semvec/pypi-packages huggingface dataset:
mopactools 0.1.3
Productivity tools for MOPAC
License: Apache-2.0
Requires Python: >=3.6

# mopactools

This Python package is primarily a Python wrapper for API calls to the [Molecular Orbital PACkage (MOPAC)](https://github.com/openmopac/mopac),
which includes launching conventional MOPAC calculations from an input file. For supported environments, it packages the MOPAC shared library
and can be used as a dependency for other Python packages that run MOPAC calculations. It is also intended to be
collection of pre-processing and post-processing tools to enhance the productivity of MOPAC users.

User contributions of new tools are welcome and encouraged. As an open-source project, the future of MOPAC now depends on contributions
from its large user base and the broader community of computational chemistry and materials science. While MOPAC's Fortran codebase is
not easily approachable to new developers, the intent is that an adjacent ecosystem of Python-based tools is more approachable. Also,
activity in this Python layer may motivate the development of more API-based connections to MOPAC's Fortran-based core functionality in the future.
memory-ai-sdk 1.0.0
Simple 3-line integration for AI agents with memory - The most advanced memory API
License: Apache-2.0
Requires Python: >=3.8
Keywords: ai, memory, agents, llm, chatbot, assistant, ml, reinforcement-learning, knowledge-graph, vector-search, rag

# Memory AI Python SDK

[![PyPI version](https://img.shields.io/pypi/v/memory-ai.svg)](https://pypi.org/project/memory-ai/)
[![Python versions](https://img.shields.io/pypi/pyversions/memory-ai.svg)](https://pypi.org/project/memory-ai/)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

Official Python SDK for the AI Memory API - **the only memory API with Reinforcement Learning**.

## 🚀 Features

- ✅ **Complete API Coverage** - All 50+ endpoints supported
- ✅ **Reinforcement Learning** - Train AI agents to optimize memory operations
- ✅ **Temporal Knowledge Graphs** - Track facts over time with bi-temporal model
- ✅ **Procedural Memory** - Store and execute learned skills
- ✅ **Working Memory** - Short-term context buffer for conversations
- ✅ **Memory Consolidation** - Automatic compression of episodic memories
- ✅ **Async/Await** - Full async support with httpx
- ✅ **Type Hints** - Complete type annotations
- ✅ **Clean API** - Pythonic, intuitive interface

## 📦 Installation

```bash
pip install memory-ai
```

## 🔥 Quick Start

```python
import asyncio
from memory_ai import MemoryClient

async def main():
    # Initialize client
    client = MemoryClient(api_key="mem_sk_your_api_key")

    # Create a collection
    collection = await client.collections.create(
        name="My AI Agent",
        description="Personal memory for my chatbot"
    )

    # Store a memory
    memory = await client.memories.create(
        collection_id=collection["id"],
        content="User prefers dark mode and loves Python",
        importance=0.9
    )

    # Search memories
    results = await client.search(
        query="What programming language does user like?",
        collection_id=collection["id"],
        limit=5
    )

    print(results)

    # Close client
    await client.close()

asyncio.run(main())
```

## 📚 Complete Documentation

Visit https://docs.memory-ai.com for full documentation.

## 🔗 Links

- **Documentation**: https://docs.memory-ai.com
- **API Reference**: https://api.memory-ai.com/docs
- **GitHub**: https://github.com/memory-ai/memory-ai
- **Examples**: https://github.com/memory-ai/examples

## 📄 License

MIT License - see [LICENSE](LICENSE) for details

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**Built with ❤️ by the Memory AI team**