langchain-architecture

Build autonomous AI agents using LangChain by instantiating an LLM, loading tools, and selecting an agent type—ReAct, OpenAI Functions, Structured Chat, Conversational, or Self-Ask with Search—then executing queries. The agent orchestrates tool calls and reasoning loops to complete multi-step tasks.

LangChain memory systems preserve conversation context: ConversationBufferMemory stores all messages, ConversationSummaryMemory compresses history, ConversationBufferWindowMemory retains recent exchanges, EntityMemory tracks entities, and VectorStoreMemory enables semantic retrieval. Choose based on context window constraints and retrieval needs.

Integrate external systems by loading documents with loaders, splitting text for chunking, storing vectors in vector stores, creating retrievers for semantic search, and building indexes. Bind these to agents and chains via tool definitions to enable API calls and data source queries within agent workflows.

Yes. LangChain document processing pipelines handle loaders, text splitters, vector stores, and retrievers to ingest, chunk, embed, and index large datasets. Use MapReduceChain or retrieval-augmented generation (RAG) patterns for production-grade document processing and semantic search across external data sources.

LangChain supports LLMChain for single-step prompting, SequentialChain for ordered task pipelines, RouterChain for conditional logic, TransformChain for data transformation, and MapReduceChain for parallel processing. Select by workflow structure: linear, branching, or map-reduce patterns.

LangChain callbacks provide production-grade observability: log execution traces, capture metrics and latency, handle errors, and monitor agent behavior. Attach callbacks to chains and agents to instrument logging, performance tracking, and error resilience across your application.