What is the NPC Context-Agent-Tool data layer?
It is a framework within `npcpy` that structures interactions between personas (NPCs), agents, and tools to ensure compliance with directives through software rather than relying solely on prompts.
@cagostino
Basic information
The python library for research and development in NLP, multimodal LLMs, Agents, ML, Knowledge Graphs, and more.
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npcpy is a Python library that provides key primitives for research and development with multimodal language models, agentic AI, and knowledge graphs. It supports both local providers (ollama, llama.cpp, onnx, LM Studio) and cloud providers, and enables building multi‑agent teams with a Context‑Agent‑Tool data layer that enforces compliance through software rather than prompts.
Install with pip install npcpy. Then import the library to create NPC personas, run direct LLM calls, build agents with built‑in or custom tools, orchestrate multi‑agent debates via NPCArray, or manage knowledge graphs with sleep/dream lifecycle functions. Example code is provided for each major feature.
It is a framework within `npcpy` that structures interactions between personas (NPCs), agents, and tools to ensure compliance with directives through software rather than relying solely on prompts.
Run `pip install npcpy` in your environment.
`npcpy` supports local providers such as `ollama`, `llama.cpp`, `onnx`, and `LM Studio`, as well as cloud providers (e.g., `ollama`’s cloud models).
Use the `NPCArray` class to create a team of `NPC` objects, then call `team.infer()` with a prompt, optionally followed by `chain()` for iterative refinement. The README includes a full example with role‑based personas and debate rounds.
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