It is a Plan-Validate-Solve (PVS) Agent designed for accurate, reliable, and reproducible workflow automation. The Lemon Agent enables users to interact with tools like Airtable, Hubspot, Discord, Notion, and GitHub by allowing large language models (LLMs) to perform read and write operations reliably. It is a standalone supervised agent based on the “Plan-and-Solve Prompting” framework from the ACL 2023 paper, which improves zero-shot chain-of-thought reasoning in LLMs. The agent separates tasks and incorporates human-in-the-loop interactions, offering unlimited configuration options and UI flexibility. It is model and framework agnostic, with future updates planned to enhance this capability.
The Lemon Agent operates through a user-initiated session where the user interacts with a planner agent via a UI to define task solutions. The planner agent then communicates with a solver agent, which executes the workflow step-by-step using available tools. Users can define and configure workflows in a JSON file, with options like pre-execution conditions (e.g., userPermissionRequired) to ensure controlled execution. The agent supports over 120 tools across various services, enabling powerful workflow automations.
To start the Lemon Agent, users can run specific commands depending on their operating system. For Unix-like systems, commands like `pnpm exec turbo dev` or `pnpm exec turbo build` are used, followed by navigating to the CLI client and running `pnpm run dev`. For Windows, similar steps are followed. Once running, the agent waits for tasks to solve. The agent also provides analytics visualization to help users identify weak spots in decision-making and improve workflow determinism.
The repository is currently not under active maintenance, and users should be aware that things might break unexpectedly. Contributions are welcome, and users can find more information in the CONTRIBUTING.md file or reach out via Discord for further assistance. The project is open-source and maintained by a team of contributors, with gratitude extended to those who help improve the codebase.
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