Philosophy

AI Agents are only as reliable as the tools they wield. In production, tools are expensive, unreliable, and slow.

ToolOps is a framework-agnostic middleware SDK that treats every tool call as a first-class operation. By wrapping your tools in a single decorator, you instantly upgrade them with industrial-grade caching, resilience, and observability.

"ToolOps is to AI Tools what Service Mesh is to Microservices."

The Production Wall

Every agent developer hits the same wall when moving from demo to production:

Quickstart

Install the SDK (Core is zero-dependency):

pip install toolops[all]

Supercharge your tools in seconds:

from toolops import readonly, sideeffect, cache_manager
from toolops.cache import MemoryCache, PostgresCache

# 1. Plug-and-play Backends
cache_manager.register("fast", MemoryCache(), is_default=True)

# 2. Add Intelligence & Resilience to any function
@readonly(cache_backend="fast", cache_ttl=3600, retry_count=3)
async def get_market_data(ticker: str):
    return await api.fetch(ticker) # Automatically cached & retried

@sideeffect(circuit_breaker=True, timeout=5.0)
async def execute_trade(order: dict):
    return await broker.submit(order) # Protected by Circuit Breaker

Engineering Features

Semantic Caching

Don't just cache exact strings. ToolOps uses vector embeddings to understand the meaning of a query. If an agent asks "How's the weather in Paris?" and then "What is the Parisian weather like?", ToolOps serves the cached result. Reduces LLM latency by up to 90%.

Industrial Resilience

• Circuit Breakers: Stop pounding failing APIs before they take down your system.
• Stale-if-Error: If an upstream API fails, ToolOps can automatically serve the last known good value from the cache.
• Request Coalescing: If 50 agents call the same tool simultaneously, ToolOps executes it once and multicasts the result.

MCP Integration (Model Context Protocol)

ToolOps is "AI-Native". It includes built-in support for MCP, allowing you to instantly expose your decorated tools to Claude Desktop, Cursor, or any MCP-compatible host without writing a single line of JSON Schema.

Ecosystem Compatibility

ToolOps is designed to be the "glue" of the AI ecosystem. It works natively with:

Built-in Helpers

• LangChain / LangGraph
• CrewAI
• LlamaIndex
• Model Context Protocol (MCP)

General Compatibility

• PydanticAI
• AutoGPT
• Any Python function-based agent framework

Observability & Metrics

ToolOps doesn't just run your tools; it measures them.

• Structured Logging: Every hit, miss, failure, and retry is logged in production-ready JSON.
• OpenTelemetry: Native traces and spans to visualize tool execution in Jaeger, Honeycomb, or Datadog.
• Prometheus: Real-time metrics for cache hit rates and tool latency.

🗺 Roadmap

• Web Dashboard: Real-time metrics, cost attribution, and hit rates UI.
• Budget Control: Hard limits on tool-induced API costs per hour/day.
• Native MCP Server: One-click deployment of ToolOps tools as a standalone host.
• Streaming Middleware: Support for streaming tool outputs in real-time.
• New Backends: MariaDB, ChromaDB, and Pinecone support.

CLI & Operations

ToolOps includes a command-line tool to inspect and manage your tool infrastructure.

# See all available commands
toolops --help

# Check system health and backend readiness
toolops doctor

# View real-time cache statistics
toolops stats --app my_app:setup_toolops

# Clear a specific cache backend
toolops clear postgres --app my_app:setup_toolops

Maintenance & Support

ToolOps is an open-source project by Hedi MANAI. I am building the future of Agentic Operations through lightweight, industrial-grade tools.

• LinkedIn Connect with me
• GitHub Follow my work
• Website Hedi Manai Portfolio
• X (Twitter) Follow @hedi_manaii
• Discord @hedimanai
• Reddit Profile
• Contact hedi.manai.pro@gmail.com

📄 License

Apache License 2.0 — see LICENSE for details.

Created by Hedi MANAI
Empowering the next generation of Agentic Workflows.