Eric Hare 6610091697 fix(bundles): republish lfx-* at 0.1.1 with corrected pin + relax lfx floor for RC builds (#13542)
* fix(bundles): bump lfx-* bundles to 0.1.1 to republish with corrected lfx pin

The published 0.1.0 artifacts on PyPI carry stale lfx pins from before the lfx 0.5.0->1.10.0 version realignment:

- lfx-arxiv, lfx-duckduckgo: lfx>=0.5.0,<0.6.0 (hard-broken; the <0.6.0 cap can never resolve lfx 1.10.0)
- lfx-docling, lfx-ibm: lfx>=0.5.0 (uncapped floor/cap mismatch)

The source pin was already corrected to lfx>=1.10.0,<2.0.0 in #13516, but the bundles were never re-published. PyPI versions are immutable, so shipping the fix requires a version bump. Bump all four to 0.1.1 so the Release Bundles workflow cuts fresh wheels carrying the correct pin. Root pyproject keeps its >=0.1.0 floors (satisfied by 0.1.1); only the bundle dist versions and their uv.lock stamps change.

* fix(release): relax bundle lfx floor for pre-release builds + idempotent bundle publish

The RC pre-release run builds lfx as 1.10.0rc0, but bundles floor lfx at >=1.10.0. Under PEP 440 a pre-release sorts below the final, so 1.10.0rc0 fails >=1.10.0 and the cross-platform install test cannot resolve the bundle wheels against the RC lfx wheel.

build-base/build-main/build-lfx already rewrite their inter-package deps to the pre-release version when pre_release=true; build-bundles was the only release artifact missing that step. Add it: when pre_release=true, rewrite each bundle's lfx floor to the exact pre-release version, keeping the wide <2.0.0 BUNDLE_API cap. Stable source stays at >=1.10.0 -- only RC wheels are relaxed, at build time, so no source churn or re-tag.

Also make publish-bundles tolerate 'already exists' duplicate wheels on rerun, matching release_bundles.yml.
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2026-04-23 17:49:53 -07:00
2024-06-04 09:26:13 -03:00

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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 3.103.14 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.

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Langflow is completely open source and you can deploy it to all major deployment clouds. To learn how to deploy Langflow, see our Langflow deployment guides.

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