"If you've read the blog post How to Build Chatbots With LangChain, you may want to know more about LangChain. This blog post will dive deeper into what LangChain offers and guide you through a few more real-world use cases. And even if you haven't read the first post, you might still find the info in this one helpful for building your next AI agent. LangChain fundamentals Let's have a look at what LangChain is."
"LangChain provides a standard framework for building AI agents powered by LLMs, like the ones offered by OpenAI, Anthropic, Google, etc., and is therefore the easiest way to get started. LangChain supports most of the commonly used LLMs on the market today. LangChain is a high-level tool built on LangGraph, which provides a low-level framework for orchestrating the agent and runtime and is suitable for more advanced users. Beginners and those who only need a simple agent build are definitely better off with LangChain."
LangChain provides a standard framework to build AI agents that leverage large language models from providers such as OpenAI, Anthropic, and Google. LangChain runs as a high-level layer on top of LangGraph, which supplies low-level orchestration and runtime capabilities for advanced users. Agents combine LLMs with external tools to reason about tasks, choose appropriate tools for each step, inspect intermediate results, and iteratively reach solutions. Creating an agent can be as simple as calling a create_agent function with an LLM name and tool list. Agent models can be static, configured at creation and unchanged during execution, or dynamic for flexible behavior.
Read at The JetBrains Blog
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