This article introduces Langchain, a platform for productionizing large language model (LLM) applications, and discusses the first principles of building LLM agents. The author explains the difference between simple LLM usage and techniques such as 'chain of thought' and 'tree of thoughts'. The article also provides examples of how to use Langchain's built-in tools and custom tools for planning, memory, and tools in LLM agents.
This article guides you through the process of building a simple agent in LangChain using Tools and Toolkits. It explains the basics of Agents, their components, and how to build a Mathematics Agent that can perform simple mathematical operations.
The article discusses the use of large language models (LLMs) as reasoning engines for powering agent workflows, focusing specifically on ReAct agents. It explains how these agents combine reasoning and action capabilities and provides examples of how they function. Challenges faced while implementing such agents are also mentioned, along with ways to overcome them. Additionally, the integration of open-source models within LangChain is highlighted.
