Learning LangChain

Index

Symbols

• {} (curly braces), Making LLM Prompts Reusable

A

• abstractions, LangChain and Why It’s Important
• agency, Cognitive Architectures with LangGraph, The Plan-Do Loop, Patterns to Make the Most of LLMs
• agency-reliability trade-off, Cognitive Architectures with LangGraph, Patterns to Make the Most of LLMs-Patterns to Make the Most of LLMs
• agent architecture
  • agent abstractions, LangChain and Why It’s Important
  • always calling tools first, Always Calling a Tool First-Always Calling a Tool First
  • dealing with many tools, Dealing with Many Tools-Dealing with Many Tools
  • LangGraph agent creation, Building a LangGraph Agent-Building a LangGraph Agent
  • multi-agent architectures, Multi-Agent Architectures-Supervisor Architecture, Multitasking LLMs
  • plan-do loop, The Plan-Do Loop-The Plan-Do Loop
  • reflection prompting technique, Reflection-Reflection
  • unique aspects of, Agent Architecture
• agents, evaluating performance of
  • final response, Testing an agent’s final response-Testing a single step of an agent
  • levels of testing, Evaluating an Agent’s End-to-End Performance
  • single steps, Testing a single step of an agent-Testing a single step of an agent
  • trajectory, Testing an agent’s trajectory-Testing an agent’s trajectory
• AI chatbots (see chatbots)
• AIMessage interface, Using LLMs in LangChain
• alerts, Ambient Computing
• algorithms, Preface
• ambient computing, Ambient Computing
• Anthropic, Preface, LangChain and Why It’s Important, LLM Fundamentals with LangChain
• API access, protecting, Security
• API keys, retrieving, Large ...