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Basic Information

Lemon AI is an open-source, full-stack agentic AI framework intended to run advanced agent workflows entirely on local hardware. The repository provides the core code, deployment instructions and documentation to run a local alternative to hosted agent platforms, with an integrated Code Interpreter virtual machine that sandboxes code writing and execution. It supports agent capabilities such as planning, action, reflection and memory and is designed to work with local large language models via Ollama, referencing models like DeepSeek, Qwen, Llama and Gemma. The project targets researchers and organizations that need private, fully local agentic systems for tasks such as deep search, web browsing, coding and data analysis, and it includes Docker-based quick deployment options plus desktop client builds for macOS and Windows.

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Features
The README highlights a set of concrete features: a VM sandboxed code interpreter for safe code execution and editing, integration with local LLMs through Ollama, multi-tool support including browser operations and web search, and facilities for code generation and data analysis. The framework includes an experience repository for self-learning and enterprise customization, supports optional cloud model APIs for enhanced results, and offers multiple deployment modes including open-source code, container images and a client application. The project emphasizes rapid one-click deployment, documented Docker quick-start commands, cross-platform support for macOS, Linux and Windows with WSL, and resource guidance such as a minimum recommended 4GB of RAM.
Use Cases
Lemon AI is useful where privacy, local control and safe code execution are priorities. Running agents and LLMs entirely on local hardware avoids cloud data exposure and enables zero cloud dependency. The VM sandbox reduces the risk of uncontrolled filesystem or OS changes when executing or editing code. The framework bundles agentic functions for research, automated web browsing, code generation and data analysis, making it suitable for teams that need reproducible, extensible agent workflows. It also offers cost advantages according to the README, claims reduced operating costs compared to other agent products, and provides documented deployment paths to get systems running quickly on personal machines or servers. The project is positioned for contributors and enterprise customization.

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