Agents Component of LangChain

Agents are systems that use large language models (LLMs) to decide what actions to take and in what order to accomplish a task.

They act as intelligent decision-makers, dynamically interacting with tools, APIs, or data sources to achieve a goal.

Types of Agents

  1. Single-Action Agents:
    • Perform one specific task or action based on the input.
    • Example: An agent that retrieves weather data from an API.
  2. Multi-Action Agents:
    • Perform a sequence of actions or make decisions iteratively to solve complex problems.
    • Example: An agent that plans a trip by booking flights, hotels, and transportation.

Why Are Agents Important?

  • They enable autonomous and intelligent behavior, allowing systems to handle complex, multi-step tasks without predefined workflows.
  • Agents are essential for building applications that require reasoning, planning, or interaction with external systems.

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LangChain Tutorial
LangChain with RAG - Workflow
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