Semantic Frame

Official

by Anarkitty1 · Python

Token-efficient semantic compression for numerical data. 95%+ token reduction.

Token-efficient semantic compression for numerical data with 95%+ token reduction.

README.md14 KBView on GitHub

Semantic Frame

Token-efficient semantic compression for numerical data.

Semantic Frame converts raw numerical data (NumPy, Pandas, Polars) into natural language descriptions optimized for LLM consumption. Instead of sending thousands of data points to an AI agent, send a 50-word semantic summary.

The Problem

LLMs are terrible at arithmetic. When you send raw data like [100, 102, 99, 101, 500, 100, 98] to GPT-4 or Claude:

Token waste: 1000 data points = ~2000 tokens
Hallucination risk: LLMs guess trends instead of calculating them
Context overflow: Large datasets fill the context window

The Solution

Semantic Frame provides deterministic analysis using NumPy, then translates results into token-efficient narratives:

python

from semantic_frame import describe_series
import pandas as pd

data = pd.Series([100, 102, 99, 101, 500, 100, 98])
print(describe_series(data, context="Server Latency (ms)"))

Output:

code

The Server Latency (ms) data shows a flat/stationary pattern with stable
variability. 1 anomaly detected at index 4 (value: 500.00).
Baseline: 100.00 (range: 98.00-500.00).

Result: 95%+ token reduction, zero hallucination risk.

Installation

bash

pip install semantic-frame

Or with uv:

bash

uv add semantic-frame

🤖 Claude Integration (MCP)

Semantic Frame is available on the official MCP Registry, enabling direct integration with Claude.

Claude Code CLI:

bash

claude mcp add semantic-frame

Claude Desktop - Add to your claude_desktop_config.json:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

json

{
  "mcpServers": {
    "semantic-frame": {
      "command": "uvx",
      "args": ["--from", "semantic-frame[mcp]", "semantic-frame-mcp"]
    }
  }
}

Once configured, Claude can use these tools:

describe_data - Analyze a single data series
describe_batch - Analyze multiple series at once
describe_json - Get structured JSON output

Quick Start

Analyze a Series

python

from semantic_frame import describe_series
import numpy as np

# Works with NumPy arrays
data = 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..."

Analyze a DataFrame

python

from semantic_frame import describe_dataframe
import 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..."

Get Structured Output

python

result = describe_series(data, output="full")

print(result.trend)          # TrendState.RISING_SHARP
print(result.volatility)     # VolatilityState.MODERATE
print(result.anomalies)      # [AnomalyInfo(index=4, value=500.0, z_score=4.2)]
print(result.compression_ratio)  # 0.95

JSON Output for APIs

python

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

Supported Data Types

NumPy: np.ndarray
Pandas: pd.Series, pd.DataFrame
Polars: pl.Series, pl.DataFrame
Python: list

Analysis Features

Feature	Method	Output
Trend	Linear regression slope	RISING_SHARP, RISING_STEADY, FLAT, FALLING_STEADY, FALLING_SHARP
Volatility	Coefficient of variation	COMPRESSED, STABLE, MODERATE, EXPANDING, EXTREME
Anomalies	Z-score / IQR adaptive	Index, value, z-score for each outlier
Seasonality	Autocorrelation	NONE, WEAK, MODERATE, STRONG
Distribution	Skewness + Kurtosis	NORMAL, LEFT_SKEWED, RIGHT_SKEWED, BIMODAL, UNIFORM
Step Change	Baseline shift detection	NONE, STEP_UP, STEP_DOWN
Data Quality	Missing value %	PRISTINE, GOOD, SPARSE, FRAGMENTED

LLM Integration

System Prompt Injection

python

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

LangChain Tool Output

python

from semantic_frame.interfaces import format_for_langchain

output = format_for_langchain(result)
# {"output": "narrative...", "metadata": {...}}

Multi-Column Agent Context

python

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

Framework Integrations

Anthropic Claude (Native Tool Use)

bash

pip install semantic-frame[anthropic]

python

import anthropic
from 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 response
for block in response.content:
    if block.type == "tool_use" and block.name == "semantic_analysis":
        result = handle_tool_call(block.input)
        print(result)

LangChain

bash

pip install semantic-frame[langchain]

python

from semantic_frame.integrations.langchain import get_semantic_tool

tool = get_semantic_tool()
# Use as a LangChain BaseTool in your agent

CrewAI

bash

pip install semantic-frame[crewai]

python

from semantic_frame.integrations.crewai import get_crewai_tool

tool = get_crewai_tool()
# Use with CrewAI agents

MCP (Model Context Protocol)

bash

pip install semantic-frame[mcp]

Run the MCP server:

bash

mcp run semantic_frame.integrations.mcp:mcp

Exposes describe_data tool for MCP clients like:

ElizaOS: TypeScript-based agent framework
Claude Desktop: Anthropic's desktop app
Claude Code: Anthropic's CLI for Claude
Any MCP-compatible client

Claude Code

Add Semantic Frame as a native tool in Claude Code:

bash

# Install MCP dependencies
pip install semantic-frame[mcp]

# Add MCP server to Claude Code
claude mcp add semantic-frame -- uv run --project /path/to/semantic-frame mcp run /path/to/semantic-frame/semantic_frame/integrations/mcp.py

# Restart Claude Code, then verify connection
claude mcp list
# semantic-frame: ... - ✓ Connected

Once configured, ask Claude to analyze data and it will use the describe_data tool automatically.

Advanced Tool Use (Beta)

Semantic Frame supports Anthropic's Advanced Tool Use features for efficient tool orchestration in complex agent workflows.

Features

Feature	Benefit	API
Input Examples	+18% parameter accuracy	Included by default
Tool Search	1000+ tools without context bloat	`defer_loading=True`
Programmatic Calling	Batch analysis via code execution	`allowed_callers=["code_execution"]`

Quick Start (Advanced)

python

import anthropic
from 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..."}]
)

Configuration Options

python

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
)

MCP Batch Analysis

python

from semantic_frame.integrations.mcp import describe_batch

# Analyze multiple series in one call
result = describe_batch(
    datasets='{"cpu": [45, 47, 95, 44], "memory": [60, 61, 60, 61]}',
)

See docs/advanced-tool-use.md for complete documentation.

Use Cases

Crypto Trading

python

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."

DevOps Monitoring

python

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 Analytics

python

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)."

IoT Sensor Data

python

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."

📈 Trading Module (v0.4.0)

Specialized semantic analysis for trading agents, portfolio managers, and financial applications.

Trading Tools

Tool	Description
`describe_drawdown`	Equity curve drawdown analysis with severity
`describe_trading_performance`	Win rate, Sharpe, profit factor metrics
`describe_rankings`	Multi-agent/strategy comparison
`describe_anomalies`	Enhanced anomaly detection with PnL context
`describe_windows`	Multi-timeframe trend alignment
`describe_regime`	Market regime detection (bull/bear/sideways)
`describe_allocation`	Portfolio allocation suggestions ⚠️

Quick Examples

python

from semantic_frame.trading import (
    describe_trading_performance,
    describe_drawdown,
    describe_regime,
    describe_allocation,
)

# Trading Performance
pnl = [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 Analysis
equity = [10000, 10500, 10200, 9800, 9500, 10100]
result = describe_drawdown(equity, context="Strategy")
print(result.narrative)
# "Strategy max drawdown: 9.5% (moderate). Currently recovering..."

# Market Regime
returns = [0.01, 0.015, 0.02, -0.01, 0.025, 0.018]  # Daily returns
result = 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..."

MCP Integration

All trading tools are available via MCP:

bash

semantic-frame-mcp

Tools: describe_drawdown, describe_trading_performance, describe_rankings, describe_anomalies, describe_windows, describe_regime, describe_allocation

📖 Full Trading Documentation | Quick Reference

API Reference

`describe_series(data, context=None, output="text")`

Analyze a single data series.

Parameters:

data: Input data (NumPy array, Pandas Series, Polars Series, or list)
context: Optional label for the data (appears in narrative)
output: Format - "text" (string), "json" (dict), or "full" (SemanticResult)

Returns: Semantic description in requested format.

`describe_dataframe(df, context=None)`

Analyze all numeric columns in a DataFrame.

Parameters:

df: Pandas or Polars DataFrame
context: Optional prefix for column context labels

Returns: Dict mapping column names to SemanticResult objects.

`SemanticResult`

Full analysis result with:

narrative: Human-readable text description
trend: TrendState enum
volatility: VolatilityState enum
data_quality: DataQuality enum
anomaly_state: AnomalyState enum
anomalies: List of AnomalyInfo objects
seasonality: Optional SeasonalityState
distribution: Optional DistributionShape
step_change: Optional StructuralChange (STEP_UP, STEP_DOWN, NONE)
step_change_index: Optional int (index where step change occurred)
profile: SeriesProfile with statistics
compression_ratio: Token reduction ratio

Development

bash

# Clone and install
git clone https://github.com/yourusername/semantic-frame
cd semantic-frame
uv sync

# Run tests
uv run pytest

# Run with coverage
uv run pytest --cov=semantic_frame

License

MIT License - see LICENSE file.

Install

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "semantic-frame": {
      "command": "uvx",
      "args": [
        "semantic-frame"
      ]
    }
  }
}