agents-langchainlisted

# 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. ## When to use - Turning a hand-rolled prompt script into a maintainable multi-step pipeline. - Giving a model tools (search, calculator, database) via a standard agent loop. - Building RAG: load documents, split them, embed, store, retrieve, generate. - Adding conversation or entity memory to a prototype agent. - Evaluating and streaming: you need token streaming, callbacks, or trace hooks out of the box. ## Core concepts - **Prompt templates**: parameterized prompt strings with named variables (`{question}`, `{context}`) so prompts are testable artifacts, not inline string soup. Keep few-shot examples in the template, not in code. - **Chains**: sequential composition — `prompt | model | parser`. The classic pat