Open Source Linux AI Agents - Page 3
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AI Agents for Linux
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Google Agent Skills
Agent Skills for Google products and technologies
Google Skills is a repository of modular “agent skills” designed to extend AI agents with structured knowledge about Google technologies and workflows. Each skill provides guidance, best practices, and procedural instructions that agents can use to perform tasks more effectively. The repository includes skills for services like BigQuery, Cloud Run, Firebase, and Kubernetes, as well as onboarding and architectural patterns. It is designed to integrate with agent platforms through a standardized installation system. The project emphasizes reusable, composable knowledge units that can enhance agent reasoning and execution. It is actively developed and intended to support modern AI-driven development workflows. The system helps bridge the gap between documentation and actionable agent behavior. -
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Kheish
Kheish: A multi-role LLM agent for tasks like code auditing
Kheish is a framework designed for cybersecurity professionals to automate penetration testing tasks, providing tools to streamline security assessments. -
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Nanobrowser
Open-Source Chrome extension for AI-powered web automation
Nanobrowser is an open-source AI web automation tool that runs in your browser. A free alternative to OpenAI Operator with flexible LLM options and a multi-agent system. Nanobrowser, as a chrome extension, delivers premium web automation capabilities while keeping you in complete control. No subscription fees or hidden costs. Just install and use your own API keys, and you only pay what you use with your own API keys. Everything runs in your local browser. Your credentials stay with you, never shared with any cloud service. Connect to your preferred LLM providers with the freedom to choose different models for different agents. -
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GBrain
Garry's Opinionated OpenClaw/Hermes Agent Brain
GBrain is an open-source AI memory system designed to give autonomous agents persistent, structured, and scalable long-term memory across interactions and workflows. It operates by transforming large collections of markdown documents, personal notes, and external data into a searchable knowledge base backed by PostgreSQL and vector embeddings, enabling both semantic and keyword-based retrieval. The system is tightly integrated with agent frameworks such as OpenClaw and Hermes, allowing AI agents to read from and write to memory continuously, effectively evolving their understanding over time. GBrain introduces a hybrid retrieval model that combines embeddings with ranking strategies to improve relevance when querying large datasets. It also organizes knowledge into structured documents with summaries and timelines, helping agents maintain context and track changes in information. -
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Grok CLI
An open-source AI agent that brings the power of Grok
Grok CLI is a command-line interface built around the Grok AI model that brings programmatic and conversational AI capabilities directly to developer terminals. It lets you run Grok queries from your shell, scripting environment, or automation workflows without switching to a browser, enabling utility in scripting, quick data exploration, code generation, and assistant-guided tasks directly where you write code. The CLI supports streaming responses, so outputs appear in real time as the Grok model generates them, making interactions feel responsive and fluid in terminal contexts. Grok CLI is designed to integrate with existing terminal habits—aliases, pipes, editors, and tooling—so you can combine AI assistance with native command-line workflows like grep, awk, and git. It also includes authentication support, configuration management, and caching options so frequent queries are efficient. -
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Obscura
The headless browser for AI agents and web scraping
Obscura is a security-focused project aimed at providing tools and techniques for enhancing privacy, anonymity, and operational security in digital environments. It is designed for users who need to obscure their digital footprint and reduce traceability across systems. The project typically includes utilities for masking identity, managing secure communication, and mitigating surveillance risks. It emphasizes practical implementations of privacy-preserving workflows rather than purely theoretical approaches. Obscura is particularly relevant for researchers, security professionals, and privacy-conscious users. Its architecture focuses on modular tools that can be adapted to different threat models. The project reflects modern concerns around digital surveillance and data exposure. -
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Self-Operating Computer
A framework to enable multimodal models to operate a computer
The Self-Operating Computer Framework is an innovative system that enables multimodal models to autonomously operate a computer by interpreting the screen and executing mouse and keyboard actions to achieve specified objectives. 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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Skills For Real Engineers
Skills for Real Engineers. Straight from my .claude directory
Skills For Real Engineers is a curated collection of modular AI “skills” designed to improve how developers interact with coding agents by enforcing structured engineering workflows. Each skill is a small, focused instruction set that guides an AI through tasks such as planning, refactoring, testing, or architectural analysis. Instead of relying on vague prompts, the system encodes repeatable processes that ensure consistent and higher-quality outputs. The repository includes tools for converting conversations into product requirements, breaking plans into actionable issues, and stress-testing ideas through structured questioning. It emphasizes disciplined thinking before coding, encouraging developers to fully explore design decisions. Skills can be installed individually and integrated into agent environments, making them highly composable. Overall, the project transforms AI from a reactive assistant into a process-driven engineering collaborator. -
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Agency Agents
A complete AI agency at your fingertips
Agency Agents is a framework focused on orchestrating multiple AI agents that collaborate to complete complex tasks through structured coordination. It is designed around the idea of “agency,” where each agent has a defined role, responsibility, and interaction pattern within a larger system. The framework enables developers to define workflows where agents communicate, delegate tasks, and share context, creating a distributed problem-solving environment. It supports modular design, allowing agents to be composed into reusable systems that can be adapted across different use cases. The project emphasizes clarity in agent responsibilities, which helps reduce ambiguity and improve reliability in multi-agent workflows. It also provides mechanisms for managing interactions, ensuring that communication between agents remains structured and predictable. Overall, agency-agents serves as a foundation for building collaborative AI systems that operate through coordinated agent behavior. -
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Agentic
AI agent stdlib that works with any LLM and TypeScript AI SDK
Agentic is an open source, TypeScript, AI agent standard library that works with any LLM and TS AI SDK. Agentic’s standard library of TypeScript AI tools are optimized for both TS-usage as well as LLM-based usage, which is really important for testing and debugging. -
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AutoCoder
A long-running autonomous coding agent powered by the Claude Agent
Autocoder is an experimental auto-generation engine that transforms high-level prompts or structured descriptions into functioning source code, models, or systems with minimal manual intervention. Rather than hand-writing boilerplate or repetitive patterns, users supply a specification—such as a description of a feature, a function prototype, or a module outline—and Autocoder fills in complete implementations that compile and run. It is built to support iterative refinement: after generating an initial draft, you can provide feedback or corrections, and the system will adjust the output to match evolving intentions. The core idea is to accelerate software production while preserving correctness and readability, minimizing the cognitive overhead that comes from switching between concept and implementation. Its architecture typically integrates language models with static analysis and template logic so that generated code is not only syntactically valid but also idiomatic and testable. -
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Beads
A memory upgrade for your coding agent
Beads is an open-source project providing a distributed, structured memory system for AI coding agents, replacing ad-hoc text plans with a git-backed graph that represents tasks, dependencies, and progress in a persistent, queryable format. Instead of storing plans as unstructured Markdown or ephemeral notes, Beads organizes agent state, task artifacts, and relationships as nodes and edges in a version-controlled graph so that long-horizon projects don’t lose context or coherence as the agent proceeds. This approach helps coding agents — and human collaborators — track which tasks depend on others, what has been done, and where workflows branch or reunify without losing important data. By leveraging Git as the storage backbone, the project ensures that memory is persistent, diffable, and sharable, with the ability to roll back, branch, or merge memory states just like source code. -
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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. It is particularly useful for experimenting with autonomous or semi-autonomous systems that rely on prompt-driven logic and tool usage. The design encourages transparency and control over how agents operate, making it suitable for both prototyping and production scenarios. -
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Claude Context
Code search MCP for Claude Code
Claude Context is a tool designed to enhance the contextual understanding of large language models by managing and injecting relevant information into prompts. It focuses on improving response quality by ensuring that models have access to the most relevant data when generating outputs. The system integrates with vector databases and retrieval systems, enabling efficient storage and retrieval of contextual information. It supports workflows such as retrieval-augmented generation, where external knowledge is dynamically incorporated into model responses. The project emphasizes scalability, allowing it to handle large datasets and complex queries efficiently. It also provides tools for organizing and managing context, making it easier to maintain structured knowledge bases. Overall, Claude-context acts as a bridge between raw data and AI models, improving the relevance and accuracy of generated outputs. -
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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. The CLI is typically lightweight, easy to install, and designed to integrate with existing toolchains and shells without friction. -
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uAgents
A fast and lightweight framework for creating decentralized agents
uAgents is a library developed by Fetch.ai that allows for creating autonomous AI agents in Python. With simple and expressive decorators, you can have an agent that performs various tasks on a schedule or takes action on various events. -
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Agent Skills
Skills for AI coding agents
Agent Skills by Vercel Labs is a curated collection of modular “skills” designed to extend the capabilities of AI coding agents by packaging human-ready instructions, workflows, and optional scripts that tell an agent how to perform specific development tasks. In this repository, each skill adheres to the Agent Skills specification, meaning they’re defined as folders with a SKILL.md file (containing task descriptions and step-by-step guidance) and can include helper scripts and reference material that the agent can execute or consult when invoked. The goal of the project is to make it easy for AI assistants like Claude Code, OpenCode, Cursor, Codex, and others that support this open ecosystem to automatically apply best practices or perform concrete actions when a relevant user intent is detected. For example, some skills guide the agent in applying React and Next.js performance best practices, auditing UI and accessibility standards. -
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AskUI Vision Agent
Enable AI to control your desktop, mobile and HMI devices
AskUI’s Vision Agent is an automation framework that allows you—and AI agents—to control real desktops, mobile devices, and HMI systems by perceiving the UI and performing actions like clicking, typing, scrolling, and drag-and-drop. It is designed for multi-platform compatibility and supports multiple AI models so you can tailor perception and decision-making to your workload. The repository presents a feature overview, sample media, and frequent release notes, which show ongoing improvements such as CORS checks and other operational tweaks. The broader AskUI documentation covers the Python Vision Agent along with suite services and inference APIs, indicating a productized ecosystem rather than a single library. Community-curated lists also recognize Vision Agent as part of the broader “GUI agents” landscape, placing it among other computer-use agents. -
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Bolna
Conversational voice AI agents
Bolna is an end-to-end open-source platform for building conversational voice AI agents, enabling developers to create voice-first conversational assistants efficiently. -
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Khazix Skills
Digital Life Kazik Open Source AI Skills Collection
Khazix Skills project is an automation framework designed to transform GitHub repositories into structured, reusable AI agent skills. It acts as a pipeline that analyzes a repository’s metadata, extracts relevant information such as README content and commit hashes, and converts it into a standardized skill format that can be integrated into agent ecosystems. The system emphasizes lifecycle management by embedding versioning, traceability, and metadata directly into generated skill files, allowing future updates and synchronization with the original repository. It also generates wrapper scripts that enable AI agents to interact with the underlying repository functionality without requiring deep manual integration. By enforcing a consistent schema, the project ensures interoperability between skills and simplifies deployment across environments. This makes it especially useful for teams building modular AI agents that rely on external tools or open-source repositories. -
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OpenSandbox
OpenSandbox is a general-purpose sandbox platform for AI applications
OpenSandbox is a general purpose sandbox platform designed to securely run and isolate AI applications and untrusted workloads in controlled environments. The project focuses on providing a unified sandbox API that simplifies the process of executing code safely across different runtime backends. It supports multiple programming languages through SDKs, allowing developers to integrate sandbox capabilities into their systems without building custom isolation layers. The platform is built to work with container technologies such as Docker and Kubernetes, enabling scalable and production ready deployments. 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. -
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PentAGI
Perform penetration testing tasks
PentAGI is a fully autonomous AI agent system designed to perform complex penetration testing tasks by orchestrating multiple intelligent components into a coordinated offensive security workflow. The platform aims to automate significant portions of the penetration testing lifecycle, including reconnaissance, vulnerability discovery, and exploitation planning, reducing the amount of manual effort required from security professionals. It leverages agent-based architecture and AI reasoning to chain together tools and strategies in a way that mimics experienced human testers. The project is built to be modular and extensible so researchers and red teams can customize behavior or integrate additional tools as needed. By focusing on autonomous decision-making in cybersecurity contexts, PentAGI represents part of the broader trend toward AI-assisted offensive security automation. -
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Phidata
Build multi-modal Agents with memory, knowledge, tools and reasoning
Phidata is an open source platform for building, deploying, and monitoring AI agents. It enables users to create domain-specific agents with memory, knowledge, and external tools, enhancing AI capabilities for various tasks. The platform supports a range of large language models and integrates seamlessly with different databases, vector stores, and APIs. Phidata offers pre-configured templates to accelerate development and deployment, allowing users to quickly go from building agents to shipping them into production. It includes features like real-time monitoring, agent evaluations, and performance optimization tools, ensuring the reliability and scalability of AI solutions. Phidata also allows developers to bring their own cloud infrastructure, offering flexibility for custom setups. The platform provides robust support for enterprises, including security features, agent guardrails, and automated DevOps for smoother deployment processes. -
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PraisonAI
PraisonAI application combines AutoGen and CrewAI or similar framework
PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration. Chat with your ENTIRE Codebase. Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralized framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing ease of use, customization, and human-agent interaction. -
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SafeClaw
Chat with it via text and voice
SafeClaw is an open-source, entirely local alternative to cloud-based AI assistants like OpenClaw, enabling users to build a personal assistant that runs on their own machine without incurring API usage charges or exposing data to third-party services. It emphasizes privacy and predictability by using traditional programming, rule-based intent parsing, and established machine learning tools rather than large language models, meaning there are no per-token API costs and deterministic behavior. The assistant offers features such as voice control using fully local speech-to-text (Whisper) and text-to-speech (Piper) capabilities, news aggregation with extractive summarization, and smart home or Bluetooth device control. SafeClaw supports multiple channels, including CLI and Telegram, and avoids prompt injection risk because it doesn’t rely on LLMs for core operations.
