Author(s): Sayanteka Chakraborty

Originally published on Towards AI.

Build AI Agents in 5 Minutes Using LangChain

AI agents are becoming an important part of modern AI applications. Unlike a traditional chatbot that primarily generates responses from its training and supplied context, an AI agent can use tools to perform tasks, retrieve information, execute functions, and combine the results to solve more complex problems.

The article walks through building AI agents with LangChain’s create_agent framework: defining tools (e.g., an audio-cleansing function and a mocked weather tool) using the @tool decorator, registering those tools, and creating an agent with a system prompt that instructs it to choose the right tool. It then extends the approach to a multi-tool travel-planning agent that chains outputs across several steps (weather → packing advice → luggage estimation → budget), explaining agent reasoning and tool orchestration concepts like observing tool results and deciding whether to continue. Finally, it covers optimizing repeated workflows with prompt caching by adding a prompt_cache_key, discusses how caching can reduce cost and latency for reusable prompt prefixes, and concludes with guidance on what matters for reliable agent design beyond simply exposing functions.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI