OpenAI functions agent in LangChain

Introduction

An OpenAI functions agent helps your program talk to OpenAI's AI models and use special functions easily. It makes your app smarter by letting it ask AI for help and get answers or actions back.

You want your app to answer questions using AI.

You need to call specific functions based on AI responses.

You want to automate tasks by combining AI with your code.

You want to build a chatbot that can do things beyond just chatting.

You want to handle user requests with AI and custom logic.

Syntax

LangChain

from langchain.agents import create_openai_functions_agent
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(temperature=0)
agent = create_openai_functions_agent(llm, functions)

llm is the AI model you use to chat with OpenAI.

functions is a list of Python functions you want the agent to use.

Examples

This example creates an agent with one function called my_function.

LangChain

from langchain.agents import create_openai_functions_agent
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(temperature=0)
functions = [my_function]
agent = create_openai_functions_agent(llm, functions)

Here, the agent uses two functions and a bit more creative AI with temperature 0.5.

LangChain

from langchain.agents import create_openai_functions_agent
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(temperature=0.5)
functions = [func1, func2]
agent = create_openai_functions_agent(llm, functions)

Sample Program

This program creates an agent that can greet people by name. It asks the agent to greet Alice and prints the reply.

LangChain

from langchain.agents import create_openai_functions_agent
from langchain.chat_models import ChatOpenAI

# Define a simple function the agent can call
def greet(name: str) -> str:
    return f"Hello, {name}!"

# List of functions the agent can use
functions = [greet]

# Create the AI chat model
llm = ChatOpenAI(temperature=0)

# Create the agent with the model and functions
agent = create_openai_functions_agent(llm, functions)

# Ask the agent to greet someone
response = agent.invoke({"input": "Greet Alice"})
print(response["output"])

Important Notes

Make sure your functions have clear input and output types for the agent to use them well.

Keep the AI temperature low (like 0) for predictable results when calling functions.

Test your functions separately before adding them to the agent.

Summary

An OpenAI functions agent connects AI chat with your own functions.

Use it to make apps that understand and act on user requests smartly.

It needs an AI model and a list of functions to work.

Practice

1. What is the main purpose of an OpenAI functions agent in Langchain?

easy

A. To store large datasets for AI processing

B. To train new AI models from scratch

C. To create user interfaces for AI applications

D. To connect AI chat with your own custom functions for smarter responses

Solution

1. Step 1: Understand the role of an OpenAI functions agent

   An OpenAI functions agent links AI chat capabilities with user-defined functions to perform tasks.

2. Step 2: Compare options to the definition

   Only To connect AI chat with your own custom functions for smarter responses describes connecting AI chat with custom functions, which matches the agent's purpose.

3. Final Answer:

   To connect AI chat with your own custom functions for smarter responses -> Option D

4. Quick Check:

   Agent purpose = connect AI chat + functions [OK]

Hint: Remember: functions agent links AI chat to your code [OK]

Common Mistakes:

• Confusing agent with AI model training
• Thinking it stores data instead of connecting functions
• Assuming it builds user interfaces

2. Which of the following is the correct way to create an OpenAI functions agent in Langchain?

easy

A. agent = OpenAIFunctionsAgent(functions, model)

B. agent = OpenAIChatAgent(model, funcs)

C. agent = OpenAIFunctionsAgent(llm=model, tools=funcs)

D. agent = FunctionsAgent(llm=model, funcs=functions)

Solution

1. Step 1: Recall the correct constructor syntax

   The OpenAI functions agent requires named parameters: llm for the model and tools for the list of tools.

2. Step 2: Check each option for correct names and syntax

   agent = OpenAIFunctionsAgent(llm=model, tools=funcs) uses correct class name and named parameters. Others either use wrong class names or positional arguments incorrectly.

3. Final Answer:

   agent = OpenAIFunctionsAgent(llm=model, tools=funcs) -> Option C

4. Quick Check:

   Correct constructor = agent = OpenAIFunctionsAgent(llm=model, tools=funcs) [OK]

Hint: Look for named parameters llm and tools in constructor [OK]

Common Mistakes:

• Using positional arguments instead of named
• Wrong class names like OpenAIChatAgent
• Mixing parameter names like funcs vs tools

3. Given the code snippet:

from langchain.agents import OpenAIFunctionsAgent

model = OpenAI()
functions = [get_weather, get_news]
agent = OpenAIFunctionsAgent(llm=model, tools=functions)
response = agent.invoke({'input': 'What is the weather today?'})
print(response)

What will print(response) most likely output?

medium

A. A string response from the AI calling get_weather function

B. A syntax error due to missing parameters

C. An empty dictionary because no functions are called

D. A runtime error because invoke method does not exist

Solution

1. Step 1: Understand agent.invoke behavior

   The agent uses the AI model and tools list to process input and call the right function, here likely get_weather.

2. Step 2: Analyze the code flow

   Input asks about weather, so the agent calls get_weather and returns its result as a string response.

3. Final Answer:

   A string response from the AI calling get_weather function -> Option A

4. Quick Check:

   invoke calls function and returns response [OK]

Hint: Input about weather triggers get_weather function call [OK]

Common Mistakes:

• Assuming invoke method does not exist
• Expecting empty output without function calls
• Confusing syntax errors with runtime behavior

4. What is wrong with this code snippet for creating an OpenAI functions agent?

model = OpenAI()
functions = [get_time]
agent = OpenAIFunctionsAgent(functions, model)
response = agent.invoke({'input': 'What time is it?'})

medium

A. The agent constructor is missing named parameters for llm and tools

B. The tools list should be a dictionary, not a list

C. The invoke method requires a string, not a dictionary

D. The OpenAI model must be passed as a string, not an object

Solution

1. Step 1: Check constructor parameter usage

   The OpenAIFunctionsAgent requires named parameters: llm= and tools=, not positional arguments.

2. Step 2: Verify other parts of the code

   Tools as list is correct, invoke accepts a dictionary input, and model is an object as expected.

3. Final Answer:

   The agent constructor is missing named parameters for llm and tools -> Option A

4. Quick Check:

   Constructor needs named params llm= and tools= [OK]

Hint: Always use named parameters llm= and tools= in constructor [OK]

Common Mistakes:

• Passing positional arguments instead of named
• Thinking tools must be a dictionary
• Misunderstanding invoke input type

5. You want to build a Langchain app that answers user questions by calling either get_weather or get_news functions based on input. Which approach correctly sets up the OpenAI functions agent to handle this?

hard

A. Create two separate agents, one for weather and one for news, and switch manually

B. Pass both functions in a list to OpenAIFunctionsAgent and let it decide which to call

C. Use only get_weather function and ignore get_news for simplicity

D. Call functions directly without using an agent

Solution

1. Step 1: Understand agent's function selection

   The OpenAI functions agent can receive multiple functions and uses AI to pick the right one based on input.

2. Step 2: Evaluate options for best design

   Passing both functions in a list lets the agent decide automatically, which is the intended use.

3. Final Answer:

   Pass both functions in a list to OpenAIFunctionsAgent and let it decide which to call -> Option B

4. Quick Check:

   Agent selects function from list automatically [OK]

Hint: Give all functions to agent; it picks based on input [OK]

Common Mistakes:

• Manually switching between agents instead of one agent
• Ignoring needed functions for simplicity
• Bypassing agent and losing AI routing benefits