Open Source BSD AI Coding Tools
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AI Coding Tools for BSD
Browse free open source AI Coding tools and projects for BSD below. Use the toggles on the left to filter open source AI Coding tools by OS, license, language, programming language, and project status.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Endpoint Management with Remote Control Built InSave 50% on your first year when you switch from your existing RMM to Splashtop AEM. Automate patching, track inventory, and jump directly into 1-click unattended remote sessions without switching tools.
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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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Graphify
AI coding assistant skill (Claude Code, Codex, OpenCode, OpenClaw)
Graphify is a data visualization and transformation tool designed to convert structured or semi-structured data into graph-based representations, enabling better understanding of relationships and dependencies. It focuses on building visual models such as nodes and edges that represent entities and their connections, making complex datasets easier to interpret. The system likely supports dynamic updates, allowing graphs to evolve as data changes or new inputs are introduced. It is particularly useful in domains such as network analysis, knowledge graphs, and system architecture visualization. The architecture emphasizes flexibility, enabling users to customize how data is mapped and displayed. It may also include analytical features to explore patterns, clusters, or anomalies within the graph. Overall, Graphify serves as a bridge between raw data and visual insight. -
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Bolt.new
Prompt, run, edit, and deploy full-stack web applications
Bolt.new is an AI-powered full-stack development platform created by StackBlitz that enables users to build, run, edit, and deploy complete web applications directly from the browser without requiring any local setup or traditional development environment. It operates as an intelligent coding agent where users describe what they want to build in natural language, and the system generates functional applications, including frontend, backend, and infrastructure components. The platform is built on StackBlitz’s WebContainers technology, which allows Node.js environments to run entirely in the browser, eliminating the need for installations while maintaining real development capabilities. Bolt.new is designed to significantly lower the barrier to entry for software creation, making it accessible not only to developers but also to product managers, designers, and non-technical users who want to quickly prototype or launch applications. -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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Multica
The open-source managed agents platform
Multica is an open-source platform designed to manage and orchestrate AI coding agents as if they were real team members within a development workflow. It introduces a paradigm where agents can be assigned tasks, participate in discussions, and autonomously execute work while reporting progress and blockers in real time. The system integrates with multiple AI coding tools and provides a unified interface for managing tasks, compute environments, and agent execution pipelines. It includes both a web interface and a CLI that connects local or cloud-based runtimes to the platform, enabling flexible deployment and scaling. Multica emphasizes collaboration between humans and AI by allowing agents to operate alongside developers in shared workspaces. It also supports reusable skill accumulation, meaning that solutions generated by agents can be reused across projects to improve efficiency over time. -
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Every Code
Local AI coding agent CLI with multi-agent orchestration tools
Every Code (often referred to simply as Code) is a fast, local AI-powered coding agent designed to run directly in the terminal environment. It is a community-driven fork of the Codex CLI, with a strong emphasis on improving real-world developer ergonomics and workflows. Every Code enhances the traditional coding assistant model by introducing multi-agent orchestration, allowing multiple AI agents to collaborate, compare solutions, and refine outputs in parallel. It supports integration with various AI providers, enabling users to route tasks across different models depending on their needs. Every Code also includes browser integration and automation capabilities, extending its usefulness beyond simple code generation into more complex development tasks. Customization is a key focus, with support for theming, configurable settings, and reasoning controls that allow developers to fine-tune how the agent behaves. -
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Alook
The collaboration layer for your AI workforce
Alook is an open-source, self-hosted platform for running local AI coding agents as a coordinated workforce. It lets users give agents defined roles, email addresses, task boards, calendars, and shared workflows so they can collaborate more like a real team. The platform connects local agents to external communication channels while keeping the actual agents and codebase running on the user’s own machine. It is designed for developers, solo founders, and teams that want multiple AI agents to handle development, operations, research, and routine work with clearer structure. Alook also emphasizes memory and traceability, so agents can remember past decisions, learn preferences, and keep a record of instructions and replies. Its main value is turning separate AI coding tools into an organized, always-on operating system for agent-based work. -
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claurst
Your favorite Terminal Coding Agent, now in Rust
claurst is an experimental AI agent framework that appears to focus on structured reasoning and task execution within coding or automation environments. The project likely explores how agents can be designed to handle complex workflows through modular components and clearly defined execution steps. It may include abstractions for managing context, decision-making, and interaction with external tools, enabling agents to perform multi-step tasks efficiently. The architecture suggests a focus on flexibility, allowing developers to adapt the system to different use cases or domains. It is likely intended as a lightweight but extensible platform for experimenting with agent behavior and orchestration. The project may also emphasize simplicity, making it accessible for developers who want to prototype agent systems quickly. -
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Google Antigravity SDK
Python library for building agents that leverages Google Antigravity
Google Antigravity SDK for Python is a Python library for building AI agents powered by Antigravity and Gemini. It provides a secure, scalable, and stateful infrastructure layer so developers can focus on agent behavior instead of manually implementing the full agent loop. The SDK includes a high-level Agent class for quick setup, as well as lower-level conversation and connection abstractions for more controlled workflows. It supports streaming responses, stateful sessions, custom Python tools, MCP server integration, hooks, policies, and event-driven triggers. The package relies on a compiled runtime binary distributed through platform-specific PyPI wheels, so installation from PyPI is required for normal use. Its main value is giving developers a structured Python framework for creating local, tool-using, multimodal, policy-controlled AI agents. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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pi-autoresearch
Autonomous experiment loop extension for pi
pi-autoresearch is an automation-oriented research assistant project that focuses on orchestrating iterative information gathering, analysis, and synthesis workflows with minimal human intervention. It is designed to simulate a continuous research loop where queries are generated, refined, and expanded based on previous outputs, enabling deeper exploration of complex topics. The system likely integrates with external data sources or APIs to retrieve information and process it into structured insights. Its architecture suggests a focus on autonomy, allowing it to run multi-step research pipelines that mimic human investigative processes. This makes it particularly useful for exploratory analysis, trend discovery, or generating structured knowledge from large information spaces. Overall, pi-autoresearch represents a step toward self-directed research agents capable of producing increasingly refined outputs over time. -
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Open SWE
Open source async coding agent that plans, codes, and opens PRs
Open SWE is an open source asynchronous coding agent designed to automate software engineering workflows across entire repositories. Built with LangGraph, it can understand a codebase, generate a structured plan, and execute code changes from start to finish without constant human intervention. It operates in a cloud-based environment where tasks are processed asynchronously, allowing multiple coding jobs to run in parallel in isolated sandboxes. It integrates directly with development workflows by responding to triggers from tools like GitHub, enabling users to initiate tasks through issues or comments. Open SWE is capable of creating commits and automatically opening pull requests once implementation is complete, effectively closing the loop on development tasks. It also supports interactive feedback during execution, allowing users to guide or adjust the process mid-task. Despite its advanced capabilities, the project has been officially marked as deprecated. -
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Oh My OpenCode Slim
Slimmed, cleaned and fine-tuned oh-my-opencode fork
Oh My OpenCode Slim is a lightweight, optimized fork of the broader oh-my-opencode ecosystem, designed to deliver high-performance multi-agent coding workflows while significantly reducing token consumption and system overhead. It retains the core concept of orchestrating multiple specialized AI agents but streamlines their configuration, execution, and communication to make the system more efficient and practical for everyday use. The framework introduces a structured “pantheon” of agents, each with a defined role such as orchestration, exploration, and execution, allowing tasks to be automatically delegated and completed through coordinated workflows. It supports multiple AI providers and models, enabling users to mix and match capabilities depending on cost, speed, and performance requirements. -
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M3E Canvas
Sketch Material 3 Expressive screens in the browser
M3E Canvas is a browser-based design tool for sketching Material 3 Expressive interfaces and turning them into prompts for AI coding tools. Users can drag buttons, cards, navigation bars, dialogs, fields, chips, images, and many other components onto a canvas. Screens can be linked together and tested as simple interactive flows before implementation. A Tidy function automatically aligns and groups nearby elements using Material-style spacing. Designs can be exported as PNG images or copied as prompts for tools such as Claude Code, Codex, Gemini CLI, and Cursor. Optional AI helpers can generate behavior notes and screen descriptions while keeping provider keys in the browser. Projects are stored locally and can also be shared through links. -
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Crush
The glamourous AI CLI coding agent for your favourite terminal 💘
Crush is a next-generation, terminal-based AI coding assistant developed by Charm, designed to seamlessly integrate with your tools, workflows, and preferred LLMs. It provides developers with an intuitive, session-based experience where multiple contexts can be managed across projects. With flexible model switching, Crush allows you to change providers mid-session while retaining conversation history. It enhances productivity by combining LSP (Language Server Protocol) support with extensible MCP (Model Context Protocol) integrations for richer coding context and external tool connectivity. Built for portability, it offers first-class support across macOS, Linux, Windows (PowerShell and WSL), and BSD systems. Backed by the Charm ecosystem, Crush is a stable, actively maintained evolution of the original OpenCode project. -
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Expect
Let agents test your code in a real browser
Expect is a developer-focused utility designed to simplify validation, testing, and assertion workflows across software environments by providing a clean and expressive interface for defining expected outcomes. The project likely centers on improving readability and maintainability in testing scenarios, allowing developers to write expectations in a concise and human-readable format. It may support chaining conditions, enabling complex validation logic without introducing unnecessary verbosity. The design suggests a focus on productivity, reducing cognitive load when writing and reviewing tests or validation scripts. It is likely adaptable across multiple contexts, including unit testing, integration testing, and runtime assertions. By abstracting repetitive validation logic, expect helps developers focus on behavior rather than implementation details. Overall, it serves as a lightweight but powerful tool for improving software reliability and clarity in testing workflows. -
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Fulling
Full-stack Engineer Agent. Built with Next.js, Claude, shadcn/ui
Fulling is an open-source AI-powered development environment designed to function as an autonomous full-stack engineering assistant. The platform provides a sandboxed workspace where developers can build complete applications with the help of an integrated AI coding agent. Instead of manually configuring development environments, the system automatically provisions the required infrastructure including a Linux environment, database services, and development tools. It integrates an AI pair programmer that can generate code, implement features, and assist with debugging tasks through natural language instructions. The environment also includes web-based terminals, file management tools, and version control capabilities to support collaborative software development workflows. Developers can connect external services by simply providing API credentials, allowing the AI system to automatically integrate features such as authentication or payment processing. -
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Modelence
Modelence is an all-in-one TypeScript platform
Modelence is an all-in-one TypeScript platform aimed at helping teams ship production web apps with far less boilerplate than a typical full-stack setup. It positions itself as a Supabase-style experience tailored toward MongoDB-centric development, bundling common backend needs like authentication, database integration, and observability into a cohesive framework. The project is built to support modern application workflows where product teams want to move quickly without stitching together many separate services and libraries. It includes scaffolding and tooling to create a new application quickly, then run a local development server with a predictable structure that’s easy to extend. Modelence also focuses on “standard features” that most apps require, so developers can spend more time on product logic rather than setup and glue code. -
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GitHub Copilot SDK
Multi-platform SDK for integrating GitHub Copilot Agent into apps
The GitHub Copilot SDK is a developer toolkit that enables creators to build custom AI-assisted experiences powered by Copilot models within their own applications, editors, and workflows. Instead of being limited to editors like VS Code, this SDK lets teams embed Copilot-style code suggestions, natural language assistance, and predictive completions anywhere they see fit—such as internal IDEs, browser extensions, documentation portals, or bespoke tools tailored to specific languages or frameworks. It provides a structured API surface for invoking the Copilot model in context with the surrounding user state, capturing document content, cursor position, and invocation triggers so suggestions are relevant and responsive. The SDK includes helpers for streaming completions, managing rate limits, handling authentication, and integrating with telemetry and analytics pipelines. -
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Markdown Viewer Agent Skills
Opinionated skills for AI coding agents to create stunning diagrams
Markdown Viewer Agent Skills is a repository that provides a collection of modular AI “skills” designed to extend the capabilities of agents or tools that operate on markdown-based workflows. These skills are typically structured as self-contained instruction sets that define how an AI should perform specific tasks, such as formatting, analysis, or content transformation. The project emphasizes simplicity and composability, allowing developers to integrate these skills into existing systems without requiring complex infrastructure. Each skill is designed to be reusable and adaptable, making it easy to customize behavior for different use cases. The repository aligns with the broader concept of agent skills, where capabilities are encapsulated into modular units that can be dynamically loaded and executed. It supports workflows that rely heavily on markdown as a primary medium for content and communication. -
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VibeWise
A Claude Code / Codex plugin that helps you learn how to build
VibeWise is a Claude Code and Codex plugin designed to keep developers actively learning while AI handles implementation work. Instead of immediately generating code, it first asks the user to reason through behavior, architecture, and tradeoffs. Build checkpoints focus on how a feature should work, while design checkpoints record agreed technical decisions. Implementation checkpoints explicitly authorize the agent to write the planned code and run its tests. After coding, the agent explains what changed and why, preserving the educational context of the task. Learning notes persist between sessions and can be shared between Claude Code and Codex, making the plugin useful for beginners and experienced developers exploring unfamiliar stacks. -
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CodexPro
Use ChatGPT Developer Mode as a local coding agent for your repo
CodexPro is a local MCP bridge that lets ChatGPT Developer Mode work inside a specific code repository. It starts a local server for the current workspace so ChatGPT can read files, search code, apply scoped edits, run guarded shell checks, and review changes. The project is designed around local control rather than hosting code or proxying model access through a third-party service. It supports setup, start, doctor, settings, handoff, and pro modes for different levels of agent involvement. Public access can be routed through options such as Cloudflare tunnels, ngrok, Tailscale Funnel, or local-only mode. CodexPro is useful for developers who want ChatGPT to act like a repo-aware coding assistant while keeping the actual workspace on their own machine. -
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deepclaude
Use Claude Code's agent loop with DeepSeek V4 Pro, OpenRouter & more
deepclaude is a lightweight proxy tool that enables developers to run Claude Code’s autonomous coding agent loop using alternative AI backends like DeepSeek V4 Pro, OpenRouter, or other Anthropic-compatible models. It preserves the full Claude Code experience—including file editing, terminal execution, and multi-step agent workflows—while dramatically reducing operational costs. By swapping out the underlying model instead of the interface, deepclaude delivers the same familiar UX with significantly cheaper token pricing. The platform supports seamless backend switching in real time, allowing users to choose between cost efficiency and higher reasoning power when needed. It also includes built-in cost tracking and benchmarking tools to help developers monitor usage and optimize performance. Designed for flexibility and efficiency, deepclaude is ideal for developers who want powerful AI coding agents without the premium price tag. -
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Qwen2.5-Coder
Qwen2.5-Coder is the code version of Qwen2.5, the large language model
Qwen2.5-Coder, developed by QwenLM, is an advanced open-source code generation model designed for developers seeking powerful and diverse coding capabilities. It includes multiple model sizes—ranging from 0.5B to 32B parameters—providing solutions for a wide array of coding needs. The model supports over 92 programming languages and offers exceptional performance in generating code, debugging, and mathematical problem-solving. Qwen2.5-Coder, with its long context length of 128K tokens, is ideal for a variety of use cases, from simple code assistants to complex programming scenarios, matching the capabilities of models like GPT-4o. -
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Agent Skills Tech Leads Club
The secure, validated skill registry for professional AI coding agents
Agent Skills is a managed registry of reusable instruction packages for professional AI coding agents. Skills bundle prompts, templates, references, and specialized workflows that can be installed into tools such as Claude Code, Cursor, Copilot, Codex, Cline, and others. The project emphasizes security through open-source content, static analysis, content hashing, lockfiles, path isolation, and audit logging. Its CLI can browse, install, update, remove, cache, and inspect skills at local or global scope. An interactive wizard helps users choose skills, target agents, and installation methods. A companion MCP server exposes the catalog through search-first, on-demand retrieval. The registry covers development, cloud, browser automation, design, security review, and other engineering tasks. -
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AsmJit
Low-latency machine code generation
AsmJit is a low-level code generation library designed for dynamically creating machine code at runtime, enabling just-in-time (JIT) compilation for performance-critical applications. It provides a high-level API that abstracts away the complexity of writing raw assembly while still allowing fine-grained control over instruction generation. The library supports multiple architectures, including x86 and x64, making it versatile for cross-platform development. It is commonly used in applications such as emulators, compilers, and high-performance computing systems where runtime optimization is essential. asmjit emphasizes low latency and efficiency, ensuring that generated code executes quickly without significant overhead. Its modular design allows developers to integrate it into various systems with minimal friction. Overall, asmjit bridges the gap between high-level programming and low-level execution by enabling efficient runtime code generation.
