langchain

Build LLM agents that call external tools by using ReAct and tool-calling patterns to reason through tasks and chain actions. This creates autonomous pipelines that interact with external APIs to retrieve data and execute multi-step workflows.

Retrieval-augmented generation (RAG) works by embedding documents into vector stores and retrieving relevant context to ground LLM responses. This provides chatbots with accurate, source-backed answers instead of relying on pre-trained knowledge.

Add conversation memory to a multi-turn chatbot using built-in memory management building blocks. These components retain multi-turn context, allowing conversational assistants to reference previous inputs and maintain coherent dialogue flows.

Yes, you can use multiple model providers like OpenAI, Anthropic, and Google in the same pipeline. The framework provides reusable building blocks that let you swap providers without reinventing integration logic.

Monitor and observe LLM application performance in production by enabling LangSmith observability. This traces agent actions, tool calls, and retrieval pipelines to identify bottlenecks and debug reasoning chains.

Deploy a Python chatbot with vector store retrievals using production patterns like FastAPI, Docker, or Kubernetes. These strategies package your conversational application and vector integrations into scalable infrastructure.