mirror of
https://github.com/langflow-ai/langflow.git
synced 2026-07-24 23:04:14 +08:00
71fbf5ff00
* version-1.9.0-docs * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
219 lines
9.4 KiB
Plaintext
219 lines
9.4 KiB
Plaintext
---
|
|
title: Configure tools for agents
|
|
slug: /agents-tools
|
|
---
|
|
|
|
import Icon from "@site/src/components/icon";
|
|
|
|
By default, [Langflow agents](/agents) only include the functionality built-in to their base LLM.
|
|
|
|
You can attach tools to agents to provide access to additional, targeted functionality.
|
|
For example, tools can be used to create domain-specific agents, such as a customer support agent that can access a company's knowledge base, a financial agent that can retrieve stock prices, or a math tutor agent that can use advanced math functions to solve complex equations.
|
|
|
|
## Attach tools
|
|
|
|
To attach a tool to an agent, you connect any component's **Tool** output to the **Agent** component's **Tools** input.
|
|
|
|
Some components emit **Tool** output by default.
|
|
For all other components, you must enable **Tool Mode** in the [component's header menu](/concepts-components#component-menus).
|
|
Then, you can connect the tool to the agent.
|
|
|
|
You can connect multiple tools to one agent, and each tool can have multiple actions (functions) that the agent can call.
|
|
|
|
When you run your flow, the agent decides when to call on certain tools, if it determines that a tool can help it respond to the user's prompt.
|
|
|
|
### Edit a tool's actions {#edit-a-tools-actions}
|
|
|
|
When you attach components to an agent as tools, each tool can have multiple actions (functions) that the agent can call.
|
|
Available actions are listed in each tool component's **Actions** list.
|
|
|
|
You can change each action's labels, descriptions, and availability to help the agent understand how to use the tool and prevent it from using irrelevant or undesired actions.
|
|
|
|
:::tip
|
|
If an agent seems to be using a tool incorrectly, try editing the actions metadata to clarify the tool's purpose and disable unnecessary actions.
|
|
|
|
You can also try using a **Prompt Template** component to pass additional instructions or examples to the agent.
|
|
:::
|
|
|
|
To view and edit a tool's actions, click <Icon name="Settings2" aria-hidden="true"/> **Edit Tool Actions** on the tool component.
|
|
|
|
The following information is provided for each action:
|
|
|
|
* **Enabled**: A checkbox that determines whether the action is available to the agent.
|
|
If selected, the action is enabled.
|
|
If not selected, the action is disabled.
|
|
|
|
* **Name**: A human-readable string name for the action, such as `Fetch Content`. This cannot be changed.
|
|
|
|
* **Description**: A human-readable description of the action's purpose, such as `Fetch content from web pages recursively`.
|
|
To edit this value, double-click the action's row to open the edit pane.
|
|
Changes are saved automatically when you click out of the field or close the dialog.
|
|
|
|
* **Slug**: An encoded name for the action, usually the same as the name but in snake case, such as `fetch_content`.
|
|
To edit this value, double-click the action's row to open the edit pane.
|
|
Changes are saved automatically when you click out of the field or close the dialog.
|
|
|
|
Some actions allow you to provide fixed values for their inputs.
|
|
Typically, you want to leave these blank so the agent can provide its own values.
|
|
However, you might use a fixed value if you're trying to debug an agent's behavior or your use case requires a fixed input for an action.
|
|
|
|
## Use an agent as a tool
|
|
|
|
To create multi-agent flows, you can set another **Agent** component to **Tool Mode**, and then attach that agent as a tool for your primary **Agent** component.
|
|
|
|
To try this for yourself, add an additional agent to the **Simple Agent** template:
|
|
|
|
1. Create a flow based on the **Simple Agent** template.
|
|
2. Add a second **Agent** component to the flow.
|
|
3. Add your **OpenAI API Key** to both **Agent** components.
|
|
4. In the second **Agent** component, change the model to `gpt-4.1`, and then enable **Tool Mode**.
|
|
5. Click <Icon name="Settings2" aria-hidden="true"/> **Edit Tool Actions** to [edit the tool's actions](#edit-a-tools-actions).
|
|
|
|
For this example, change the action's slug to `Agent-gpt-41`, and set the description to `Use the gpt-4.1 model for complex problem solving`.
|
|
This lets the primary agent know that this tool uses the `gpt-4.1` model, which could be helpful for tasks requiring a larger context window, such as large scrape and search tasks.
|
|
|
|
As another example, you could attach several specialized models to a primary agent, such as agents that are trained on certain tasks or domains, and then the primary agent would call each specialized agent as needed to respond to queries.
|
|
|
|
You can also enable and disable tools if you want to limit the available toolset.
|
|
|
|
6. Connect the new agent's **Toolset** port to the existing agent's **Tools** port.
|
|
|
|
|
|
|
|
## Add custom components as tools {#components-as-tools}
|
|
|
|
An agent can use [custom components](/components-custom-components) as tools.
|
|
|
|
1. To add a custom component to an agent flow, click **New Custom Component** in the <Icon name="Component" aria-hidden="true" /> **Core components** or <Icon name="Blocks" aria-hidden="true" /> **Bundles** menus.
|
|
|
|
2. Enter Python code into the **Code** pane to create the custom component.
|
|
|
|
If you don't already have code for a custom component, you can use the following code snippet as an example before creating your own.
|
|
|
|
<details>
|
|
<summary>Text Analyzer custom component</summary>
|
|
|
|
This code creates a text analyzer component.
|
|
|
|
```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
|
|
```
|
|
</details>
|
|
|
|
3. Enable **Tool Mode** in the custom component.
|
|
4. Connect the custom component's tool output to the **Agent** component's **Tools** input.
|
|
5. Open the <Icon name="Play" aria-hidden="true"/> **Playground** and instruct the agent, `Use the text analyzer on this text: "Agents really are thinking machines!"`
|
|
|
|
Based on your instruction, the agent should call the `analyze_text` action and return the result.
|
|
For example:
|
|
|
|
```
|
|
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!
|
|
```
|
|
|
|
## 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 example code in [Use custom components as tools](#components-as-tools) included `tool_mode=True` to the `MessageTextInput` input so the custom component could be used as a tool:
|
|
|
|
```python
|
|
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 your other flows as tools with the [**Run Flow** component](/run-flow).
|
|
|
|
1. Add a **Run Flow** component to your flow.
|
|
2. Select the flow you want the agent to use as a tool.
|
|
3. Enable **Tool Mode**.
|
|
The selected flow becomes an [action](#edit-a-tools-actions) in the **Run Flow** component.
|
|
4. Connect the **Run Flow** component's **Tool** output to the **Agent** component's **Tools** input.
|
|
5. Open the **Playground**, and then ask the agent, `What tools are you using to answer my questions?`
|
|
Your flow should be visible in the response as an available tool.
|
|
6. Ask the agent a question that specifically uses the connected flow as a tool.
|
|
The connected flow returns an answer based on your question.
|
|
|
|
|
|
|
|
## See also
|
|
|
|
* [Agent components](/components-agents)
|
|
* [Use Langflow as an MCP client](/mcp-client)
|
|
* [Use Langflow as an MCP server](/mcp-server) |