What are the core features of LangChain?

LangChain is a framework designed to help developers build applications using large language models (LLMs) by providing modular components and tools. Its core features include chains for creating multi-step workflows, agents for dynamic decision-making, memory for retaining context across interactions, and integrations with external data sources and tools. These features simplify the process of connecting LLMs to real-world data, enabling developers to build applications like chatbots, document analyzers, or automated workflows without starting from scratch. LangChain abstracts common complexities, allowing developers to focus on application logic.

One of LangChain’s key features is its chains, which let developers combine LLMs, prompts, and external tools into structured workflows. For example, a chain might take a user’s question, use an LLM to rephrase it into a database query, fetch relevant data, and then generate a summary. Chains can also include conditional logic, such as routing a query to a specific tool based on the LLM’s analysis. Another example is a customer support bot that uses a chain to first classify a user’s request (e.g., billing vs. technical support), retrieve account details from a database, and then generate a tailored response. This modular approach allows developers to reuse components across projects, reducing redundant work and ensuring consistency.

LangChain’s agents and memory features add further flexibility. Agents use LLMs to decide which tools to call in real time, such as a calculator for math problems or a search API for real-time data. For instance, an agent could analyze a user’s question like “What’s the population of Tokyo divided by 2?” and decide to first fetch the population via an API, then run a calculation. Memory enables applications to retain context across interactions, such as storing a chat history so a user can ask follow-up questions without repeating details. Developers can customize memory storage (e.g., keeping only recent messages or summarizing past interactions) to balance performance and accuracy. Combined with integrations for databases, APIs, and LLM providers, LangChain provides a cohesive toolkit for building context-aware, data-driven applications efficiently.