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docs: Standardize style of component names, especially when used in links (#9251)

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* standardize component name style and links pt 1

* component name standardization pt 2

* standardization pt 3

* standardize component names pt 4

* peer review

* fix lnk

* update edit tool actions section

* standardize Agent, agent, agentic

* more style auditing

* missing imports
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74
docs/docs/Agents/agents-tools.mdx
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@ -7,21 +7,44 @@ import Icon from "@site/src/components/icon";
Configure tools connected to agents to extend their capabilities.
## Edit a tool component's actions
## Edit a tool's actions {#edit-a-tools-actions}
To edit a tool's actions, in the tool component, click <Icon name="SlidersHorizontal" aria-hidden="true"/> **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.
When you set any component to **Tool Mode** or **Tool** output, an agent can use the actions (functions) provided by that component.
Available actions are listed in the tool component's **Actions** list.
For example, the [URL](/components-data#url) component has two actions available when **Tool Mode** is enabled:
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.
| Tool Name | Description | Enabled |
|-----------|-------------|---------|
| `fetch_content` | Fetch content from web pages recursively | true |
| `fetch_content_as_message` | Fetch web content formatted as messages | true |
:::tip
If an agent seems to be using a tool incorrectly, try editing the action metadata to clarify the tool's purpose and disable unnecessary actions.
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.
You can also try using a **Prompt Template** component to pass additional instructions or examples to the agent.
:::
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.
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 checked, the action is enabled.
If unchecked, 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.
To edit the **Description** or **Slug**, double-click anywhere on the action's row to open the edit pane.
Note that the **Name** field on the edit page maps to the **Slug** column.
Changes are saved automatically when you click out of a field or close the dialog.
Optionally, you can provide fixed values for an action's inputs. Typically you want to leave these blank so the agent can provide its own values. 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
@ -33,9 +56,9 @@ To try this for yourself, add an additional agent to the **Simple Agent** templa
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="SlidersHorizontal" aria-hidden="true"/> **Edit Tools** to set tool names and descriptions that help the primary agent understand how to use those tools.
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 tool name to `Agent-gpt-41` and set the description to `Use the gpt-4.1 model for complex problem solving`.
For this example, change the action **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.
@ -50,7 +73,7 @@ To try this for yourself, add an additional agent to the **Simple Agent** templa
An agent can use [custom components](/components-custom-components) as tools.
1. To add a custom component to the agent flow, click **New Custom Component**.
1. To add a custom component to an agent flow, click **New Custom Component** in the **Components** menu.
2. Enter Python code into the **Code** pane to create the custom component.
@ -115,8 +138,8 @@ An agent can use [custom components](/components-custom-components) as tools.
```
</details>
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.
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.
@ -148,7 +171,7 @@ Langflow supports **Tool Mode** for the following data types:
* `MultilineInput`
* `DropdownInput`
For example, the [components as tools](#components-as-tools) example above adds `tool_mode=True` to the `MessageTextInput` input so the custom component can be used as a tool.
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 = [
@ -164,20 +187,19 @@ inputs = [
## Use flows as tools
An agent can use flows that are saved in your workspace as tools with the [Run flow](/components-logic#run-flow) component.
An agent can use your other flows as tools with the [**Run Flow** component](/components-logic#run-flow).
1. To add a **Run flow** component, click and drag a **Run flow** component to your workspace.
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** 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.
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.
For example, a Basic Prompting flow connected as a tool returns a different result depending upon its LLM and prompt instructions.
![Run Flow as tool connected to an agent](/img/agent-example-run-flow-as-tool.png)
![Run Flow component connected to an Agent component as a tool](/img/agent-example-run-flow-as-tool.png)
## See also

10
docs/docs/Agents/agents.mdx
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@ -5,7 +5,7 @@ slug: /agents
import Icon from "@site/src/components/icon";
Langflow's [**Agent** component](/components-agents) is critical for building agentic flows.
Langflow's [**Agent** component](/components-agents) is critical for building agent flows.
This component provides everything you need to create an agent, including multiple Large Language Model (LLM) providers, tool calling, and custom instructions.
It simplifies agent configuration so you can focus on application development.
@ -30,11 +30,11 @@ The `Tool` object's description tells the agent what the tool can do so that it
## Use the Agent component in a flow
The following steps explain how to create an agentic flow in Langflow from a blank flow.
For a prebuilt example, use the **Simple Agent** template or try the [Langflow quickstart](/get-started-quickstart).
The following steps explain how to create an agent flow in Langflow from a blank flow.
For a prebuilt example, use the **Simple Agent** template or the [Langflow quickstart](/get-started-quickstart).
1. Click **New Flow**, and then click **Blank Flow**.
2. Add an **Agent** component to the **Workspace**.
2. Add an **Agent** component to your flow.
3. Enter a valid OpenAI API key.
The default model for the **Agent** component is an OpenAI model.
@ -84,7 +84,7 @@ For a prebuilt example, use the **Simple Agent** template or try the [Langflow q
To help you debug and test your flows, the **Playground** displays the agent's tool calls, the provided input, and the raw output the agent received before generating the summary.
With the given example, the agent should call the **News Search** component's `search_news` action.
You've successfully created a basic agentic flow that uses some generic tools.
You've successfully created a basic agent flow that uses some generic tools.
To continue building on this tutorial, try connecting other tool components or [use Langflow as an MCP client](/mcp-client) to support more complex and specialized tasks.