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Comparison

Ori-Mnemos vs graphify

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 graphify if graphify transforms a variety of inputs into a unified knowledge graph, ideal for creating searchable insights from mixed content types.

Markdown twin · Ori-Mnemos alternatives · graphify alternatives

GraphCanon updated today

Ori-Mnemos logo

Ori-Mnemos

aayoawoyemi/Ori-Mnemos

★ 314pushed Jun 21, 2026
vs
graphify logo

graphify

Graphify-Labs/graphify

★ 82kpushed Jul 11, 2026

Trust & integrity

SignalOri-Mnemosgraphify
Maintenance
Active (20d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Personal account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No MCP manifest
As of today · mcp_manifest
No lockfile
As of today · none
Ori-Mnemos trust reportgraphify trust reportTrust methodology

Tagline

Ori-Mnemos
Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).
graphify
Turn any code or documentation into a queryable knowledge graph

Stars

Ori-Mnemos
314
graphify
82k

Forks

Ori-Mnemos
28
graphify
8.1k

Open issues

Ori-Mnemos
5
graphify
452

Language

Ori-Mnemos
TypeScript
graphify
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.
graphify
Graphify transforms a variety of inputs into a unified knowledge graph, ideal for creating searchable insights from mixed content types.

Persona

Ori-Mnemos
-
graphify
-

Runtime

Ori-Mnemos
-
graphify
-

License

Ori-Mnemos
Apache-2.0
graphify
MIT

Last pushed

Ori-Mnemos
Jun 21, 2026
graphify
Jul 11, 2026

Categories

Ori-Mnemos
AI Agents, Data & Retrieval
graphify
AI Agents, Data & Retrieval

Trust and health

Maintenance

Ori-Mnemos
Active (82%)
graphify
Very active (96%)

Days since push

Ori-Mnemos
20d
graphify
0d

Open issues (now)

Ori-Mnemos
5
graphify
452

Owner type

Ori-Mnemos
User
graphify
Organization

Security scan

Ori-Mnemos
No MCP manifest
graphify
No lockfile

Full report

Ori-Mnemos
Trust report
graphify
Trust report

Choose Ori-Mnemos if…

  • Ori-Mnemos is primarily TypeScript; graphify is Python.
  • License: Ori-Mnemos is Apache-2.0, graphify is MIT.
  • Tags unique to Ori-Mnemos: markdown, persistent-memory, llm, model-context-protocol.
  • 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.
GitHubDetails

Choose graphify if…

  • graphify is primarily Python; Ori-Mnemos is TypeScript.
  • License: graphify is MIT, Ori-Mnemos is Apache-2.0.
  • Requirements: Ensure to install from the correct PyPI package named `graphifyy` (with double 'y') and not other similar-named packages which are unaffiliated.; Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed..
  • Tags unique to graphify: gemini, rag, codex, skills.
  • When you need to turn diverse file types (code, SQL schemas, documents, images) into a single queryable data structure that can be searched and analyzed together.

When NOT to use graphify

  • If the project exclusively involves text-based content without requiring integration or querying across different file types (e.g., plain documents with no need for cross-referencing).
  • Avoid using Graphify if you are looking for a tool that focuses solely on visual graph representation without the depth of semantic querying capabilities.
GitHubDetails

Explore

Ori-Mnemos trust report →graphify trust report →AI Agents category →Data & Retrieval category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: Ori-Mnemos 314 · graphify 82k (synced Jul 11, 2026).

Common questions

What is the difference between Ori-Mnemos and graphify?
Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. graphify: Turn any code or documentation into a queryable knowledge graph. See the comparison table for live GitHub stats and shared categories.
When should I choose Ori-Mnemos over graphify?
Choose Ori-Mnemos over graphify when Ori-Mnemos is primarily TypeScript; graphify is Python; License: Ori-Mnemos is Apache-2.0, graphify is MIT; Tags unique to Ori-Mnemos: markdown, persistent-memory, llm, model-context-protocol; 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 graphify over Ori-Mnemos?
Choose graphify over Ori-Mnemos when graphify is primarily Python; Ori-Mnemos is TypeScript; License: graphify is MIT, Ori-Mnemos is Apache-2.0; Requirements: Ensure to install from the correct PyPI package named graphifyy (with double 'y') and not other similar-named packages which are unaffiliated.; Installation involves setting up a Python environment ('venv') and ensuring all required extras are installed.; Tags unique to graphify: gemini, rag, codex, skills; When you need to turn diverse file types (code, SQL schemas, documents, images) into a single queryable data structure that can be searched and analyzed together.
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 graphify?
If the project exclusively involves text-based content without requiring integration or querying across different file types (e.g., plain documents with no need for cross-referencing). Avoid using Graphify if you are looking for a tool that focuses solely on visual graph representation without the depth of semantic querying capabilities.
Is Ori-Mnemos or graphify more popular on GitHub?
graphify has more GitHub stars (82,139 vs 314). Stars measure visibility, not whether either tool fits your constraints.
Are Ori-Mnemos and graphify open source?
Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, graphify: MIT).
Where can I find alternatives to Ori-Mnemos or graphify?
GraphCanon lists graph-backed alternatives at Ori-Mnemos alternatives and graphify alternatives (Ori-Mnemos markdown twin, graphify 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 graphify?
Ori-Mnemos: Active. graphify: 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 graphify?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Ori-Mnemos trust report; graphify trust report.