Comparison
Ori-Mnemos vs Agent-Reach
Verdict
Pick Ori-Mnemos when ori-Mnemos is primarily TypeScript; Agent-Reach is Python; pick Agent-Reach when agent-Reach is primarily Python; Ori-Mnemos is TypeScript.
Markdown twin · Ori-Mnemos alternatives · Agent-Reach alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Ori-Mnemos | Agent-Reach |
|---|---|---|
| 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 · Personal account As of today · github_public_v1 |
| Security (OSV) | No MCP manifest As of today · mcp_manifest | No MCP manifest As of today · mcp_manifest |
Tagline
- Ori-Mnemos
- Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).
- Agent-Reach
- Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
Stars
- Ori-Mnemos
- 314
- Agent-Reach
- 55k
Forks
- Ori-Mnemos
- 28
- Agent-Reach
- 4.5k
Open issues
- Ori-Mnemos
- 5
- Agent-Reach
- 144
Language
- Ori-Mnemos
- TypeScript
- Agent-Reach
- 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.
- Agent-Reach
- -
Persona
- Ori-Mnemos
- -
- Agent-Reach
- -
Runtime
- Ori-Mnemos
- -
- Agent-Reach
- -
License
- Ori-Mnemos
- Apache-2.0
- Agent-Reach
- MIT
Last pushed
- Ori-Mnemos
- Jun 21, 2026
- Agent-Reach
- Jul 10, 2026
Categories
- Ori-Mnemos
- AI Agents, Data & Retrieval
- Agent-Reach
- LLM Frameworks, AI Agents, Developer Tools
Trust and health
Maintenance
- Ori-Mnemos
- Active (82%)
- Agent-Reach
- Very active (96%)
Days since push
- Ori-Mnemos
- 20d
- Agent-Reach
- 0d
Open issues (now)
- Ori-Mnemos
- 5
- Agent-Reach
- 144
Full report
- Ori-Mnemos
- Trust report
- Agent-Reach
- Trust report
Choose Ori-Mnemos if…
- Ori-Mnemos is primarily TypeScript; Agent-Reach is Python.
- License: Ori-Mnemos is Apache-2.0, Agent-Reach is MIT.
- Tags unique to Ori-Mnemos: markdown, persistent-memory, llm, model-context-protocol.
- Also covers Data & Retrieval.
- 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 Agent-Reach if…
- Agent-Reach is primarily Python; Ori-Mnemos is TypeScript.
- License: Agent-Reach is MIT, Ori-Mnemos is Apache-2.0.
- Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code.
- Also covers LLM Frameworks, Developer Tools.
When NOT to use Agent-Reach
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
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 (Panniantong/Agent-Reach) · observed Jul 11, 2026
- GitHub forks (Panniantong/Agent-Reach) · observed Jul 11, 2026
- Last push (Panniantong/Agent-Reach) · observed Jul 10, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Ori-Mnemos 314 · Agent-Reach 55k (synced Jul 11, 2026).
Common questions
- What is the difference between Ori-Mnemos and Agent-Reach?
- Ori-Mnemos: Local-first persistent agentic memory powered by Recursive Memory Harness (RMH).. Agent-Reach: Give your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Ori-Mnemos over Agent-Reach?
- Choose Ori-Mnemos over Agent-Reach when Ori-Mnemos is primarily TypeScript; Agent-Reach is Python; License: Ori-Mnemos is Apache-2.0, Agent-Reach is MIT; Tags unique to Ori-Mnemos: markdown, persistent-memory, llm, model-context-protocol; Also covers Data & Retrieval; 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 Agent-Reach over Ori-Mnemos?
- Choose Agent-Reach over Ori-Mnemos when Agent-Reach is primarily Python; Ori-Mnemos is TypeScript; License: Agent-Reach is MIT, Ori-Mnemos is Apache-2.0; Tags unique to Agent-Reach: agent-infrastructure, ai-search, bilibili, claude-code; Also covers LLM Frameworks, Developer Tools.
- 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 Agent-Reach?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model.
- Is Ori-Mnemos or Agent-Reach more popular on GitHub?
- Agent-Reach has more GitHub stars (54,715 vs 314). Stars measure visibility, not whether either tool fits your constraints.
- Are Ori-Mnemos and Agent-Reach open source?
- Yes - both are open-source projects on GitHub (Ori-Mnemos: Apache-2.0, Agent-Reach: MIT).
- Where can I find alternatives to Ori-Mnemos or Agent-Reach?
- GraphCanon lists graph-backed alternatives at Ori-Mnemos alternatives and Agent-Reach alternatives (Ori-Mnemos markdown twin, Agent-Reach 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 Agent-Reach?
- Ori-Mnemos: Active. Agent-Reach: 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 Agent-Reach?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Ori-Mnemos trust report; Agent-Reach trust report.