langflow/docs/docs/Starter-Projects/starter-projects-simple-agent.md

title	slug
Simple agent	/starter-projects-simple-agent

Build a Simple Agent flow for an agentic application using the Tool-calling agent component.

An agent uses an LLM as its "brain" to select among the connected tools and complete its tasks.

In this flow, the Tool-calling agent reasons using an Open AI LLM. The agent selects the Calculator tool for simple math problems and the URL tool to search a URL for content.

Prerequisites

• A running Langflow instance
• An OpenAI API key

Open Langflow and start a new flow

Click New Flow, and then select the Simple Agent flow.

This opens a starter flow with the necessary components to run an agentic application using the Tool-calling agent.

Simple Agent flow

The Simple Agent flow consists of these components:

• The Tool calling agent component uses the connected LLM to reason through the user's input and select among the connected tools to complete its task.
• The URL tool component searches a list of URLs for content.
• The Calculator component performs basic arithmetic operations.
• The Chat Input component accepts user input to the chat.
• The Chat Output component prints the flow's output to the chat.

Run the Simple Agent flow

1. Add your credentials to the Agent component.
2. Click Playground to start a chat session.
3. To confirm the tools are connected, ask the agent, What tools are available to you? The response is similar to the following:

I have access to the following tools:
Calculator: Perform basic arithmetic operations.
fetch_content: Load and retrieve data from specified URLs.
fetch_content_text: Load and retrieve text data from specified URLs.
as_dataframe: Load and retrieve data in a structured format (dataframe) from specified URLs.
get_current_date: Returns the current date and time in a selected timezone.

4. Ask the agent a question. For example, ask it to create a tabletop character using your favorite rules set. The agent tells you when it's using the URL-fetch_content_text tool to search for rules information, and when it's using CalculatorComponent-evaluate_expression to generate attributes with dice rolls. The final output should be similar to this:

Final Attributes
Strength (STR): 10
Constitution (CON): 12
Size (SIZ): 14
Dexterity (DEX): 9
Intelligence (INT): 11
Power (POW): 13
Charisma (CHA): 8

Now that your query has completed the journey from Chat input to Chat output, you have completed the Simple Agent flow.