Comparison
Ori-Mnemos vs memsearch
Verdict
Pick Ori-Mnemos if ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents; pick memsearch if memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage.
Markdown twin · Ori-Mnemos alternatives · memsearch alternatives
GraphCanon updated Sep 30, 2026
12views this month
vs
Trust & integrity
| Signal | Ori-Mnemos | memsearch |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 | Very active (0d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Ori-Mnemos
- Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).
- memsearch
- A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.
Stars
- Ori-Mnemos
- 324
- memsearch
- 2.6k
Forks
- Ori-Mnemos
- 31
- memsearch
- 253
Open issues
- Ori-Mnemos
- 1
- memsearch
- 254
Language
- Ori-Mnemos
- TypeScript
- memsearch
- Python
Adopt for
- Ori-Mnemos
- Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents.
- memsearch
- memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage.
Persona
- Ori-Mnemos
- -
- memsearch
- -
Runtime
- Ori-Mnemos
- -
- memsearch
- -
License
- Ori-Mnemos
- Apache-2.0
- memsearch
- MIT
Last pushed
- Ori-Mnemos
- Sep 20, 2026
- memsearch
- Sep 20, 2026
Categories
- Ori-Mnemos
- AI Agents, Data & Retrieval
- memsearch
- AI Agents, Data & Retrieval, Vector Databases
Trust and health
Open issues (now)
- Ori-Mnemos
- 1
- memsearch
- 254
Stars delta
- Ori-Mnemos
- +10 (30d)
- memsearch
- +291 (30d)
Open issues delta
- Ori-Mnemos
- +1 (30d)
- memsearch
- +23 (30d)
Owner type
- Ori-Mnemos
- User
- memsearch
- Organization
Full report
- Ori-Mnemos
- Trust report
- memsearch
- Trust report
Choose Ori-Mnemos if…
- Ori-Mnemos is primarily TypeScript; memsearch is Python.
- License: Ori-Mnemos is Apache-2.0, memsearch is MIT.
- Tags unique to Ori-Mnemos: ai-agents, knowledge-graph, llm, local-first.
- Ori-Mnemos ships an MCP server manifest.
- When you need a robust, local-first solution that prioritizes offline capabilities and security.
When NOT to use Ori-Mnemos
- When real-time synchronization across devices or cloud integration is a non-negotiable requirement for your application.
- If you are looking for a memory system that leverages distributed databases for scalable access patterns; Ori-Mnemos focuses on local storage using SQLite.
- In environments where complex, multi-node architectures and high availability requirements demand more than a single point of data persistence.
Choose memsearch if…
- memsearch is primarily Python; Ori-Mnemos is TypeScript.
- License: memsearch is MIT, Ori-Mnemos is Apache-2.0.
- Tags unique to memsearch: long-term-memory, milvus, semantic-search.
- Also covers Vector Databases.
- When you need robust integration with AI agents like Claude Code or Codex
When NOT to use memsearch
- If your application doesn't require integration with specific AI agents like Claude Code
- In cases where only simple text data storage without semantic search is needed
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aayoawoyemi/Ori-Mnemos) · observed Sep 20, 2026
- GitHub forks (aayoawoyemi/Ori-Mnemos) · observed Sep 20, 2026
- Last push (aayoawoyemi/Ori-Mnemos) · observed Sep 20, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zilliztech/memsearch) · observed Sep 20, 2026
- GitHub forks (zilliztech/memsearch) · observed Sep 20, 2026
- Last push (zilliztech/memsearch) · observed Sep 20, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Ori-Mnemos 324 · memsearch 2.6k (synced Sep 20, 2026).
Common questions
- What is the difference between Ori-Mnemos and memsearch?
- Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. memsearch: A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Ori-Mnemos over memsearch?
- Choose Ori-Mnemos over memsearch when Ori-Mnemos is primarily TypeScript; memsearch is Python; License: Ori-Mnemos is Apache-2.0, memsearch is MIT; Tags unique to Ori-Mnemos: ai-agents, knowledge-graph, llm, local-first; Ori-Mnemos ships an MCP server manifest; When you need a robust, local-first solution that prioritizes offline capabilities and security.
- When should I choose memsearch over Ori-Mnemos?
- Choose memsearch over Ori-Mnemos when memsearch is primarily Python; Ori-Mnemos is TypeScript; License: memsearch is MIT, Ori-Mnemos is Apache-2.0; Tags unique to memsearch: long-term-memory, milvus, semantic-search; Also covers Vector Databases; When you need robust integration with AI agents like Claude Code or Codex.
- When should I avoid Ori-Mnemos?
- When real-time synchronization across devices or cloud integration is a non-negotiable requirement for your application. If you are looking for a memory system that leverages distributed databases for scalable access patterns; Ori-Mnemos focuses on local storage using SQLite. In environments where complex, multi-node architectures and high availability requirements demand more than a single point of data persistence.
- When should I avoid memsearch?
- If your application doesn't require integration with specific AI agents like Claude Code In cases where only simple text data storage without semantic search is needed
- Is Ori-Mnemos or memsearch more popular on GitHub?
- memsearch has more GitHub stars (2,627 vs 324). Stars measure visibility, not whether either tool fits your constraints.
- Are Ori-Mnemos and memsearch open source?
- Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, memsearch: MIT).
- Where can I find alternatives to Ori-Mnemos or memsearch?
- GraphCanon lists graph-backed alternatives at Ori-Mnemos alternatives and memsearch alternatives (Ori-Mnemos markdown twin, memsearch markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Ori-Mnemos or memsearch?
- Ori-Mnemos: Very active. memsearch: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for Ori-Mnemos and memsearch?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Ori-Mnemos trust report; memsearch trust report.