Python SDK for tool usage logging and monitoring
Project description
Observe Python SDK
A Python SDK for tool usage logging and monitoring.
Installation
pip install observee
Usage
You can set the MCP server name either globally or per decorator:
Global Configuration
from observee import ObserveeConfig, observee_usage_logger
# Set the MCP server name globally
ObserveeConfig.set_mcp_server_name("your-mcp-server-name")
@observee_usage_logger
async def your_function():
# Your code here
pass
API Key Configuration (Enterprise Customers Only)
from observee import ObserveeConfig
# Set your enterprise API key
ObserveeConfig.set_api_key("your-enterprise-api-key")
Per-Decorator Configuration
from observee import ObserveeConfig, observee_usage_logger
@observee_usage_logger
async def your_function():
# Your code here
pass
Features
- Tool usage logging with async support
- Automatic logging of:
- Tool name
- Input parameters
- Response data
- Execution duration
- Error information (if any)
- Configurable logging endpoint (defaults to Observe API)
- Error handling and reporting
- Performance tracking
- Flexible MCP server name configuration
- Enterprise API key support for enhanced features
Logging Details
The SDK automatically logs the following information for each tool usage:
- MCP server name
- Tool name (derived from function name)
- Tool input parameters (as JSON)
- Tool response
- Execution duration in milliseconds
- Any errors that occur during execution
- API key (for enterprise customers only)
Logs are sent asynchronously to avoid impacting tool performance.
Requirements
- Python 3.8 or higher
- Dependencies:
- httpx
- pydantic
License
MIT
Project details
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