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* feat: upgrade Docker images to Python 3.14 (experimental) ## Changes - Update pyproject.toml: requires-python from <3.14 to <3.15 - Update all 5 Dockerfiles to use Python 3.14: - Builder stage: python3.12-trixie-slim → python3.14-trixie-slim - Runtime stage: python:3.12-slim-trixie → python:3.14-slim-trixie ## Files Updated - src/backend/base/pyproject.toml - docker/build_and_push_base.Dockerfile - docker/build_and_push.Dockerfile - docker/build_and_push_backend.Dockerfile - docker/build_and_push_with_extras.Dockerfile - docker/build_and_push_ep.Dockerfile ## Rationale Python 3.14.5 was released on May 10, 2026. Upgrading to the latest Python version should help reduce CVE vulnerabilities in Docker images. ## Testing Strategy This is an experimental change to test Python 3.14 compatibility: - Docker images will use Python 3.14 - CI/CD workflows still test on Python 3.10-3.13 - Nightly build will validate if dependencies work with 3.14 - Can be reverted quickly if issues are found ## Next Steps - Monitor nightly build for failures - If successful, update CI/CD workflows to add Python 3.14 to test matrix - If failures occur, revert and investigate compatibility issues * chore: support Python 3.14 Bump requires-python upper bound to <3.15 across langflow, langflow-base, lfx, and langflow-sdk, and add 3.14 to CI test matrices so PRs are gated on 3.14 compatibility. Conditional pins for transitive deps without 3.14 wheels at the existing caps: - onnxruntime: >=1.26 on 3.14 (existing <1.24 cap retained for 3.10) - faiss-cpu: >=1.13.2 on 3.14 (existing ==1.9.0.post1 retained for <3.14) * chore: marker-gate IBM watsonx packages for Python 3.14 ibm-watsonx-ai 1.3.x cannot import on Python 3.14: its StrEnum subclasses override __init__ in a way that conflicts with 3.14's reworked enum __set_name__ path (TypeError on KnowledgeBaseFieldRole class creation). Cap ibm-watsonx-ai, langchain-ibm, and the ibm-watsonx-orchestrate-* extras at python_version<'3.14' until upstream adapts. Watsonx component imports are already lazy, so this surfaces only at component-access time on 3.14, where the existing ImportError handler in lfx.components.ibm.__init__ degrades it gracefully. test_model_utils.py imports ChatWatsonx at module top to verify get_model_name resolves model_id; guard that import so the test module skips on 3.14 instead of breaking collection. * [autofix.ci] apply automated fixes * chore: upgrade remaining Docker images to Python 3.14 build_and_push*.Dockerfile already moved to 3.14 in the merge from feat/upgrade-python-3.14-docker-images. Apply the same bump to the dev, devcontainer, and lfx Docker images so all in-repo Dockerfiles share one Python version. * [autofix.ci] apply automated fixes * chore: gate 3.14-broken extras to python_version<'3.14' cuga (and its transitive fastembed -> py-rust-stemmers source build), altk/agent-lifecycle-toolkit (re-pulls ibm-watsonx-ai which was already gated), langchain-pinecone, langwatch, ragstack-ai-knowledge-store, and OpenDsStar all pin themselves below 3.14 upstream. Without a marker guard, the workspace lock still tries to install them and a Docker `uv sync` then attempts source builds that require Rust (CI Docker test failure with py-rust-stemmers 0.1.5). Add python_version<'3.14' (or <'3.13' for ragstack) to each pin so those packages are simply omitted from the 3.14 install set until upstream catches up. uv pip check is now clean on 3.14.2. * test: skip test_altk_agent on Python 3.14 altk (agent-lifecycle-toolkit) is gated to python_version<'3.14' upstream and now via the langflow-base [altk] extra marker. The test imports altk at module top to exercise ALTKAgentComponent; guard the import so collection skips on 3.14 instead of failing. * test: skip remaining altk tests on Python 3.14 Three more test modules import lfx.base.agents.altk_* at module top, which transitively imports altk. Add the same module-level skip guard as test_altk_agent.py: - test_altk_agent_logic.py - test_altk_agent_tool_conversion.py - test_conversation_context_ordering.py * fix(calculator): replace removed ast.Num with ast.Constant for Python 3.14 ast.Num was deprecated in Python 3.8 in favor of ast.Constant and removed entirely in 3.14. The calculator tool and its core walker crashed on 3.14 with 'module ast has no attribute Num'. Switch the isinstance checks to ast.Constant + isinstance(node.value, (int, float)), and drop the now-dead ast.Num backwards-compat branch in calculator_core.py (the ast.Constant branch already handles every input it would have matched). Update the parser unit-test fixtures to construct ast.Constant nodes. * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * test: skip LangWatch HTTP instrumentation tests on Python 3.14 langwatch is gated to python_version<'3.14' upstream. The TestLangWatchHttpInstrumentation class patches langwatch.setup and langwatch.trace inside a fixture, which requires langwatch to be importable; on 3.14 the test errors with ModuleNotFoundError. Guard the class with a skipif on langwatch availability. * test: allow IBM and altk components to be missing on Python 3.14 test_all_modules_importable enforces that every component in __all__ imports cleanly. On 3.14 the ibm-watsonx-ai, langchain-ibm, and altk packages are gated upstream and intentionally not installed, so the WatsonxAI, WatsonxEmbeddings, and ALTKAgent components fail to import as designed. - test_all_components_in_categories_importable: accept a known-gated deny-list on 3.14 instead of failing. - test_all_lfx_component_modules_directly_importable: extend the existing 'missing optional dependency' allowlist with altk, langchain_ibm, and ibm_watsonx_ai. * fix(lfx): pass ensure_exists to user_cache_dir for Python 3.14 Python 3.14 tightened PurePath.__init__ to reject unknown keyword arguments. The KeyedWorkerLockManager constructor was passing ensure_exists=True to Path() instead of user_cache_dir(), which silently worked on 3.10-3.13 but raises TypeError on 3.14 and also meant the cache directory was never actually being created. Move the kwarg to user_cache_dir() where it belongs and update the two unit tests that asserted the buggy call shape. * [autofix.ci] apply automated fixes * test: accept either ZIP or JSON error path on Python 3.14 Python 3.14's zipfile.is_zipfile() now validates the central-directory signature in addition to EOCD, so the garbage+EOCD payload no longer passes the is_zipfile() dispatch in the /flows/upload/ route. The endpoint still returns 400 with a descriptive detail (now from the JSON branch); only the message wording differs, which the test was asserting verbatim. * fix(lfx): normalize cors_origins ['*'] back to '*' on Python 3.14 Pydantic-settings on Python 3.14 parses the env var '*' into ['*'] before the cors_origins field validator runs (the list[str] | str union resolves differently than on 3.10-3.13). Collapse that back to the bare-string wildcard so downstream consumers — including warn_about_future_cors_changes and the test suite — see the same shape on every supported Python version. --------- Co-authored-by: vijay kumar katuri <vijay.katuri@ibm.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
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.
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.
🛡️ Security
For security information, see our Security Policy.
🚀 Deployment
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.
⭐ Stay up-to-date
Star Langflow on GitHub to be instantly notified of new releases.
👋 Contribute
We welcome contributions from developers of all levels. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.