Semantic Frame

from semantic_frame import describe_seriesimport pandas as pd
data = pd.Series([100, 102, 99, 101, 500, 100, 98])print(describe_series(data, context="Server Latency (ms)"))

from semantic_frame import describe_seriesimport numpy as np
# Works with NumPy arraysdata = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])result = describe_series(data, context="Daily Sales")print(result)# "The Daily Sales data shows a rapidly rising pattern with moderate variability..."

from semantic_frame import describe_dataframeimport pandas as pd
df = pd.DataFrame({    'cpu': [40, 42, 41, 95, 40, 41],    'memory': [60, 61, 60, 60, 61, 60],})
results = describe_dataframe(df, context="Server Metrics")print(results['cpu'].narrative)# "The Server Metrics - cpu data shows a flat/stationary pattern..."

result = describe_series(data, output="full")
print(result.trend)          # TrendState.RISING_SHARPprint(result.volatility)     # VolatilityState.MODERATEprint(result.anomalies)      # [AnomalyInfo(index=4, value=500.0, z_score=4.2)]print(result.compression_ratio)  # 0.95

result = describe_series(data, output="json")# Returns dict ready for JSON serialization

from semantic_frame.interfaces import format_for_system_prompt
result = describe_series(data, output="full")prompt = format_for_system_prompt(result)# Returns formatted context block for system prompts

from semantic_frame.interfaces import format_for_langchain
output = format_for_langchain(result)# {"output": "narrative...", "metadata": {...}}

from semantic_frame.interfaces import create_agent_context
results = describe_dataframe(df)context = create_agent_context(results)# Combined narrative for all columns with attention flags

import anthropicfrom semantic_frame.integrations.anthropic import get_anthropic_tool, handle_tool_call
client = anthropic.Anthropic()tool = get_anthropic_tool()
response = client.messages.create(    model="claude-sonnet-4-20250514",    max_tokens=1024,    tools=[tool],    messages=[{"role": "user", "content": "Analyze this sales data: [100, 120, 115, 500, 118]"}])
# Handle tool use in responsefor block in response.content:    if block.type == "tool_use" and block.name == "semantic_analysis":        result = handle_tool_call(block.input)        print(result)

from semantic_frame.integrations.langchain import get_semantic_tool
tool = get_semantic_tool()# Use as a LangChain BaseTool in your agent

from semantic_frame.integrations.crewai import get_crewai_tool
tool = get_crewai_tool()# Use with CrewAI agents

import anthropicfrom semantic_frame.integrations.anthropic import get_advanced_tool, handle_tool_call
client = anthropic.Anthropic()tool = get_advanced_tool()  # All advanced features enabled
response = client.beta.messages.create(    betas=["advanced-tool-use-2025-11-20"],    model="claude-sonnet-4-5-20250929",    max_tokens=4096,    tools=[        {"type": "tool_search_tool_regex_20251119", "name": "tool_search"},        {"type": "code_execution_20250825", "name": "code_execution"},        tool,    ],    messages=[{"role": "user", "content": "Analyze all columns in this dataset..."}])

from semantic_frame.integrations.anthropic import (    get_anthropic_tool,          # Standard (includes examples)    get_tool_for_discovery,      # For Tool Search    get_tool_for_batch_processing,  # For code execution    get_advanced_tool,           # All features enabled)

from semantic_frame.integrations.mcp import describe_batch
# Analyze multiple series in one callresult = describe_batch(    datasets='{"cpu": [45, 47, 95, 44], "memory": [60, 61, 60, 61]}',)

btc_prices = pd.Series(hourly_btc_prices)insight = describe_series(btc_prices, context="BTC/USD Hourly")# "The BTC/USD Hourly data shows a rapidly rising pattern with extreme variability.#  Step up detected at index 142. 2 anomalies detected at indices 89, 203."

cpu_data = pd.Series(cpu_readings)insight = describe_series(cpu_data, context="CPU Usage %")# "The CPU Usage % data shows a flat/stationary pattern with stable variability#  until index 850, where a critical anomaly was detected..."

sales = pd.Series(daily_sales)insight = describe_series(sales, context="Daily Revenue")# "The Daily Revenue data shows a steadily rising pattern with weak cyclic pattern#  detected. Baseline: $12,450 (range: $8,200-$18,900)."

temps = pl.Series("temperature", sensor_readings)insight = describe_series(temps, context="Machine Temperature (C)")# "The Machine Temperature (C) data is expanding with extreme outliers.#  3 anomalies detected at indices 142, 156, 161."

from semantic_frame.trading import (    describe_trading_performance,    describe_drawdown,    describe_regime,    describe_allocation,)
# Trading Performancepnl = [100, -50, 75, -25, 150, -30, 80]result = describe_trading_performance(pnl, context="My Bot")print(result.narrative)# "My Bot shows good performance with 57.1% win rate. Profit factor: 2.53..."
# Drawdown Analysisequity = [10000, 10500, 10200, 9800, 9500, 10100]result = describe_drawdown(equity, context="Strategy")print(result.narrative)# "Strategy max drawdown: 9.5% (moderate). Currently recovering..."
# Market Regimereturns = [0.01, 0.015, 0.02, -0.01, 0.025, 0.018]  # Daily returnsresult = describe_regime(returns, context="BTC")print(result.narrative)# "BTC is in a strong bullish regime. Conditions favor trend-following..."
# Portfolio Allocation (⚠️ Educational only, not financial advice)assets = {"BTC": [40000, 42000, 44000], "ETH": [2500, 2650, 2800]}result = describe_allocation(assets, method="risk_parity")print(result.narrative)# "Suggested allocation: BTC (55%), ETH (45%). Risk: high..."