Metadata-Version: 2.5
Name: noscium
Version: 0.0.1
Summary: Institutional memory for enterprise AI assistants — governed, multi-user, organizational memory framework.
Project-URL: Homepage, https://github.com/YOUR_GITHUB_USERNAME/noscium
Project-URL: Repository, https://github.com/YOUR_GITHUB_USERNAME/noscium
Project-URL: Issues, https://github.com/YOUR_GITHUB_USERNAME/noscium/issues
Author: Ragesh
License: MIT
License-File: LICENSE
Keywords: agents,ai,enterprise-ai,institutional-memory,llm,memory,organizational-memory,rag
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
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
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown

# Noscium

Institutional memory for enterprise AI assistants.

Unlike retrieval systems (RAG) or single-user memory libraries, Noscium is
designed for organizations: multiple users, teams, roles, and the governance
that comes with it.

> **Status: pre-alpha.** This repo currently holds the initial scaffold and
> roadmap. Follow along via the [Institutional Intelligence newsletter](#)
> for build updates.

## Why Noscium

Most AI agent memory systems (Mem0, Zep, and others) optimize for *individual
recall* — one user's memories, retrieved well over time. Noscium's focus is
different: *organizational* memory — many users, teams, and roles, and the
access control, consolidation, and conflict resolution that come with that.

## Planned Features

- **Multi-user access control** — memories scoped by role and team
- **Organizational consolidation** — individual facts become org-level knowledge
- **Conflict resolution** — when memories contradict, track and resolve
- **Compliance & retention** — configurable policies, audit trails, provenance
- **Importance judging** — LLM + user feedback decides what's worth remembering
- **Memory types** — episodic (what happened), semantic (what is true), procedural (how to do X)

## Roadmap

| Phase | Focus | Status |
|---|---|---|
| Phase 1 | Core memory model, extraction, scoped retrieval | In progress |
| Phase 2 | Organizational consolidation, conflict resolution, memory graph | Planned |
| Phase 3 | Governance, compliance, retention policies, full audit trail | Planned |

## Installation

```bash
pip install noscium
```

(Pre-alpha — API will change without notice until a 0.1.0 release.)

## Quickstart

```python
from noscium import Memory, MemoryType, VisibilityScope

m = Memory(
    id="mem_001",
    fact="Customer X prefers async standups",
    type=MemoryType.SEMANTIC,
    team_id="support-team",
    visibility_scope=VisibilityScope.TEAM,
)
```

## License

MIT
