Key Features

Key Features

Tracing Capabilities

LLM Call Tracing

Monitor and analyze LLM interactions with detailed metrics:

• Input/output tokens

• Response times

• Cost tracking

• Model parameters

• Prompt analysis

@tracer.trace_llm("market_analysis")
async def analyze_market(data):
    response = await openai.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": f"Analyze this market data: {data}"}]
    )
    return response.choices[0].message.content

Tool Tracing

Track tool usage and performance:

• Execution time

• Input/output validation

• Error rates

• Usage patterns

@tracer.trace_tool("data_processor")
def process_market_data(raw_data):
    # Processing logic
    return processed_data

Agent Tracing

Monitor agent behavior and decision-making:

• Task decomposition

• Tool selection

• Goal achievement

• Interaction patterns

@tracer.trace_agent("trading_agent")
def execute_trade_strategy(market_conditions):
    # Trading logic
    return trade_decision

Monitoring Features

Real-time Dashboard

• Live execution tracking

• Interactive visualizations

• Performance metrics

• Resource utilization

Data Storage

• SQLite backend

• JSON log files

• Custom storage adapters

• Data export capabilities

Analytics

• Token usage trends

• Cost analysis

• Performance bottlenecks

• Error patterns

Evaluation Tools

• Goal decomposition efficiency

• Tool usage effectiveness

• Response quality metrics

• Cost optimization insights