Open Source JavaScript AI Coding Tools for Linux
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JavaScript AI Coding Tools for Linux
View 93 business solutionsBrowse free open source JavaScript AI Coding Tools for Linux and projects below. Use the toggles on the left to filter open source JavaScript AI Coding Tools for Linux by OS, license, language, programming language, and project status.
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TokenTracker
Local-first AI token usage & cost tracker for 31 coding tools
TokenTracker is an open-source, local-first application for monitoring AI token consumption, usage patterns, and associated costs across coding assistants. It supports dozens of tools, including Claude Code, Codex, Cursor, Gemini CLI, and GitHub Copilot. The dashboard visualizes token activity, model usage, project attribution, and estimated spending. Automatic tool detection and hooks simplify setup without requiring API keys or cloud accounts. Its pricing engine calculates costs using model-specific rates and locally cached pricing information. Desktop applications provide convenient access through system menus, widgets, and activity indicators. Additional features include usage limit monitoring, achievement tracking, and an animated desktop companion. Optional cloud synchronization and leaderboards extend functionality while keeping local tracking independent. -
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Claude-Mem
Claude Code plugin that automatically captures everything Claude does
Claude-Mem is a persistent memory compression system built specifically for Claude Code to preserve context across coding sessions. It automatically captures Claude’s tool usage, observations, and decisions, then compresses them into semantic memories that carry forward into future sessions. By enabling long-term continuity, Claude-Mem helps Claude “remember” project history, past fixes, and prior reasoning even after restarts or reconnects. Its progressive disclosure approach intelligently injects only the most relevant context, balancing usefulness with token efficiency. Claude-Mem runs automatically in the background with no manual workflow changes required. Designed for serious developers, it transforms Claude Code into a continuously learning, project-aware coding assistant. -
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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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9Router
Unlimited FREE AI coding. Connect Claude Code, Codex, Cursor, Cline
9Router is an AI routing and proxy platform designed to unify access to dozens of AI providers and coding assistants through a single local endpoint. The project allows developers to connect tools such as Claude Code, Cursor, Codex, Cline, Copilot, Gemini CLI, and other AI coding environments to multiple commercial, free, and fallback model providers without manually switching APIs. It includes smart routing logic that can automatically redirect requests between premium, low-cost, or free providers based on quotas, availability, or configuration rules. The system also offers a local dashboard for monitoring quotas, providers, models, and routing behavior in real time. 9router emphasizes flexibility and uninterrupted workflows by supporting failover mechanisms, provider pooling, and configuration-driven request management. Overall, it functions as a centralized AI gateway optimized for coding, experimentation, and multi-provider orchestration workflows. -
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
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PyGPT
Open source personal AI Assistant for Linux, Windows and Mac
PyGPT is a desktop application that allows you to talk to OpenAI's LLM models such as GPT4 and GPT3 using your own computer and OpenAI API. It allows you to talk in chat mode and in completion mode, as well as generate images using DALL-E 2. PyGPT also adds access to the Internet for GPT via Google Custom Search API and Wikipedia API and includes voice synthesis using Microsoft Azure Text-to-Speech API. Moreover, the application has implemented context memory support, context storage, history of contexts, which can be restored at any time and e.g. continue the conversation from point in history, and also has a convenient and intuitive system of presets that allows you to quickly and pleasantly create and manage your prompts. Plugins support is also available. -
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Clawd on Desk
A pixel desktop pet that watches Claude Code, Codex, Cursor
Clawd on Desk is a free, open-source desktop companion that visually monitors AI coding agents through an animated pixel-art pet. It integrates with Claude Code, Codex, Cursor, GitHub Copilot, Gemini, and numerous other coding assistants. The pet changes its animations to reflect agent activity, including thinking, executing tools, requesting approval, and completing tasks. Users can monitor multiple coding sessions through a dashboard with status indicators and terminal shortcuts. Interactive permission notifications allow tool requests to be approved or denied without switching windows. The application also supports remote notifications and monitoring through mobile interfaces. Available for Windows, macOS, and Linux, it offers customizable characters, themes, and multilingual support. -
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Get Shit Done
A light-weight and powerful meta-prompting, context engineering
Get Shit Done is a high-impact, open-source meta-prompting and spec-driven development system designed to streamline building software with AI assistants like Claude Code, OpenCode, and Gemini CLI. It solves “context rot” — the degradation of AI quality as a chat session grows — by structuring your idea into precise, context-engineered steps that are researched, scoped, planned, executed, and verified with clear commands and outputs instead of ad-hoc prompts. The project emphasizes simplicity and effectiveness over bureaucratic workflows like story points, sprint ceremonies, or enterprise processes, making it especially useful for solo developers or small teams aiming to get reliable execution without overhead. GSD breaks down big goals into atomic plans, keeps AI context fresh, and automates task execution while generating standardized documentation, roadmaps, and commit histories. -
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Open Vibe
Open Vibe turns Claude Code into a SaaS-building assistant
Open Vibe is an open-source course and agent workflow that turns Claude Code, Codex, Copilot, Open Code, or another terminal-capable AI coding agent into a SaaS-building assistant. It is built around Open SaaS, a free Wasp-powered SaaS boilerplate, so learners can create a real app while understanding the architecture behind production-ready SaaS systems. The workflow starts with setup instructions that install Node.js, install the Wasp CLI, and verify the local environment. After creating a new Wasp app, the user opens an AI coding agent inside the project and lets it fetch course module instructions. The agent then works as both tutor and pair programmer, explaining the system while helping the user build features from plain-language requests. Progress is tracked through JSON files written into the project, making the learning path structured while still letting the user build their own app idea. -
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Claude HUD
A Claude Code plugin that shows what's happening
Claude HUD is a real-time monitoring add-on for Claude Code that places a persistent heads-up display directly in your interactive session, giving developers clear insight into what the AI engine is doing at every moment. Instead of guessing about hidden processes behind the scenes, users see the amount of context remaining in the current session, tools being used, active running agents, and the progress of TODO tasks that the AI has planned or is executing. This plugin was designed to reduce cognitive load and make agentic workflows more transparent, helping developers diagnose stalled tasks, understand resource usage, and manage multi-step reasoning sequences more effectively. It installs with zero configuration and appears immediately in Claude Code, making it helpful for both newcomers and seasoned users who want better situational awareness without interrupting their workflow. -
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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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GSD Core
Meta-prompting, context engineering, spec-driven development system
GSD Core is the core framework behind Open GSD, a workflow system for AI-assisted software engineering. It is designed to keep agent-driven development organized by turning vague goals into structured research, plans, tasks, implementation phases, verification steps, and handoffs. The project focuses on cleaner context, verified work, and more honest project history when working with coding agents. It supports installation across several AI coding runtimes and offers profiles for different levels of workflow surface area. The system is especially useful for developers who use AI agents heavily and need more discipline around planning, execution, and review. It acts less like a single app and more like an execution layer for shipping software with agents. -
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Promptr
Promptr is a CLI tool that lets you use plain English to instruct GPT3
Promptr is a CLI tool that lets you use plain English to instruct OpenAI LLM models to make changes to your codebase. Changes are applied directly to the files that you reference from your prompt. -
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CodinIT.dev
Free, local, open-source AI app builder
CodinIT.dev is a free, local, open source AI app builder that lets you go from idea to full-stack application entirely on your machine, no coding required, just chat with AI. You can build unlimited apps with real-time previews, instant undo, and responsive, frictionless workflows. Deep Supabase integration means you can create UI and backend logic in one cohesive environment, while the model-agnostic architecture lets you connect to any AI, whether cloud-based (Gemini 3 Pro, GPT-5, Claude Sonnet 4.5) or local via Ollama, so you’re never locked in. All source code remains on your device and integrates seamlessly with your preferred IDE. A natural-language API enables powerful data queries and updates, automating tasks without leaving the chat interface. By running entirely locally, CodinIT.dev delivers maximum privacy, minimal latency, and smooth developer experiences free from cloud-based inconsistencies. -
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Mermaid.js to SVG Converter
Visualize the diagrams of your projects
While coding with AI assist, I noticed bigger projects need something more rigid as context so the AI coder doesn't stray away when I try to add new features. You know, to keeps its memory fresh and not change the stuff that already works. The trick is to ask the AI to write a diagram in Mermaid.js format that solidifies the structure of the project and then use that as context to keep the AI reminded at all times what the project is as a whole. This will prevent it from changing things its not supposed to change. This standalone offline web app will convert that mermaid.js code into a visual SVG image so that YOU as a human will be able to understand what the AI think about the structure of your projects so you can see it and fix any misconceptions until the diagram is correct for your project. (Source code is included in the html itself. Open it in a web browser to use.) -
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49 Agents IDE
Open-source 2D IDE for managing AI agents in native CLIs
49Agents is an open-source “agentic IDE” that reimagines how developers interact with multiple AI agents, terminals, and development environments by placing everything onto a single infinite, zoomable canvas. Instead of relying on traditional tab-based workflows, it provides a spatial interface where terminals, editors, Git views, and monitoring tools coexist as movable panes, enabling users to manage complex multi-agent systems visually and intuitively. The platform is designed to work across multiple machines simultaneously, allowing agents running on different devices or servers to connect to a unified workspace without requiring SSH, which simplifies distributed development workflows. It supports real-time synchronization across devices, meaning users can monitor and control their agents from laptops, tablets, or phones with the same interface. -
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BMad Method
Breakthrough Method for Agile Ai Driven Development
BMad Method is a comprehensive AI-driven software development framework that structures the entire lifecycle of building applications through coordinated agent workflows and agile methodologies. It transforms AI from a reactive assistant into a structured team of specialized roles such as product manager, architect, developer, and QA, each operating within predefined workflows. The system guides users through phases including analysis, planning, solution design, and implementation, ensuring that projects are approached systematically rather than through ad hoc prompting. It adapts dynamically to project complexity, offering lightweight flows for small tasks and more rigorous processes for enterprise-scale systems. The framework also emphasizes repeatability and consistency by storing workflows, templates, and roles as reusable artifacts. Its integration with modern AI coding tools allows it to function as a full development operating system rather than a simple plugin. -
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Better Harness
Help your coding agents get better at getting better
Better Harness is an open-source evaluation system for improving the workflows used by AI coding agents. Instead of reviewing only the final code change, it examines how an agent understood the task, executed work, validated results, delivered safely, and captured reusable lessons. It gathers project and session evidence while marking missing or unobserved behavior explicitly. Reports organize findings across five Agent Work Loop dimensions and connect each conclusion to visible supporting evidence. Supported gaps become prioritized recommendations with expected outcomes, repair boundaries, and acceptance checks. Repeated reports can be compared through a history view to observe workflow trends without claiming unsupported causation. Better Harness supports Claude Code, Codex, Qoder, and Cursor through host-specific plugins, commands, and report formats. -
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Context Hub
Makes coding agents get smarter with every task
Context Hub is a curated documentation system built to help coding agents write more accurate code. It gives agents versioned, language-specific reference material instead of forcing them to rely on noisy web searches or stale model memory. The project includes a CLI called chub that agents can use to search for available docs, fetch specific API guidance, and request only the files they need. It also supports local annotations, allowing an agent to remember project-specific notes, pitfalls, or workarounds across future sessions. Feedback can be sent back to maintainers so shared documentation improves over time. Context Hub is especially useful for teams that use AI coding assistants and want more reliable API usage, fewer hallucinated calls, and a transparent source of agent-readable context. -
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DevOpsGPT
Multi agent system for AI-driven software development
Welcome to the AI Driven Software Development Automation Solution, abbreviated as DevOpsGPT. We combine LLM (Large Language Model) with DevOps tools to convert natural language requirements into working software. This innovative feature greatly improves development efficiency, shortens development cycles, and reduces communication costs, resulting in higher-quality software delivery. The automated software development process significantly reduces delivery time, accelerating software deployment and iterations. By accurately understanding user requirements, DevOpsGPT minimizes the risk of communication errors and misunderstandings, enhancing collaboration efficiency between development and business teams. DevOpsGPT generates code and performs validation, ensuring the quality and reliability of the delivered software. -
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Frontman
AI coding agent for visual frontend fixes in your browser
Frontman is an open-source AI coding agent that lives inside your running web app. Click any element, describe the change, and Frontman edits the real source files with hot reload. Unlike IDE-only coding tools, Frontman sees the live DOM, component tree, computed CSS, routes, source maps, screenshots, console output, and server logs. That runtime context helps product managers, designers, and frontend teams fix copy, spacing, colors, layout bugs, and internal UI polish without guessing which file owns a rendered element. Works with Next.js, Astro, Vite, React, Vue, Svelte, and SvelteKit. BYOK model support includes OpenAI, Anthropic, OpenRouter, Google, xAI, Fireworks, NVIDIA, and more. Use Frontman when visual frontend edits get stuck in design QA, product review, or developer handoff. -
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Loop Engineering
Practical patterns, starters & CLI tools for loop engineering with AI
Loop Engineering is a practical reference repository for designing loop-based workflows with AI coding agents. It focuses on replacing repeated manual prompting with systems that prompt, verify, schedule, and hand off work over time. The project is aimed at developers using tools such as Grok, Claude Code, Codex, Cursor, and other coding agents. It explains core building blocks such as scheduling, worktrees, skills, plugins, connectors, sub-agents, and persistent memory or state. The repository includes production loop patterns, quickstarts, starters, checklists, safety notes, and real-world stories. It also provides CLI tools such as loop-audit, loop-init, and loop-cost to scaffold, evaluate, and estimate agent loop workflows. -
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Luna Code Checker
An advanced web-based tool for checking JavaScript code using ESLint.
Luna Code Checker An advanced web-based tool for checking JavaScript code using ESLint. How to Use Clone the repository. Run npm install to install the dependencies. Run npm start to start the server. Open http://localhost:3000 in your web browser. Write or paste your JavaScript code in the textarea. Click the "Check Code" button to see linting results. Features Comprehensive syntax and style checking using ESLint. Detailed error messages including line numbers and descriptions. License This project is open source and available under the MIT License. -
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Skills for coding agents
Skills for coding agents
BuilderIO Skills is a collection of small, composable skills for coding agents. It is made for teams that want agents to perform better in planning, review, orchestration, validation, documentation discipline, and clear communication. Instead of acting like a heavy process framework, it lets teams install only the skills they need and adapt them to their project. The catalog includes tools for visual planning, visual PR recaps, agent audits, plan comparison, autonomous execution, model orchestration, usage-limit management, final status recaps, and documentation-first development. Installation is handled through an interactive CLI that can configure skills for multiple agent environments. It also supports managed instruction files, Claude Code skill paths, shared agent folders, and an optional GitHub Action for visual PR recaps. -
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SmallCode
AI coding agent optimized for small LLMs. 87% benchmark
SmallCode is a terminal-native AI coding agent optimized for local models running on consumer hardware. It is designed to extract useful coding performance from smaller LLMs, especially models in the 7B to 20B range. The project focuses on making local coding assistance practical without requiring massive cloud-hosted models for every task. Its workflow is built around terminal usage, which makes it suitable for developers who prefer command-line control and local project context. smallcode emphasizes efficient agent behavior, careful tool use, and benchmark-driven improvements for constrained models. Its main value is giving developers a compact coding-agent environment that treats small local models as first-class tools. -
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darwin-skill
Autoresearch-inspired autonomous skill optimization for Claude Code
darwin-skill is an experimental framework designed to automatically improve AI agent “skills” through iterative evaluation and optimization loops inspired by machine learning training processes. Instead of treating prompts or skill definitions as static assets, the system applies a continuous improvement cycle that evaluates performance, proposes changes, tests outcomes, and either retains or reverts modifications. The framework introduces a scoring system across multiple dimensions, enabling quantitative assessment of skill quality and ensuring that only improvements are preserved over time. It incorporates a “ratchet mechanism” similar to version control workflows, guaranteeing that performance never degrades as iterations progress. The system also separates the agents responsible for editing and evaluating skills to avoid bias, which improves the reliability of optimization results.
