toolops 0.1.1

Framework-agnostic middleware SDK for AI agent tools — caching, semantic deduplication, coalescing and observability in one decorator.

ToolOps

The Industrial-Grade Resilience & Efficiency Layer for AI Agent Tools

Build production-ready AI agents. Stop writing infrastructure boilerplate.

Website · Documentation · Quickstart · Changelog

---

What is ToolOps?

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

When you build AI agents, every external call — to an LLM, an API, a database — is a tool call. In production, those calls are expensive, unreliable, and slow. Yet most developers handle this by re-writing the same boilerplate across every project: a cache class here, a retry decorator there, a circuit-breaker wrapper somewhere else.

ToolOps eliminates that entirely. It is a framework-agnostic middleware SDK that wraps any Python function in a single decorator and upgrades it with caching, resilience, observability, and concurrency control — with zero changes to your business logic.

# Before ToolOps: 80+ lines of cache managers, retry logic, circuit breakers...
# After ToolOps:

@readonly(cache_backend="fast", cache_ttl=3600, retry_count=3)
async def get_market_data(ticker: str) -> dict:
    return await api.fetch(ticker)  # Automatically cached, retried, and traced

That's it. One line. Production-ready.

---

The Production Wall

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

Problem	Business Impact	Without ToolOps	With ToolOps
Redundant API calls	💸 10× cost spikes	100 calls = 100 credits	100 calls → 1 real + 99 cache hits
Similar queries	💸 LLM tokens wasted	Treated as unique	Semantic match → same result
API instability	💥 Agent crashes & loops	No protection	Circuit Breaker + auto-retry
Concurrency bursts	🐢 Thundering herd	N identical live calls	Request coalescing → 1 real call
Zero observability	🌑 Blind operations	No insight	Structured JSON + OTEL traces
Framework lock-in	🧩 Rewrites on migration	Coupled to one framework	Universal Python decorator

---

Feature Overview

Feature	Standard @lru_cache	ToolOps
Async / await support	❌	✅ Native
Semantic (meaning-aware) cache	❌	✅ Embeddings
Exact-match cache	✅ (in-memory only)	✅ Memory, Postgres, File
Distributed / persistent cache	❌	✅ Postgres (Redis coming)
Circuit Breaker	❌	✅
Automatic retries	❌	✅ With backoff
Request coalescing	❌	✅
Stale-if-error fallback	❌	✅
OpenTelemetry tracing	❌	✅
Prometheus metrics	❌	✅
CLI management tools	❌	✅
AI-native (MCP / LangChain / CrewAI)	❌	✅

---

Prerequisites

Before installing ToolOps, make sure you have:

• Python 3.9 or higher — check with python --version
• pip 21.0 or higher — check with pip --version
• A working Python environment (virtual environment strongly recommended — see below)

New to virtual environments? See the Virtual Environment Setup section below — it takes 30 seconds and avoids a lot of pain.

---

Installation

ToolOps uses a modular install system. The core package has zero external dependencies. You only install what you need.

Quick reference

Install command	What you get	Use when
pip install toolops	Core SDK only	Starting out, no extras needed
pip install "toolops[postgres]"	+ PostgreSQL cache backend	Persistent/distributed cache
pip install "toolops[semantic]"	+ Semantic cache support	NLP/RAG similarity matching
pip install "toolops[otel]"	+ OpenTelemetry tracing	Production observability
pip install "toolops[all]"	Everything above	Full feature set

---

Platform-specific install commands

Important: The [extras] syntax requires quotes on Linux and macOS because shells like bash and zsh treat square brackets as glob patterns. Windows CMD and PowerShell use double quotes.

🐧 Linux / 🍎 macOS (bash, zsh, sh)

# Core only (no extras, no quotes needed)
pip install toolops

# Recommended: full install with all features
pip install "toolops[all]"

# Individual extras (examples)
pip install "toolops[postgres]"
pip install "toolops[semantic]"
pip install "toolops[otel]"

# Combine multiple extras
pip install "toolops[postgres,semantic,otel]"

🪟 Windows (Command Prompt or PowerShell)

# Core only
pip install toolops

# Recommended: full install with all features
pip install "toolops[all]"

# Individual extras
pip install "toolops[postgres]"
pip install "toolops[semantic]"
pip install "toolops[otel]"

# Combine multiple extras
pip install "toolops[postgres,semantic,otel]"

Windows note: Both CMD and PowerShell accept double-quoted package specifiers. Single quotes (') do not work in CMD — use double quotes only.

Alternative: using python -m pip (all platforms)

This form is more explicit and avoids PATH confusion, especially when you have multiple Python versions installed:

# Linux / macOS
python -m pip install "toolops[all]"

# Windows
py -m pip install "toolops[all]"

---

Virtual environment setup

We strongly recommend isolating your project in a virtual environment before installing ToolOps.

Linux / macOS

# Create a virtual environment
python -m venv .venv

# Activate it
source .venv/bin/activate

# Install ToolOps
pip install "toolops[all]"

# Verify installation
toolops --version

Windows (Command Prompt)

:: Create a virtual environment
python -m venv .venv

:: Activate it
.venv\Scripts\activate.bat

:: Install ToolOps
pip install "toolops[all]"

:: Verify installation
toolops --version

Windows (PowerShell)

# Create a virtual environment
python -m venv .venv

# Activate it
.venv\Scripts\Activate.ps1

# Install ToolOps
pip install "toolops[all]"

# Verify installation
toolops --version

PowerShell note: If you see an execution policy error, run: Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

---

Verify your installation

After installing, confirm everything is working:

# Check CLI is available
toolops --version

# Run a system health check (checks all registered backends)
toolops doctor

Expected output from toolops doctor:

✔ ToolOps core        OK
✔ MemoryCache         OK
✔ PostgresCache       Connected (postgresql://localhost:5432/...)
✔ SemanticCache       OK (model: all-MiniLM-L6-v2)
✔ OpenTelemetry       Exporter configured

---

Quickstart

This minimal example gets you from install to a working, cached, resilient tool in under 2 minutes.

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

# Step 1: Register a cache backend (do this once at startup)
cache_manager.register("memory", MemoryCache(), is_default=True)

# Step 2: Decorate any async function with @readonly for read operations
# This adds: automatic caching (1 hour TTL) + 3 retries on failure
@readonly(cache_backend="memory", cache_ttl=3600, retry_count=3)
async def fetch_weather(city: str) -> dict:
    # Simulate an external API call
    # In production, replace with your real API client
    return {"city": city, "temp": 22, "condition": "sunny"}

# Step 3: Decorate write operations with @sideeffect (no caching, but protected)
@sideeffect(circuit_breaker=True, timeout=5.0, retry_count=2)
async def send_alert(message: str) -> bool:
    # Simulate sending a notification
    print(f"Alert sent: {message}")
    return True

async def main():
    # First call hits the API
    result = await fetch_weather("Paris")
    print(f"First call (live): {result}")

    # Second call is served from cache — no API call made
    result = await fetch_weather("Paris")
    print(f"Second call (cached): {result}")

    # Write operation with circuit breaker protection
    await send_alert("Agent completed successfully.")

asyncio.run(main())

What you get with zero extra configuration:

• ✅ fetch_weather("Paris") is cached for 1 hour — subsequent calls return instantly
• ✅ If the API fails, it retries up to 3 times automatically
• ✅ send_alert is protected by a circuit breaker — it won't hammer a failing service
• ✅ Every call is logged as structured JSON — ready for your log aggregator

---

Core Concepts

1. Cache Backends

Register backends once at application startup, then reference them by name in any decorator. ToolOps supports multiple backends simultaneously — for example, a fast in-memory cache for hot data and a persistent Postgres cache for expensive computations.

from toolops import cache_manager
from toolops.cache import MemoryCache, PostgresCache, FileCache

# In-memory: fastest, cleared on restart, no dependencies
cache_manager.register("memory", MemoryCache(), is_default=True)

# Postgres: persistent across restarts, shareable across processes
# Requires: pip install "toolops[postgres]"
cache_manager.register(
    "db",
    PostgresCache(connection_string="postgresql://user:pass@localhost:5432/mydb"),
)

# File-based: lightweight persistence without a database
cache_manager.register("disk", FileCache(directory="/tmp/toolops-cache"))

Backend comparison:

Backend	Speed	Persistence	Multi-process	When to use
MemoryCache	⚡ Fastest	❌ Lost on restart	❌ Single process	Dev, testing, single-instance apps
FileCache	🐇 Fast	✅ Survives restarts	⚠️ Read-safe	Local scripts, prototyping
PostgresCache	🐢 Moderate	✅ Durable	✅ Fully shared	Production, microservices, audit trails
SemanticCache	🐢 Moderate	Depends on backend	Depends	NLP queries, RAG pipelines

Tip: You can register as many backends as you need. Use the cache_backend= parameter on each decorator to choose which one a specific function uses.

---

2. The @readonly Decorator

Use @readonly for any function that reads data and has no side effects: API lookups, database queries, LLM calls, file reads. It adds caching and retries.

from toolops import readonly

@readonly(
    cache_backend="memory",   # Which registered backend to use
    cache_ttl=3600,           # Cache Time-to-Live in seconds (1 hour)
    retry_count=3,            # Number of retry attempts on failure
    timeout=10.0,             # Max seconds to wait per attempt
    stale_if_error=True,      # Serve stale cache if the live call fails
    stale_ttl=86400,          # How long stale data is acceptable (24h)
)
async def get_stock_price(ticker: str) -> dict:
    return await market_api.fetch(ticker)

How caching works under the hood:

1. ToolOps hashes the function name + arguments into a cache key
2. On each call, it checks the cache first
3. Cache hit → return the stored result immediately (no API call)
4. Cache miss → call the real function, store the result, return it
5. If the real function fails, stale_if_error=True serves the last known good value

---

3. The @sideeffect Decorator

Use @sideeffect for any function that writes data or triggers an action: sending emails, executing trades, posting messages, modifying state. Side effects are never cached (calling the same function twice should produce two real effects), but they are protected by retries and circuit breakers.

from toolops import sideeffect

@sideeffect(
    circuit_breaker=True,              # Enable circuit breaker protection
    circuit_failure_threshold=5,       # Open circuit after 5 consecutive failures
    circuit_recovery_timeout=60,       # Try recovery after 60 seconds
    retry_count=3,                     # Retry on transient failures
    timeout=5.0,                       # Timeout per attempt in seconds
)
async def execute_trade(order: dict) -> dict:
    return await broker_api.submit(order)

When to use which decorator:

• Does the function have an observable side effect (writes, sends, modifies)? → @sideeffect
• Is the function purely reading/querying with the same input always producing the same output? → @readonly

---

4. Resilience Patterns

Circuit Breaker

A circuit breaker prevents your agent from hammering a failing service and causing cascading failures. When a service fails repeatedly, the circuit "opens" and all calls fail fast — until the service recovers.

Normal state (Closed) → Too many failures → Circuit opens (Open)
         ↑                                          ↓
         └─────────── Recovery timeout ─────────────┘
                      (Half-Open probe)

@sideeffect(
    circuit_breaker=True,
    circuit_failure_threshold=5,   # Open after 5 failures in a row
    circuit_recovery_timeout=60,   # Wait 60s before probing the service again
)
async def call_payment_api(payload: dict) -> dict:
    return await payment_service.process(payload)

Stale-if-Error

When a live API call fails, instead of raising an exception, ToolOps serves the last known good cached value. Useful for data that changes slowly (exchange rates, configuration, metadata).

@readonly(
    cache_backend="db",
    cache_ttl=3600,         # Normally refresh every hour
    stale_if_error=True,
    stale_ttl=86400,        # But accept data up to 24h old if API is down
)
async def get_exchange_rates(base: str = "USD") -> dict:
    return await forex_api.fetch(base)

Request Coalescing

If 50 agents call get_stock_price("AAPL") simultaneously during a cache miss, ToolOps executes the real API call once and multicasts the result to all 50 callers. Without this, a cache miss under load can cause a thundering herd that overwhelms your API rate limits.

# Coalescing is automatic when you use @readonly.
# All concurrent callers with the same arguments wait for a single execution.
@readonly(cache_backend="memory", cache_ttl=60)
async def get_stock_price(ticker: str) -> dict:
    return await market_api.fetch(ticker)
    # 50 concurrent calls for "AAPL" → 1 real API call, 49 coalesced responses

---

5. Semantic Cache

The standard cache only matches exact inputs — "weather in Paris" and "Paris weather" are treated as different keys. The Semantic Cache uses vector embeddings to match by meaning, not by string equality. If the semantic similarity between two queries exceeds a configurable threshold, they share the same cached result.

Requires: pip install "toolops[semantic]"

from toolops import readonly, cache_manager
from toolops.cache import SemanticCache, SentenceTransformerEmbedder

# Initialize the embedder (downloads model on first run, ~90MB)
embedder = SentenceTransformerEmbedder("all-MiniLM-L6-v2")

# Create a semantic cache with a similarity threshold of 0.92
# (1.0 = identical, 0.0 = completely different — 0.92 is a good default)
semantic_cache = SemanticCache(embedder=embedder, threshold=0.92)
cache_manager.register("semantic", semantic_cache)

@readonly(cache_backend="semantic")
async def answer_question(query: str) -> str:
    return await llm.complete(query)

# Example — these two calls share the same cache entry:
r1 = await answer_question("What's the weather in Paris?")
r2 = await answer_question("How's the weather in Paris today?")  # Cache hit ✅
r3 = await answer_question("What is the Parisian weather like?")  # Cache hit ✅

# This is a different enough query to miss:
r4 = await answer_question("What's the temperature on the Moon?")  # Cache miss ✅

Performance note: Semantic cache adds ~5–20ms of embedding inference per call (for the query vector). The first run downloads the model weights (~90MB). Subsequent runs load from disk in milliseconds. The payoff: up to 90% reduction in LLM calls for agents that handle natural language queries.

---

Observability

ToolOps instruments every tool call automatically. You don't need to add logging — it's built in.

Structured JSON Logging

Every cache hit, miss, retry, circuit-breaker event, and timeout is logged as structured JSON, ready for any log aggregator (Datadog, Loki, CloudWatch, etc.).

{"event": "cache_hit",   "fn": "get_stock_price", "backend": "memory", "ttl_remaining": 2847, "latency_ms": 0.4}
{"event": "cache_miss",  "fn": "get_stock_price", "backend": "memory", "latency_ms": 142.7}
{"event": "retry",       "fn": "execute_trade",   "attempt": 2, "error": "ConnectionTimeout", "latency_ms": 5002}
{"event": "circuit_open","fn": "call_payment_api","failures": 5, "recovery_in": 60}

OpenTelemetry (OTEL) Tracing

Requires: pip install "toolops[otel]"

from toolops.observability import configure_otel

# Point at any OTEL-compatible backend
configure_otel(
    service_name="my-agent",
    exporter_endpoint="http://localhost:4317",  # Jaeger, Honeycomb, Datadog, etc.
)

# From this point, every @readonly and @sideeffect call emits a span
# with attributes: fn_name, cache_status, retry_count, latency_ms

You'll see spans like this in Jaeger or Honeycomb:

agent_run (450ms)
  ├── get_market_data (12ms)  [cache: hit]
  ├── get_news_feed (310ms)   [cache: miss, retries: 1]
  └── send_report (128ms)     [circuit: closed]

Prometheus Metrics

Requires: pip install "toolops[otel]"

from toolops.observability import configure_prometheus

configure_prometheus(port=8000)
# Metrics available at http://localhost:8000/metrics

Key metrics exposed:

Metric	Type	Description
toolops_cache_hits_total	Counter	Total cache hits by function + backend
toolops_cache_misses_total	Counter	Total cache misses
toolops_tool_latency_seconds	Histogram	Per-function execution time distribution
toolops_retries_total	Counter	Total retry attempts by function
toolops_circuit_opens_total	Counter	Total circuit breaker open events

---

Framework Integration

ToolOps decorates plain Python async functions, so it works with any agent framework without modification. Below are integration patterns for the most common frameworks.

LangChain / LangGraph

from langchain.tools import tool
from toolops import readonly, cache_manager
from toolops.cache import MemoryCache

cache_manager.register("memory", MemoryCache(), is_default=True)

# Decorate before @tool — ToolOps wraps the raw function
@tool
@readonly(cache_backend="memory", cache_ttl=600, retry_count=3)
async def search_web(query: str) -> str:
    """Search the web and return a summary."""
    return await web_search_api.run(query)

# Use in your LangGraph agent as normal
# Every call to search_web is now automatically cached and retried

CrewAI

from crewai import Agent, Task, Crew
from crewai.tools import BaseTool
from toolops import readonly, cache_manager
from toolops.cache import PostgresCache

cache_manager.register(
    "db",
    PostgresCache(connection_string="postgresql://..."),
    is_default=True,
)

class ResearchTool(BaseTool):
    name: str = "Research Tool"
    description: str = "Fetches and caches research data."

    @readonly(cache_backend="db", cache_ttl=3600, retry_count=3)
    async def _run(self, query: str) -> str:
        return await research_api.fetch(query)

researcher = Agent(
    role="Researcher",
    tools=[ResearchTool()],
    # ...
)

LlamaIndex

from llama_index.core.tools import FunctionTool
from toolops import readonly, cache_manager
from toolops.cache import SemanticCache, SentenceTransformerEmbedder

embedder = SentenceTransformerEmbedder("all-MiniLM-L6-v2")
cache_manager.register("semantic", SemanticCache(embedder=embedder, threshold=0.92))

@readonly(cache_backend="semantic")
async def query_knowledge_base(question: str) -> str:
    return await vector_store.query(question)

knowledge_tool = FunctionTool.from_defaults(async_fn=query_knowledge_base)

Model Context Protocol (MCP)

ToolOps has built-in support for MCP. Expose any decorated function as an MCP tool — compatible with Claude Desktop, Cursor, and any MCP-compatible host — without writing JSON schema by hand.

from toolops import readonly, cache_manager
from toolops.cache import MemoryCache
from toolops.integrations.mcp import MCPIntegration

cache_manager.register("memory", MemoryCache(), is_default=True)

@readonly(cache_backend="memory", cache_ttl=600, retry_count=2)
async def get_weather(city: str) -> dict:
    """Get current weather for a city."""
    return await weather_api.fetch(city)

# Generate a fully typed MCP tool definition automatically
mcp_definition = MCPIntegration.to_mcp_definition(get_weather)
# Returns: {"name": "get_weather", "description": "...", "inputSchema": {...}}

# Register with your MCP server
mcp_server.register_tool(mcp_definition)

---

CLI Reference

ToolOps ships with a command-line tool for managing and inspecting your tool infrastructure.

# Display all available commands and options
toolops --help

# Check the health of all registered backends
toolops doctor

# View live cache statistics for an app
# Replace 'my_app:setup_toolops' with your module:function path
toolops stats --app my_app:setup_toolops

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

# Clear all backends
toolops clear all --app my_app:setup_toolops

Example output of toolops stats:

Backend: memory
  Hit rate:       87.3%
  Total hits:     12,481
  Total misses:   1,814
  Avg latency:    0.3ms

Backend: postgres
  Hit rate:       94.1%
  Total hits:     8,320
  Total misses:   492
  Avg latency:    4.1ms
  Oldest entry:   2026-05-08 09:41:22

---

Configuration Reference

@readonly — all parameters

Parameter	Type	Default	Description
cache_backend	str	"default"	Name of the registered backend to use
cache_ttl	int	300	Cache Time-to-Live in seconds
retry_count	int	0	Number of retry attempts on exception
retry_delay	float	1.0	Base delay (seconds) between retries (exponential backoff)
timeout	float	None	Max execution time in seconds per attempt
stale_if_error	bool	False	Serve stale cache if the live call fails
stale_ttl	int	None	Max age (seconds) of stale data to serve on error
circuit_breaker	bool	False	Enable circuit breaker
circuit_failure_threshold	int	5	Failures before circuit opens
circuit_recovery_timeout	int	60	Seconds before attempting recovery

@sideeffect — all parameters

Parameter	Type	Default	Description
retry_count	int	0	Number of retry attempts
retry_delay	float	1.0	Base delay between retries
timeout	float	None	Max execution time per attempt
circuit_breaker	bool	False	Enable circuit breaker
circuit_failure_threshold	int	5	Failures before circuit opens
circuit_recovery_timeout	int	60	Seconds before attempting recovery

---

Ecosystem Compatibility

ToolOps is designed as framework-agnostic middleware — the "glue layer" of any Python-based agent stack.

First-class integrations (built-in helpers)

• LangChain / LangGraph — decorator-compatible with @tool
• CrewAI — compatible with BaseTool._run()
• LlamaIndex — compatible with FunctionTool
• Model Context Protocol (MCP) — MCPIntegration.to_mcp_definition()

General compatibility

Works with any framework that calls Python async functions:

• PydanticAI
• AutoGPT
• Haystack
• Agno
• Any custom function-based agent

Note: ToolOps wraps the raw function. Apply the ToolOps decorator before any framework-specific decorator (e.g., @tool goes on top of @readonly), so the framework receives the fully-instrumented function.

---

Common Patterns

Pattern 1: Multi-backend strategy (hot + cold cache)

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

# Hot cache: in-memory, very fast, short TTL
cache_manager.register("hot", MemoryCache())

# Cold cache: persistent, shared across processes, longer TTL
cache_manager.register(
    "cold",
    PostgresCache(connection_string="postgresql://..."),
    is_default=True,
)

# Frequently accessed, low-latency need → hot cache
@readonly(cache_backend="hot", cache_ttl=60)
async def get_user_session(user_id: str) -> dict: ...

# Expensive computation, less frequent → cold cache
@readonly(cache_backend="cold", cache_ttl=86400)
async def generate_monthly_report(user_id: str) -> dict: ...

Pattern 2: Full production setup

# app/toolops_setup.py

from toolops import cache_manager
from toolops.cache import MemoryCache, PostgresCache, SemanticCache, SentenceTransformerEmbedder
from toolops.observability import configure_otel, configure_prometheus
import os

def setup_toolops():
    """Call this once at application startup."""

    # Register cache backends
    cache_manager.register("memory", MemoryCache(), is_default=True)
    cache_manager.register(
        "db",
        PostgresCache(connection_string=os.environ["DATABASE_URL"]),
    )

    embedder = SentenceTransformerEmbedder("all-MiniLM-L6-v2")
    cache_manager.register(
        "semantic",
        SemanticCache(embedder=embedder, threshold=0.92),
    )

    # Configure observability
    configure_otel(
        service_name=os.environ.get("SERVICE_NAME", "my-agent"),
        exporter_endpoint=os.environ.get("OTEL_ENDPOINT", "http://localhost:4317"),
    )
    configure_prometheus(port=int(os.environ.get("METRICS_PORT", "8000")))

Pattern 3: Protecting expensive LLM calls

from toolops import readonly, cache_manager
from toolops.cache import SemanticCache, SentenceTransformerEmbedder

embedder = SentenceTransformerEmbedder("all-MiniLM-L6-v2")
cache_manager.register(
    "semantic",
    SemanticCache(embedder=embedder, threshold=0.90),
    is_default=True,
)

@readonly(
    cache_backend="semantic",
    cache_ttl=7200,       # 2-hour TTL for semantic results
    retry_count=3,        # Retry on rate limits or transient failures
    timeout=30.0,         # LLM calls can be slow
    stale_if_error=True,  # Return last known answer if the LLM is down
    stale_ttl=3600,       # Accept 1-hour-old answers as fallback
)
async def ask_llm(prompt: str) -> str:
    return await openai_client.chat(prompt)

# These three calls result in only ONE real LLM call:
a = await ask_llm("Summarize the latest news about AI")
b = await ask_llm("Give me a summary of recent AI news")       # Cache hit ✅
c = await ask_llm("What's happening in AI recently?")           # Cache hit ✅

---

Troubleshooting

zsh: no matches found: toolops[all]

You're on macOS/Linux and forgot the quotes. Use:

pip install "toolops[all]"

ModuleNotFoundError: No module named 'toolops.cache.postgres'

You installed the core package without the Postgres extra. Run:

pip install "toolops[postgres]"

toolops doctor shows a backend as FAILED

Common causes:

• PostgresCache: Check your connection string and that the Postgres server is running and reachable
• SemanticCache: The sentence-transformer model may not have downloaded yet — run a quick test call to trigger the download
• OTEL: Verify your exporter endpoint is reachable from your machine

Retries are not triggering

retry_count only retries on Exception subclasses. If your function catches exceptions internally and returns an error dict instead of raising, ToolOps won't see the failure. Make sure your tool functions raise on error.

Cache is not persisting between restarts

You're likely using MemoryCache. Switch to PostgresCache or FileCache for persistence across process restarts.

---

Roadmap

• Web Dashboard — Real-time cache hit rates, cost attribution, and tool latency UI
• Budget Control — Hard limits on API costs per tool per hour/day
• Native MCP Server — One-command deployment of ToolOps tools as a standalone MCP host
• Streaming Middleware — Support for streaming tool outputs in real-time agents
• Redis Backend — High-performance distributed caching for microservice architectures
• MariaDB / ChromaDB / Pinecone — Additional cache backends
• Async Dashboard CLI — Live top-style monitoring of tool calls

---

Contributing

Contributions, bug reports, and feature requests are welcome!

1. Fork the repository: github.com/hedimanai-pro/toolops
2. Create a feature branch: git checkout -b feature/my-improvement
3. Make your changes and add tests
4. Submit a pull request with a clear description

For larger changes, please open an issue first to discuss the approach.

---

Support & Community

ToolOps is built and maintained by Hedi MANAI.

Channel	Link
🐛 Bug Reports / Feature Requests	GitHub Issues
💼 LinkedIn	linkedin.com/in/hedimanai
🐦 X (Twitter)	@hedi_manaii
🌐 Website	hedimanai.vercel.app
📦 PyPI	pypi.org/project/toolops
📧 Email	[e-mail]
💬 Discord	@hedimanai

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License

Distributed under the Apache License 2.0. See LICENSE for full details.

You are free to use, modify, and distribute ToolOps in personal and commercial projects.

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Built with ❤️ by Hedi MANAI

Empowering the next generation of production-ready agentic workflows.

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