Adam-AghiliandEric Hare
26b19cba5e
* chore: security patch security patch * chore: upgrade package-lock.json * chore: smolagents and transformer update * chore: redis upgrade * chore: litellm upgrade * fix: Pin click to avoid lower versions ## Root cause **litellm 1.83.5+** introduced an exact pin `click==8.1.8` in its `requires_dist` (upstream bug [BerriAI/litellm#26154](https://github.com/BerriAI/litellm/issues/26154) — "Pinning exact dependency versions breaks downstream consumers"). When this branch bumped litellm to 1.83.11, uv was forced to downgrade click from 8.3.2 → 8.1.8. Click 8.2+ captures stderr separately by default; 8.1 merges it — hence `ValueError: stderr not separately captured`. ## Fix - [pyproject.toml:155](pyproject.toml:155) — added `"click>=8.3.0"` to `[tool.uv] override-dependencies` with comment linking the upstream issue. - [src/backend/base/pyproject.toml:112](src/backend/base/pyproject.toml:112) — same override so `langflow-base` Docker builds pick it up too. - Regenerated `uv.lock` (click: 8.1.8 → 8.3.3). - Patched the `click` entry in `src/backend/base/uv.lock` to 8.3.3 (that lockfile is used by `docker/build_and_push_base.Dockerfile` via `uv sync --frozen`; uv treats backend/base as a workspace member so I updated its click block directly — no other references to 8.1.8 remain). - Reverted [src/lfx/tests/unit/cli/test_run_real_flows.py](src/lfx/tests/unit/cli/test_run_real_flows.py) back to its original `result.stderr` usage — now passes cleanly with click 8.3.3. All 19 tests in the file pass. * Update build_and_push_backend.Dockerfile The `uv pip install` command doesn't read `[tool.uv] override-dependencies` from the target packages' pyproject.toml — those overrides only apply during `uv lock`/`uv sync`. The fix is to pass the overrides explicitly via `--overrides`. Now let me verify it works by running the same install locally. Resolution succeeded — click==8.3.3, aiohttp==3.13.5, litellm==1.83.11 all coexist. Let me also test without `--overrides` to confirm I reproduce the original failure. Reproduced the Docker failure locally. Now test with the overrides file: Fix verified — resolution succeeds with the overrides file, failing without. Let me clean up and show the final Dockerfile change. ## Summary **Root cause:** The Dockerfile uses `uv pip install ./src/...` which **does not read** `[tool.uv] override-dependencies` from the target packages' `pyproject.toml` — those overrides only apply during `uv lock`/`uv sync`. On Linux, `cuga>=0.2.20` requires `aiohttp>=3.13.5`, but `litellm==1.83.11` exact-pins `aiohttp==3.13.3` (same upstream bug as the click pin, [BerriAI/litellm#26154](https://github.com/BerriAI/litellm/issues/26154)). Without an active override, resolution fails. **Fix:** Write a `/tmp/uv-overrides.txt` file mirroring the workspace's `override-dependencies` (litellm, python-dotenv, openai, aiohttp, click) and pass `--overrides /tmp/uv-overrides.txt` to `uv pip install`. Verified locally by reproducing the exact failure in an isolated tmp directory (to escape the workspace's pyproject.toml auto-apply), then confirming the overrides file resolves it: - aiohttp==3.13.5 ✓ - click==8.3.3 ✓ - litellm==1.83.11 ✓ - cuga==0.2.22 ✓ * Templates version update * Update .secrets.baseline * chore: update overrides * chore: bump version * chore: bump main version * chore: bump SDK version to 0.1.1 * chore: run uv lock and uv sync after SDK version bump * chore: read the overrides from a single source security patch * chore: add pip check toggle add pip check toggle * chore: litellm base uv.lock update * fix(chore): Pin litellm back to last working release * fix(chore): Pin to 1.83 and higher litellm * Update build_and_push_backend.Dockerfile * chore: update base uv.lock * fix: lazyload toolguard since its an optional extra * Update .secrets.baseline * Update component_index.json * Rebuild component index * Update component_index.json --------- Co-authored-by: Eric Hare <[email protected]> Co-authored-by: vijay kumar katuri <[email protected]> (cherry picked from commit 60a8f76c3fde17124057626c671ea8162872837a)
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.
⚡️ Quickstart
Install locally (recommended)
Requires Python 3.10–3.13 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.
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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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