Showing 24 of 24 tools
Fork, customize, and ship AI agents on Vercel in minutes
Open Agents is a Vercel Labs open-source template for building and deploying cloud-hosted AI agents. It provides a production-ready Next.js starter with built-in tool use, streaming responses, multi-model support, and deployment on Vercel infrastructure. Developers can fork, customize agent behavior and tools, then ship agent-backed apps in minutes with automatic scaling and edge routing.
Human-in-the-loop web agent you can co-pilot in real time
Magentic-UI is a Microsoft Research web agent with a human-in-the-loop interface for browsing, coding, and file tasks. It plans multi-step actions, asks for approval before executing, and lets users co-pilot by taking over the browser mid-task. Built on AutoGen, it runs a team of specialized agents for web browsing, file handling, and code execution with full action transparency and safety guardrails.
Modular AI prompt framework for everyday tasks
Fabric is an open-source framework that organizes AI prompts into reusable patterns for solving everyday tasks like summarizing content, explaining code, extracting insights from videos, and generating social media posts. Written in Go with support for 20+ AI providers including OpenAI, Claude, Gemini, and Ollama, it runs from the command line and can serve as a REST API. With 40,000+ GitHub stars, Fabric bridges the gap between AI capabilities and practical workflow automation.
Context engineering patterns for AI coding assistants
Context Engineering Intro is an open-source repository by Cole Medin providing structured context engineering patterns for AI coding assistants. Built around Claude Code, it includes .claude command files, PRP templates, and the WISC framework for managing AI context in coding sessions. The repo shows how to structure project context and rules so AI assistants produce reliable, architecture-aware code. With 13K+ GitHub stars, it is a go-to reference for context-first AI coding.
Agent harness performance system with 30+ agents and 136 skills
Everything Claude Code is a comprehensive agent harness performance optimization system providing 30 specialized agents, 136 skills, 60 commands, and automated hook workflows for AI-assisted development. Born from an Anthropic hackathon winner and evolved over 10+ months of intensive daily use, it works across Claude Code, Codex, Cursor, and OpenCode with built-in security scanning via AgentShield, continuous learning, and research-first development patterns.
Virtual engineering team as Claude Code skills by YC CEO Garry Tan
GStack transforms Claude Code into a structured virtual engineering team through 23 opinionated slash command skills created by Y Combinator CEO Garry Tan. It assigns specialist roles including CEO product review, engineering manager architecture oversight, designer visual audit, QA lead with real browser testing, and release engineer deployment. Each skill enforces focused workflows with clear decision principles for running parallel coding sessions.
Open standard for guiding AI coding agents at the repository level
AGENTS.md is an open standard format adopted by 60,000+ open-source projects for providing AI coding agents with repository-level instructions. With 20,000+ GitHub stars, it has been adopted by GitHub Copilot, OpenAI Codex, Google Gemini CLI, and multiple IDEs as the de facto way to communicate project context and coding conventions to AI agents.
Twelve-lesson curriculum for building AI coding agents from scratch
Learn Claude Code is a comprehensive educational framework with 12 structured lessons teaching AI coding agent construction from first principles. With over 46,000 GitHub stars and 7,000 forks, it provides complete runnable Python implementations progressing from a 50-line bash agent to a 550-line skills agent with modular extensibility and subagent orchestration.
Meta-prompting and context engineering system for Claude Code agents
GSD is a meta-prompting, context engineering, and spec-driven development system designed for Claude Code and compatible AI coding agents. With over 46,000 GitHub stars, it implements a structured four-phase workflow of Discuss, Plan, Execute, and Verify to combat context rot in long AI coding sessions. The system uses multi-agent orchestration with persistent file-based memory.
AI-driven development workflow template
A template system that bootstraps AI-driven development workflows for your projects. Provides structured workflows, templates, and configurations for integrating AI agents into your development process. Reduces setup time by giving teams a proven starting point for organizing AI-assisted coding, task management, and quality assurance in new and existing repositories.
Curated collection of agent skills and capabilities
An opinionated project scaffolding tool that generates AI-ready codebases with pre-configured CLAUDE.md files, git hooks, and CI/CD templates. Ensures new projects follow best practices for AI-assisted development from day one, including structured prompts, context files, and workflow configurations that help AI coding agents understand and navigate the codebase effectively.
Spec-driven development for AI coding assistants
OpenSpec is an open-source spec-driven development framework by Fission-AI for AI coding assistants. It adds a lightweight spec layer so developers and AI agents agree on what to build before any code is written. OpenSpec organizes changes in dedicated folders for proposals, specs, design docs, and tasks, and works across 21+ AI coding tools including Claude Code, Cursor, Windsurf, Continue, Gemini CLI, GitHub Copilot, and Amazon Q — bringing structured planning to agentic development.
Toolkit for spec-driven development with AI
GitHub's official toolkit for spec-driven development. Write specifications in natural language and let AI coding agents implement them with structure, consistency, and traceability. Bridges the gap between product requirements and AI-generated code by providing a standardized format that agents can follow reliably across complex projects.
Markdown-based project management for AI agents
A tool for managing project collaboration between humans and AI agents in a git ecosystem. Uses markdown files as the source of truth for backlogs, tasks, and progress tracking. Keeps everything version-controlled and transparent, making it easy to assign work to AI agents and review their contributions alongside human commits.
TypeScript AI agent standard library
Standard library of AI tools and integrations for TypeScript-based agents. Works with any AI SDK and includes ready-made integrations for search, web scraping, email, and other common tool patterns. Saves developers from rebuilding common agent capabilities from scratch, providing well-tested, type-safe building blocks for rapid AI agent development.
LLM testing and evaluation toolkit
Open-source tool for testing, evaluating, and red-teaming LLM applications. Promptfoo lets developers define test cases, run prompts across multiple models and configurations, and score outputs with built-in metrics like factuality, relevance, and toxicity. Includes red-teaming for jailbreak and hallucination detection plus CI/CD integration for automated prompt regression testing.
Programming — not prompting — LLMs
Declarative framework from Stanford University for programming language models rather than prompting them. DSPy treats LLM interactions as programmable modules with input-output signatures and uses optimization algorithms to automatically compile these modules into effective prompts or fine-tuned weights, replacing brittle prompt strings with structured, modular AI software.
Type-safe LLM function builder
BAML is a domain-specific language by BoundaryML for building reliable AI workflows and agents through schema engineering. It turns prompt engineering into a structured, type-safe discipline by letting developers declaratively define function schemas, validate LLM responses, and version prompts without fragile JSON parsing or boilerplate. BAML reframes prompt engineering as schema definition, making AI workflows testable and maintainable across models.
Structured generation for LLMs
Outlines is an open-source Python library for structured text generation that guarantees LLM outputs conform to a defined schema or format. It constrains the model's token selection at each step so only tokens leading to valid output are considered, eliminating fragile post-processing. Supports multiple-choice constraints, regex patterns, JSON Schema, and type-safe Pydantic models — helping teams extract reliable structured data from any LLM.
Structured LLM outputs with validation
Instructor is the most popular Python library for extracting structured, validated data from large language models, with over 3 million monthly downloads and ports across Python, TypeScript, Go, Ruby, Elixir, and Rust. It uses Pydantic models to define output schemas and automatically handles validation, retries, and error correction when the LLM output does not match. Instructor patches existing client libraries instead of replacing them, preserving full access to the underlying API.
OpenAI's custom chatbot builder and GPT Store
Create personalized GPT assistants with custom instructions, knowledge files, and tool integrations including browsing, DALL-E, and code interpreter. Publish to the GPT Store or keep private with no coding required. Enables anyone to build specialized AI assistants for specific domains, workflows, or audiences using OpenAI's consumer-friendly builder interface.
Claude's inline code and document generation tool
Claude's built-in capability to generate and render interactive artifacts — code, documents, SVGs, React components, and HTML — directly inline within the conversation. No setup required. Turns Claude from a text-only assistant into a creative tool that can produce runnable applications, visualizations, and interactive prototypes during natural conversation.
Curated Claude Code resources
Official Anthropic-curated list of Claude Code tips, CLAUDE.md templates, hooks, MCP servers, and community tools. Essential reference for Claude Code users looking to optimize their workflow. Covers everything from initial setup and configuration best practices to advanced patterns like custom hooks and multi-agent orchestration with the Claude Code CLI.
Community cursor rules directory
Community-maintained collection of .cursorrules files that customize Cursor IDE's AI behavior for specific frameworks, languages, and project types. Define coding conventions, preferred libraries, architectural patterns, and style guidelines that the AI follows consistently. Popular rules exist for Next.js, React, Python, TypeScript, Tailwind, and more. Hosted on cursor.directory with 1-click installation. Essential for getting consistent, project-aware AI completions in Cursor.
