--- title: "MemOS vs honcho" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/memtensor-memos-vs-plastic-labs-honcho" tools: ["memtensor-memos", "plastic-labs-honcho"] --- # MemOS vs honcho *GraphCanon updated Aug 18, 2026* ## Verdict Pick MemOS if memOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks; pick honcho if memory Library for Stateful Agents. [MemOS](https://memos.openmem.net) reports 11k GitHub stars, 994 forks, and 84 open issues, last pushed Aug 18, 2026. [honcho](https://docs.honcho.dev) has 6.7k stars, 824 forks, and 163 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [MemOS's repository](https://github.com/MemTensor/MemOS) and [honcho's repository](https://github.com/plastic-labs/honcho). | | [MemOS](/tools/memtensor-memos.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Tagline | Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse | Memory library for building stateful agents | | Stars | 10,758 | 6,703 | | Forks | 994 | 824 | | Open issues | 84 | 163 | | Language | TypeScript | Python | | Adopt for | MemOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks. | Memory Library for Stateful Agents | | Persona | - | - | | Runtime | - | - | | License | MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions. | AGPL-3.0 | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MemOS](/tools/memtensor-memos.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Open issues (now) | 84 | 163 | | Stars delta | +497 (30d) | +659 (30d) | | Open issues delta | +4 (30d) | -3 (30d) | | Full report | [trust report](/tools/memtensor-memos/trust.md) | [trust report](/tools/plastic-labs-honcho/trust.md) | ## Decision facts: MemOS - **Pricing:** unknown - Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs. - **Requirements:** Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components. - **Adopt for:** MemOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks. - **License detail:** MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions. ## Decision facts: honcho - **Adopt for:** Memory Library for Stateful Agents ## Choose when ### Choose MemOS if… - MemOS is primarily TypeScript; honcho is Python. - License: MemOS is Apache-2.0, honcho is AGPL-3.0. - Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs.. - Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components.. - Tags unique to MemOS: agent, agentic-ai, llm, memory-management. - If you require ultra-persistent memory management within your application for long-term storage. ### Choose honcho if… - honcho is primarily Python; MemOS is TypeScript. - License: honcho is AGPL-3.0, MemOS is Apache-2.0. - Tags unique to honcho: agent-memory, ai-agents, embeddings, langchain. - Requires managing long-term and contextual memory in AI agents for statefulness. ## When NOT to use MemOS - If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud). - When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance. - In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services. ## When NOT to use honcho - Do not need complex SDK support or a customizable framework approach. - Not interested in using stateful capabilities leveraging multiple APIs such as Gemini, Anthropic, and OpenAI. ## Common questions ### What is the difference between MemOS and honcho? MemOS: Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse. honcho: Memory library for building stateful agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose MemOS over honcho? Choose MemOS over honcho when MemOS is primarily TypeScript; honcho is Python; License: MemOS is Apache-2.0, honcho is AGPL-3.0; Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs.; Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components.; Tags unique to MemOS: agent, agentic-ai, llm, memory-management; If you require ultra-persistent memory management within your application for long-term storage. ### When should I choose honcho over MemOS? Choose honcho over MemOS when honcho is primarily Python; MemOS is TypeScript; License: honcho is AGPL-3.0, MemOS is Apache-2.0; Tags unique to honcho: agent-memory, ai-agents, embeddings, langchain; Requires managing long-term and contextual memory in AI agents for statefulness. ### When should I avoid MemOS? If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud). When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance. In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services. ### When should I avoid honcho? Do not need complex SDK support or a customizable framework approach. Not interested in using stateful capabilities leveraging multiple APIs such as Gemini, Anthropic, and OpenAI. ### Is MemOS or honcho more popular on GitHub? MemOS has more GitHub stars (10,758 vs 6,703). Stars measure visibility, not whether either tool fits your constraints. ### Are MemOS and honcho open source? Yes - both are open-source projects on GitHub (MemOS: Apache-2.0, honcho: AGPL-3.0). ### Where can I find alternatives to MemOS or honcho? GraphCanon lists graph-backed alternatives at [MemOS alternatives](/tools/memtensor-memos/alternatives) and [honcho alternatives](/tools/plastic-labs-honcho/alternatives) ([MemOS markdown twin](/tools/memtensor-memos/alternatives.md), [honcho markdown twin](/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 [this comparison](/compare/memtensor-memos-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, MemOS or honcho? MemOS: Very 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 MemOS and honcho? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MemOS trust report](/tools/memtensor-memos/trust); [honcho trust report](/tools/plastic-labs-honcho/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=memtensor-memos`](/api/graphcanon/graph?tool=memtensor-memos) - 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/_