Ori-Mnemos
Enables AI agents to maintain coherent, contextual memory across sessions without cloud dependency, reducing latency and vendor lock-in while preserving inst…
Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). Open source must win.
- Ask Claude to retain context across multiple coding sessions without external databases.
- Generate persistent agent memory that survives restarts while staying completely local.
- Automate recursive memory updates as your development codebase grows and evolves.
Enables AI agents to maintain coherent, contextual memory across sessions without cloud dependency, reducing latency and vendor lock-in while preserving institutional knowledge in development workflows.
Development teams building AI-assisted coding systems requiring persistent agent state and local data residency.
https://github.com/aayoawoyemi/Ori-Mnemos
By aayoawoyemi
How to Get It
claude plugins install aayoawoyemi/Ori-Mnemos
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Retain context across multiple coding sessions without external databases
Trust Signals Auto-scanned
Community Pulse Emerging
Discussed on Hacker News
- Ori Mnemos – an open-source persistent memory for AI agents — Hacker News · 2 pts
- 3AM Coding:cracking persistent open-source memory for agents — Hacker News · 1 pts
- Open Source, Decentralized Memory Layer for AI Agents — Hacker News · 1 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 328 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 328 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.