--- title: "Ori-Mnemos vs graphify" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aayoawoyemi-ori-mnemos-vs-graphify-labs-graphify" tools: ["aayoawoyemi-ori-mnemos", "graphify-labs-graphify"] --- # Ori-Mnemos vs graphify *GraphCanon updated Jul 11, 2026* ## 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. [Ori-Mnemos](https://orimnemos.com.) reports 314 GitHub stars, 28 forks, and 5 open issues, last pushed Jun 21, 2026. [graphify](https://graphifylabs.ai/) has 82k stars, 8.1k forks, and 452 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [Ori-Mnemos's repository](https://github.com/aayoawoyemi/Ori-Mnemos) and [graphify's repository](https://github.com/Graphify-Labs/graphify). | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [graphify](/tools/graphify-labs-graphify.md) | | --- | --- | --- | | Tagline | Local-first persistent agentic memory powered by Recursive Memory Harness (RMH). | Turn any code or documentation into a queryable knowledge graph | | Stars | 314 | 82,139 | | Forks | 28 | 8,086 | | Open issues | 5 | 452 | | Language | TypeScript | Python | | Adopt for | Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents. | Graphify transforms a variety of inputs into a unified knowledge graph, ideal for creating searchable insights from mixed content types. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Ori-Mnemos](/tools/aayoawoyemi-ori-mnemos.md) | [graphify](/tools/graphify-labs-graphify.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 20d | 0d | | Open issues (now) | 5 | 452 | | Owner type | User | Organization | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/aayoawoyemi-ori-mnemos/trust.md) | [trust report](/tools/graphify-labs-graphify/trust.md) | ## Decision facts: Ori-Mnemos - **Adopt for:** Ori-Mnemos is a local-first, persistent agentic memory system leveraging SQLite and TypeScript. It incorporates Recursive Memory Harness (RMH) for AI agents. ## Decision facts: graphify - **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. - **Adopt for:** Graphify transforms a variety of inputs into a unified knowledge graph, ideal for creating searchable insights from mixed content types. ## Choose when ### 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: agent-memory, ai-agents, llm, local-first. - Ori-Mnemos ships an MCP server manifest. - When you need a robust, local-first solution that prioritizes offline capabilities and security. ### 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: claude-code, codex, gemini, rag. - 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 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 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. ## 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: agent-memory, ai-agents, 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 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: claude-code, codex, gemini, rag; 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](/tools/aayoawoyemi-ori-mnemos/alternatives) and [graphify alternatives](/tools/graphify-labs-graphify/alternatives) ([Ori-Mnemos markdown twin](/tools/aayoawoyemi-ori-mnemos/alternatives.md), [graphify markdown twin](/tools/graphify-labs-graphify/alternatives.md)), 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](/compare/aayoawoyemi-ori-mnemos-vs-graphify-labs-graphify.md) 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](/tools/aayoawoyemi-ori-mnemos/trust); [graphify trust report](/tools/graphify-labs-graphify/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aayoawoyemi-ori-mnemos`](/api/graphcanon/graph?tool=aayoawoyemi-ori-mnemos) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_