Framework for embodied AI systems that maintain continuity across physical interactions, environments, and time.
Project description
Persistence & Continuity Framework
A framework for AI systems that maintain meaningful continuity across interactions, contexts, and time.
This project treats memory as lived contextual experience so agents can retain skills, world models, and performance history across sessions. The goal is to enable safe, reliable long-term collaboration between humans and AI in shared organizational and personal environments.
What it provides
- Layered memory for episodic, semantic, and procedural experience
- Skill retention across sessions and multi-session task resumption
- Performance awareness, drift/degradation monitoring, and adaptive compensation
- Safety-first incident memory, explainable recall, and governance-aware retention
Status
Early research and framework design. APIs and storage formats are expected to change.
Technical details
| Field | Value |
|---|---|
| Hardware | CPU-first; no GPU or CUDA required |
| Acceleration | Optional (hardware-agnostic) |
| Project Name | edyant-persistence |
| Description | Framework for AI systems that maintain continuity across interactions, contexts, and time |
| Python Requirement | Python >= 3.11 |
| License | Apache License 2.0 |
| Author | Edyant Labs |
| Contact | arsalan@edyant.com |
Install
python -m pip install edyant-persistence
Test releases (TestPyPI):
python -m pip install \
--index-url https://pypi.org/simple \
--extra-index-url https://test.pypi.org/simple/ \
edyant-persistence
Learn more
See about.md for the full framework narrative, research threads, and governance considerations. The license is in LICENSE.
Project details
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