---
title: Langflow release notes
slug: /release-notes
---

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This page summarizes significant changes to Langflow in each release.
For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases/latest).

Due to strict SemVer requirements, Langflow Desktop can have different patch versions than the core Langflow OSS Python package, but the major and minor versions are aligned.

## Prepare to upgrade

:::warning
Whenever possible, the Langflow team recommends installing new Langflow versions in a new virtual environment or VM before upgrading your primary installation.
This allows you to [import flows](/concepts-flows-import#import-a-flow) from your existing installation and test them in the new version without disrupting your existing installation.
In the event of breaking changes or bugs, your existing installation is preserved in a stable state.
:::

To avoid the impact of potential breaking changes and test new versions, the Langflow team recommends the following upgrade process:

1. Recommended: [Export your projects](/api-projects#export-a-project) to create backups of your flows:

    ```bash
    curl -X GET \
    "$LANGFLOW_SERVER_URL/api/v1/projects/download/$PROJECT_ID" \
      -H "accept: application/json" \
      -H "x-api-key: $LANGFLOW_API_KEY"
    ```

   To export flows from the visual editor, see [Import and export flows](/concepts-flows-import).

2. Install the new version:

   * **Langflow OSS Python package**: Install the new version in a new virtual environment. For instructions, see [Install and run the Langflow OSS Python package](/get-started-installation#install-and-run-the-langflow-oss-python-package).
   * **Langflow Docker image**: Run the new image in a separate container, or upgrade your existing image. For more information, see [Upgrade the Langflow Docker image](/deployment-docker#upgrade-the-langflow-docker-image).
   * **Langflow Desktop**: To upgrade in place, open Langflow Desktop, and then click **Upgrade Available** in the Langflow header. If you want to isolate the new version, you must install Langflow Desktop on a separate physical or virtual machine, and then [import your flows](/concepts-flows-import) to the new installation.

3. [Import your flows](/concepts-flows-import) to test them in the new version, [upgrading components](/concepts-components#component-versions) as needed.

    When upgrading components, you can use the **Create backup flow before updating** option if you didn't previously export your flows.

4. If you installed the new version in isolation, upgrade your primary installation after testing the new version.

    If you made changes to your flows in the isolated installation, you might want to export and import those flows back to your upgraded primary installation so you don't have to repeat the component upgrade process.

## 1.11.x

Highlights of this release include the following changes.
For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).

:::tip
If Langflow fails to start with a `get_body_field` error after installing version 1.11.0, see [`get_body_field` error after installing Langflow](/troubleshoot#get_body_field-error-after-installing-langflow).
:::

### Breaking changes

- Default superuser password removed

    Langflow no longer creates or accepts the legacy `langflow`/`langflow` default superuser credentials.
    If `LANGFLOW_AUTO_LOGIN=False`, set `LANGFLOW_SUPERUSER_PASSWORD` to a strong password before startup.
    The legacy value `langflow` is not allowed, even if `LANGFLOW_AUTO_LOGIN=True`.

    ```bash
    export LANGFLOW_SUPERUSER_PASSWORD=SUPERUSER_PASSWORD
    ```

    Replace `SUPERUSER_PASSWORD` with a strong password for the Langflow superuser.

    If `LANGFLOW_AUTO_LOGIN=true`, setting `LANGFLOW_SUPERUSER_PASSWORD` is optional. If you omit it, Langflow generates a random bootstrap password for the auto-login account.

    If you're running Langflow with Docker and `LANGFLOW_AUTO_LOGIN=false`, pass the password at startup:

    ```bash
    docker run -d \
      --name ${CONTAINER_NAME} \
      --restart unless-stopped \
      -p 7860:7860 \
      -e LANGFLOW_HOST=0.0.0.0 \
      -e LANGFLOW_PORT=7860 \
      -e LANGFLOW_AUTO_LOGIN=false \
      -e LANGFLOW_SUPERUSER_PASSWORD=SUPERUSER_PASSWORD \
      -v langflow-data:/app/langflow \
      ${IMAGE}
    ```

    Replace `SUPERUSER_PASSWORD` with a strong password for the Langflow superuser.

- Docker images disable auto-login by default

    Official Langflow Docker images set `LANGFLOW_AUTO_LOGIN=false`.
    You must set `LANGFLOW_SUPERUSER_PASSWORD` (and optionally `LANGFLOW_SUPERUSER`) before the container can start, unless you explicitly set `LANGFLOW_AUTO_LOGIN=true`.

    For more information, see [Docker image defaults](/deployment-docker#docker-image-security-defaults).

- Ongoing breaking changes: bundle separation

    Langflow 1.10.x introduced the Extension bundles model.
    In Langflow 1.11.x, many third-party provider integrations are now shipped as *long-tail bundles* inside the `lfx-bundles` package, instead of in the core `langflow` installation.

    `uv pip install langflow` still includes these bundles automatically, but if you install standalone `lfx`, you will need to install the providers your flows require:

    ```bash
    # Install one third party provider
    uv pip install "lfx-bundles[<bundle-name>]"

    # Install all third party providers
    uv pip install "lfx[bundles]"
    ```

    For more information, see [Langflow Extensions overview](./extensions-overview).

- PyTorch components are opt-in by default

    **CUGA**, **Code Agents**, and **Docling** local conversion are excluded from the default `uv pip install langflow` installation because they require PyTorch.

    To install these components, see [Torch opt-in installs](./components-bundle-components#torch-opt-in).

- Workflow API request schema (Beta)

    The v2 [Workflow API (Beta)](/workflow-api) request body and interaction pattern changes in 1.11.
    Any client that calls `POST /api/v2/workflows` against 1.10.x must update as follows:

    Example 1.10.x request:

    ```json
    {
      "flow_id": "67ccd2be-17f0-8190-81ff-3bb2cf6508e6",
      "background": false,
      "inputs": {
        "ChatInput-abc.input_value": "what is 2+2",
        "LLMComponent-123.temperature": 0.7
      }
    }
    ```

    Example 1.11.x request:

    ```json
    {
      "flow_id": "67ccd2be-17f0-8190-81ff-3bb2cf6508e6",
      "input_value": "what is 2+2",
      "mode": "stream",
      "stream_protocol": "agui",
      "tweaks": {
        "LLMComponent-123": {
          "temperature": 0.7
        }
      }
    }
    ```

    For more information, see [Workflow API (Beta)](/workflow-api).

- Input Schema pane replaced by in-component Parameters

    The **Input Schema** pane under **Share** > **API access** is removed, but the fields can still be exposed to requests.
    The `tweaks` object in API requests is unchanged.
    **Endpoint Name** remains available from the **API access** pane.

    For details, see [Tweaks (API inputs)](/concepts-publish#input-schema).

### New features and enhancements

- Human-in-the-Loop (HITL)

    Human-in-the-Loop (HITL) pauses an agent when the agent calls a tool and creates a stateful checkpoint.
    After a human responds by approving, rejecting, or editing the request, the agent resumes from the checkpoint.

    For more information, see [Human-in-the-Loop](./human-in-the-loop).

- Agent2Agent (A2A) protocol support

    Publish a flow as an A2A agent so other agents can discover and call it, and call remote A2A agents from inside a flow with the **A2A Agent** component.

    For more information, see [Use Langflow as an A2A server](./a2a-server) and the [**A2A Agent** component](./a2a-agent-component).

- AG-UI compatible streaming for the Workflow API

    The v2 [Workflow API](/workflow-api) now supports streaming with the [AG-UI (Agent–User Interaction) protocol](https://docs.ag-ui.com/introduction) streaming format.

    For more information, see [Workflow API (Beta)](/workflow-api#stream-with-ag-ui).

- OpenAI Compatible model provider

    Point Langflow's model provider at any OpenAI-compatible endpoint, and Langflow discovers models live from the `/v1/models` endpoint.
    These models can power flows, Langflow Assistant, and any component that uses Langflow’s global model providers.

    For more information, see [OpenAI Compatible](./bundles-openai-compatible).

- Unified **Data Operations** component

    **Text Operations**, **JSON Operations**, and **Table Operations** are consolidated into a single [**Data Operations** component](./operations).
    Saved flows that use the separate components continue to work.
    For more information, see the [**Data Operations** component](./operations).

- IBM watsonx Orchestrate: Python 3.14 compatibility

    The `ibm-watsonx-orchestrate-core` and `ibm-watsonx-orchestrate-clients` packages are upgraded to version 2.12, which supports Python 3.14.
    IBM watsonx Orchestrate is no longer excluded from Python 3.14 installs.

    For more information, see [Deploy flows on watsonx Orchestrate](/deployment-wxo).

    The following optional integrations remain excluded from installations on Python 3.14:

    - [ALTK](/bundles-altk)
    - [CUGA](/bundles-cuga)
    - [LangWatch](/integrations-langwatch)
    - [Pinecone](/bundles-pinecone)
    - [OpenDsStar](/bundles-files-ingestion)
    - [litellm](/bundles-lite-llm)
    - [opik](/integrations-opik)
    - [toolguard](/policies)

- NextPlaid multi-vector bundle

    The **NextPlaid** bundle adds two new components for ColBERT-style multi-vector retrieval.

    The **NextPlaid** vector store, backed by a running [NextPlaid](https://github.com/meetdoshi90/next-plaid) server, stores each document as a matrix of token embeddings, and the **vLLM Multivector Embeddings** component generates the token-level multi-vector embeddings required by NextPlaid.

    For more information, see [NextPlaid bundle](./bundles-nextplaid).

- PaddleOCR bundle

    The **Paddle** bundle (`lfx-paddle`) adds a **PaddleOCR** component that calls the PaddleOCR AI Studio Job API for layout-aware document parsing into Markdown.

    For more information, see [Paddle bundle](./bundles-paddle).

- Oracle Extension bundle

    The **Oracle** bundle adds Oracle Database integration for vector search, document loading, and embeddings.

    For more information, see [Oracle bundle](./bundles-oracle).

- Valkey bundle

    The **Valkey** bundle adds a vector store and chat memory components for [Valkey](https://valkey.io/), an open-source Redis fork.

    For more information, see [Valkey bundle](./bundles-valkey).

- LFX is now engine-only

    `uv pip install lfx` now installs the LFX executor only, with no bundle components included.
    `uv pip install langflow` is unchanged.
    This only affects users who install `lfx` directly.

    If you install `lfx` directly and your flows use bundle components, install the required packages in the same environment.

    For more information, see [Extensions overview](./extensions-overview).

## 1.10.x

For 1.10.x release notes, see the [1.10.x documentation](https://docs.langflow.org/1.10.0/release-notes).

## 1.9.x

For 1.9.x release notes, see the [1.9.x documentation](https://docs.langflow.org/1.9.0/release-notes).

## 1.8.x

For 1.8.x release notes, see the [1.8.x documentation](https://docs.langflow.org/1.8.0/release-notes).

## Earlier releases

See the [Changelog](https://github.com/langflow-ai/langflow/releases).