source agentic AI physicist
...The project focuses on reducing the friction involved in setting up experiments, running simulations, and analyzing results, allowing researchers to focus more on scientific insight rather than infrastructure. It emphasizes automation and reproducibility, ensuring that experiments can be easily replicated and extended by other researchers. -
6
DB-GPT
Revolutionizing Database Interactions with Private LLM Technology
DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure. -
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CowAgent
AI assistant based on large models that can actively think and plan
CowAgent, based on the chatgpt-on-wechat project, is an open-source AI agent framework that integrates large language models into the WeChat ecosystem to create intelligent conversational assistants. It enables automated message handling by connecting WeChat accounts with AI models that can generate contextual replies, process voice messages, and produce images directly inside chats. -
8
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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Clawith
OpenClaw for Teams
...Its architecture suggests support for multi-agent collaboration, enabling distributed problem-solving and task delegation. It may also include monitoring and control features to ensure that agent behavior remains aligned with user goals. The project reflects a broader trend toward building AI systems that act as autonomous operators rather than passive assistants. Overall, Clawith serves as a foundation for building advanced, action-oriented AI workflows. -
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10
Agent Skills for Context Engineering
A comprehensive collection of Agent Skills for context engineering
Agent Skills for Context Engineering is a curated collection of reusable “agent skills” focused on helping AI agents perform better on long-horizon, multi-step work by managing context deliberately. Rather than being a single application, it packages practical guidance into skill modules that agents can load to improve planning, retrieval, memory usage, and overall reliability in real workflows. The repository emphasizes context engineering as a discipline, covering why agents fail when... -
11
Self-Operating Computer
A framework to enable multimodal models to operate a computer
...This framework is compatible with various multimodal models and currently integrates with GPT-4o, o1, Gemini Pro Vision, Claude 3, and LLaVa. Notably, it was the first known project to implement a multimodal model capable of viewing and controlling a computer screen. The framework supports features like Optical Character Recognition (OCR) and Set-of-Mark (SoM) prompting to enhance visual grounding capabilities. It is designed to be compatible with macOS, Windows, and Linux (with X server installed), and is released under the MIT license. -
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GPT All Star
AI-powered code generation tool for scratch development of web apps
AI-powered code generation tool for scratch development of web applications with a team collaboration of autonomous AI agents. This is a research project, and its primary value is to explore the possibility of autonomous AI agents. -
13
AutoGroq
Revolutionizes the way users interact with Autogen
AutoGroq is a groundbreaking tool that revolutionizes the way users interact with Autogen™ and other AI assistants. By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency. AutoGroq was born out of the realization that the traditional approach to building AI agents was backwards. Instead of creating agents in anticipation of problems, AutoGroq uses the syntax of the users' needs as the basis for constructing the perfect AI team. ... -
14
Claude Code Plugins
Intelligent automation and multi-agent orchestration for Claude Code
Claude Code Plugins is a lightweight framework designed to define, manage, and execute AI agents in a modular and extensible way, typically focusing on orchestrating tasks using large language models and tool integrations. The project provides abstractions for building agents that can interpret instructions, execute commands, and interact with external systems in a structured workflow. It emphasizes simplicity and composability, allowing developers to define agent behaviors through reusable components rather than monolithic logic. The framework supports integration with various tools and APIs, enabling agents to perform actions such as data retrieval, automation, and decision-making processes. ... -
15
OpenAI Agent Skills
Skills Catalog for Codex
...It organizes reusable, task-specific workflows, instructions, scripts, and resources into modular skill folders so that an AI agent can reliably perform complex tasks without repeated custom prompting, making agent behavior more predictable and composable. Each skill is defined with clear metadata and instructions organizing how an AI assistant should complete specific tasks ranging from project management to code generation and documentation assistance. The repository supports community contributions, allowing developers to add new skills or update existing ones to keep the catalog relevant and practical for evolving use cases. -
16
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 across different domains. The architecture suggests scalability, allowing the system to handle multiple agents or complex workflows simultaneously. ... -
17
Agent SOP
Natural language workflows for AI agents
Agent SOP is a framework that implements structured operational procedures (SOPs) for autonomous agents so that they can carry out complex multi-step tasks reliably and in a defined order. Instead of relying solely on broad language model reasoning, this project enforces explicit step sequences with checkpoints, conditional transitions, and rollback logic, making agent workflows more predictable and auditable. It defines reusable SOP templates that agents can instantiate with context-specific parameters, allowing organizations to codify best practices for customer support, data processing, document workflows, or incident response. ... -
18
Mini Agent
A minimal yet professional single agent demo project
Mini-Agent is a minimal yet production-minded demo project that shows how to build a serious command-line AI agent around the MiniMax-M2 model. It is designed both as a reference implementation and as a usable agent, demonstrating a full execution loop that includes planning, tool calls, and iterative refinement. The project exposes an Anthropic-compatible API interface and fully supports interleaved thinking, letting the agent alternate between reasoning steps and tool invocations during long, complex tasks. ... -
19
OpenHarness
Open Agent Harness with a built-in personal agent, Ohmo
OpenHarness is an open-source framework developed to support large-scale machine learning workflows, particularly in the context of training, evaluating, and benchmarking AI models. It provides a structured environment for orchestrating experiments, managing datasets, and standardizing evaluation processes across different models. The project focuses on reproducibility and scalability, allowing researchers and engineers to run consistent experiments while tracking results effectively. It often includes modular components that can be adapted to different machine learning pipelines, enabling flexibility across use cases such as recommendation systems, natural language processing, or multimodal tasks. ... -
20
MoneyPrinter V2
Automate the process of making money online
MoneyPrinter V2 is an open-source automation platform designed to streamline and scale online income generation workflows by combining content creation, social media automation, and marketing strategies into a single system. It is a complete rewrite of the original MoneyPrinter project, focusing on modularity, extensibility, and broader functionality across multiple monetization channels. The platform operates primarily through Python-based scripts that automate tasks such as generating and publishing YouTube Shorts, posting on social media platforms like Twitter, and executing affiliate marketing campaigns. It integrates scheduling mechanisms that allow users to run automated workflows at defined intervals, enabling continuous content production and distribution without manual intervention. -
21
GenericAgent
Self-evolving autonomous agent framework
The GenericAgent project is a flexible framework for building autonomous AI agents that can operate across diverse tasks and environments. It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. -
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Hello-Agents
Building an Intelligent Agent from Scratch
Hello Agents is an open educational project designed to teach developers how to understand, design, and build AI-native agents from the ground up through structured tutorials and practical examples. The project focuses on guiding learners beyond superficial framework usage toward deeper comprehension of agent architecture, reasoning loops, and real-world implementation patterns. -
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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 changes. The system centers on a simple workflow where the agent modifies a single training file while human researchers guide the process through a program.md instruction file. ... -
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BlenderMCP
Blender Model Context Protocol Integration
...The system establishes a two-way communication channel between Blender and the AI, where commands can be sent and results retrieved in real time. It includes features for object manipulation, material editing, and scene inspection, giving the AI deep control over the modeling environment. The project also supports integration with external asset sources such as Sketchfab and Poly Haven, expanding the range of available resources. Additionally, it allows execution of Python scripts within Blender through AI commands, enabling advanced automation and customization. -
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Cua
Open-source infrastructure for Computer-Use Agents. Sandboxes
Cua is an open-source command-line utility and workflow orchestrator designed to help developers define, compose, and run common tasks with a unified interface, promoting consistency and reuse across projects. It introduces a declarative syntax for specifying build scripts, automation pipelines, environment setups, and project-specific commands so contributors don’t need to memorize disparate scripts or tooling across languages and ecosystems. Cua can also manage task dependencies, handle cross-platform invocations, and simplify complex workflows into simple aliases or compound commands that are easy to share in teams. By centralizing shared commands in a structured, documented config, it helps reduce errors, accelerates onboarding of new contributors, and keeps task definitions versioned with the codebase. ...
