Langchain Agents | Langchain Basic Series

The core idea of agents is to use an LLM to choose a sequence of actions to take. In chains, a sequence of actions is hardcoded (in code). In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.
This is the chain responsible for deciding what step to take next. This is powered by a language model and a prompt. The inputs to this chain are:

List of available tools
User input
Any previously executed steps (intermediate_steps)
This chain then returns either the next action to take or the final response to send to the user (AgentAction or AgentFinish).

Different agents have different prompting styles for reasoning, different ways of encoding input, and different ways of parsing the output.

In this video we get started with creating agents from scratch and also with inbuilt functions. We supply those agents with functions and test their sequencing intelligence.
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0:00 Introduction
0:37 What are agents and why we need them
1:44 ChatGPT example
5:26 Agent terminology
11:06 Demo with Agents. Single tool
24:30 Demo with multiple sequence tools
33:07 Using initialize_agent function to create agents
36:20 Agent Types
38:05 Like Share and Subscribe

Langchain playlist - Langchain and Large language models

Langchain Agents link - https://python.langchain.com/docs/mod...

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