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agents-langchain

Build LLM applications with chain-style orchestration frameworks — prompts, chains, tool-calling agents, memory, and retrieval pipelines. Use when structuring multi-step LLM programs, adding tools to a model, or composing RAG systems in code.

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# Agents with Chain-Style LLM Frameworks

Chain-style orchestration libraries give you composable building blocks — prompt templates, model 
wrappers, output parsers, memory, document loaders, and agent loops — so you write pipelines 
instead of raw API calls.

## Overview

The core idea is composition: small, typed units (prompt -> model -> parser) that snap together 
into chains, which in turn become tools inside an agent loop. A model provider wrapper normalizes 
API differences; a prompt template keeps prompts version-controlled and parameterizable; a parser 
turns model text into structured data; a retriever plus chain gives you retrieval-augmented 
generation. Agents emerge when a model is allowed to choose between tools in a loop until the task 
is done.