LangChain chain

LangChain chain is a reusable pipeline that strings together prompts, models, memory, and custom logic in the LangChain framework. Each chain exposes a single invoke method: it ingests an input dict, runs the defined sequence—calling an LLM, querying a vector store, parsing JSON, or triggering a tool—and returns an output dict. Built-in chain types cover common patterns such as LLMChain (prompt → response), SequentialChain (multi-step workflows), and RetrievalQA (RAG). Developers can subclass chain to add validation, streaming callbacks, or parallel branches, enabling complex agentic behavior without boilerplate. Because all steps share a memory object, a chain maintains conversation context across turns, supports token counting for cost control, and logs events for observability. In production you compose chains inside a Runnable graph, serialize them to JSON for versioning, and deploy behind FastAPI or AWS Lambda—cutting time-to-market for chatbots, copilots, and automation scripts.

Related terms

• Agentic in Supply Chain
• AI Agents in Supply Chain
• Alternatives to LangChain
• Autogen vs LangChain
• Chain of Thought Prompting

Vstorm builds production systems that use LangChain chain: LangChain development company, Agentic AI consulting.

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