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Fabric an open-source framework for augmenting humans using AI. Its stated goal is to address an integration problem rather than a capabilities problem: while many AI applications exist, folding their functionality into everyday workflows is difficult. Fabric approaches this by treating prompts as the fundamental unit of AI work and organizing them by real-world task. Core concept: Patterns Fabric collects and organizes prompts, which it calls Patterns, into a single place for use in the user's preferred tools. The README lists example use cases such as extracting highlights from YouTube videos and podcasts, drafting essays in a personal voice, summarizing academic papers, generating AI art prompts, rating content quality, explaining code, improving documentation, and creating social media posts. Patterns can be used directly through the Fabric command-line interface or copied into other tools. Interfaces and usage Fabric is distributed primarily as a command-line tool. It supports one-line installers for Unix/Linux/macOS and Windows PowerShell, manual binary downloads, package managers including Homebrew, AUR, Winget and Scoop, installation from source with Go, and pre-built Docker images. A REST API server mode is available, including an Ollama compatibility mode, and interactive API documentation is provided via Swagger/OpenAPI. A web interface is also referenced. Shell completions are documented for Zsh, Bash and Fish, and the README shows how to generate shell aliases so each Pattern can be invoked as its own command. Provider support The README lists native integrations with OpenAI, OpenAI Codex, Anthropic (Claude), Google Gemini, Ollama for local models, Azure OpenAI, Amazon Bedrock, Vertex AI, LM Studio and Perplexity. It also lists many OpenAI-compatible providers, including Abacus, AIML, Cerebras, DeepSeek, DigitalOcean, GitHub Models, GrokAI, Groq, Langdock, LiteLLM, MiniMax, Mistral, Novita AI, OpenRouter, Pzero, SiliconCloud, Synthorai, Together, Venice AI and Z AI. Setup is performed with a setup command, and vendors can be listed from the CLI. Per-pattern model mapping is supported through environment variables. Customization and extensions The README describes custom Patterns, prompt strategies, helper apps such as to_pdf, code2context and generate_changelog, and a pbpaste helper. It documents debug levels, a dry run mode, and extensions. Speech-to-text support is mentioned with flags for transcribing files, choosing a transcription model, and splitting media files. Internationalization is a recurring theme in the release notes, with locale support across multiple languages and locale normalization for language variants. Project context The project is MIT licensed and written primarily in Go, having migrated from an earlier Python implementation; migration and upgrade instructions are included. The README emphasizes a philosophy that AI magnifies human creativity and that problems should be broken into components and addressed one at a time. It links to documentation, a changelog, community videos, and a DeepWiki resource. The material is a project README and should be treated as descriptive documentation rather than independent evaluation; claims about features come from the project itself.