Comparison
Ori-Mnemos vs ragflow
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 ragflow if rAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license.
Markdown twin · Ori-Mnemos alternatives · ragflow alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Ori-Mnemos | ragflow |
|---|---|---|
| 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 | 4 low (4 low) As of today · osv@v1 |
Tagline
- Ori-Mnemos
- Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).
- ragflow
- Retrieval-Augmented Generation engine with agent capabilities
Stars
- Ori-Mnemos
- 314
- ragflow
- 85k
Forks
- Ori-Mnemos
- 28
- ragflow
- 9.9k
Open issues
- Ori-Mnemos
- 5
- ragflow
- 2.3k
Language
- Ori-Mnemos
- TypeScript
- ragflow
- Go
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.
- ragflow
- RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license.
Persona
- Ori-Mnemos
- -
- ragflow
- -
Runtime
- Ori-Mnemos
- -
- ragflow
- -
License
- Ori-Mnemos
- Apache-2.0
- ragflow
- Apache-2.0 License
Last pushed
- Ori-Mnemos
- Jun 21, 2026
- ragflow
- Jul 11, 2026
Categories
- Ori-Mnemos
- AI Agents, Data & Retrieval
- ragflow
- AI Agents, Data & Retrieval
Trust and health
Maintenance
- Ori-Mnemos
- Active (82%)
- ragflow
- Very active (96%)
Days since push
- Ori-Mnemos
- 20d
- ragflow
- 0d
Open issues (now)
- Ori-Mnemos
- 5
- ragflow
- 2.3k
Owner type
- Ori-Mnemos
- User
- ragflow
- Organization
Security scan
- Ori-Mnemos
- No MCP manifest
- ragflow
- 4 low (4 low)
Full report
- Ori-Mnemos
- Trust report
- ragflow
- Trust report
Choose Ori-Mnemos if…
- Ori-Mnemos is primarily TypeScript; ragflow is Go.
- 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.
Choose ragflow if…
- ragflow is primarily Go; Ori-Mnemos is TypeScript.
- Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services..
- Tags unique to ragflow: context-management, rag, agentic-ai, retrieval-augmented-generation.
- ragflow ships Docker support for self-hosted deployment.
- - You need an integrated RAG system with AI agent capabilities for better context management in your applications.
When NOT to use ragflow
- - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go.
- - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB).
- - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function.
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 Jul 11, 2026
- GitHub forks (aayoawoyemi/Ori-Mnemos) · observed Jul 11, 2026
- Last push (aayoawoyemi/Ori-Mnemos) · observed Jun 21, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (infiniflow/ragflow) · observed Jul 11, 2026
- GitHub forks (infiniflow/ragflow) · observed Jul 11, 2026
- Last push (infiniflow/ragflow) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Ori-Mnemos 314 · ragflow 85k (synced Jul 11, 2026).
Common questions
- What is the difference between Ori-Mnemos and ragflow?
- Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. ragflow: Retrieval-Augmented Generation engine with agent capabilities. See the comparison table for live GitHub stats and shared categories.
- When should I choose Ori-Mnemos over ragflow?
- Choose Ori-Mnemos over ragflow when Ori-Mnemos is primarily TypeScript; ragflow is Go; 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 ragflow over Ori-Mnemos?
- Choose ragflow over Ori-Mnemos when ragflow is primarily Go; Ori-Mnemos is TypeScript; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Tags unique to ragflow: context-management, rag, agentic-ai, retrieval-augmented-generation; ragflow ships Docker support for self-hosted deployment; - You need an integrated RAG system with AI agent capabilities for better context management in your applications.
- 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 ragflow?
- - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function.
- Is Ori-Mnemos or ragflow more popular on GitHub?
- ragflow has more GitHub stars (84,818 vs 314). Stars measure visibility, not whether either tool fits your constraints.
- Are Ori-Mnemos and ragflow open source?
- Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, ragflow: Apache-2.0).
- Where can I find alternatives to Ori-Mnemos or ragflow?
- GraphCanon lists graph-backed alternatives at Ori-Mnemos alternatives and ragflow alternatives (Ori-Mnemos markdown twin, ragflow 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 ragflow?
- Ori-Mnemos: Active. ragflow: 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 ragflow?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Ori-Mnemos trust report; ragflow trust report.