Ram Gopal Srikar Katakam 355a4aa7fc feat: use uv sources for CPU-only PyTorch (#11833)
* feat: use uv sources for CPU-only PyTorch

Configure [tool.uv.sources] with pytorch-cpu index to avoid ~6GB CUDA
dependencies in Docker images. This replaces hardcoded wheel URLs with
a cleaner index-based approach.

- Add pytorch-cpu index with explicit = true
- Add torch/torchvision to [tool.uv.sources]
- Add explicit torch/torchvision deps to trigger source override
- Regenerate lockfile without nvidia/cuda/triton packages

* fix: address CodeRabbit review issues for PR #11833

- Add Windows AMD64 platform support to required-environments
- Add inline comments explaining architecture naming differences across OSes
- Document why loose version ranges are used (avoid conflicts with transitive deps like altk)
- Exclude Windows ARM64 due to missing wheels for dependencies like faiss-cpu
- Update comments to reflect multi-platform support (Linux, macOS, Windows)

Resolves CodeRabbit issues:
1. Windows platform missing from required-environments
2. Clarifies rationale for loose torch/torchvision version ranges

The loose version ranges (>=2.0.0) are intentional to avoid conflicts with
transitive dependencies (e.g., agent-lifecycle-toolkit requires torch==2.2.2).
The uv sources configuration ensures CPU-only PyTorch wheels are used for all
platforms (verified in lockfile: torch 2.10.0+cpu for Linux/Windows).

* fixed

* fix: reduce Node.js heap size to 4GB in Docker builds to prevent OOM

The Vite frontend build was configured with --max-old-space-size=12288
(12GB), which exceeds available RAM on ARM64 CI runners, causing the
build process to be OOM-killed during the transform phase.

Reduced to 4GB (4096MB) which is sufficient for the Vite build and
prevents OOM kills in memory-constrained Docker BuildKit environments.

* fix: avoid redundant recursive chown on /app in backend Dockerfile

The recursive chown -R on /app was re-owning the entire .venv (~2.6GB,
40k+ files) which was already correctly owned via COPY --chown=1000:0.
This was causing the build to be killed on ARM64 runners.

Changed to non-recursive chown on /app since only the directory itself
needs ownership set. /app/data still gets recursive chown (it's empty).

* fix: add Docker cleanup between image builds to prevent disk full

The 40GB ARM64 runner runs out of disk when building 3 Docker images
sequentially. Each image (main ~8GB layers, backend ~5GB, frontend)
accumulates build cache and layers that exhaust the disk.

Added cleanup steps between builds that:
- Remove the tested image (no longer needed)
- Prune all unused Docker data and buildx cache
- Log disk usage before/after for debugging

---------

Co-authored-by: vijay kumar katuri <vijay.katuri@ibm.com>
2026-02-24 12:59:01 -05:00
2026-02-24 14:20:34 +00:00
2025-03-20 00:05:55 +00:00
2024-06-04 09:26:13 -03:00
2025-11-24 02:02:01 +00:00
2025-12-19 16:48:08 +00:00
2026-02-24 14:20:34 +00:00

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Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.

Highlight features

  • Visual builder interface to quickly get started and iterate.
  • Source code access lets you customize any component using Python.
  • Interactive playground to immediately test and refine your flows with step-by-step control.
  • Multi-agent orchestration with conversation management and retrieval.
  • Deploy as an API or export as JSON for Python apps.
  • Deploy as an MCP server and turn your flows into tools for MCP clients.
  • Observability with LangSmith, LangFuse and other integrations.
  • Enterprise-ready security and scalability.

🖥️ Langflow Desktop

Langflow Desktop is the easiest way to get started with Langflow. All dependencies are included, so you don't need to manage Python environments or install packages manually. Available for Windows and macOS.

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Quickstart

Requires Python 3.103.13 and uv (recommended package manager).

Install

From a fresh directory, run:

uv pip install langflow -U

The latest Langflow package is installed. For more information, see Install and run the Langflow OSS Python package.

Run

To start Langflow, run:

uv run langflow run

Langflow starts at http://127.0.0.1:7860.

That's it! You're ready to build with Langflow! 🎉

📦 Other install options

Run from source

If you've cloned this repository and want to contribute, run this command from the repository root:

make run_cli

For more information, see DEVELOPMENT.md.

Docker

Start a Langflow container with default settings:

docker run -p 7860:7860 langflowai/langflow:latest

Langflow is available at http://localhost:7860/. For configuration options, see the Docker deployment guide.

Caution

  • Users must update to Langflow >= 1.7.1 to protect against CVE-2025-68477 and CVE-2025-68478.
  • Langflow version 1.7.0 has a critical bug where persisted state (flows, projects, and global variables) cannot be found when upgrading. Version 1.7.0 was yanked and replaced with version 1.7.1, which includes a fix for this bug. DO NOT upgrade to version 1.7.0. Instead, upgrade directly to version 1.7.1.
  • Langflow versions 1.6.0 through 1.6.3 have a critical bug where .env files are not read, potentially causing security vulnerabilities. DO NOT upgrade to these versions if you use .env files for configuration. Instead, upgrade to 1.6.4, which includes a fix for this bug.
  • Windows users of Langflow Desktop should not use the in-app update feature to upgrade to Langflow version 1.6.0. For upgrade instructions, see Windows Desktop update issue.
  • Users must update to Langflow >= 1.3 to protect against CVE-2025-3248
  • Users must update to Langflow >= 1.5.1 to protect against CVE-2025-57760

For security information, see our Security Policy and Security Advisories.

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