sherlock-ai 1.14.9

AI-powered observability and debugging toolkit for Python applications

Documentation: https://docs.sherlockai.dev

Source Code: https://github.com/pranawmishra/sherlock-ai

What is Sherlock AI?

Sherlock AI is a Python observability toolkit that automatically monitors, logs, and analyzes your application — performance, memory, resources, and errors — with minimal setup. Drop in a decorator and you're done.

• ⚡ Auto-Instrumentation — Zero-code setup for FastAPI (Sentry-style monkey-patching)
• 🤖 AI-Powered Insights — Error analysis & performance suggestions via Groq or Azure OpenAI
• 📊 Full-Stack Monitoring — Performance · Memory · CPU/I/O · Resources
• 🗄️ Flexible Storage — MongoDB or HTTP API ingestion
• 🔄 Async/Sync — Works seamlessly with both

• 📋 JSON Logs — Structured output ready for log aggregators

Installation

pip install sherlock-ai

Quick Start

One-liner setup (FastAPI / any app)

from sherlock_ai import SherlockAI, LoggingConfig, get_logger

# Auto-instruments all FastAPI routes — do this BEFORE creating the app
SherlockAI(config=LoggingConfig(auto_instrument=True)).setup()

logger = get_logger(__name__)

from fastapi import FastAPI

app = FastAPI()

@app.get("/health")
def health_check():
    # ✅ Automatically monitored: performance, memory, resources, errors
    return {"status": "healthy"}

Decorator-based setup

from sherlock_ai import sherlock_ai, get_logger, log_performance
from sherlock_ai.monitoring import sherlock_error_handler

sherlock_ai()  # call once at startup
logger = get_logger(__name__)

@log_performance
@sherlock_error_handler
def process_data(user_id: str):
    logger.info("Processing user %s", user_id)
    return {"result": "ok"}

Core Features

Performance Monitoring

from sherlock_ai import log_performance, PerformanceTimer

@log_performance(min_duration=0.1, include_args=True)
def slow_query(user_id, limit=10):
    ...

with PerformanceTimer("batch_job"):
    run_batch()

Memory & Resource Monitoring

from sherlock_ai import monitor_memory, monitor_resources

@monitor_memory(trace_malloc=True)
@monitor_resources(include_io=True, include_network=True)
def heavy_computation():
    ...

AI Error Analysis

import os
from sherlock_ai.monitoring import sherlock_error_handler

os.environ["GROQ_API_KEY"] = "your-key"          # default provider
# os.environ["LLM_PROVIDER"] = "azure_openai"    # or Azure

@sherlock_error_handler
def risky_operation():
    return 1 / 0  # error auto-analyzed and stored

Configuration

Custom config

from sherlock_ai import SherlockAI, LoggingConfig, LogFileConfig

config = LoggingConfig(
    logs_dir="my_logs",
    log_format_type="json",          # creates .json files
    auto_instrument=True,
    log_files={
        "app":         LogFileConfig("application", max_bytes=100*1024*1024),
        "errors":      LogFileConfig("errors", level="ERROR"),
        "performance": LogFileConfig("perf"),
    }
)
SherlockAI(config).setup()

LLM Providers

# Groq
export GROQ_API_KEY="gsk_..."

# Azure OpenAI
export LLM_PROVIDER="azure_openai"
export AZURE_OPENAI_API_KEY="..."
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4-turbo"

API / MongoDB storage

# Send insights to your own backend
export SHERLOCK_AI_API_KEY="your-key"
export SHERLOCK_AI_API_BASE_URL="https://your-backend.com/v1"  # optional

# Or use local MongoDB
export MONGO_URI="mongodb://localhost:27017"

Log Format

Standard

2025-07-15 20:51:19 - aa580b62 - PerformanceLogger - INFO - PERFORMANCE | my_module.fn | SUCCESS | 1.003s

JSON (log_format_type="json")

{"timestamp": "2025-07-15 20:51:19", "level": "INFO", "message": "PERFORMANCE | my_module.fn | SUCCESS | 1.003s", "request_id": "aa580b62"}

Requirements

• Python ≥ 3.8
• psutil · astor · groq · openai · pymongo · requests

Authors

Pranaw Mishra · pranawmishra73@gmail.com

Kunal Aggarwal · aggarwalkunu263@gmail.com

License

MIT