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Basic Information

Langflow is an open source framework for building, visualizing and deploying AI-powered agents and automated workflows. It provides developers a visual authoring experience together with programmatic source code access so workflows can be customized in Python and extended. The project includes built-in API and MCP servers that expose flows as services and tools for integration into applications on any stack. Langflow supports major LLMs, vector databases and a growing library of AI tools, and includes an interactive playground for step-by-step testing and refinement. The repository contains installation and deployment guidance, observability integration options and notes on required Python and tooling versions, and it highlights an important security update requirement.

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Features
Langflow offers a visual builder interface to create and iterate workflows, source code access to customize components in Python, and an interactive playground to immediately test and refine flows. It provides multi-agent orchestration with conversation management and retrieval and can export flows as JSON or run them as a hosted API. The project can be deployed as an MCP server so flows become tools for MCP clients. Observability integrations such as LangSmith and LangFuse are supported. Langflow is compatible with major LLMs and vector databases and includes deployment guidance for Docker and cloud environments. The README documents quickstart commands and required dependencies including Python 3.10–3.13 and uv.
Use Cases
Langflow helps developers and teams accelerate prototyping and productionization of AI agents by combining a visual composition environment with code-level customization. Users can assemble model, retrieval and tool components in the visual builder, iterate and troubleshoot flows in the interactive playground, then expose working flows as APIs or MCP tools for integration into other applications. Built-in support for multiple LLMs, vector stores and observability tools reduces integration overhead, while export and deployment options simplify moving from prototype to deployed service. The repository includes documentation, contributing guidelines and deployment instructions to support customization, scalability and operational monitoring, and it calls out a required update to mitigate a disclosed vulnerability.

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