Langflow
Low-code builder for LLM apps and agents with a visual workflow editor.
Open the official app on www.langflow.org
This tool is hosted by its maintainers. Click below to open www.langflow.org in a new tab — it's their official demo.
Browse text & tools tools →What's next with Langflow?
Choose how you want to get started.
Use it free
Open the official tool or demo — no account needed.
Self-host it
Run the open-source version on your own infrastructure.
What is Langflow?
Langflow is an open-source, Python-based framework designed for building and deploying AI applications, particularly focused on creating agents and workflows using the Model Context Protocol (MCP). It enables developers to construct AI systems without being tied to specific large language models (LLMs) or vector databases, offering flexibility in integrating various AI tools and services. The tool's primary purpose is to streamline the development process by providing a visual editor that simplifies the prototyping of application workflows, allowing users to turn their ideas into functional solutions quickly. This framework is utilized by AI development teams and individuals looking to build and deploy AI-powered applications efficiently. Langflow addresses the challenge of complex AI system development by reducing the need for extensive coding, making it accessible to a broader range of developers while maintaining the capability for customization through Python scripting. By abstracting much of the complexity involved in AI application development, Langflow allows users to focus on the core logic of their applications. Its visual interface enables rapid iteration and testing, which is crucial for developing AI systems. The framework supports a wide array of AI functionalities, including agents and MCP servers, and integrates with major LLMs and vector databases. This makes it a versatile tool for both prototyping and deploying AI applications in various environments, from local development to enterprise-grade cloud platforms. The ease of use and flexibility provided by Langflow make it an attractive option for developers seeking to build AI applications without the overhead of traditional development workflows.
How it works
Langflow is built using Python and relies on libraries such as FastAPI for API handling and SQLAlchemy for database interactions. It supports deployment on cloud platforms that provide enterprise-grade infrastructure, with compatibility across modern web browsers. Data flows through a series of interconnected components managed via a visual interface, which abstracts the underlying processing logic. Privacy considerations include the handling of user data through secure APIs and the potential for data exposure if sensitive information is not properly encrypted or anonymized.
How to use it
- 1Install Langflow by following the installation instructions on the official documentation. 2. Use the visual editor to create a new workflow by dragging and dropping components such as input models, vector databases, and AI tools. 3. Configure the components by setting parameters like API keys, temperature settings, and response lengths. 4. Run the workflow to test the AI application and make adjustments as needed. Practical tips include utilizing the pre-built components to speed up development, customizing workflows with Python scripts for advanced functionality, and leveraging the cloud deployment options for scaling applications. It is also advisable to test workflows thoroughly in a development environment before deploying to production to ensure reliability and performance.
What it can do
- low code AI builder
Use cases
Assumptions and limitations
Assumptions
- source: https://github.com/langflow-ai/langflow
- license: MIT — free to use
- privacy: Self-hosted — you control your data
Limitations
- Self-hosted — requires setup, maintenance, and your own infrastructure.
- Relies on an external source (github.com); availability depends on that service.
- Focused on the text tools category: Low-code builder for LLM apps and agents with a visual workflow editor..
Understanding the result
Low-code builder for LLM apps and agents with a visual workflow editor.
Tool details
- Clearly flagged when a network request is needed.
- No account, no sign-up, and no tracking of your content.
- Powered by (MIT).
- Built with
- (langflow-ai/langflow)
- License
- MIT
- Runs locally
- No — requires a network request
- Verification
- Not yet verified
- Input
- Query
- Output
- Text
Built with langflow-ai/langflow. OpenToolVault provides the discovery and browser interface while crediting the original project maintainers.
- Built with
- License
- MIT
Open-source project
OpenToolVault is an independent directory. We are not affiliated with or endorsed by this project.
References
- / — GitHub Repository
Upstream project · GitHub
- MIT License
Upstream project
Frequently asked
How does Langflow support the Model Context Protocol (MCP)?
Langflow supports the Model Context Protocol (MCP) by providing a framework for building and deploying MCP servers. This allows developers to create applications that can interact with users and other systems in a structured manner, facilitating the exchange of context and information. The framework abstracts the complexity of MCP implementation, enabling developers to focus on the core functionality of their applications while ensuring compatibility with the protocol's requirements.
Can I use Langflow with any LLM?
Langflow is designed to work with major LLMs such as llama-3.2 and supports integration with various other LLMs through its flexible architecture. Developers can choose from a range of LLMs and configure them within the framework, allowing for customization based on specific project requirements. However, the extent of compatibility may depend on the availability of APIs and the specific features of the chosen LLM.
How do I deploy a Langflow application to production?
To deploy a Langflow application to production, you can use the enterprise-grade cloud platform provided by the framework. This involves configuring the deployment settings, ensuring that all components are properly integrated, and testing the application in a staging environment before going live. The cloud platform offers scalability and security features, making it suitable for deploying AI applications in production environments.
How does Langflow compare to other AI development tools?
Langflow differs from other AI development tools by focusing on the Model Context Protocol (MCP) and providing a visual interface for workflow creation. Unlike some tools that require extensive coding, Langflow abstracts much of the complexity, making it accessible to a broader range of developers. It also integrates with a wide range of LLMs and vector databases, offering flexibility in application development. However, it may not offer the same level of customization or advanced features as more specialized tools.
What should I do if my Langflow workflow is not working as expected?
If your Langflow workflow is not working as expected, start by checking the configuration settings for any errors, such as incorrect API keys or misconfigured components. Review the logs for any error messages that may indicate the source of the problem. If the issue persists, consider simplifying the workflow to isolate the problem or reaching out to the Langflow community for support. Ensuring that all dependencies are properly installed and updated can also help resolve unexpected issues.