Custom agent logic in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a custom agent in LangChain?

A custom agent in LangChain is a user-defined logic that controls how the agent decides which tools to use and how to respond, allowing tailored behavior beyond default agents.

intermediate

Which method do you typically override to implement custom agent logic in LangChain?

You override the plan or plan_intermediate_steps method to define how the agent decides the next action based on inputs and observations.

intermediate

Why would you create a custom agent instead of using a built-in agent in LangChain?

To handle specific workflows, use custom decision-making, integrate unique tools, or control the agent's reasoning steps tailored to your application needs.

beginner

What role do tools play in custom agent logic?

Tools are external functions or APIs the agent can call. Custom agent logic decides when and how to use these tools to answer questions or perform tasks.

intermediate

How can you test your custom agent logic effectively?

By running example inputs through the agent, checking if it chooses the right tools and produces expected outputs, and iterating on the logic for improvements.

What is the main purpose of custom agent logic in LangChain?

A To change the language model used

B To define how the agent decides actions and uses tools

C To create new language models

D To store data permanently

Custom agent logic controls the agent's decision-making process and tool usage.

Which method is commonly overridden to customize agent behavior?

A plan_intermediate_steps

B run_tool

C initialize_agent

D save_agent

The plan_intermediate_steps method defines the agent's next action logic.

In custom agent logic, what are 'tools'?

A User interface components

B The language models used

C Database tables

D External functions or APIs the agent can call

Tools are external capabilities the agent can invoke to perform tasks.

Why might you prefer a custom agent over a built-in one?

A To tailor decision-making and tool usage to your needs

B Because built-in agents are slower

C To avoid using any tools

D To reduce code size

Custom agents allow specific workflows and logic not possible with defaults.

What is a good way to check if your custom agent logic works well?

A Change the language model frequently

B Only read the code without running it

C Run test inputs and verify outputs and tool choices

D Use it without any tests

Testing with examples ensures the agent behaves as expected.

Explain how custom agent logic controls the behavior of an agent in LangChain.

Think about how the agent decides what to do next.

Describe the role of tools within custom agent logic and why they are important.

Consider how the agent interacts with outside helpers.

Practice

1. What is the main purpose of creating custom agent logic in Langchain?

easy

A. To define specific rules for how the agent plans and acts

B. To change the user interface of the agent

C. To improve the speed of the Langchain library

D. To add new data sources automatically

Solution

1. Step 1: Understand the role of custom agent logic

   Custom agent logic is about controlling how the agent decides what to do next.

2. Step 2: Identify the main purpose

   It is used to write your own rules for planning and acting, not for UI or speed improvements.

3. Final Answer:

   To define specific rules for how the agent plans and acts -> Option A

4. Quick Check:

   Custom agent logic = planning and acting rules [OK]

Hint: Custom logic controls agent decisions, not UI or speed [OK]

Common Mistakes:

• Thinking it changes the user interface
• Assuming it speeds up the library
• Believing it adds data sources automatically

2. Which method should you override to customize how an agent decides its next action in Langchain?

easy

A. run

B. plan

C. initialize

D. execute

Solution

1. Step 1: Identify key methods for custom logic

   Langchain agents use methods like plan and run for behavior.

2. Step 2: Determine which controls decision making

   The plan method decides the next action, so overriding it customizes decisions.

3. Final Answer:

   plan -> Option B

4. Quick Check:

   Decision method = plan [OK]

Hint: Override plan() to change agent's next action [OK]

Common Mistakes:

• Overriding run() instead of plan() for decisions
• Using initialize() which is not for planning
• Confusing execute() with plan()

3. Given this custom agent code snippet, what will be printed?

class MyAgent:
    def plan(self, input_text):
        if 'hello' in input_text.lower():
            return 'Greet'
        return 'Ignore'

agent = MyAgent()
print(agent.plan('Hello there!'))

medium

A. Greet

B. Ignore

C. hello

D. Error

Solution

1. Step 1: Analyze the plan method logic

   The method checks if 'hello' is in the input text (case-insensitive). If yes, returns 'Greet'.

2. Step 2: Apply input to the method

   Input is 'Hello there!', which contains 'hello' ignoring case, so it returns 'Greet'.

3. Final Answer:

   Greet -> Option A

4. Quick Check:

   Input contains 'hello' -> returns 'Greet' [OK]

Hint: Check string contains 'hello' ignoring case [OK]

Common Mistakes:

• Ignoring case sensitivity in string check
• Expecting the method to print input text
• Assuming method returns 'Ignore'

4. What is wrong with this custom agent code?

class CustomAgent:
    def plan(self, input_text):
        if input_text.contains('test'):
            return 'Found'
        return 'Not Found'

medium

A. Missing return statement in plan method

B. plan method should not take input_text parameter

C. Indentation error in class definition

D. Using .contains() method which does not exist in Python strings

Solution

1. Step 1: Check string method usage

   Python strings do not have a .contains() method; use 'in' keyword instead.

2. Step 2: Identify correct string membership check

   Correct way is: 'test' in input_text, so .contains() causes an error.

3. Final Answer:

   Using .contains() method which does not exist in Python strings -> Option D

4. Quick Check:

   Python strings use 'in', not .contains() [OK]

Hint: Use 'in' keyword for substring check in Python [OK]

Common Mistakes:

• Using .contains() instead of 'in'
• Thinking plan method can't take parameters
• Assuming missing return statement

5. You want to create a custom Langchain agent that first plans actions based on input, then logs each action before running it. Which approach correctly combines planning and running with logging?

hard

A. Override only run() to plan, log, and execute actions

B. Override plan() to log actions and run() to decide actions

C. Override plan() to decide actions and override run() to log and execute actions

D. Use default plan() and run(), add logging outside the agent

Solution

1. Step 1: Understand responsibilities of plan() and run()

   plan() decides what to do next; run() executes actions.

2. Step 2: Combine logging with correct methods

   Override plan() for custom decisions; override run() to add logging before execution.

3. Final Answer:

   Override plan() to decide actions and override run() to log and execute actions -> Option C

4. Quick Check:

   plan() decides, run() logs and executes [OK]

Hint: Plan decides; run executes and logs actions [OK]

Common Mistakes:

• Putting planning logic inside run() only
• Logging inside plan() without running actions
• Not overriding methods and logging externally