OpenMemory: The Neuro-Symbolic Consolidation Engine
OpenMemory is a Tier-1, enterprise-grade AI memory system designed for infinite scalability, zero-hallucination auditing, and military-grade security. Built for production LLM infrastructures (like those at OpenAI and Anthropic) or large enterprise deployments.
🚀 Features
- Asynchronous Scalability: Built on pure
async/awaitFastAPI andaiosqlite/asyncpg. It will never block the event loop, easily handling thousands of concurrent AI agents. - Envelope Encryption (AWS KMS): Security is not an afterthought. OpenMemory uses mathematically secure Envelope Encryption via
boto3. Master keys are held securely in the cloud, while the database only stores encrypted data keys. - Redis Token-Bucket Rate Limiting: Prevent API bankruptcy and DDoS attacks from runaway AI scripts. Malicious or looping agents are automatically throttled (HTTP 429).
- Neuro-Symbolic Consolidation (NSCE): Avoids standard vector DB hallucination loops by employing adversarial pruning before context injection.
- Docker & Kubernetes Ready: A multi-stage Dockerfile and Docker Compose setup is included for immediate, highly-available deployment.
📦 Installation
pip install openmemory-ai
🛠️ Quick Start
1. Local Deployment (Docker)
The easiest way to start OpenMemory, ChromaDB, and Redis locally:
git clone https://github.com/shivay00001/openmemory.git
cd openmemory
docker-compose up --build
2. Manual Start
pip install -r requirements.txt
uvicorn api.server:app --reload
🔐 Security Audit
OpenMemory has passed a rigorous simulated Vendor Security Review (VSR). It scored 100/100 for architecture (KMS, Redis, Async Postgres).
👨💻 About the Author
Built by Shivam Kumar (shivay00001).
📄 License & Commercial Use
OpenMemory uses a Dual-Licensing model:
- Free / Non-Commercial: Free for personal, academic, and non-commercial hobbyist use.
- Enterprise / Commercial: Paid. Any commercial use, production deployment, or integration into a revenue-generating product requires a paid Commercial License.
For enterprise licensing, VSR (Vendor Security Review) documentation, or Big Tech acquisitions, please contact the author.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file shivay00001-1.0.0.tar.gz.
File metadata
- Download URL: shivay00001-1.0.0.tar.gz
- Upload date:
- Size: 3.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/7.0.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0491f7a20c31064a1116b5c2f01ea5f186d6c9f0ee40b30d0f57ed49beec6124
|
|
| MD5 |
e7c4755edca2beda756e2103a02fef06
|
|
| BLAKE2b-256 |
0bccc18802270eb2f8dd1c363cafa94ae83c4f5bbd64d264a379564595ea8850
|
File details
Details for the file shivay00001-1.0.0-py3-none-any.whl.
File metadata
- Download URL: shivay00001-1.0.0-py3-none-any.whl
- Upload date:
- Size: 3.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/7.0.0 CPython/3.11.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
78926358879dd697d0b2e7019e86f34be2de354696245bd832c37270d754dd6b
|
|
| MD5 |
e8702f6165c45868344d8706775c7458
|
|
| BLAKE2b-256 |
b04ad15db6ec9e2399c2a3266ac8d3ae9e581e4e2267ebffd05f888e4a0bbb61
|