How Do Agents Differ from Chains? When to Use Agents vs Chains?

If you’re learning LangChain or building AI apps, you’ve probably heard about agents and chains. They might sound similar — both connect large language models (LLMs) with tools and logic — but they work in very different ways.
Let’s break it down simply!

🔗 What Are Chains?

Chains are like step-by-step workflows.
They follow a fixed sequence of actions — once you define the steps, the model runs them in that exact order every time.

✅ Example
A simple question-answer chain might:

1. Take a user’s question

2. Search the knowledge base

3. Generate an answer

No matter what the question is, the process always stays the same.

💡 Think of it like: A recipe — each step happens in a fixed order.

🤖 What Are Agents?

Agents, on the other hand, are more flexible and dynamic.
They don’t follow a strict script — instead, they decide what to do next based on the situation.

Agents use reasoning to choose the right tools or actions to achieve a goal.
They often rely on something called the ReAct framework (Reason + Act) — meaning they can:

1. Analyze the input

2. Decide what tool or step to use

3. Execute the action

4. Reevaluate and continue until the task is complete

✅ Example
If a user asks, “Find today’s weather and summarize it in simple words,” an agent might:

• Use a weather API tool

• Read the data

• Generate a short summary
  — all without predefining the steps.

💡 Think of it like: A smart assistant that figures out the best path on its own.

⚖️ Agents vs Chains — Key Differences

Feature	Chains	Agents
Flow Type	Fixed	Dynamic
Decision-Making	Predefined	AI-driven
Best For	Repetitive, structured tasks	Complex, flexible tasks
Control	High (predictable)	Lower (more autonomy)
Example Use Case	FAQ bot, text summarizer	Research assistant, task planner

🧩 When to Use Chains

Use chains when:

• The steps are clear and predictable

• You want consistent, repeatable results

• Performance and speed matter more than flexibility

✅ Example use cases:

• Text summarization

• Data extraction

• Formatted Q&A systems

• Chatbots with defined flows

⚙️ When to Use Agents

Use agents when:

• Tasks need dynamic reasoning

• You want your system to choose tools or data sources automatically

• The workflow might change depending on user input

✅ Example use cases:

• AI personal assistants

• Multi-tool automation

• Research and report generation

• Customer support with open-ended queries

Conclusion

In simple terms:

Chains are like well-planned routes, while agents are smart navigators who find the best path on the go.

If your task has a clear path, use chains.
If your task requires flexibility and decision-making, go for agents.

Together, they form the backbone of powerful AI apps built with LangChain.