fabric
RepositoryFreeApply AI to everyday challenges in the comfort of your terminal. Help’s to get better results with tried and tested library of prompt pattern’s.
Capabilities15 decomposed
task-oriented prompt pattern library with variable substitution
Medium confidenceFabric organizes AI prompts as reusable Patterns—YAML-based templates organized by real-world tasks (summarize, extract_wisdom, analyze_claims). Each pattern supports variable substitution via {{variable}} syntax, enabling dynamic context injection. Patterns are stored in a file-system registry, discoverable via metadata tags, and loaded at runtime with full support for custom user-defined patterns alongside built-in library.
Organizes prompts by real-world task intent rather than model capability, with file-system-based pattern discovery and metadata-driven pattern selection via suggest_pattern function. Decouples prompt logic from execution environment, enabling same pattern to run across CLI, Web UI, REST API, and Ollama-compatible server without modification.
Unlike prompt management tools that focus on versioning and collaboration, Fabric's pattern system prioritizes task-oriented organization and cross-interface portability, making it stronger for teams building consistent AI workflows across multiple deployment contexts.
multi-vendor ai provider abstraction with unified interface
Medium confidenceFabric implements a plugin-based vendor abstraction layer (ai.Vendor interface) that normalizes API calls across 15+ AI providers including OpenAI, Anthropic, Gemini, Azure, Ollama, Bedrock, and others. Each vendor plugin handles provider-specific authentication, request formatting, streaming, and error handling. The Chatter orchestrator selects vendors at runtime based on configuration, enabling seamless provider switching without code changes.
Implements vendor abstraction as a pluggable interface rather than a wrapper library, allowing each provider to optimize for its specific API design while maintaining a unified Chatter orchestrator. Supports both cloud and local providers (Ollama) in the same configuration, with Ollama compatibility mode enabling Fabric to act as a drop-in replacement for Ollama clients.
More flexible than LangChain's provider abstraction because it doesn't enforce a lowest-common-denominator API; vendor plugins can expose provider-specific features while maintaining interface compatibility. Lighter weight than full LLM frameworks for CLI-first workflows.
output formatting and notification system with multiple formats
Medium confidenceFabric supports multiple output formats (plain text, JSON, markdown, YAML) and notification methods (stdout, file, system notifications). Output format is selectable via CLI flag or config. The system includes a notification layer for non-blocking status updates (pattern execution started, completed, failed) that can be sent to system notification daemon or logged to file. Output formatting respects pattern-specific requirements (e.g., JSON patterns output structured data).
Integrates output formatting and notifications as first-class features of the Chatter orchestrator, rather than post-processing steps. Format selection is pattern-aware; patterns can specify preferred output format, with user overrides supported.
More integrated than piping to separate formatting tools (jq, yq); output formatting is built into Fabric. Notification system reduces need for external monitoring tools for background tasks.
custom pattern creation and extension system
Medium confidenceFabric enables users to create custom patterns by writing YAML files with system prompt, user message template, and metadata. Custom patterns are stored in user-defined directories and loaded at runtime alongside built-in patterns. Pattern creation requires no programming; patterns are pure YAML with variable substitution via {{variable}} syntax. The system supports pattern inheritance and composition, enabling patterns to reference other patterns.
Enables pattern creation via pure YAML without programming, lowering barrier to entry for non-developers. Patterns are first-class citizens with full metadata support, enabling discovery and composition alongside built-in patterns.
More accessible than prompt engineering tools requiring code; YAML syntax is simpler than Python or JavaScript. Patterns are portable and version-controllable as files, unlike cloud-based prompt management systems.
ollama compatibility mode for local model execution
Medium confidenceFabric implements Ollama compatibility mode, enabling it to act as a drop-in replacement for Ollama clients. When running in Ollama mode, Fabric exposes the same API endpoints as Ollama, allowing existing Ollama clients to communicate with Fabric. This enables local LLM execution without cloud dependencies while maintaining compatibility with Ollama ecosystem tools.
Implements Ollama compatibility as a first-class execution mode rather than a separate tool, enabling Fabric to seamlessly switch between cloud and local models. Ollama mode is transparent to patterns; same patterns execute identically against Ollama or cloud providers.
More integrated than running Ollama separately; Fabric provides unified interface for cloud and local models. Enables privacy-first workflows without sacrificing Fabric's multi-interface capabilities.
automated changelog generation with ai summarization
Medium confidenceFabric includes an automated changelog generation system that processes Git history, GitHub PR metadata, and release information to generate human-readable changelogs. The system uses AI to summarize commit messages and PR descriptions, grouping changes by category (features, fixes, breaking changes). Changelog generation is integrated into CI/CD workflows via GoReleaser, enabling automatic changelog creation on each release.
Integrates changelog generation as a built-in capability with AI summarization, rather than relying on external tools. Changelog system is aware of Git history, GitHub metadata, and release structure, enabling intelligent categorization and summarization.
More automated than manual changelog writing; AI summarization reduces effort. Tighter integration with release process than standalone changelog tools; changelog generation is part of Fabric's release workflow.
vendor plugin development framework for extending ai provider support
Medium confidenceFabric provides a plugin development framework enabling developers to add support for new AI providers by implementing the ai.Vendor interface. Vendor plugins handle provider-specific authentication, request formatting, response parsing, streaming, and error handling. The framework includes utilities for common patterns (API key management, HTTP client setup, response normalization). New vendors are registered in the plugin registry and automatically available to Chatter orchestrator.
Provides a structured plugin framework for vendor implementation, rather than requiring vendors to be hardcoded. Plugin interface is minimal and focused, enabling vendors to optimize for their specific API design while maintaining compatibility with Chatter orchestrator.
More extensible than monolithic vendor support; new providers can be added without modifying core Fabric code. Plugin framework reduces boilerplate for common vendor patterns (auth, HTTP, response parsing).
content extraction and preprocessing from multiple sources
Medium confidenceFabric integrates specialized content processors for YouTube (transcript extraction), web pages (readability-based scraping), PDFs (text extraction), audio/video (transcription via external services), and Spotify (metadata extraction). Each processor normalizes content into plain text suitable for AI analysis. Processors are invoked via CLI flags (--youtube, --pdf, --web) and output is piped to patterns for downstream analysis.
Integrates content extraction as first-class CLI operations (--youtube, --pdf, --web flags) rather than separate tools, enabling single-command workflows that extract, normalize, and analyze content in one pipeline. Uses readability algorithm for web scraping instead of regex, improving robustness across diverse page structures.
More integrated than chaining separate tools (youtube-dl + pdftotext + curl); provides unified interface for multi-source content ingestion. Lighter than full ETL frameworks for ad-hoc content analysis workflows.
stateful conversation management with file-system persistence
Medium confidenceFabric maintains conversation history in a file-system database, enabling multi-turn interactions where each message is stored with metadata (timestamp, model, vendor). Sessions are identified by unique IDs and stored in structured directories. The Chatter orchestrator loads prior messages when --session flag is used, reconstructing context for follow-up queries. Conversation state is human-readable and portable across machines.
Uses file-system database instead of external storage, making conversations portable and auditable without infrastructure dependencies. Session state is human-readable YAML/JSON, enabling manual inspection and editing. Integrates session management directly into Chatter orchestrator rather than as a separate layer.
Simpler than database-backed conversation systems for single-user or small-team use; no schema migrations or connection management. More portable than cloud-based conversation storage; sessions can be version-controlled or shared as files.
pattern discovery and recommendation via semantic matching
Medium confidenceFabric implements a suggest_pattern function that analyzes user intent (via natural language description or task keywords) and recommends matching patterns from the library. The function uses semantic matching against pattern metadata (name, description, tags) to surface relevant patterns. Pattern metadata includes structured fields (category, tags, input_type, output_type) enabling multi-dimensional discovery.
Implements pattern discovery as a first-class operation (suggest_pattern) rather than a secondary feature, enabling programmatic pattern selection. Uses pattern metadata registry for efficient matching without requiring vector embeddings or external search infrastructure.
Lighter than embedding-based pattern search; no vector database required. More structured than free-text search; metadata-driven matching enables precise filtering by category, input type, and tags.
unified cli with multi-interface access (cli, rest api, web ui)
Medium confidenceFabric provides three execution interfaces: (1) CLI via Go binary with rich flag support for patterns, models, vendors, and content sources; (2) REST API server exposing pattern execution, session management, and vendor configuration as HTTP endpoints; (3) SvelteKit-based Web UI for interactive pattern execution and conversation management. All three interfaces share the same underlying Chatter orchestrator and pattern system, ensuring consistent behavior.
Implements three execution interfaces (CLI, API, Web) as first-class citizens sharing the same Chatter orchestrator, rather than bolting on API/Web as afterthoughts. CLI is primary interface with full feature parity; API and Web UI are projections of CLI capabilities. Enables same pattern to execute identically across all three interfaces.
More integrated than separate CLI and API tools; single codebase ensures consistency. Lighter than full web frameworks for API-first approaches; CLI remains the primary, most efficient interface.
configuration management with environment variable and file-based overrides
Medium confidenceFabric uses a hierarchical configuration system: environment variables (highest priority), config files (YAML/JSON), and built-in defaults. Configuration includes vendor credentials, model selection, pattern paths, output formats, and language preferences. The system supports multiple config file locations (~/.config/fabric, project-local .fabric.yml) with automatic discovery. Configuration is validated at startup and provides clear error messages for missing or invalid settings.
Implements hierarchical configuration with environment variable override as first-class feature, enabling seamless CI/CD integration without config file modifications. Supports multiple config file locations with automatic discovery, reducing setup friction for new users.
More flexible than single-file configuration; environment variable overrides enable CI/CD without secrets management. Simpler than full configuration management systems; no external tools or services required.
internationalization and language-aware output formatting
Medium confidenceFabric supports multiple languages for CLI output, help text, and error messages via an i18n system. Language selection is configurable via environment variable or config file. Output formatting respects language-specific conventions (date formats, number formatting, text direction). Built-in patterns can be localized by providing language-specific pattern variants.
Integrates i18n as a core system rather than an afterthought, with language selection affecting both CLI output and pattern execution. Supports language-specific pattern variants, enabling teams to maintain patterns in multiple languages without code duplication.
More integrated than adding i18n as a plugin; language support is built into core CLI and pattern systems. Enables non-English speakers to use Fabric without translation tools.
shell completion generation for cli discoverability
Medium confidenceFabric generates shell completion scripts (bash, zsh, fish) that enable tab-completion for patterns, flags, and options. Completion logic is aware of available patterns, configured vendors, and valid flag values. Completion scripts are generated dynamically based on installed patterns and configuration, ensuring completions stay in sync with available options.
Generates dynamic completion scripts that reflect current pattern library and configuration, rather than static completion files. Completion logic is aware of pattern metadata, enabling intelligent suggestions based on available patterns.
More dynamic than static completion files; completions update automatically when patterns are added or removed. Reduces CLI learning curve for new users by exposing available options via tab-completion.
streaming response handling with real-time output
Medium confidenceFabric supports streaming responses from AI models, enabling real-time output display as tokens arrive rather than waiting for complete response. Streaming is implemented at the vendor plugin level, with each provider handling streaming according to its API (Server-Sent Events, WebSocket, etc.). The Chatter orchestrator buffers streamed tokens and outputs them to stdout in real-time, with optional formatting (markdown, JSON).
Implements streaming at vendor plugin level, allowing each provider to optimize for its streaming protocol (SSE, WebSocket, etc.). Streaming is transparent to pattern system; patterns work identically with or without streaming enabled.
More responsive than buffered responses for interactive workflows. Vendor-level implementation enables provider-specific optimizations rather than forcing all providers through a lowest-common-denominator streaming interface.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓teams building repeatable AI workflows
- ✓developers integrating AI into CLI tools
- ✓non-technical users who want templated AI interactions
- ✓teams evaluating multiple AI providers
- ✓developers building provider-agnostic AI tools
- ✓organizations with multi-cloud or hybrid AI strategies
- ✓developers integrating Fabric into scripts or applications
- ✓teams processing Fabric output programmatically
Known Limitations
- ⚠Pattern discovery relies on file-system scanning—no centralized registry or versioning system
- ⚠Variable substitution is simple string replacement, not context-aware templating
- ⚠No built-in pattern validation or schema enforcement before execution
- ⚠Vendor plugins must be explicitly implemented—no automatic provider discovery
- ⚠Streaming response handling varies by provider; some providers have latency overhead in normalization
- ⚠Model-specific features (vision, function calling) require vendor-specific configuration
Requirements
Input / Output
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Apply AI to everyday challenges in the comfort of your terminal. Help’s to get better results with tried and tested library of prompt pattern’s.
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