OpenAI functions agent in LangChain - Step-by-Step Execution

Concept Flow - OpenAI functions agent

User Input

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Agent Receives Input

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Agent Checks for Function Call

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Call Function

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Function Executes

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Return Function Output

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Agent Incorporates Output

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Respond to User

The agent receives user input, decides if a function call is needed, executes it if yes, then responds with the function output or text.

Execution Sample

LangChain

from langchain.agents import create_openai_functions_agent, AgentExecutor

agent = create_openai_functions_agent(llm, tools)
agent_executor = AgentExecutor(agent=agent, tools=tools)
response = agent_executor.invoke({'input': 'What is the weather in Paris?'})

This code creates an OpenAI functions agent with executor and sends a user question to get a response, possibly calling a function.

Execution Table

Step	Action	Input	Agent Decision	Function Called	Function Output	Agent Response
1	Receive user input	What is the weather in Paris?	Check if function needed
2	Decide to call function	What is the weather in Paris?	Yes, call weather function	get_weather(location='Paris')
3	Execute function	location='Paris'			Sunny, 25°C
4	Incorporate function output	Sunny, 25°C				The weather in Paris is Sunny, 25°C.
5	Return response to user					The weather in Paris is Sunny, 25°C.

💡 Agent returns final response after function call and output incorporation.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
user_input		What is the weather in Paris?	What is the weather in Paris?	What is the weather in Paris?	What is the weather in Paris?
agent_decision		Check if function needed	Call function	Function output received	Response ready
function_called			get_weather(location='Paris')	get_weather(location='Paris')	get_weather(location='Paris')
function_output				Sunny, 25°C	Sunny, 25°C
agent_response					The weather in Paris is Sunny, 25°C.

Key Moments - 3 Insights

How does the agent know when to call a function instead of just replying?

The agent checks the input and matches it to available functions. If a function fits the request, it chooses to call it (see execution_table step 2).

What happens if the function output is empty or missing?

The agent waits for the function output before responding. If missing, it may fallback to a text response or error handling (not shown here).

Can the agent call multiple functions in one interaction?

Yes, but this example shows a single function call. The agent can chain calls by repeating the decision and execution steps.

Visual Quiz - 3 Questions

Look at the execution_table, what function does the agent call at step 2?

A send_email(address='Paris')

B get_news(topic='Paris')

C get_weather(location='Paris')

D No function called

💡 Hint

Check the 'Function Called' column at step 2 in the execution_table.

At which step does the agent receive the function output?

A Step 3

B Step 1

C Step 4

D Step 5

💡 Hint

Look at the 'Function Output' column in the execution_table.

If the user input was 'Tell me a joke', what would likely change in the execution_table?

A Agent calls a joke function instead of weather

B Agent skips function call and replies directly

C Agent calls get_weather function anyway

D Agent crashes

💡 Hint

Think about when the agent decides to call a function or just reply (see agent decision in execution_table step 2).

Concept Snapshot

OpenAI functions agent:
- Receives user input
- Checks if a function call fits the request
- Calls the function if needed
- Gets function output
- Responds using output or text
- Enables dynamic, real-time info in conversations

Full Transcript

The OpenAI functions agent works by first receiving what the user says. It then decides if it needs to call a special function to answer. If yes, it calls that function with the right details. The function runs and sends back an answer. The agent uses this answer to reply to the user. If no function is needed, the agent just replies with text. This way, the agent can give smart answers using live data or tools.

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