Docs: 1.8 release

docs/docs/Agents/mcp-client.mdx

@@ -6,6 +6,8 @@ slug: /mcp-client
 import Icon from "@site/src/components/icon";
 import McpIcon from '@site/static/logos/mcp-icon.svg';
 import PartialMcpNodeTip from '@site/docs/_partial-mcp-node-tip.mdx';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
 
 Langflow integrates with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) as both an MCP server and an MCP client.
 
@@ -24,6 +26,8 @@ This component has two modes, depending on the type of server you want to access
 
 ### Connect to a non-Langflow MCP server {#mcp-stdio-mode}
 
+<PartialMcpNodeTip />
+
 1. Add an **MCP Tools** component to your flow.
 
 2. In the **MCP Server** field, select a previously connected server or click <Icon name="Plus" aria-hidden="true"/> **Add MCP Server**.
@@ -36,35 +40,31 @@ This component has two modes, depending on the type of server you want to access
     * **HTTP/SSE**: Enter your MCP server's **Name**, **URL**, and any **Headers** and **Environment Variables** the server uses, and then click **Add Server**.
     The default **URL** for Langflow MCP servers is `http://localhost:7860/api/v1/mcp/project/PROJECT_ID/streamable` or `http://localhost:7860/api/v1/mcp/streamable`. For more information, see [Connect to a Langflow MCP server](#mcp-http-mode).
 
-    <PartialMcpNodeTip />
-
-3. To use environment variables in your server command, enter each variable in the **Env** fields as key-value pairs.
+3. To configure headers for your MCP server, enter each header in the **Headers** fields as key-value pairs.
+    You can use [global variables](/configuration-global-variables) in header values by entering the global variable name as the header value.
+    For more information, see [Use global variables in MCP server headers](#use-global-variables-in-mcp-server-headers).
 
-    :::tip
-    Langflow passes environment variables from the `.env` file to MCP, but it doesn't pass global variables declared in your Langflow **Settings**.
-    To define an MCP server environment variable as a global variable, add it to Langflow's `.env` file at startup.
-    For more information, see [global variables](/configuration-global-variables).
-    :::
+4. To use environment variables in your server command, enter each variable in the **Env** fields as key-value pairs.
 
-4. In the **Tool** field, select a tool that you want this component to use, or leave the field blank to allow access to all tools provided by the MCP server.
+5. In the **Tool** field, select a tool that you want this component to use, or leave the field blank to allow access to all tools provided by the MCP server.
 
     If you select a specific tool, you might need to configure additional tool-specific fields. For information about tool-specific fields, see your MCP server's documentation.
 
     At this point, the **MCP Tools** component is serving a tool from the connected server, but nothing is using the tool. The next steps explain how to make the tool available to an [**Agent** component](/components-agents) so that the agent can use the tool in its responses.
 
-5. In the [component's header menu](/concepts-components#component-menus), enable **Tool mode** so you can use the component with an agent.
+6. In the [component's header menu](/concepts-components#component-menus), enable **Tool mode** so you can use the component with an agent.
 
-6. Connect the **MCP Tools** component's **Toolset** port to an **Agent** component's **Tools** port.
+7. Connect the **MCP Tools** component's **Toolset** port to an **Agent** component's **Tools** port.
 
     If not already present in your flow, make sure you also attach **Chat Input** and **Chat Output** components to the **Agent** component.
 
     ![MCP Tools component in STDIO mode](/img/component-mcp-stdio.png)
 
-7.  Test your flow to make sure the MCP server is connected and the selected tool is used by the agent. Open the **Playground**, and then enter a prompt that uses the tool you connected through the **MCP Tools** component.
+8.  Test your flow to make sure the MCP server is connected and the selected tool is used by the agent. Open the **Playground**, and then enter a prompt that uses the tool you connected through the **MCP Tools** component.
 
     For example, if you use `mcp-server-fetch` with the `fetch` tool, you could ask the agent to summarize recent tech news. The agent calls the MCP server function `fetch`, and then returns the response.
 
-8. If you want the agent to be able to use more tools, repeat these steps to add more tools components with different servers or tools.
+9. If you want the agent to be able to use more tools, repeat these steps to add more tools components with different servers or tools.
 
 ### Connect a Langflow MCP server {#mcp-http-mode}
 
@@ -110,6 +110,167 @@ To add a new MCP server, click **Add MCP Server**, and then follow the steps in
 
 Click <Icon name="Ellipsis" aria-hidden="true"/> **More** to edit or delete an MCP server connection.
 
+## Modify MCP server environment variables with the API {#mcp-api-tweaks}
+
+You can modify MCP server environment variables at runtime when running flows through the [Langflow API](/api-reference-api-examples) by tweaking the **MCP Tools** component.
+
+You can include tweaks with any Langflow API request that supports the `tweaks` parameter, such as POST requests to the `/run` or `/webhook` endpoints.
+For more information, see [Input schema (tweaks)](/concepts-publish#input-schema).
+
+To modify the **MCP Tools** component's environment variables with tweaks, do the following:
+
+1. Open the flow that contains your **MCP Tools** component.
+2. To find the **MCP Tools** component's unique ID, in your **MCP Tools** component, click <Icon name="SlidersHorizontal" aria-hidden="true"/> **Controls**.
+The component's ID is displayed in the **Controls** pane, such as `MCPTools-Bzahc`.
+3. Send a POST request to the Langflow server's `/run` endpoint, and include tweaks to the **MCP Tools** component.
+
+    The following examples demonstrate a request structure with the `env` object nested under `mcp_server` in the `tweaks` payload:
+
+    <Tabs groupId="language">
+    <TabItem value="python" label="Python" default>
+
+    ```python
+    import requests
+    import os
+
+    LANGFLOW_SERVER_ADDRESS = "http://localhost:7860"
+    FLOW_ID = "your-flow-id"
+    LANGFLOW_API_KEY = os.getenv("LANGFLOW_API_KEY")
+    MCP_TOOLS_COMPONENT_ID = "MCPTools-Bzahc"
+
+    url = f"{LANGFLOW_SERVER_ADDRESS}/api/v1/run/{FLOW_ID}?stream=false"
+    headers = {
+        "Content-Type": "application/json",
+        "x-api-key": LANGFLOW_API_KEY
+    }
+    payload = {
+        "output_type": "chat",
+        "input_type": "chat",
+        "input_value": "What sales data is available to me?",
+        "tweaks": {
+            MCP_TOOLS_COMPONENT_ID: {
+                "mcp_server": {
+                    "env": {
+                        "API_URL": "https://api.example.com",
+                        "API_KEY": "your-mcp-server-api-key",
+                        "ENVIRONMENT": "production"
+                    }
+                }
+            }
+        }
+    }
+
+    response = requests.post(url, json=payload, headers=headers)
+    print(response.json())
+    ```
+
+    </TabItem>
+    <TabItem value="typescript" label="TypeScript">
+
+    ```typescript
+    const LANGFLOW_SERVER_ADDRESS = "http://localhost:7860";
+    const FLOW_ID = "your-flow-id";
+    const LANGFLOW_API_KEY = process.env.LANGFLOW_API_KEY || "";
+    const MCP_TOOLS_COMPONENT_ID = "MCPTools-Bzahc";
+
+    const url = `${LANGFLOW_SERVER_ADDRESS}/api/v1/run/${FLOW_ID}?stream=false`;
+
+    const response = await fetch(url, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        "x-api-key": LANGFLOW_API_KEY,
+      },
+      body: JSON.stringify({
+        output_type: "chat",
+        input_type: "chat",
+        input_value: "What sales data is available to me?",
+        tweaks: {
+          [MCP_TOOLS_COMPONENT_ID]: {
+            mcp_server: {
+              env: {
+                API_URL: "https://api.example.com",
+                API_KEY: "your-mcp-server-api-key",
+                ENVIRONMENT: "production",
+              },
+            },
+          },
+        },
+      }),
+    });
+
+    const data = await response.json();
+    console.log(data);
+    ```
+
+    </TabItem>
+    <TabItem value="curl" label="cURL">
+
+    ```bash
+    curl --request POST \
+      --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false" \
+      --header "Content-Type: application/json" \
+      --header "x-api-key: LANGFLOW_API_KEY" \
+      --data '{
+        "output_type": "chat",
+        "input_type": "chat",
+        "input_value": "What sales data is available to me?",
+        "tweaks": {
+          "MCP_TOOLS_COMPONENT_ID": {
+            "mcp_server": {
+              "env": {
+                "API_URL": "https://api.example.com",
+                "API_KEY": "your-mcp-server-api-key",
+                "ENVIRONMENT": "production"
+              }
+            }
+          }
+        }
+      }'
+    ```
+
+    </TabItem>
+    </Tabs>
+
+    Replace `MCP_TOOLS_COMPONENT_ID`, `LANGFLOW_API_KEY`, `LANGFLOW_SERVER_ADDRESS`, and `FLOW_ID` with the actual values from your Langflow deployment.
+
+    Langflow doesn't automatically discover or expose which environment variables your MCP server accepts from the **MCP Tools** component.
+    To determine which environment variables your MCP server accepts, see the MCP server's documentation. For example, the [Astra DB MCP server](https://github.com/datastax/astra-db-mcp) requires `ASTRA_DB_APPLICATION_TOKEN` and `ASTRA_DB_API_ENDPOINT`, with an optional variable for `ASTRA_DB_KEYSPACE`, as documented in its repository.
+
+## Use global variables in MCP server headers {#use-global-variables-in-mcp-server-headers}
+
+You can use [global variables](/configuration-global-variables) in MCP server header values to securely store and reference API keys, authentication tokens, and other sensitive values. This is particularly useful for deployment scenarios where you need to pass user-specific credentials at runtime.
+
+Enter a global variable name as the header value, and Langflow resolves the global variable name to its actual value before making the MCP server request. Langflow only passes the token value to your server; it doesn't validate tokens on behalf of your MCP server.
+
+For example, to create a global variable named `TEST_BEARER_TOKEN` for MCP server bearer authentication, do the following:
+
+1. To open the **Global Variables** pane, click your profile icon, select **Settings**, and then click <Icon name="Globe" aria-hidden="true"/> **Global Variables**.
+2. Create a **Credential** global variable named `TEST_BEARER_TOKEN`.
+3. In the **Value** field, enter your MCP server's bearer token value. The value must include the `Bearer` prefix with a space, for example: `Bearer eyJhbG...`.
+4. Click **Save Variable**.
+5. To manage MCP server connections for your Langflow client, click <McpIcon /> **MCP servers** in the visual editor, or click your profile icon, select **Settings**, and then click **MCP Servers**.
+6. Click <Icon name="Plus" aria-hidden="true"/> **Add MCP Server**.
+7. Select the following:
+    * **Name**: test-mcp-server
+    * **Streamable HTTP/SSE URL**: Your MCP server's URL, such as `http://127.0.0.1:8000/mcp`.
+    * **Headers**: In the key field, enter the literal string `Authorization`. For the key's value, enter `TEST_BEARER_TOKEN`, or the exact name of your global variable.
+8. Click **Create Server**.
+
+   If the connection succeeds, Langflow shows the number of tools exposed by the server.
+
+   After creating the server and global variable, you can connect to the server with the **MCP Tools** component, as explained in the next steps.
+
+9. Add the **MCP Tools** component to a flow.
+10. In the **MCP Tools** component, select the **MCP Server** you created.
+The MCP server configuration already includes the headers you configured earlier, so no further configuration is needed in the component. The global variable `TEST_BEARER_TOKEN` is automatically resolved when the component makes requests to the MCP server.
+
+11. Optional: To override headers or add additional headers to the **MCP Tools** component, click <Icon name="SlidersHorizontal" aria-hidden="true"/> **Controls** in the [component's header menu](/concepts-components#component-menus), enable **Show** for the **Headers** parameter, and then add header key values. Headers configured in the component take precedence over the headers configured in the MCP server settings.
+
+12. Test your flow to make sure the agent uses your server to respond to queries. Open the **Playground**, and then enter a prompt that uses a tool that you connected through the **MCP Tools** component.
+
+    Langflow automatically resolves `TEST_BEARER_TOKEN` to its actual value before sending the request to the MCP server. When your MCP server receives the request, the `Authorization` header contains the resolved token value.
+
 ## See also
 
 - [Use Langflow as an MCP server](/mcp-server)

docs/docs/Agents/mcp-component-astra.mdx

@@ -28,7 +28,7 @@ This guide demonstrates how to [use Langflow as an MCP client](/mcp-client) by u
 
     1. In the **MCP Server** field, click <Icon name="Plus" aria-hidden="true"/> **Add MCP Server**.
     2. Select **Stdio** mode.
-    3. EIn the **Name** field, enter a name for the MCP server.
+    3. In the **Name** field, enter a name for the MCP server.
     4. In the **Commmand** field, add the following code to connect to an Astra DB MCP server:
 
         ```bash

docs/docs/Components/components-prompts.mdx

@@ -19,10 +19,10 @@ The **Prompt Template** component can also output variable instructions to other
 
 ## Prompt Template parameters
 
-| Name     | Display Name   | Description                                                       |
-|----------|----------------|-------------------------------------------------------------------|
-| template | Template       | Input parameter. Create a prompt template with dynamic variables in curly braces, such as `{VARIABLE_NAME}`. <PartialCurlyBraces /> |
-| prompt   | Prompt Message | Output parameter. The built prompt message returned by the `build_prompt` method. |
+| Name                | Display Name        | Description                                                       |
+|---------------------|---------------------|-------------------------------------------------------------------|
+| template            | Template            | Input parameter. Create a prompt template with dynamic variables in curly braces, such as `{VARIABLE_NAME}`. <PartialCurlyBraces /> |
+| use_double_brackets | Use Double Brackets | When enabled, use Mustache syntax `{{variable}}` instead of f-string syntax `{variable}`. For more information, see [Use Mustache templating in prompt templates](#use-mustache-templating-in-prompt-templates). |
 
 ## Define variables in prompts
 
@@ -70,6 +70,39 @@ The following steps demonstrate how to add variables to a **Prompt Template** co
 You can add as many variables as you like in your template.
 For example, you could add variables for `{references}` and `{instructions}`, and then feed that information in from other components, such as **Text Input**, **URL**, or **Read File** components.
 
+### Use Mustache templating in prompt templates
+
+F-string escaping can become confusing when you mix escaped braces with variables in the same template.
+For example:
+
+```text
+Generate a response in this JSON format:
+{{"name": "{name}", "age": {age}, "city": "{city}"}}
+
+The user's name is {name}, age is {age}, and they live in {city}.
+```
+
+The characters `{{` and `}}` are escaped literal braces for the JSON structure, but `{name}` is a variable.
+This can make prompts error-prone and difficult to parse.
+Use [Mustache](https://mustache.github.io) in your prompt templates to make the differences clearer.
+
+To enable Mustache templating, do the following:
+
+1. In the **Prompt Template** component, enable **Use Double Brackets**.
+2. In your prompt template, change the variables from `{variable}` to `{{variable}}`.
+    Mustache uses `{` `}` for literal braces and `{{variable}}` for variables.
+
+    ```text
+    Generate a response in this JSON format:
+    {"name": "{{name}}", "age": {{age}}, "city": "{{city}}"}
+
+    The user's name is {{name}}, age is {{age}}, and they live in {{city}}.
+    ```
+
+3. Click **Check & Save**.
+    The component lints the template code and returns **Prompt is ready** if there are no errors.
+    Your prompt is now ready to use in a flow.
+
 ## See also
 
 * [**LangChain Prompt Hub** component](/bundles-langchain#prompt-hub)

docs/docs/Components/smart-transform.mdx

@@ -12,15 +12,17 @@ import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
 This component has been renamed multiple times.
 Its previous names include **Lambda Filter** and **Smart Function**.
 
-The **Smart Transform** component uses an LLM to generate a Lambda function to filter or transform structured data based on natural language instructions.
+The **Smart Transform** component uses an LLM and natural language instructions to generate a Lambda function that can filter or transform [`Data`](/data-types#data), [`DataFrame`](/data-types#dataframe), or [`Message`](/data-types#message) input.
 You must connect this component to a [language model component](/components-models), which is used to generate a function based on the natural language instructions you provide in the **Instructions** parameter.
-The LLM runs the function against the data input, and then outputs the results as [`Data`](/data-types#data).
+The LLM runs the function against the input, and then outputs the results as [`Data`](/data-types#data), [`DataFrame`](/data-types#dataframe), or [`Message`](/data-types#message).
 
 :::tip
 Provide brief, clear instructions, focusing on the desired outcome or specific actions, such as `Filter the data to only include items where the 'status' is 'active'`.
 One sentence or less is preferred because end punctuation, like periods, can cause errors or unexpected behavior.
 
 If you need to provide more details instructions that aren't directly relevant to the Lambda function, you can input them in the **Language Model** component's **Input** field or through a **Prompt Template** component.
+
+For the most reliable results, the **Smart Transform** component's output type must match the input type. For example, select **Message** output for [`Message`](/data-types#message) input.
 :::
 
 The following example uses the **API Request** endpoint to pass JSON data from the `https://jsonplaceholder.typicode.com/users` endpoint to the **Smart Transform** component.
@@ -35,9 +37,8 @@ From there, the LLM generates a filter function that extracts email addresses fr
 
 | Name | Display Name | Info |
 |------|--------------|------|
-| data | Data | Input parameter. The structured data to filter or transform using a Lambda function. |
-| llm | Language Model | Input parameter. Connect [`LanguageModel`](/data-types#languagemodel) output from a **Language Model** component. |
+| data | Data | Input parameter. The [`Data`](/data-types#data), [`DataFrame`](/data-types#dataframe), or [`Message`](/data-types#message) input to filter or transform using the generated Lambda function. |
+| model | Language Model | Input parameter. Connect [`LanguageModel`](/data-types#languagemodel) output from a **Language Model** component. |
 | filter_instruction | Instructions | Input parameter. The natural language instructions for how to filter or transform the data. The LLM uses these instructions to create a Lambda function. |
 | sample_size | Sample Size | Input parameter. For large datasets, the number of characters to sample from the dataset head and tail. Only applied if the dataset meets or exceeds `max_size`. Default: `1000`. |
-| max_size | Max Size | Input parameter. The number of characters for the dataset to be considered large, which triggers sampling by the `sample_size` value. Default: `30000`. |
-
+| max_size | Max Size | Input parameter. The number of characters for the dataset to be considered large, which triggers sampling by the `sample_size` value. Default: `30000`. |