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NPi is an open-source platform and SDK that provides tool-use APIs to enable AI agents to take actions and interact with external software and applications. It presents a Natural-language Programming Interface that lets developers expose functions as callable tools for language models. The README highlights a quick start showing how to define a FunctionTool, annotate tool methods with a decorator, and integrate with an OpenAI client to pass tool schemas to the model and execute tool calls. The project is installable via pip as npiai, offers examples and documentation, a web playground, and a community Discord. The project is under active development, APIs may change, and the maintainers recommend using the command line tool or examples to try it out.

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https://github.com/sheet0/npi

Github Repository

https://github.com/sheet0/npi/blob/main/README.md

Features

NPi exposes a FunctionTool base class and a @function decorator to register Python callables as tools with generated JSON schemas compatible with LLM function-calling formats. It integrates with OpenAI chat completions by supplying a tools package and supports tool_choice and async execution of tool calls. The repo includes a one-minute quick start, example code showing schema and function-call results, installation via pip, links to documentation and examples, an online playground, and a community Discord. The package returns structured tool_call entries and tool results in the model response format so developers can implement programmatic handlers for model-invoked actions.

Use Cases

NPi streamlines building agents that can perform real actions by turning ordinary Python functions into LLM-callable tools and handling the plumbing between the model and executable code. Developers can rapidly prototype action-capable agents, generate function schemas automatically for OpenAI function-calling, receive tool_call objects from model responses, and execute those calls asynchronously to return results. The quick start demonstrates a Fibonacci tool, showing how the model requests a tool and the tool returns the computed value. Documentation, examples, and a playground help users learn patterns for agent tool use, while an active community and planned cloud offerings signal ongoing ecosystem support.

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