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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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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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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Joget
AI Powered Open Source Platform to Easily Build Enterprise Web Apps
Joget offers an open-source, AI-powered platform that converges no-code/low-code development with AI to rapidly build and customize enterprise applications at scale. By combining AI with visual app builders—not raw code—Joget makes app generation faster, safer, and more accessible for everyone. With Generative AI and Agentic AI capabilities, Joget Intelligence enables organizations to automate and enhance processes while maintaining oversight and compliance. Unlike typical AI code generation, Joget's visual-first approach ensures applications are maintainable and governed within collaborative human workflows. As an Application and Integration Fabric, Joget connects legacy and modern systems seamlessly. Its extensible, open-source core and plugin architecture offer unmatched flexibility, and its White Label solution allows OEMs and digital solution providers to fully rebrand the platform. Trusted by startups, global enterprises, and government agencies across the globe. -
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The Replica skill
Eleven free Claude skills that clone any app
Replica Skill is an open-source collection of eleven Claude AI skills that guide developers through analyzing, rebuilding, testing, and launching applications inspired by existing software. It begins by examining publicly available interfaces, screenshots, documentation, and user workflows to map an application's structure. Specialized skills generate architecture plans, database designs, visual design systems, and implementation instructions. Additional workflows address backend functionality, authentication, payment integration, and automated testing. Comparison tools evaluate visual differences and feature parity between the reference application and its reconstruction. The toolkit also analyzes customer reviews to identify usability problems and opportunities for product improvement. Branding, marketing, and deployment skills help transform the reconstructed application into a distinct product. It integrates with Claude Code and includes Python utilities for development. -
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Taste Skill
Taste-Skill - gives your AI good taste. stops the AI
Taste Skill is a project focused on structured skill development, likely combining curated content, learning paths, and interactive elements to help users build expertise in specific domains. It appears to emphasize personalized or modular learning, allowing users to explore topics based on interest or progression level. The platform may integrate recommendation systems or categorized content to guide users through a learning journey efficiently. Its design suggests an emphasis on accessibility and practical skill acquisition rather than purely theoretical knowledge. The repository likely includes tools or interfaces that support content organization, tracking, and user engagement. Overall, it positions itself as a lightweight, adaptable system for continuous learning and skill discovery. -
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Claude Code Router
Use Claude Code as the foundation for coding infrastructure
Claude Code Router is a tool for routing Claude Code requests to different model providers and customizing how those requests are handled. It acts as a configurable layer between Claude Code and multiple LLM backends, allowing users to control routing, provider selection, transformations, and workflow behavior. The project is useful for developers who want to keep the Claude Code interface while experimenting with different models, cost profiles, and specialized request handling. It supports project-level configuration, making it possible to adapt behavior depending on the codebase or use case. It also includes an extensible agent-oriented architecture for custom tools and workflows, including support for image-related tasks. Overall, it gives technical users more control over Claude Code infrastructure without abandoning the familiar coding assistant workflow. -
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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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PawWork
Selection-first web agent for Chrome
PawWork ZhuaZhua is a Chrome extension that turns an already logged-in browser session into a workspace for an AI web agent. The agent runs from a side panel and can act on the current tab instead of requiring a separate automated browser profile. Users can select content on a live page, describe the desired outcome, and keep generated artifacts inside the session. The extension can execute browser-side actions and guest JavaScript through its sandboxed runtime. It supports editable artifacts such as spreadsheets, document-style content, and generated site HTML. Model access is bring-your-own-key and credentials are stored locally in Chrome rather than routed through a hosted PawWork server. The project installs as an unpacked Manifest V3 extension and requires a recent Chrome release. -
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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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Reef
Continual learning infra for self-improving agents
Reef is open-source infrastructure for building AI agents that continually improve from interaction and feedback. It connects live inference, feedback collection, learning, evaluation, and versioned deployment in one lifecycle. The system can improve either underlying model weights or the surrounding agent harness, including prompts, skills, and rules. Its core loop follows four stages: serve requests, observe feedback, grow candidate improvements, and commit accepted updates. Weight-training workflows can integrate with systems such as Slime and SGLang, while harness optimization can run without local training GPUs. Versioned artifacts let deployments stay operational while new candidates are evaluated and released. Reef also supports test-time learning scenarios where measurable task outcomes can drive iterative improvement. -
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OpenConnector
Open-source auth gateway connecting 1000+ SaaS providers to AI agents
OpenConnector is an open-source integration gateway that gives AI agents controlled access to external applications and services. It exposes a shared catalog of more than 1,000 providers and over 10,000 predefined actions. Applications can call those actions through a TypeScript SDK, command-line relay, MCP, HTTP endpoints, or generated OpenAPI specifications. The runtime manages OAuth, API keys, custom credentials, scopes, tokens, policies, temporary files, and redacted logs. A web console helps administrators browse connectors, configure accounts, create runtime tokens, and inspect activity. It can run locally with Node.js or Docker, on Fly.io, on Cloudflare-compatible infrastructure, or through OOMOL's hosted environment. Its main purpose is to keep provider credentials outside agent processes while maintaining inspectable and reusable access contracts. -
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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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500+ AI Agent Projects
The 500 AI Agents Projects is a curated collection of AI agent
500 AI Agents Projects is a curated repository that aggregates hundreds of real-world AI agent use cases across multiple industries, serving as a centralized knowledge hub for developers, researchers, and businesses. Rather than being a single application, it functions as a structured index linking to open-source implementations and conceptual examples of agent-based systems. The repository highlights how AI agents can be applied in domains such as healthcare, finance, education, and retail, offering inspiration and practical entry points for building solutions. It simplifies exploration by organizing diverse projects into a single accessible location, making it easier to discover trends and patterns in agentic AI development. The project encourages collaboration and contributions, positioning itself as a living resource that evolves with the ecosystem. Its value lies in breadth, discoverability, and practical orientation toward implementation. -
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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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Sepia
De-AI writing skill for any Agent Skills-compatible agent
Sepia is a portable agent skill for reducing recognizable AI-writing patterns in fiction and professional prose. Rather than focusing only on vocabulary substitutions, it targets higher-level narrative structure and document-specific writing habits. Fiction processing emphasizes narrative architecture before sentence-level polishing. Professional modes apply different rules to release notes, technical articles, pull request replies, postmortems, tickets, and other workplace formats. Four workflows are provided: writing from scratch, diagnostic review, minimal refactoring, and complete recreation. The skill follows the standard Agent Skills format and can therefore be installed across many compatible assistants. Native packaging is also provided for selected environments such as Claude Code, Codex, Grok Build, Antigravity, and QwenPaw. -
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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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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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Office Agents
Agent plugins for Microsoft Office but BYOK for any model and provider
Office Agents is a modular framework that brings AI-powered agents directly into Microsoft Office applications through add-ins equipped with integrated chat interfaces. It enables users to interact with large language models inside tools like Excel, Word, and PowerPoint, allowing real-time automation, content generation, and data manipulation within familiar productivity environments. The system is built as a monorepo with multiple packages, including a core SDK for agent runtime, a React-based chat interface, and specialized add-ins tailored to each Office application. It supports a bring-your-own-key model, allowing users to connect to a wide range of AI providers, including OpenAI, Anthropic, and other compatible APIs. The framework also includes advanced capabilities such as a sandboxed execution environment, virtual file systems, and extensible skill modules that expand agent functionality. -
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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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Antigravity Kit
AI Agent templates with Skills, Agents, and Workflows
Antigravity Kit is an AI agent capability expansion toolkit that provides structured templates, skills, and workflows designed to boost the productivity of AI assistants and autonomous agents in coding environments. It comes with an extensive library of predefined agent personas and domain-specific skills that help in performing targeted tasks such as frontend development, backend engineering, quality assurance, and more. With this kit, developers can rapidly initialize a project scaffold and gain access to specialist agents and command workflows that guide the agent through multi-step tasks and slash-command procedures. The repository follows a clear pattern for defining agents and their behaviors, making it easier to extend with new skills or tailor existing ones to your use case. Because it’s framework-agnostic at its core, the Antigravity Kit can integrate with IDEs and other development tooling for smoother AI-augmented coding. -
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Academic Research Skills for Claude Code
Academic Research Skills for Claude Code
Academic Research Skills is a structured learning repository aimed at improving users’ ability to conduct rigorous academic research, particularly in technical and scientific domains. It compiles methodologies, frameworks, and best practices for literature review, critical analysis, and research writing. The project is designed as a self-guided resource, helping learners understand how to evaluate sources, synthesize information, and develop strong arguments. It likely integrates examples, templates, and conceptual explanations to bridge the gap between theory and practical research execution. The repository emphasizes skill-building rather than automation, making it especially useful for students and early-career researchers. Its overall goal is to enhance research literacy and reproducibility in academic workflows. -
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AutoGen
An Open-Source Programming Framework for Agentic AI
AutoGen is an open-source programming framework for building AI agents and facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns. AutoGen provides multi-agent conversation framework as a high-level abstraction. With this framework, one can conveniently build LLM workflows. AutoGen offers a collection of working systems spanning a wide range of applications from various domains and complexities. AutoGen supports enhanced LLM inference APIs, which can be used to improve inference performance and reduce cost. -
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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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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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FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. The framework also tends to include automation layers for deployment, enabling trained models to operate in live or simulated environments with scheduled re-training and risk controls in place.
