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Langflow release notes

docs/versioned_docs/version-1.11.0/Support/release-notes.mdx

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This page summarizes significant changes to Langflow in each release. For all changes, see the Changelog.

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 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 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.

  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.
    • 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.
    • 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 to the new installation.

  3. Import your flows to test them in the new version, upgrading components 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.

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.

  • 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.

  • 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.

  • Workflow API request schema (Beta)

    The v2 Workflow API (Beta) 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).

  • 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).

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.

  • 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 and the A2A Agent component.

  • AG-UI compatible streaming for the Workflow API

    The v2 Workflow API now supports streaming with the AG-UI (Agent–User Interaction) protocol streaming format.

    For more information, see Workflow API (Beta).

  • 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.

  • Unified Data Operations component

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

  • 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.

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

  • 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 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.

  • 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.

  • Oracle Extension bundle

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

    For more information, see Oracle bundle.

  • Valkey bundle

    The Valkey bundle adds a vector store and chat memory components for Valkey, an open-source Redis fork.

    For more information, see Valkey bundle.

  • 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.

1.10.x

For 1.10.x release notes, see the 1.10.x documentation.

1.9.x

For 1.9.x release notes, see the 1.9.x documentation.

1.8.x

For 1.8.x release notes, see the 1.8.x documentation.

Earlier releases

See the Changelog.