LangChain Agents Mind Map

Components, Tools, and Relationships

Core Foundation: LLM
Agent Types
ZERO_SHOT_REACT_DESCRIPTION
OPENAI_FUNCTIONS
CONVERSATIONAL_REACT_DESCRIPTION
Tools
Search Tools: SerpAPI, GoogleSearch
Math Tools: LLM-Math, Calculator
Code Tools: Python REPL, Shell
Memory & Execution
ConversationBufferMemory
AgentExecutor
ReAct Framework

Relationship Explanation:

LLM serves as the core foundation, powering all agent capabilities.

Agent Types define how the agent makes decisions, while Tools provide specific capabilities that agents can use. The Memory & Execution layer manages conversation context and handles the execution flow of agent actions.

How Agents Work

Agent Execution Flow

  1. User provides a query or task
  2. LLM analyzes the task and decides which tool to use
  3. Tool executes and returns results
  4. LLM processes the results and decides next steps
  5. Process repeats until task is complete
  6. Final answer is returned to the user

Key Components

  • Agent: The decision-making component powered by an LLM
  • Tools: Functions that agents can use to interact with external systems
  • AgentExecutor: Manages the execution loop and handles errors
  • Memory: Stores conversation history and context
  • Prompt: Instructs the LLM on how to use tools and respond

Example Code

from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI

# Initialize the language model
llm = OpenAI(temperature=0)

# Load tools the agent can use
tools = load_tools(["serpapi", "llm-math"], llm=llm)

# Create an agent with the tools and LLM
agent = initialize_agent(
    tools, 
    llm, 
    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

# Run the agent on a specific task
result = agent.run(
    "What was the high temperature in SF yesterday? " 
    "What is that number raised to the .023 power?"
)