357ea19156
Robustness pass on the execution seam after using it to integrate an external
executor end-to-end. Four things this pins down:
1. Reusable executor contract suite
- tests/unit/execution/test_executor_contract.py
- ExecutorContract with 7 universal seam guarantees: kind shape, execute()
returns an AsyncIterator, stream ends with exactly one RunComplete,
instance is reusable across runs, concurrent runs are isolated, consumer
cancellation does not hang or leak, execute() signature is stable.
- TestInProcessExecutorContract subclasses and provides fixtures. Downstream
executors (stepflow, future remote/sandbox) get the same battery by
subclassing and overriding two fixtures.
2. Cancellation/cleanup correctness
- tests/unit/execution/test_cancellation.py
- InProcessExecutor wraps graph.async_start() in try/finally with explicit
aclose so consumer aclose cascades to the underlying graph generator.
Previously the inner generator was abandoned and only finalized on a GC
pass: a real leak for executors holding subprocesses or sockets.
- Coordinator.run and Coordinator.stream propagate aclose the same way so
the full chain (consumer -> coordinator -> executor -> graph) cleans up
deterministically.
3. Documented seam contracts
- Unit.runtime_options: free-form bag, "_"-prefixed keys reserved for
executor-internal flags, common in-process keys listed.
- StepResult.payload: untyped on purpose; each executor defines its own
event vocabulary; consumers normalize at consumption site.
- RunComplete.outputs: only the legacy in-process path populates it;
streaming consumers should collect from StepResult.payload.
- Executor ABC: lifecycle (shared instance, must be reusable, must tolerate
concurrent execute() calls and consumer aclose).
4. Coordinator-routed E2E suite
- tests/unit/execution/test_coordinator_e2e.py
- Real Graph through Coordinator -> registry -> InProcessExecutor chain.
- Asserts payload shape, RunComplete never leaks to stream() consumers,
dispatch routes to configured kind, registry returns same instance.
Full execution suite: 63 passing. flow_executor coordinator test: passing.
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 3.10–3.14 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.
🛡️ 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.