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AI systems that use language models to determine which actions to take and in what sequence

Core Idea: Agents are systems that leverage language models as reasoning engines to decide which actions to perform, determine the inputs for those actions, execute them, and make further decisions based on the results.

Key Elements

• Components:

  • Language model (the "brain")
  • Tools (capabilities the agent can use)
  • Memory systems (for maintaining context)
  • Agent executor (orchestrates the workflow)

• Operation Flow:

  1. Agent receives input from user
  2. LLM determines which tool to use and with what inputs
  3. Tool is executed and returns results
  4. Results are fed back to the LLM
  5. LLM decides whether to use another tool or respond to user

• Types of Agents:

  • ReAct agents (reason-then-act pattern)
  • Stateless agents (no memory of past interactions)
  • Stateful agents (maintain conversation history)

• Design Considerations:

  • Tool selection and integration
  • Prompt engineering for effective reasoning
  • Error handling and fallback mechanisms
  • Memory management for context preservation

Implementation Approaches

from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent

# Create the components
memory = MemorySaver()
model = ChatAnthropic(model_name="claude-3-sonnet-20240229")
search = TavilySearchResults(max_results=2)
tools = [search]

# Build the agent
agent_executor = create_react_agent(model, tools, checkpointer=memory)

# Use the agent
config = {"configurable": {"thread_id": "abc123"}}
response = agent_executor.invoke(
    {"messages": [HumanMessage(content="What's the weather in San Francisco?")]},
    config
)

Common Use Cases

• Information Retrieval: Using search tools to find up-to-date information
• Multi-step Problem Solving: Breaking complex tasks into manageable steps
• Conversational Assistants: Creating assistants that can access external systems
• Workflow Automation: Automating sequences of operations that require reasoning

Connections

• Related Concepts: ReAct Agent Pattern (foundation for many LangChain agents), Function Calling (enables agents to use tools)
• Broader Context: LangChain (framework that provides the agent architecture)
• Applications: RAG Systems (often implemented using agents), Conversational AI (enhanced with agent capabilities)
• Components: LangGraph (orchestration system for agents), LLM Memory Systems (enables stateful agents)

References

1. LangChain documentation on agents (https://python.langchain.com/docs/concepts/agents/)
2. LangGraph documentation for agent orchestration

#langchain #agents #llm #tool-use #ai-systems

Connections:

Sources:

• From: Build an Agent 🦜️🔗 LangChain