Open Source AI Agents - Page 15
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Real time strategy zombie siege and survival game written in Java. You and your friends team together and try to survive while the AI controlled zombies pound on your barricades. Only time will tell if you will hear the rescue helicopter.
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agents.md
A simple, open format for guiding coding agents
openai/agents.md is a repository whose primary file is AGENTS.md, a proposed open, lightweight convention (i.e. Markdown file) for guiding coding agents in software repositories. The idea is that AGENTS.md acts as a “README for agents”: a predictable, structured place where humans can put instructions, conventions, build/test commands, environment setup, and other guidance that generative agents (e.g. code-writing, code-assisting tools) should consult when operating in the repo. Instead of putting everything in README or doc files (which are more human-oriented and might mix high-level narrative), AGENTS.md is intended to surface agent-relevant details that help them “do the right thing” (tests, style, project structure, tooling). -
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airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
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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. Designed to run on a single GPU, it keeps the research loop minimal and self-contained to make autonomous experimentation practical. Over time, the agent logs experiments, evaluates improvements, and gradually evolves the model through automated trial-and-error. -
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butlerbot
Artificial intelligent agent
Artificial intelligent agent which uses pypy (python), nltk, PyAIML, and various other opensource libraries to make a self contained AI agent server / client enviroment. It is meant to work in Win, Mac and Linux. just unzip it into the dir which will be its home. -
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gpt-engineer
Full stack AI software engineer
gpt-engineer is an open-source platform designed to help developers automate the software development process using natural language. The platform allows users to specify software requirements in plain language, and the AI generates and executes the corresponding code. It can also handle improvements and iterative development, giving users more control over the software they’re building. Built with a terminal-based interface, gpt-engineer is customizable, enabling developers to experiment with AI-assisted programming and refine their development process. It is especially useful for automating the coding and iterative feedback loop in software development. -
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litlyx
Analytics for developers, setup Analytics in 30 seconds
The easiest, developer-centric analytics tool. Litlyxis an open-source, self-hostable analytics solution for the modern framework. Litlyx offers a unique eyewear cleaning system that includes a special cleaning solution and reusable microfiber swabs. This system is designed to provide a more thorough and eco-friendly way to clean glasses, lenses, and screens. The brand emphasizes sustainability by reducing single-use plastics and promoting long-term use of their products. Their cleaning kit is compact, portable, and designed to be effective for everyday use, ensuring that users can maintain clear vision without the hassle of disposable wipes or sprays. -
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mcp-use
A solution to build and deploy MCP agents and applications
mcp-use is an open source development platform offering SDKs, cloud infrastructure, and a developer-friendly control plane for building, managing, and deploying AI agents that leverage the Model Context Protocol (MCP). It enables connection to multiple MCP servers, each exposing specific tool capabilities like browsing, file operations, or specialized integrations, through a unified MCPClient. Developers can create custom agents (via MCPAgent) that dynamically select the most appropriate server for each task using configurable pipelines or a built-in server manager. It simplifies authentication, access control, audit logging, observability, sandboxed runtime environments, and deployment workflows, whether self-hosted or managed, making MCP development production-ready. With integrations for popular frameworks like LangChain (Python) and LangChain.js (TypeScript), mcp-use accelerates the creation of tool-enabled AI agents. -
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n-skills
Curated plugin marketplace for AI agents
n-skills is a curated plugin marketplace and universal skills collection for AI coding agents that standardizes how skills are defined, discovered, and installed across multiple frameworks and agent platforms. It organizes skills into categories such as workflow orchestration, tools, automation, and documentation support, making it easy for developers to add capabilities like browser automation, multi-agent workflow coordination, or repo maintenance assistance. The repository includes a universal AGENTS.md discovery file and a shared SKILL.md format so that once a skill is published, it can be recognized and used by Claude Code, GitHub Copilot, Codex, Cursor, and other AI coding assistants with minimal friction. Installation of skills is supported through native installers or via universal installers like openskills, enabling seamless adoption in diverse development environments. -
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opc-methodology
The second edition of "One-person Enterprise Methodology"
opc-methodology is a structured framework for designing, organizing, and executing AI-driven workflows using prompt-based systems. It focuses on breaking down complex AI tasks into clear stages that improve reliability, reproducibility, and clarity when working with language models. The methodology emphasizes defining objectives, processes, and constraints explicitly, ensuring that AI outputs are guided by structured reasoning rather than vague prompts. It is particularly useful for building consistent workflows in areas such as content generation, automation, and decision support. The framework promotes modular thinking, allowing users to reuse and refine components across different tasks. It also encourages iterative refinement, helping users improve prompt performance through systematic adjustments. Overall, OPC Methodology provides a practical approach to prompt engineering that prioritizes clarity, structure, and repeatability. -
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pump-fun-skills
This repo contains pump fun skills for AI agents
pump-fun-skills is a repository that provides a collection of predefined “skills” or modules designed for use with AI agents in the pump.fun ecosystem. These skills represent reusable capabilities that agents can execute, enabling more structured and modular behavior. The project focuses on expanding the functionality of agents by providing ready-to-use components that can be combined into larger workflows. It supports customization, allowing developers to adapt or extend skills to meet specific requirements. The repository is particularly useful for developers building agent-based systems who want to accelerate development by reusing existing logic. It emphasizes modularity and composability, making it easier to build complex systems from smaller parts. Overall, pump-fun-skills serves as a library of building blocks for agent-based applications. -
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smolagents
Agents write python code to call tools and orchestrate other agents
This library is the simplest framework out there to build powerful agents. We provide our definition in this page, where you’ll also find tips for when to use them or not (spoilers: you’ll often be better off without agents). smolagents is a lightweight framework for building AI agents using large language models (LLMs). It simplifies the development of AI-driven applications by providing tools to create, train, and deploy language model-based agents. -
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ticket
Fast, powerful, git-native ticket tracking in a single bash script
ticket is a lightweight, git-native ticket management tool implemented as a single Bash script that brings powerful issue tracking directly into your Git workflows without requiring a database or complex setup. It stores each ticket as a Markdown file with YAML frontmatter, making them human-readable and easy to version control alongside your code, while also allowing IDEs to jump straight to ticket definitions. The CLI provides common subcommands to create, list, edit, close, and manage dependencies between tickets, enabling clear hierarchical task structures and visual dependency trees. Its design is rooted in the Unix philosophy of simplicity, composability, and transparency, meaning it integrates well with other standard tools like grep, jq, and ripgrep when installed. Teams can use ticket to track bugs, features, chores, and epics with priority levels and tags, all by staying within the terminal and Git ecosystem. -
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typeui.sh
Design system skills for agentic tools
typeui.sh is a type-safe UI framework designed to streamline the development of user interfaces by leveraging strong typing and structured definitions. It focuses on improving developer productivity by reducing runtime errors and ensuring consistency across UI components. The framework integrates closely with TypeScript, allowing developers to define interfaces and components with strict type validation. It supports component-based architecture, making it easier to build scalable and maintainable applications. TypeUI also emphasizes reusability, enabling developers to create standardized components that can be shared across projects. Its design encourages clean and predictable code, which is especially valuable in large-scale applications. Overall, TypeUI provides a structured and reliable approach to building modern user interfaces with strong typing guarantees. -
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zhangxuefeng-skill
Zhang Xuefeng's cognitive operating system
zhangxuefeng-skill is an AI agent skill package that distills the decision-making frameworks and practical reasoning style of Chinese educator Zhang Xuefeng into a reusable, executable cognitive system. Rather than functioning as a simple quote collection, it encodes structured heuristics, mental models, and decision logic derived from books, interviews, and real-life case analysis. The skill is designed to be used within AI coding agents such as Claude Code, where it can be invoked to provide guidance on topics like college major selection, career planning, and long-term life decisions. It emphasizes pragmatic, outcome-driven reasoning focused on employment prospects, income potential, and social constraints rather than abstract ideals. The architecture organizes knowledge into reproducible decision flows, enabling consistent outputs across different scenarios. It also demonstrates how human expertise can be translated into modular AI skills that can be reused and adapted.
