What is the difference between Chains and Agents in LangChain? When should each be used?

In LangChain, Chains are sequences of steps (components) that process input data and return a result. Chains are typically used to build predictable, deterministic workflows where each step is executed sequentially.

Agents are more complex structures that can decide which actions to take based on intermediate results and external data. Agents are used when interactivity, dynamic tool selection, or executing multiple chains depending on the context is required.

When to use:

• Chains are suitable for simple and fixed scenarios, such as sequential text processing, calling multiple models or functions.
• Agents are employed when the system needs to decide on actions autonomously, for example, when integrating with multiple APIs, searching for information, or performing complex logical tasks.

Example:

from langchain.chains import SimpleSequentialChain
from langchain.agents import initialize_agent, Tool

# Chain: sequential execution of two functions
chain = SimpleSequentialChain(chains=[chain1, chain2])
result = chain.run(input_text)

# Agent: selecting tools based on a query
tools = [Tool(name="Search", func=search_func), Tool(name="Calculator", func=calc_func)]
agent = initialize_agent(tools, llm, agent_type="zero-shot-react-description")
response = agent.run(user_input)