Converts numerical data into token-efficient natural language summaries for Large Language Models.
Sponsored
Semantic Frame addresses the inefficiencies of feeding raw numerical data to Large Language Models (LLMs) by transforming it into concise, natural language descriptions. LLMs often struggle with arithmetic, leading to token waste, hallucination risks, and context overflow when processing large datasets. This Python library performs deterministic analysis using NumPy, Pandas, and Polars, then translates the results into token-efficient narratives, ensuring accuracy and significantly reducing token consumption for LLM-based applications. It provides detailed analysis of trends, volatility, anomalies, seasonality, and more, making data insights readily available for agentic workflows.
Key Features
01Token-efficient semantic compression for numerical data
02Deterministic analysis of trends, volatility, anomalies, and distribution
03Native integrations with Anthropic Claude, LangChain, and CrewAI
04Structured output (JSON, SemanticResult) for programmatic use
05Specialized trading module for financial performance, drawdown, and regime analysis
061 GitHub stars
Use Cases
01Summarizing complex data for crypto trading and sales analytics agents
02Optimizing LLM context windows for numerical data analysis
03Automated insights generation for DevOps monitoring and alerts
