15637403ad* standardize component name style and links pt 1 * component name standardization pt 2 * standardization pt 3 * standardize component names pt 4 * peer review * fix lnk * update edit tool actions section * standardize Agent, agent, agentic * more style auditing * missing imports
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---
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title: What is Langflow?
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slug: /about-langflow
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---
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Langflow is an open-source, Python-based, customizable framework for building AI applications.
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It supports important AI functionality like agents and the Model Context Protocol (MCP), and it doesn't require you to use specific large language models (LLMs) or vector stores.
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The visual editor simplifies prototyping of application workflows, enabling developers to quickly turn their ideas into powerful, real-world solutions.
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:::tip Try it
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Build and run your first flow in minutes: [Install Langflow](/get-started-installation), and then try the [Quickstart](/get-started-quickstart).
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:::
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## Application development and prototyping
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Langflow can help you develop a wide variety of AI applications, such as chatbots, document analysis systems, content generators, and agentic applications.
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Langflow includes several pre-built templates that are ready to use or customize to your needs.
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### Create flows in minutes
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The primary purpose of Langflow is to create and serve flows, which are functional representations of application workflows.
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To [build a flow](/concepts-flows), you connect and configure component nodes. Each component is a single step in the workflow.
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With Langflow's [visual editor](/concepts-overview), you can drag and drop components to quickly build and test a functional AI application workflow.
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For example, you could build a chatbot flow for an e-commerce store that uses an LLM and a product data store to allow customers to ask questions about the store's products.
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### Test flows in real-time
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You can use the [**Playground**](/concepts-playground) to test flows without having to build your entire application stack.
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You can interact with your flows and get real-time feedback about flow logic and response generation.
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You can also run individual components to test dependencies in isolation.
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### Run and serve flows
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You can use your flows as prototypes for more formal application development, or you can use the Langflow API to embed your flows into your application code.
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For more extensive development, you can build Langflow as a dependency or deploy a Langflow server to serve flows over the public internet.
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For more information, see the following:
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* [Trigger flows with the Langflow API](/concepts-publish)
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* [Containerize a Langflow application](/develop-application)
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## Endless modifications and integrations
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Langflow provides [components](/concepts-components) that support many services, tools, and functionality that are required for AI applications.
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Some components are generalized, such as inputs, outputs, and data stores.
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Others are specialized, such as agents, language models, and embedding providers.
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All components offer parameters that you can set to fixed or variable values. You can also use tweaks to temporarily override flow settings at runtime.
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### Agent and MCP support
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In addition to building agent flows with Langflow, you can leverage Langflow's built-in agent and MCP features:
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* [Use Langflow Agents](/agents)
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* [Use components and flows as agent tools](/agents-tools)
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* [Use Langflow as an MCP server](/mcp-server)
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* [Use Langflow as an MCP client](/mcp-client)
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### Extensibility
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In addition to the core components, Langflow supports custom components.
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You can use custom components developed by others, and you can develop your own custom components for personal use or to share with other Langflow users.
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For more information, see the following:
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* [Contribute to Langflow](/contributing-how-to-contribute)
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* [Create custom Python components](/components-custom-components)
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## Next steps
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* [Install Langflow](/get-started-installation)
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* [Quickstart](/get-started-quickstart) |