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"""Logger utility for LocalMate - Structured logging for debugging.
Provides colored console logging with structured output for:
- API request/response
- Tool execution
- LLM calls
- Workflow tracing
"""
import logging
import json
import sys
from datetime import datetime
from typing import Any
from dataclasses import dataclass, field, asdict
# Configure root logger
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%H:%M:%S",
stream=sys.stdout,
)
# Color codes for terminal
COLORS = {
"RESET": "\033[0m",
"BOLD": "\033[1m",
"CYAN": "\033[36m",
"GREEN": "\033[32m",
"YELLOW": "\033[33m",
"MAGENTA": "\033[35m",
"BLUE": "\033[34m",
"RED": "\033[31m",
}
def colorize(text: str, color: str) -> str:
"""Add color to text for terminal output."""
return f"{COLORS.get(color, '')}{text}{COLORS['RESET']}"
class LocalMateLogger:
"""Structured logger for LocalMate with colored output."""
def __init__(self, name: str):
self.logger = logging.getLogger(name)
self.name = name
def _format_data(self, data: Any, max_len: int = 500) -> str:
"""Format data for logging, truncating if needed."""
if data is None:
return "None"
if isinstance(data, (dict, list)):
try:
formatted = json.dumps(data, ensure_ascii=False, default=str)
if len(formatted) > max_len:
return formatted[:max_len] + "..."
return formatted
except:
return str(data)[:max_len]
text = str(data)
return text[:max_len] + "..." if len(text) > max_len else text
def api_request(self, endpoint: str, method: str, params: dict = None, body: Any = None):
"""Log API request."""
msg = f"{colorize('→ REQUEST', 'CYAN')} {colorize(method, 'BOLD')} {endpoint}"
if params:
msg += f"\n Params: {self._format_data(params)}"
if body:
msg += f"\n Body: {self._format_data(body)}"
self.logger.info(msg)
def api_response(self, endpoint: str, status: int, data: Any = None, duration_ms: float = None):
"""Log API response."""
status_color = "GREEN" if status < 400 else "RED"
msg = f"{colorize('← RESPONSE', status_color)} {endpoint} [{status}]"
if duration_ms:
msg += f" ({duration_ms:.0f}ms)"
if data:
msg += f"\n Data: {self._format_data(data)}"
self.logger.info(msg)
def tool_call(self, tool_name: str, arguments: dict):
"""Log tool call start."""
msg = f"{colorize('🔧 TOOL', 'MAGENTA')} {colorize(tool_name, 'BOLD')}"
msg += f"\n Args: {self._format_data(arguments)}"
self.logger.info(msg)
def tool_result(self, tool_name: str, result_count: int, sample: Any = None):
"""Log tool result."""
msg = f"{colorize('✓ RESULT', 'GREEN')} {tool_name} → {result_count} results"
if sample:
msg += f"\n Sample: {self._format_data(sample, max_len=200)}"
self.logger.info(msg)
def llm_call(self, provider: str, model: str, prompt_preview: str = None):
"""Log LLM call."""
msg = f"{colorize('🤖 LLM', 'BLUE')} {provider}/{model}"
if prompt_preview:
preview = prompt_preview[:100] + "..." if len(prompt_preview) > 100 else prompt_preview
msg += f"\n Prompt: {preview}"
self.logger.info(msg)
def llm_response(self, provider: str, response_preview: str = None, tokens: int = None):
"""Log LLM response."""
msg = f"{colorize('💬 LLM RESPONSE', 'BLUE')} {provider}"
if tokens:
msg += f" ({tokens} tokens)"
if response_preview:
preview = response_preview[:150] + "..." if len(response_preview) > 150 else response_preview
msg += f"\n Response: {preview}"
self.logger.info(msg)
def workflow_step(self, step: str, details: str = None):
"""Log workflow step."""
msg = f"{colorize('▶', 'YELLOW')} {step}"
if details:
msg += f": {details}"
self.logger.info(msg)
def error(self, message: str, error: Exception = None):
"""Log error."""
msg = f"{colorize('❌ ERROR', 'RED')} {message}"
if error:
msg += f"\n {type(error).__name__}: {str(error)}"
self.logger.error(msg)
def debug(self, message: str, data: Any = None):
"""Log debug info."""
msg = f"{colorize('DEBUG', 'CYAN')} {message}"
if data:
msg += f": {self._format_data(data)}"
self.logger.debug(msg)
@dataclass
class WorkflowStep:
"""A step in the agent workflow."""
step_name: str
tool_name: str | None = None
purpose: str = ""
input_summary: str = ""
output_summary: str = ""
result_count: int = 0
duration_ms: float = 0
@dataclass
class AgentWorkflow:
"""Complete workflow trace for a chat request."""
query: str
intent_detected: str = ""
steps: list[WorkflowStep] = field(default_factory=list)
total_duration_ms: float = 0
tools_used: list[str] = field(default_factory=list)
def add_step(self, step: WorkflowStep):
"""Add a step to the workflow."""
self.steps.append(step)
if step.tool_name and step.tool_name not in self.tools_used:
self.tools_used.append(step.tool_name)
def to_dict(self) -> dict:
"""Convert to dictionary for JSON serialization."""
return {
"query": self.query,
"intent_detected": self.intent_detected,
"tools_used": self.tools_used,
"steps": [
{
"step": s.step_name,
"tool": s.tool_name,
"purpose": s.purpose,
"results": s.result_count,
}
for s in self.steps
],
"total_duration_ms": round(self.total_duration_ms, 1),
}
def to_summary(self) -> str:
"""Generate human-readable workflow summary."""
lines = [f"📊 **Workflow Summary**"]
lines.append(f"- Query: \"{self.query[:50]}{'...' if len(self.query) > 50 else ''}\"")
lines.append(f"- Intent: {self.intent_detected}")
lines.append(f"- Tools: {', '.join(self.tools_used) or 'None'}")
if self.steps:
lines.append("\n**Steps:**")
for i, step in enumerate(self.steps, 1):
tool_info = f" ({step.tool_name})" if step.tool_name else ""
results_info = f" → {step.result_count} results" if step.result_count else ""
lines.append(f"{i}. {step.step_name}{tool_info}{results_info}")
lines.append(f"\n⏱️ Total: {self.total_duration_ms:.0f}ms")
return "\n".join(lines)
# Global logger instances
agent_logger = LocalMateLogger("agent")
api_logger = LocalMateLogger("api")
tool_logger = LocalMateLogger("tools")
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