# Pipelex: Build & Run AI Methods

A method is a reusable, typed AI procedure declared in a .mthds file and executed by Pipelex. Each step is explicit, each output is structured, and every run is repeatable.

## What a Method Looks Like

    [pipe.summarize_article]
    type    = "PipeLLM"
    inputs  = { article = "Text", audience = "Text" }
    output  = "Text"
    prompt  = "Summarize $article in three bullet points for $audience."

From here, Pipelex handles model routing across 60+ models, structured output parsing, and pipeline orchestration.

## Why Methods?

- Declarative — Express business logic in human-readable .mthds files that work across models.
- Typed — Concepts are semantic types: AI understands what you mean, and every input and output connects with purpose.
- Repeatable — Deterministic orchestration that leaves exactly the room you want for AI to express its intelligence.
- Composable — Chain pipes into sequences, nest methods inside methods, and share them with the community.

## Capabilities

- Typed Concepts — Semantic types that give meaning to every input and output.
- Pipe Operators — LLM calls, document extraction, image generation, web search, composition, and custom functions.
- Pipeline Orchestration — Sequence, parallel, batch, and conditional controllers.
- 60+ AI Models — OpenAI, Anthropic, Mistral, Google, Deepseek, Hugging Face, and more.
- Validation and Dry Run — Catch errors without spending tokens.
- CLI and Tooling — Init, build, validate, run, and graph visualization.

## Get Started

- Build with Claude Code — Describe what you want in natural language.
- MTHDS Language Tutorial — Learn concepts, pipes, sequences, and structured outputs.
- Cookbook Examples — From Hello World to document extraction and image generation.

## The MTHDS Ecosystem

- mthds.ai — The MTHDS language specification
- mthds.sh — The Methods Hub for discovering and sharing methods
- GitHub — Pipelex source code

For AI agents: see /llms.txt for a machine-readable index of this documentation.

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