Agent Components: Tools, Toolkits, and Memory
Explore the core components that enable LLM agents to operate effectively, including tools that interact with real-world functions, toolkits for domain-specific bundles, prompt templates for structured reasoning, and various memory types for context retention. Understand how to configure these components in LangChain to build accurate and cost-efficient multi-agent workflows.
We'll cover the following...
In the previous lesson, you traced the perceive-reason-act loop that gives every LLM agent its operational backbone. That loop explained what an agent does at each phase, but it left a critical question unanswered: what concrete components power each phase? An agent is only as capable as the tools it can call, the instructions it receives, and the context it retains. Without well-configured tools, prompt templates, and memory, even the most powerful LLM will hallucinate actions or lose track of multi-step tasks.
To make this concrete, consider a customer-support agent that must look up order status, query a knowledge base for return policies, and remember that the user already provided their order ID three turns ago. Each of those capabilities maps to a specific building block. This lesson walks through all four of them: tools, toolkits, prompt templates, and memory. It also shows how they are configured in LangChain. By the end, you will know how to wire these pieces together, preparing you for the React and Structured Chat agent patterns covered in the next lesson.
Tools: giving agents real-world capabilities
A tool is any external function or API that an agent can invoke during the act phase of its loop. Search engines, databases, code interpreters, and custom business logic all qualify. Think of tools as the agent’s hands: the LLM can reason all day, but without tools, it cannot reach into the outside world to fetch data or trigger side effects.
How LangChain represents a tool
In LangChain, a tool is a Python callable wrapped with three pieces of metadata: a name, a description, and an input schema. The name gives the tool a unique identifier. The input schema tells the framework what arguments the function expects. The description is the most critical field, because the LLM reads it (injected directly into the prompt) to decide when to use the tool. A vague description like “does stuff with orders” leads to misrouted calls, while a precise one like “Returns the current status and estimated delivery date for a given order ID” lets the model match the user’s intent to the right function. ...