Open Source BSD AI Agents
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AI Agents for BSD
Browse free open source AI Agents and projects for BSD below. Use the toggles on the left to filter open source AI Agents by OS, license, language, programming language, and project status.
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Huashu Design
Huashu Design · HTML-native design skill for Claude Code
Huashu-design is a framework focused on designing and optimizing conversational scripts, particularly for persuasive or structured communication scenarios such as sales, marketing, or customer interaction. The project emphasizes the creation of “huashu,” or structured dialogue patterns, that guide interactions toward specific goals. It provides methodologies and tools for organizing conversation flows, ensuring that responses are consistent, effective, and aligned with intended outcomes. The system is designed to be adaptable, allowing users to customize scripts for different domains or audiences. It also encourages iterative refinement, enabling continuous improvement of conversational strategies based on feedback and performance. The framework can be applied to both human-driven and AI-driven interactions, making it versatile across use cases. Overall, huashu-design offers a systematic approach to crafting and managing effective communication patterns. -
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Claude Code
Claude Code is an agentic coding tool that lives in your terminal
Claude Code is an intelligent agentic coding assistant that lives in your terminal and understands your entire codebase. It helps developers code faster by executing routine tasks, explaining complex code snippets, and managing git workflows—all via natural language commands. Claude Code integrates seamlessly into your terminal, IDE, or GitHub by tagging @claude to interact with your code context. The tool is designed to simplify development by automating repetitive work and providing instant clarifications on code behavior. User feedback and usage data are collected responsibly, with strict privacy safeguards and limited retention, ensuring no feedback is used to train generative models. Claude Code is open and actively maintained with community-driven bug reporting and feature requests. Its natural language interface makes advanced coding workflows accessible without leaving your coding environment. -
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OpenManus
Open-source AI agent framework
OpenManus is an open-source AI agent framework designed to autonomously execute complex, multi-step tasks by combining reasoning, planning, and tool use. It enables developers to build agents that can think, act, and iterate toward goals rather than simply responding to prompts. The platform emphasizes task decomposition, allowing agents to break down objectives into smaller steps and execute them sequentially or recursively. OpenManus supports integration with external tools, APIs, and environments, making it suitable for real-world automation workflows. It is built to be flexible and extensible, enabling customization of agent behaviors, tools, and reasoning strategies. Overall, OpenManus provides a foundation for creating more capable, autonomous AI systems that can handle dynamic and goal-driven tasks. -
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Paperclip
Open-source orchestration for zero-human companies
Paperclip is an open-source tool designed to help AI systems and developer tools access academic research papers through a standardized interface. The project implements a server based on the Model Context Protocol (MCP), a framework that allows large language models and AI agents to connect to external data sources and tools in a consistent way. By acting as a middleware layer, Paperclip aggregates multiple academic databases and exposes them through a single interface, allowing AI applications to search and retrieve scholarly papers without needing to integrate with each provider individually. The system supports repositories such as arXiv, OpenAlex, and the Open Science Framework, giving AI agents access to a large body of research literature. Instead of requiring separate APIs and authentication flows for each service, Paperclip provides unified search and retrieval capabilities that simplify integration into AI workflows. -
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The Pope Bot
Autonomous AI agent that you can configure and build
The Pope Bot is an autonomous AI agent framework that lets users configure and run an AI-powered agent that can perform tasks continuously, day in and day out, by leveraging GitHub Actions, commit history, and secure workflows. It’s designed so that every action taken by the agent is logged as a git commit, giving users complete visibility into what the agent did, why it did it, and when, which makes actions auditable and reversible. The framework treats the repository itself as the agent’s “brain,” and GitHub Actions serve as the compute layer, enabling tasks to run securely without exposing sensitive API keys to the underlying AI. The system integrates with messaging platforms like Telegram, where users can interact with the bot, trigger actions, or receive notifications, and supports scheduling and automation through patterns of request handling. -
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OpenAI Python
The official Python library for the OpenAI API
The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.7+ application. The library includes type definitions for all request params and response fields, and offers both synchronous and asynchronous clients powered by httpx. -
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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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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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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. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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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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OpenAI Agent Skills
Skills Catalog for Codex
OpenAI Agent Skills is an open-source repository that serves as a broad catalog of agent skills designed to extend the capabilities of OpenAI Codex and other AI coding agents. 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. -
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HybridClaw
The enterprise operating layer for open agents
HybridClaw is an emerging open-source framework focused on enabling hybrid AI agent systems that combine local execution, tool integration, and multi-agent orchestration into a cohesive development environment. It is designed to work alongside modern agent ecosystems such as OpenClaw, Claude Code, and similar agentic coding tools, providing a flexible infrastructure for managing agent behaviors, workflows, and capabilities. The project emphasizes modularity, allowing developers to define and compose “skills” or capabilities that agents can invoke dynamically, enabling more adaptive and context-aware automation. HybridClaw aims to bridge the gap between isolated AI tools and fully orchestrated agent systems by enabling communication, coordination, and shared context across multiple agents or processes. It is particularly relevant in scenarios where developers want to build complex autonomous systems that interact with codebases. -
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OpenSpace
OpenSpace: Make Your Agents: Smarter, Low-Cost, Self-Evolving
OpenSpace is a self-evolving agent framework designed to improve the performance, efficiency, and collaboration of AI agents through continuous learning and shared knowledge. It introduces a system where agents develop reusable “skills” based on real task execution, allowing them to improve over time without retraining underlying models. The platform emphasizes collective intelligence, enabling multiple agents to share learned behaviors and benefit from each other’s experiences. It also focuses on cost efficiency by reducing redundant computations and reusing successful workflows, significantly lowering token usage in repeated tasks. The framework includes monitoring and evaluation mechanisms to track skill performance and ensure reliability as systems evolve. It supports integration with various agent platforms, making it flexible and extensible across different environments. -
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Alan AI
In-App assistant SDK to build a multimodal conversational UX websites
Quickly add voice to your app with the Alan Platform. Create an in-app voice assistant to enable human-like conversations and provide a personalized voice experience for every user. Alan is a conversational voice AI platform that lets you create an intelligent voice assistant for your app. It offers all the necessary tools to design, embed, and host your voice solutions. A powerful web-based IDE where you can write, test and debug dialog scenarios for your voice assistant or chatbot. Alan's AI-backend powered by the industry’s best Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Speech Synthesis. The Alan Cloud provisions and handles the infrastructure required to maintain your voice deployments and perform all the voice processing tasks. To voice enable your app, you only need to get the Alan Client SDK and drop it to your app. No need to plan for, deploy and maintain any infrastructure or speech components - the Alan Platform does the bulk of the work. -
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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. It integrates with modern language models to provide planning, execution, and iterative reasoning capabilities, making it suitable for complex workflows. The project also focuses on extensibility, allowing developers to plug in custom tools or APIs and tailor agent behavior to specific use cases. By abstracting common agent patterns, it reduces the overhead of building agent systems from scratch. Overall, GenericAgent provides a foundation for scalable and reusable AI agent development. -
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Open Agents
An open source template for building cloud agents
The Open Agents project is an experimental platform developed to explore the design and deployment of open, composable AI agents. It focuses on enabling developers to create agents that can collaborate, execute tasks, and interact with tools in a structured environment. The framework provides abstractions for agent communication, task orchestration, and tool integration, allowing multiple agents to work together toward shared objectives. It emphasizes openness and interoperability, making it easier to integrate with different models, APIs, and external systems. The project also includes examples and templates that demonstrate how to build and deploy agents for real-world applications. By prioritizing composability, it allows developers to combine simple components into more complex agent systems. Overall, open-agents serves as a playground for building and experimenting with next-generation AI agent architectures. -
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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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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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AGI
The first distributed AGI system
AGI project is an experimental framework focused on building components and infrastructure for artificial general intelligence systems, emphasizing modularity, autonomy, and scalable intelligence pipelines. It aims to provide a foundation for creating agents that can reason, plan, and execute tasks across diverse domains by integrating multiple AI capabilities into a unified system. The project typically explores concepts such as agent orchestration, memory systems, task decomposition, and decision-making loops, enabling the development of more generalized and adaptive AI behaviors. It is designed to be extensible, allowing developers to plug in different models, tools, and data sources to enhance agent performance. The framework encourages experimentation with AGI-like architectures, making it useful for researchers and developers interested in advancing beyond narrow AI applications. -
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Actionbook
Browser action engine for AI agents. 10× faster, resilient by design
Actionbook is an AI-centric automation framework that equips intelligent agents with the ability to interact with real live web pages in a reliable and scalable way, eliminating the guesswork involved in navigating modern dynamic sites. Instead of having agents blindly scrape HTML or blindly try to click things, Actionbook supplies up-to-date action manuals and verified DOM structure, letting agents know exactly how to click, type, and navigate complex interfaces such as SPAs or streaming UIs. This design makes browsing up to 10× faster and far more resilient than ad-hoc approaches that break on minor page changes, because the action manuals codify expected flows and DOM targets. It provides multiple integration paths — a Rust-based CLI, MCP server support for AI IDEs, and a JavaScript SDK — so developers can plug it into a wide range of agent pipelines and toolchains. -
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Agent Starter Pack
Ship AI Agents to Google Cloud in minutes, not months
Agent Starter Pack is a production-focused framework that provides pre-built templates and infrastructure for rapidly developing and deploying generative AI agents on Google Cloud. It is designed to eliminate the complexity of moving from prototype to production by bundling essential components such as deployment pipelines, monitoring, security, and evaluation tools into a single package. Developers can create fully functional agent projects with a single command, generating both backend and frontend structures along with deployment-ready configurations. The framework supports multiple agent architectures, including ReAct, retrieval-augmented generation, and multi-agent systems, allowing flexibility across use cases. It integrates tightly with Google Cloud services like Vertex AI, Cloud Run, and Terraform-based infrastructure provisioning, enabling scalable and reliable deployments. -
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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. The system operates in a ReAct-style loop where the agent profiles baseline implementations, writes CUDA code, compiles it in a sandbox, and iteratively refines performance. CUDA-Agent has demonstrated strong benchmark results, achieving high pass rates and significant speedups compared with compiler baselines such as torch.compile. -
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Codex plugin for Claude Code
Use Codex from Claude Code to review code or delegate tasks
Codex plugin for Claude Code is an integration layer that connects OpenAI Codex-style capabilities with agent-based coding environments, enabling seamless execution of coding tasks through structured plugins. The project is designed to extend the functionality of coding agents by allowing them to delegate tasks to Codex or similar models in a controlled and modular way. It likely provides abstractions for handling code generation, editing, and analysis while maintaining consistency across workflows. The system emphasizes interoperability, allowing developers to plug Codex capabilities into broader agent ecosystems without rewriting core logic. It may also include mechanisms for managing execution context, permissions, and tool access, ensuring that generated code can be safely applied. This makes it particularly useful for complex development pipelines where multiple agents or tools need to collaborate. -
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InsForge
InsForge is the backend built for AI-assisted development
InsForge is an open-source backend development platform designed specifically for AI-assisted or agent-driven application development, positioning itself as an agent-native alternative to tools like Supabase by exposing backend primitives (auth, database, storage, serverless functions, and AI integrations) in a way that intelligent agents can understand, reason about, and act upon directly. Rather than forcing developers to manually cobble together authentication flows, database schemas, storage buckets, and cloud functions, InsForge provides a semantic layer and toolchain that let agents configured with Model Context Protocol (MCP) understand the backend state, available operations, and how to manipulate these resources end to end. This enables AI coding assistants to complement human engineers by self-configuring backend components, connecting services, and evolving apps autonomously from prompts without switching contexts or manually provisioning infrastructure.
