anti-sentinel 0.1.1

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🛡️ Sentinel Framework

Sentinel is a production-ready, async-first Python framework designed for building "Agentic" AI applications. It bridges the gap between simple LLM scripts and complex, scalable backend systems.

Built on top of FastAPI and Uvicorn, Sentinel provides a structured "framework" experience (similar to Laravel, Next.js, or Django) specifically tailored for AI Agents.

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📚 Table of Contents

1. Key Features
2. Installation
3. Quick Start
4. Project Structure
5. CLI Commands
6. Configuration
7. Core Concepts
   • Agents
   • Tools (Function Calling)
   • Memory (Mem0)
   • Workflows (Pipelines)
   • Async Queues
8. Observability & Dashboard
9. Deployment

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🚀 Key Features

• ⚡ Async-First Core: Non-blocking I/O ensures your API stays responsive even while LLMs are thinking.
• 🧠 Universal LLM Adapter: Switch between Google Gemini, OpenAI, or Ollama just by changing a config file.
• 💾 Smart Memory: Integrated Mem0 support (Cloud & Local) gives agents long-term memory of user preferences.
• 🔌 Auto-Routing: File-based routing system. Create a file in app/http/ and it automatically becomes an API endpoint.
• 🛠️ Tool System: Turn any Python function into a tool the Agent can "use" (e.g., get_weather, search_db).
• ⛓️ Workflows: Orchestrate complex tasks by chaining multiple agents together (e.g., Researcher -> Writer -> Editor).
• 👷 Job Queue: Built-in SQLite-backed background worker for handling long-running tasks without timeouts.
• 📊 Live Dashboard: Monitor request latency, errors, and agent activity in real-time.

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📦 Installation

Prerequisites

• Python 3.10 or higher
• pip

Install via pip

pip install anti-sentinel

Install from Source (Development)

git clone [https://github.com/iamaqdas/sentinel.git](https://github.com/iamaqdas/sentinel.git)
cd sentinel
pip install -e .

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⚡ Quick Start

1. Create a New Project Sentinel comes with a CLI tool to scaffold your application structure.

sentinel new my_agent_app
cd my_agent_app

1. Setup Environment Create a .env file in the root directory:

# --- LLM Keys (Choose one) ---
OPENAI_API_KEY=sk-...
GEMINI_API_KEY=AIza...

# --- Memory Key (Optional but recommended) ---
# Get free key at [https://mem0.ai](https://mem0.ai)
MEM0_API_KEY=m0-xxx...

1. Run the Server

python main.py

• API: http://127.0.0.1:8000
• Dashboard: http://127.0.0.1:8000/dashboard

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📂 Project Structure

When you run sentinel new, the following structure is created. Understanding this is key to using the framework.

my_agent_app/
├── .env                 # API Keys and Secrets (Do not commit to Git)
├── sentinel.yaml        # Main Configuration (LLM model, Memory settings)
├── main.py              # Application Entry Point
├── Dockerfile           # (Generated via CLI) for deployment
│
├── app/                 # YOUR CODE GOES HERE
│   ├── agents/          # Agent Logic definitions (e.g., writer.py, coder.py)
│   ├── http/            # API Routes (Auto-discovered)
│   │   ├── blog_routes.py   # -> /blog_routes/
│   │   └── user_routes.py   # -> /user_routes/
│   ├── workflows/       # Orchestration logic (Chaining agents)
│   └── tools/           # Python functions tools for Agents
│
└── config/              # Advanced configurations (optional)

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💻 CLI Commands

Sentinel includes a CLI tool sentinel to help you manage your project.

Command	Description
sentinel new <name>	Scaffolds a completely new Sentinel project in a folder named <name>.
sentinel docker	Generates a production-ready Dockerfile and .dockerignore in your project root.
sentinel help	Shows the help menu and available commands.

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⚙️ Configuration

sentinel.yaml

This file controls the behavior of the framework components.

app_name: "My Sentinel App"
version: "1.0.0"
debug: true

# --- Brain (LLM) ---
llm:
  provider: "gemini"  # Options: "openai", "gemini"
  model: "gemini-1.5-flash"
  # Base URL is optional, used for proxies or OpenAI-compatible endpoints
  # base_url: "..."

# --- Memory ---
memory:
  provider: "mem0"
  user_id: "default_user"  # Fallback user ID
  # API Key is read from .env automatically

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🧠 Core Concepts

1. Agents

Agents are the primary workers. They inherit from BaseAgent.

Example: app/agents/writer.py

from anti_sentinel.agents.base import BaseAgent

class ContentWriterAgent(BaseAgent):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        
    def _set_system_prompt(self, context_notes=None):
        # Define the personality here
        self.system_prompt = "You are an expert technical writer."
        # Context notes (Memory) are injected automatically if available
        if context_notes:
            self.system_prompt += f"\nUser Context: {context_notes}"
        
        self.history = [{"role": "system", "content": self.system_prompt}]

2. Tools (Function Calling)

You can give agents "hands" to perform actions. Define a standard Python function and register it.

# 1. Define the function
def get_stock_price(symbol: str):
    """
    Fetches the current stock price for a given symbol (e.g., AAPL).
    """
    return f"The price of {symbol} is $150."

# 2. Register it in your Agent
agent = ContentWriterAgent(name="BrokerBot")
agent.register_tool(get_stock_price)

# 3. Use it
# If the user asks "What is Apple's price?", the Agent will automatically 
# execute get_stock_price("AAPL") and use the result.

3. Memory (Persistent Context)

Sentinel uses Mem0 to store user facts. This happens automatically if memory is configured in sentinel.yaml.

• Saving: Every user interaction is saved to the cloud.
• Retrieving: Before answering, the Agent searches Memory for relevant context (e.g., "User likes concise answers") and injects it into the prompt.

4. Workflows (Orchestration)

Chain multiple agents together for complex tasks.

Example: app/workflows/news_flow.py

from anti_sentinel.workflows.engine import Workflow
from app.agents.researcher import ResearcherAgent
from app.agents.writer import ContentWriterAgent

async def run_pipeline(topic: str):
    # 1. Setup Agents
    researcher = ResearcherAgent(name="Sherlock")
    writer = ContentWriterAgent(name="Shakespeare")

    # 2. Create Flow
    flow = Workflow(name="News Generator")
    
    # 3. Define Steps (Output of Step 1 -> Input of Step 2)
    flow.add_step(researcher) # Finds facts
    flow.add_step(writer)     # Writes article based on facts

    # 4. Run
    return await flow.run(topic)

5. Async Background Jobs

For tasks that take longer than 30 seconds (e.g., generating a book), use the Queue system to prevent HTTP timeouts.

Pushing a Job:

from anti_sentinel.services.queue import QueueService

queue = QueueService.get_instance()
job_id = await queue.push({"topic": "Write a book about AI"})

The built-in worker process handles this in the background automatically.

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📊 Observability & Dashboard

Sentinel includes a built-in Metrics Dashboard.

• URL: http://127.0.0.1:8000/dashboard
• Features:
• Average API Latency.
• Recent Requests log.
• Status Codes (200 vs 500 errors).

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🐳 Deployment (Docker)

1. Generate Configuration:

sentinel docker

(Creates Dockerfile & .dockerignore) 2. Build Image:

docker build -t my_sentinel_app .

1. Run Container:

docker run -p 8000:8000 --env-file .env my_sentinel_app