name: npcpy-research-guide description: "All-in-one Python library for NLP, agents, and knowledge graphs" metadata: openclaw: emoji: "🎭" category: "domains" subcategory: "ai-ml" keywords: ["npcpy", "NLP", "agents", "knowledge graph", "all-in-one", "Python library"] source: "https://github.com/NPC-Worldwide/npcpy"

npcpy Research Guide

Overview

npcpy is an all-in-one Python library that combines NLP, agent orchestration, and knowledge graph capabilities in a single package. It provides tools for text processing, entity extraction, agent creation, graph-based reasoning, and research automation. Designed as a Swiss Army knife for AI researchers who need quick access to diverse NLP and agent capabilities without juggling many dependencies.

Installation

pip install npcpy

Core Modules

NLP Processing

from npcpy import NLP

nlp = NLP()

# Text processing pipeline
doc = nlp.process(
    "Transformers have revolutionized NLP since Vaswani et al. "
    "introduced the attention mechanism in 2017."
)

# Named entities
for entity in doc.entities:
    print(f"[{entity.type}] {entity.text}")
# [METHOD] Transformers
# [PERSON] Vaswani
# [CONCEPT] attention mechanism
# [DATE] 2017

# Key phrases
print(doc.key_phrases)
# ["attention mechanism", "Transformers", "NLP"]

# Sentiment / stance
print(doc.sentiment)  # positive

Agent Creation

from npcpy import Agent, Tool

# Create a research agent
agent = Agent(
    name="research_assistant",
    llm_provider="anthropic",
    tools=[
        Tool("web_search", description="Search the web"),
        Tool("paper_search", description="Search academic papers"),
        Tool("calculator", description="Math calculations"),
    ],
)

# Run a task
result = agent.run(
    "Find the top 5 most cited papers on few-shot learning "
    "from 2023 and summarize their approaches."
)
print(result.output)

Knowledge Graphs

from npcpy import KnowledgeGraph

kg = KnowledgeGraph()

# Extract knowledge from text
kg.extract_from_text(
    "BERT uses masked language modeling for pre-training. "
    "GPT uses autoregressive language modeling. "
    "Both are based on the Transformer architecture."
)

# Query the graph
results = kg.query("What models use Transformer architecture?")
# ["BERT", "GPT"]

# Visualize
kg.visualize("knowledge_graph.html")

# Export
kg.export("kg.json")

Research Workflows

from npcpy import ResearchWorkflow

workflow = ResearchWorkflow(llm_provider="anthropic")

# Literature search + synthesis
report = workflow.literature_review(
    topic="prompt engineering techniques",
    num_papers=20,
    synthesis_style="academic",
)
report.save("review.md")

# Paper analysis
analysis = workflow.analyze_paper("paper.pdf")
print(analysis.summary)
print(analysis.methodology)
print(analysis.key_findings)

Use Cases

1. Quick NLP: Text processing without heavy setup
2. Agent prototyping: Rapid agent creation and testing
3. Knowledge extraction: Build KGs from research text
4. Research automation: Literature search and synthesis
5. Teaching: Demonstrate NLP/agent concepts

References

• npcpy GitHub