Build LLM Apps with LangChain Agents & Chains

LangChain Architecture presents patterns for composing language-model applications from agents, tools, chains, memory, document-processing components, and callbacks. It explains how a model-driven agent chooses actions, how chains connect multiple steps, and how retrieval supplies relevant documents. Application patterns include conversational tool use, retrieval-augmented question answering, custom tools, and an extract-analyze-summarize workflow.

The guide distinguishes ReAct, function-calling, structured-chat, conversational, and search-oriented agent styles. Its chain overview includes sequential composition, routing, transformations, and map-reduce aggregation. These are architecture choices to match a task, rather than a claim that one agent configuration suits every application.

Use this material when planning a multi-step LangChain workflow, deciding how to retain conversation context, connecting external data, or adding monitoring and failure handling. It can help identify the components needed before implementation. Do not use it as proof that the supplied API imports match an installed LangChain release. The examples should be checked against the actual dependency version and provider setup before execution.

Supporting references and templates are explicitly not bundled in this catalog copy. Their topics include agents, memory, chains, document processing, callbacks, and configuration, including references/agents.md and assets/memory-config.yaml. Obtain and inspect the original supporting installation before following a step that depends on those files; no verified download source is supplied. An unrelated file with a matching name is not a substitute.

Inputs include the application goal, tool contracts, model configuration, conversation-state needs, source documents, and operational limits. Outputs are an application architecture and code patterns for orchestration, retrieval, memory, and observability. The source supplies this short-conversation memory example:

from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()

Other memory choices summarize older messages, retain a sliding window, track entities, or retrieve relevant history semantically. Select retention deliberately to avoid unbounded history and context-window overflow. The retrieval pattern loads and splits documents, builds embeddings in a vector store, and constructs a retriever-backed question-answering chain with source documents returned.

Custom tool examples include database search and email functions, but their bodies are placeholders rather than real integrations. Tool descriptions, input validation, execution limits, and handling failures remain necessary. The source also identifies inefficient retrieval and incorrect tool selection as common problems.

Examples reference LangChain, Chroma, model and embedding providers, SerpAPI, document loaders, and callback handlers. Callback hooks cover requests, responses, actions, errors, token usage, latency, and custom metrics. Performance examples include caching, parallel document processing, and streamed responses.

The production checklist calls for logging, cost monitoring, timeouts, rate limits, input validation, edge-case checks, fallbacks, and versioned prompts and configuration. The included illustrative checks do not establish production readiness or validate a real external tool.

This guide supports developers assembling modular language-model applications who need a component map and implementation patterns alongside version-specific documentation and environment-level verification.