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* feat: Introduce service registration decorator and enhance ServiceManager for pluggable service discovery - Added `register_service` decorator to allow services to self-register with the ServiceManager. - Enhanced `ServiceManager` to support multiple service discovery mechanisms, including decorator-based registration, config files, and entry points. - Implemented methods for direct service class registration and plugin discovery from various sources, improving flexibility and extensibility of service management. * feat: Implement VariableService for managing environment variables - Introduced VariableService class to handle environment variables with in-memory caching. - Added methods for getting, setting, deleting, and listing variables. - Included logging for service initialization and variable operations. - Created an __init__.py file to expose VariableService in the package namespace. * feat: Enhance LocalStorageService with Service integration and async teardown - Updated LocalStorageService to inherit from both StorageService and Service for improved functionality. - Added a name attribute for service identification. - Implemented an async teardown method for future extensibility, even though no cleanup is currently needed. - Refactored the constructor to ensure proper initialization of both parent classes. * feat: Implement telemetry service with abstract base class and minimal logging functionality - Added `BaseTelemetryService` as an abstract base class defining the interface for telemetry services. - Introduced `TelemetryService`, a lightweight implementation that logs telemetry events without sending data. - Created `__init__.py` to expose the telemetry service in the package namespace. - Ensured robust async methods for logging various telemetry events and handling exceptions. * feat: Introduce BaseTracingService and implement minimal TracingService - Added `BaseTracingService` as an abstract base class defining the interface for tracing services. - Implemented `TracingService`, a lightweight version that logs trace events without external integrations. - Included async methods for starting and ending traces, tracing components, and managing logs and outputs. - Enhanced documentation for clarity on method usage and parameters. * feat: Add unit tests for service registration decorators - Introduced a new test suite for validating the functionality of the @register_service decorator. - Implemented tests for various service types including LocalStorageService, TelemetryService, and TracingService. - Verified behavior for service registration with and without overrides, ensuring correct service management. - Included tests for custom service implementations and preservation of class functionality. - Enhanced overall test coverage for the service registration mechanism. * feat: Add comprehensive unit and integration tests for ServiceManager - Introduced a suite of unit tests covering edge cases for service registration, lifecycle management, and dependency resolution. - Implemented integration tests to validate service loading from configuration files and environment variables. - Enhanced test coverage for various service types including LocalStorageService, TelemetryService, and VariableService. - Verified behavior for service registration with and without overrides, ensuring correct service management. - Ensured robust handling of error conditions and edge cases in service creation and configuration parsing. * feat: Add unit and integration tests for minimal service implementations - Introduced comprehensive unit tests for LocalStorageService, TelemetryService, TracingService, and VariableService. - Implemented integration tests to validate the interaction between minimal services. - Ensured robust coverage for file operations, service readiness, and exception handling. - Enhanced documentation within tests for clarity on functionality and expected behavior. * docs: Add detailed documentation for pluggable services architecture and usage * feat: Add example configuration file for Langflow services * docs: Update PLUGGABLE_SERVICES.md to enhance architecture benefits section - Revised the documentation to highlight the advantages of the pluggable service system. - Replaced the migration guide with a detailed overview of features such as automatic discovery, lazy instantiation, dependency injection, and lifecycle management. - Clarified examples of service registration and improved overall documentation for better understanding. * [autofix.ci] apply automated fixes * [autofix.ci] apply automated fixes (attempt 2/3) * [autofix.ci] apply automated fixes (attempt 3/3) * test(services): improve variable service teardown test with public API assertions * docs(pluggable-service-layer): add docstrings for service manager and implementations * Update component index * [autofix.ci] apply automated fixes --------- 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.
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
.envfiles are not read, potentially causing security vulnerabilities. DO NOT upgrade to these versions if you use.envfiles 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.
🚀 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.