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Basic Information

COMandA (comanda) is a command-line LLM orchestration engine designed to let developers build, chain, and deploy AI processing pipelines using declarative YAML workflows. It provides a CLI that compiles into the system PATH and can be piped to or from, plus an optional HTTP server mode that exposes endpoints to upload, list, process and stream YAML pipelines and files. The project abstracts multiple LLM providers behind a unified API, supports vision models and web content, and includes runtime directory and file management. Workflows consist of named steps that accept files, STDIN, URLs, images or database queries and route outputs to files, STDOUT or databases. The tool targets reproducible, automatable, and scriptable AI tasks and offers configuration encryption and model selection to integrate LLM-based processing into development and deployment environments.

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App Details

Features
COMandA offers declarative YAML workflows for chaining multiple LLM operations, support for many providers including OpenAI, Anthropic, Google, X.AI, Ollama and Moonshot, and a command-line native experience that fits into scripts and CI/CD. It can run as an HTTP server with endpoints for file and YAML upload, processing, listing and health checks and supports SSE streaming for real-time outputs. Additional features include image/vision analysis, web scraping, wildcard and batch file processing, automatic image optimization, file chunking for large inputs, parallel processing of independent steps, conditional branching with deferred steps, database read/write (Postgres), secure configuration encryption and natural-language generation of YAML workflows.
Use Cases
COMandA helps teams and developers automate complex LLM workflows without embedding logic in application code. Its CLI and server modes enable integration into local scripts, CI pipelines or remote services and make it easy to switch models or providers without refactoring. File handling, wildcard patterns, chunking and parallel execution simplify batch and large-file processing. Deferred steps and branching enable dynamic, agentic flows that can decide subsequent work at runtime. Configuration encryption and server authentication protect API keys in shared environments. The unified API and examples accelerate prototyping, allow comparing models, and support reproducible processing pipelines for tasks like summarization, analysis, scraping and image inspection.

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