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Basic Information

NPi is an open-source platform that provides tool-use APIs to enable AI agents to operate and interact with software tools and applications. The repository supplies a Python library and examples for defining programmatic tools that can be exposed to language models, letting models request actions in a structured function-calling format. It demonstrates how to create tools as Python classes with annotated functions, automatically generate tool schemas, and integrate with LLM chat completions so agents can select and invoke tools. The project includes a command-line workflow recommendation, documentation, example projects, and an online playground for trying NPi. The codebase is under active development, distributed under the Apache 2.0 license, and is intended for developers building agent tool integrations rather than end-user chat interfaces.

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App Details

Features
Package installable via pip with the npiai package and a one-minute quick-start example. A FunctionTool base class and a function decorator let developers define callable tools and parameters in Python. Automatic generation of tool schemas compatible with LLM function-calling formats is provided. Integration examples show using OpenAI chat completions with tools, tool_choice settings, and handling tool_calls to execute functions and return structured results. The README includes runnable examples such as a Fibonacci tool that prints schema and tool call results. Project resources include documentation, example directories, an online playground, a community Discord, and mention of a forthcoming NPi cloud service. The APIs are noted as evolving under active development.
Use Cases
NPi helps developers bridge language models and actionable software by making it straightforward to expose programmatic functions as tools that LLMs can discover and invoke. It reduces friction when building agent behaviors that require calling external functions, handling parameters, and returning structured outputs in standard function-calling format. The library and examples accelerate prototyping of tool-enabled agents, demonstrate integration patterns with chat completion APIs, and provide a schema-driven approach to validate and describe tool interfaces. The playground and documentation support experimentation, while examples show end-to-end flows from prompt to tool execution. The project is suited for teams building agent tooling infrastructure and developer-facing integrations while APIs continue to mature.

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