agentic-cache

AgenticCache

This is a Cache file escpecially for Agentic approaches .

agent_core

A Python package for advanced memory management and task queuing, designed for agentic systems, AI development, and high-concurrency applications. It includes hierarchical memory, caching mechanisms, and a priority-based task queue to handle complex, stateful workflows efficiently.

Features

• Hierarchical Memory: Multi-level storage (short-term, working, long-term, context) with prefetching, indexing, and automatic consolidation.
• Caching Systems: LRU cache, FIFO buffers, and TTL-based result caching with background expiration.
• Task Queuing: Priority queue for async coroutines with stats tracking and thread safety.
• Thread-Safe: All operations use reentrant locks for concurrent access.
• Configurable: Extensive config options for capacities, TTLs, thresholds, and more.
• Extensible: Easy to integrate into agent-based systems for memory persistence and event handling.

Installation

Install via pip:

 pip install agentic_cache

or from source:

git clone https://github.com/Abinayasankar-co/AgenticCache.git
cd agentic_cache
python setup.py install

Quick Start

import logging
from agent_core import HierarchicalMemory, TaskQueue

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

# Configure and initialize memory
config = {
    "short_term_capacity": 200,
    "history_size": 500,
    "result_cache_ttl": 7200,
    "prefetch_enabled": True,
    "prefetch_patterns": {"user_data": ["profile", "history"]}
}
memory = HierarchicalMemory(config)

# Store and retrieve data
memory.put("user_id", 12345, namespace="working", persistent=True)
value = memory.get("user_id")  # Returns 12345

# Record an event
memory.record_event({"type": "login", "timestamp": time.time()})

# Initialize task queue
queue = TaskQueue(num_workers=3)
queue.start()

# Submit an async task (example coroutine)
async def example_coro(x):
    await asyncio.sleep(1)
    return x * 2

result = await queue.submit(example_coro, 10)  # Returns 20

# Stop queue when done
queue.stop()

Documentation

See USAGE.md for detailed usage and API reference.

Contributing

Pull requests welcome! Please follow standard Python conventions.

License

Apache License

AgenticCache — Usage

This document explains how to install and use the AgenticCache package. It includes instructions for the CLI entry point and programmatic usage.

Install (editable/development)

From the project root (where setup.py lives), install in editable mode:

python -m pip install -e .

To build a wheel and sdist:

python -m pip install build; python -m build

CLI

A console script agentic-cache is provided. After installing the package, run:

agentic-cache --help

Examples:

• Put a key into memory:

agentic-cache memory --put name Alice --namespace working

• Get a key from memory:

agentic-cache memory --get name --namespace working

• Submit a simple numeric task to the queue:

agentic-cache queue --submit 5 --priority 2

Notes: the CLI uses the main() function in AgenticCache/cli.py which instantiates HierarchicalMemory and TaskQueue using default parameters.

Programmatic usage

Import and use the classes directly:

from AgenticCache.memory import HierarchicalMemory
from AgenticCache.task_queue import TaskQueue

# create memory and queue
memory = HierarchicalMemory({
    "short_term_capacity": 100,
    "history_size": 500,
    "result_cache_ttl": 3600,
    "prefetch_enabled": True,
    "prefetch_patterns": {"user_id": ["profile"]}
})
queue = TaskQueue(num_workers=3)

# store and retrieve
memory.put("key", "value", namespace="working")
print(memory.get("key", namespace="working"))

# submit a coroutine task
async def my_task(x):
    await asyncio.sleep(1)
    return x * 2

import asyncio
result = asyncio.run(queue.submit(my_task, 7))
print(result)

Notes and caveats

• The package requires Python 3.8+. Ensure your environment meets that requirement.
• If you installed editable (-e), changes in the source are available immediately.
• The CLI and examples assume default constructors from the package files; tune parameters to your needs.

If you need a pyproject.toml or specific dependency pins, add them to the repo or ask me to create them for you.