langflow/docs/docs/Agents/agents-tools.md

title	slug
Configure tools for agents	/agents-tools

import Icon from "@site/src/components/icon";

Configure tools connected to agents to extend their capabilities.

Edit a tool component's actions

To edit a tool's actions, in the tool component, click Edit Tools to modify its name, description, or enabled metadata. These fields help connected agents understand how to use the action, without having to modify the agent's prompt instructions.

For example, the URL component has two actions available when Tool Mode is enabled:

Tool Name	Description	Enabled
fetch_content	Fetch content from web pages recursively	true
fetch_content_as_message	Fetch web content formatted as messages	true

A Langflow Agent has a clear idea of each tool's capabilities based on the name and description metadata. The enabled boolean controls the tool's availability to the agent. If you think an agent is using a tool incorrectly, edit a tool's description metadata to help the agent better understand the tool.

Tool names and descriptions can be edited, but the default tool identifiers cannot be changed. If you want to change the tool identifier, create a custom component.

Use an agent as a tool

The agent component itself also supports Tool Mode for creating multi-agent flows.

Add an agent to your flow that uses a different OpenAI model for a larger context window.

1. Create the Simple agent starter flow.
2. Add a second agent component to the flow.
3. Add your Open AI API Key to the Agent component.
4. In the Model Name field, select gpt-4.1.
5. Click Tool Mode to use this new agent as a tool.
6. Connect the new agent's Toolset port to the previously created agent's Tools port. The new agent will use gpt-4.1 for the larger tasks of scraping and searching information that require large context windows. The previously created agent will now use this agent as a tool, with its unique LLM and toolset.

7. The new agent's actions can be edited to help the agent understand how to use it. Click Edit Tools to modify its name, description, or enabled metadata. For example, the default tool name is Agent. Edit the name to Agent-gpt-41, and edit the description to Use the gpt-4.1 model for complex problem solving. The connected agent will understand that this is the gpt-4.1 agent, and will use it for tasks requiring a larger context window.

Add custom components as tools

An agent can use custom components as tools.

1. To add a custom component to the agent flow, click New Custom Component.

2. Add custom Python code to the custom component. For example, to create a text analyzer component, paste the below code into the custom component's Code pane.

Python

from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import Data
import re

class TextAnalyzerComponent(Component):
    display_name = "Text Analyzer"
    description = "Analyzes and transforms input text."
    documentation: str = "http://docs.langflow.org/components/custom"
    icon = "chart-bar"
    name = "TextAnalyzerComponent"

    inputs = [
        MessageTextInput(
            name="input_text",
            display_name="Input Text",
            info="Enter text to analyze",
            value="Hello, World!",
            tool_mode=True,
        ),
    ]

    outputs = [
        Output(display_name="Analysis Result", name="output", method="analyze_text"),
    ]

    def analyze_text(self) -> Data:
        text = self.input_text

        # Perform text analysis
        word_count = len(text.split())
        char_count = len(text)
        sentence_count = len(re.findall(r'\w+[.!?]', text))

        # Transform text
        reversed_text = text[::-1]
        uppercase_text = text.upper()

        analysis_result = {
            "original_text": text,
            "word_count": word_count,
            "character_count": char_count,
            "sentence_count": sentence_count,
            "reversed_text": reversed_text,
            "uppercase_text": uppercase_text
        }

        data = Data(value=analysis_result)
        self.status = data
        return data

3. To use the custom component as a tool, click Tool Mode.
4. Connect the custom component's tool output to the agent's tools input.
5. Open the Playground and instruct the agent, Use the text analyzer on this text: "Agents really are thinking machines!"

Response

``` AI gpt-4o Finished 0.6s Here is the analysis of the text "Agents really are thinking machines!": Original Text: Agents really are thinking machines! Word Count: 5 Character Count: 36 Sentence Count: 1 Reversed Text: !senihcam gnikniht era yllaer stnegA Uppercase Text: AGENTS REALLY ARE THINKING MACHINES! ```

The agent correctly calls the analyze_text action and returns the result to the Playground.

Make any component a tool

If the component you want to use as a tool doesn't have a Tool Mode button, add tool_mode=True to one of the component's inputs, and connect the new Toolset output to the agent's Tools input.

Langflow supports Tool Mode for the following data types:

• DataInput
• DataFrameInput
• PromptInput
• MessageTextInput
• MultilineInput
• DropdownInput

For example, the components as tools example above adds tool_mode=True to the MessageTextInput input so the custom component can be used as a tool.

inputs = [
    MessageTextInput(
        name="input_text",
        display_name="Input Text",
        info="Enter text to analyze",
        value="Hello, World!",
        tool_mode=True,
    ),
]

Use flows as tools

An agent can use flows that are saved in your workspace as tools with the Run flow component.

1. To add a Run flow component, click and drag a Run flow component to your workspace.
2. Select the flow you want the agent to use as a tool.
3. Enable Tool Mode in the component. The Run flow component displays your flow as an available action.
4. Connect the Run flow component's tool output to the agent's tools input.
5. Ask the agent, What tools are you using to answer my questions? Your flow should be visible in the response as a tool.
6. Ask the agent to specifically use the connected tool to answer your question. The connected flow returns an answer based on your question. For example, a Basic Prompting flow connected as a tool returns a different result depending upon its LLM and prompt instructions.