Metadata-Version: 2.4
Name: vektori
Version: 0.1.1
Summary: AI Memory that Actually Works — Graph-based memory for LLM applications
Project-URL: Homepage, https://vektori.cloud
Project-URL: Documentation, https://docs.vektori.cloud
Project-URL: Repository, https://github.com/vektori-ai/vektori
Project-URL: Issues, https://github.com/vektori-ai/vektori/issues
Author-email: Vektori AI <hello@vektori.cloud>
License-Expression: MIT
Keywords: ai,graph,llm,memory,rag,vector
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Requires-Dist: httpx>=0.25.0
Requires-Dist: pydantic>=2.0.0
Provides-Extra: dev
Requires-Dist: pytest-asyncio>=0.21.0; extra == 'dev'
Requires-Dist: pytest>=7.0.0; extra == 'dev'
Requires-Dist: ruff>=0.1.0; extra == 'dev'
Description-Content-Type: text/markdown

# Vektori Python SDK

> AI Memory that Actually Works — Graph-based memory for LLM applications.

## Installation

```bash
pip install vektori
```

## Quick Start

```python
from vektori import Vektori

# Cloud
memory = Vektori(api_key="vk_your_key")

# Self-hosted
memory = Vektori(url="http://localhost:8080")

# Store memories
memory.ingest(
    user_id="user_123",
    messages=[
        {"role": "user", "content": "I love pizza"},
        {"role": "assistant", "content": "Noted!"}
    ]
)

# Retrieve context
result = memory.retrieve(user_id="user_123", query="favorite food")
print(result.context)  # "User loves pizza"
```

## Documentation

Full documentation: [https://docs.vektori.cloud](https://docs.vektori.cloud)

## License

MIT
