langchain

LangChain provides a framework for building LLM applications by chaining prompts, models, and tools together using LCEL syntax. Start by creating a simple chain with a prompt template and language model, then run it to generate responses. You can expand to memory management, retrieval-augmented generation, and tool integration as needed.

LangChain memory management stores conversation history and context for stateful interactions. Memory-enabled pipelines maintain state across multiple invocations, enabling context-aware responses in chat models and agents. Configure memory type based on your use case—conversation buffer, summary, or entity-based approaches.

Retrieval-augmented generation (RAG) in LangChain combines a retriever component with chains to fetch relevant context from data sources and inject it into prompts. This produces grounded, fact-based responses by embedding real-world data into the LLM pipeline without retraining the model.

Yes, LangChain supports tool binding and agent integration within chains. Agents can invoke multiple tools sequentially, making decisions about which tool to call based on model outputs. This enables extensible workflows where chains delegate tasks to specialized functions or external APIs.

LangChain provides prompt templates and output parsers to enforce structured results from language models. Define templates with variables, bind them to models, and parse outputs into typed objects. This ensures consistent, machine-readable responses for downstream processing.

LangChain chains support both streaming and asynchronous execution patterns. Streaming enables real-time token output for interactive applications, while async invocation allows concurrent pipeline execution. Use these patterns to improve responsiveness in production deployments.