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Import and export flows

You can export flows to transfer them between Langflow instances, share them with others, or create backups.

Export a flow​

There are three ways to export flows:

  • Export from projects: On the Projects page, find the flow you want to export, click More, and then select Export. To export all flows in a project, click Options on the Projects list, and then select Download.

  • Export by sharing: When editing a flow, click Share, and then click Export.

  • Export with the Langflow API: To export one flow, use the /flows/download endpoint. To export all flows in a project, use the /projects/download endpoint.

Exported flows are downloaded to your local machine as JSON files named FLOW_NAME.json. If you export an entire project, the JSON files are packaged in a zip archive. For more information, see Langflow JSON file contents.

Save with my API keys​

When exporting from the Projects page or Share menu, you can select Save with my API keys to export the flow and any defined API key variables. Non-API key variables are included in the export regardless of the Save with my API keys setting.

warning

If you enter the literal key into a component's API key field, then Save with my API keys exports the literal key value.

If your key is stored in a Langflow global variable, Save with my API keys exports only the variable name.

When you or another user import the flow to another Langflow instance, that instance must have Langflow global variables with the same names and valid values in order to run the flow successfully. If any variables are missing or invalid, those variables must be created or edited after importing the flow.

Import a flow​

You can import Langflow JSON files from your local machine in the following ways:

  • Import to projects: On the Projects page, click Upload a flow, and then select the Langflow JSON file to import.
  • Import anywhere: Drag and drop Langflow JSON files from your file explorer into your Langflow window to import a flow from any Langflow page.
  • Import with the Langflow API: To import one Langflow JSON file, use the /flows/upload/ endpoint. To import a zip archive of Langflow JSON files, use the /projects/upload endpoint.

Run an imported flow​

Once imported, your flow is ready to use. If the flow contains any global variables, make sure your Langflow instance has global variables with the same names and valid values. For more information, see Save with my API keys.

Langflow JSON file contents​

An exported flow is downloaded to your local machine as a JSON file named FLOW_NAME.json.

Langflow JSON files contain nodes and edges that describe components and connections, and additional metadata that describe the flow.

For an example Langflow JSON file, examine the Basic Prompting.json file in the Langflow repository.

Nodes​

Nodes represent the components that make up the flow. For example, this object represents a Chat Input component:


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{
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"data": {
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"description": "Get chat inputs from the Playground.",
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"display_name": "Chat Input",
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"id": "ChatInput-jFwUm",
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"node": {
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"base_classes": ["Message"],
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"description": "Get chat inputs from the Playground.",
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"display_name": "Chat Input",
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"icon": "MessagesSquare",
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"template": {
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"input_value": {
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"display_name": "Text",
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"info": "Message to be passed as input.",
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"value": "Hello"
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},
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"sender": {
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"value": "User",
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"options": ["Machine", "User"]
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},
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"sender_name": {
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"value": "User"
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},
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"should_store_message": {
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"value": true
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}
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}
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},
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"type": "ChatInput"
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},
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"position": {
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"x": 689.5720422421635,
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"y": 765.155834131403
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}
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}

Each node has a unique identifier in the format of NODE_NAME-UUID, such as ChatInput-jFwUm.

Entrypoint nodes, such as the ChatInput node, are the first node executed when running a flow.

Edges​

Edges represent the connections between nodes.

The connection between the ChatInput node and the OpenAIModel node is represented as an edge:


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{
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"className": "",
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"data": {
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"sourceHandle": {
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"dataType": "ChatInput",
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"id": "ChatInput-jFwUm",
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"name": "message",
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"output_types": ["Message"]
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},
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"targetHandle": {
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"fieldName": "input_value",
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"id": "OpenAIModel-OcXkl",
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"inputTypes": ["Message"],
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"type": "str"
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}
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},
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"id": "reactflow__edge-ChatInput-jFwUm{œdataTypeœ:œChatInputœ,œidœ:œChatInput-jFwUmœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-OcXkl{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-OcXklœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
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"source": "ChatInput-jFwUm",
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"sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-jFwUmœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
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"target": "OpenAIModel-OcXkl",
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"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-OcXklœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
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}

This edge shows that the ChatInput component outputs a Message type to the target node, which is the OpenAIModel node. The OpenAIModel component accepts the Message type at the input_value field.

Additional metadata and project information​

Additional information about the flow is stored in the root data object.

  • Metadata and project information including the name, description, and last_tested_version of the flow:


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    {
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    "name": "Basic Prompting",
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    "description": "Perform basic prompting with an OpenAI model.",
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    "tags": ["chatbots"],
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    "id": "1511c230-d446-43a7-bfc3-539e69ce05b8",
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    "last_tested_version": "1.0.19.post2",
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    "gradient": "2",
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    "icon": "Braces"
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    }

  • Visual information about the flow defining the initial position of the flow in the workspace:


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    "viewport": {
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    "x": -37.61270157375441,
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    "y": -155.91266341888854,
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    "zoom": 0.7575251406952855
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    }

  • Notes are comments that help you understand the flow within the workspace. They may contain links, code snippets, and other information. Notes are written in Markdown and stored as node objects.


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    {
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    "id": "undefined-kVLkG",
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    "node": {
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    "description": "## 📖 README\nPerform basic prompting with an OpenAI model.\n\n#### Quick Start\n- Add your **OpenAI API key** to the **OpenAI Model**\n- Open the **Playground** to chat with your bot.\n..."
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    }
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    }

See also​

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