--- title: "memory-os vs honcho" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/claudiodrews-memory-os-vs-plastic-labs-honcho" tools: ["claudiodrews-memory-os", "plastic-labs-honcho"] --- # memory-os vs honcho Neutral, constraint-first comparison with live GitHub stats. | | [memory-os](/tools/claudiodrews-memory-os.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Tagline | A 7-layer memory operating system for Hermes Agent | Memory library for building stateful agents | | Stars | 1,233 | 5,846 | | Forks | 117 | 698 | | Open issues | 7 | 155 | | Language | Python | Python | | Adopt for | Provides a comprehensive, local-first memory operating system for Hermes Agent with seven layers of memory infrastructure including Qdrant and structured facts. | Honcho provides memory infrastructure for building stateful AI agents capable of dynamically understanding entities over time through message storage, event tracking, reasoning, and insight queries. | | Persona | - | - | | Runtime | - | - | | License | MIT | AGPL-3.0 | | Categories | AI Agents, Vector Databases | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [memory-os](/tools/claudiodrews-memory-os.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 27d | 0d | | Open issues (now) | 7 | 155 | | Owner type | User | Organization | | Security scan | 56 low (56 low) | No lockfile | | Full report | [trust report](/tools/claudiodrews-memory-os/trust.md) | [trust report](/tools/plastic-labs-honcho/trust.md) | **Typed relationship:** memory-os _(alternative)_ honcho Honcho and claudiodrews/memory-os both function as memory systems for AI agents, though Honcho emphasizes stateful understanding over time while Memory OS provides a layered approach. ## Shared compatibility - **Python**: [memory-os](/tools/claudiodrews-memory-os.md) - Python runtime; [honcho](/tools/plastic-labs-honcho.md) - Python runtime ## Decision facts: memory-os - **Requirements:** Min 8 GB RAM; Requires Docker; Hermes Agent + Docker (Qdrant + Redis + ARQ Worker) are required.; Python 3.11+ is needed. - **Adopt for:** Provides a comprehensive, local-first memory operating system for Hermes Agent with seven layers of memory infrastructure including Qdrant and structured facts. ## Decision facts: honcho - **Adopt for:** Honcho provides memory infrastructure for building stateful AI agents capable of dynamically understanding entities over time through message storage, event tracking, reasoning, and insight queries. ## Choose when ### Choose memory-os if… - License: memory-os is MIT, honcho is AGPL-3.0. - Requirements: Min 8 GB RAM; Requires Docker; Hermes Agent + Docker (Qdrant + Redis + ARQ Worker) are required.; Python 3.11+ is needed.. - Honcho and claudiodrews/memory-os both function as memory systems for AI agents, though Honcho emphasizes stateful understanding over time while Memory OS provides a layered approach. - Tags unique to memory-os: context-injection, persistent-memory, ground-truth, docker. - Also covers Vector Databases. - When you need persistent and structured memory that doesn't rely on cloud services. ### Choose honcho if… - License: honcho is AGPL-3.0, memory-os is MIT. - Honcho and claudiodrews/memory-os both function as memory systems for AI agents, though Honcho emphasizes stateful understanding over time while Memory OS provides a layered approach. - Tags unique to honcho: embeddings, llm, continual-learning, long-term-memory. - honcho ships Docker support for self-hosted deployment. - - When you need a system that can store messages and events to maintain long-term agent context. ## When NOT to use memory-os - When you prefer a tool that is fully cloud-based or requires minimal local setup. - If your environment does not support local memory infrastructure such as Docker, Qdrant, Redis, and ARQ Worker. - For scenarios requiring lightweight memory solutions without the complexity of multiple layers and self-hosted services. ## When NOT to use honcho - - If your application does not require stateful memory and dynamic tracking of entities over time. - - When the project does not align with Honcho's model of peer-centric reasoning and multi-peer perspective which might be unnecessary for straightforward agent needs. ## Common questions ### What is the difference between memory-os and honcho? memory-os: A 7-layer memory operating system for Hermes Agent. honcho: Memory library for building stateful agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose memory-os over honcho? Choose memory-os over honcho when License: memory-os is MIT, honcho is AGPL-3.0; Requirements: Min 8 GB RAM; Requires Docker; Hermes Agent + Docker (Qdrant + Redis + ARQ Worker) are required.; Python 3.11+ is needed.; Honcho and claudiodrews/memory-os both function as memory systems for AI agents, though Honcho emphasizes stateful understanding over time while Memory OS provides a layered approach; Tags unique to memory-os: context-injection, persistent-memory, ground-truth, docker; Also covers Vector Databases; When you need persistent and structured memory that doesn't rely on cloud services. ### When should I choose honcho over memory-os? Choose honcho over memory-os when License: honcho is AGPL-3.0, memory-os is MIT; Honcho and claudiodrews/memory-os both function as memory systems for AI agents, though Honcho emphasizes stateful understanding over time while Memory OS provides a layered approach; Tags unique to honcho: embeddings, llm, continual-learning, long-term-memory; honcho ships Docker support for self-hosted deployment; - When you need a system that can store messages and events to maintain long-term agent context. ### When should I avoid memory-os? When you prefer a tool that is fully cloud-based or requires minimal local setup. If your environment does not support local memory infrastructure such as Docker, Qdrant, Redis, and ARQ Worker. For scenarios requiring lightweight memory solutions without the complexity of multiple layers and self-hosted services. ### When should I avoid honcho? - If your application does not require stateful memory and dynamic tracking of entities over time. - When the project does not align with Honcho's model of peer-centric reasoning and multi-peer perspective which might be unnecessary for straightforward agent needs. ### Is memory-os or honcho more popular on GitHub? honcho has more GitHub stars (5,846 vs 1,233). Stars measure visibility, not whether either tool fits your constraints. ### Are memory-os and honcho open source? Yes - both are open-source projects on GitHub (memory-os: MIT, honcho: AGPL-3.0). ### Where can I find alternatives to memory-os or honcho? GraphCanon lists graph-backed alternatives at /tools/claudiodrews-memory-os/alternatives and /tools/plastic-labs-honcho/alternatives (/tools/claudiodrews-memory-os/alternatives.md, /tools/plastic-labs-honcho/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 /compare/claudiodrews-memory-os-vs-plastic-labs-honcho.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, memory-os or honcho? memory-os: Active. honcho: 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 memory-os and honcho? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: memory-os: /tools/claudiodrews-memory-os/trust; honcho: /tools/plastic-labs-honcho/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=claudiodrews-memory-os`](/api/graphcanon/graph?tool=claudiodrews-memory-os) - 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/_