--- title: "Memori vs honcho" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/memorilabs-memori-vs-plastic-labs-honcho" tools: ["memorilabs-memori", "plastic-labs-honcho"] --- # Memori vs honcho Neutral, constraint-first comparison with live GitHub stats. | | [Memori](/tools/memorilabs-memori.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Tagline | Memory infrastructure for AI agents that captures actions and conversations into a structured, persistent state. | Memory library for building stateful agents | | Stars | 15,549 | 5,846 | | Forks | 2,784 | 698 | | Open issues | 21 | 155 | | Language | Python | Python | | Adopt for | Memori is designed for enterprise users seeking seamless memory infrastructure that integrates with existing data architectures across multiple deployment environments. | 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 | Memori is licensed under the Apache License 2.0. | AGPL-3.0 | | Categories | Model Training, AI Agents | AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Memori](/tools/memorilabs-memori.md) | [honcho](/tools/plastic-labs-honcho.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 22d | 0d | | Open issues (now) | 21 | 155 | | Full report | [trust report](/tools/memorilabs-memori/trust.md) | [trust report](/tools/plastic-labs-honcho/trust.md) | **Typed relationship:** Memori _(alternative)_ honcho Both Honcho and memorilabs/memori are memory infrastructure solutions for AI agents designed to capture and manage contextual state over time. ## Shared compatibility - **Python**: [Memori](/tools/memorilabs-memori.md) - Python runtime; [honcho](/tools/plastic-labs-honcho.md) - Python runtime ## Decision facts: Memori - **Pricing:** unknown - Pricing details are not explicitly stated in the provided repository content. - **Requirements:** The tool requires set up of an API key for Memori and your LLM - **Adopt for:** Memori is designed for enterprise users seeking seamless memory infrastructure that integrates with existing data architectures across multiple deployment environments. - **License detail:** Memori is licensed under the Apache License 2.0. ## 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 Memori if… - License: Memori is Other, honcho is AGPL-3.0. - Pricing: Pricing details are not explicitly stated in the provided repository content.. - Requirements: The tool requires set up of an API key for Memori and your LLM. - Both Honcho and memorilabs/memori are memory infrastructure solutions for AI agents designed to capture and manage contextual state over time. - Tags unique to Memori: stateful, memory-management, ai-memory, llm-agnostic. - Also covers Model Training. - When you need a system to turn agent execution and conversation into structured, persistent state without disrupting your current IT environment. ### Choose honcho if… - License: honcho is AGPL-3.0, Memori is Other. - Both Honcho and memorilabs/memori are memory infrastructure solutions for AI agents designed to capture and manage contextual state over time. - Tags unique to honcho: embeddings, llm, continual-learning, long-term-memory. - - When you need a system that can store messages and events to maintain long-term agent context. ## When NOT to use Memori - Avoid if you need a tool that natively extends beyond memory management to include features like autonomous agent navigation or extensive model training utilities, as Memori focuses specifically on AI ## 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 Memori and honcho? Memori: Memory infrastructure for AI agents that captures actions and conversations into a structured, persistent state.. honcho: Memory library for building stateful agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose Memori over honcho? Choose Memori over honcho when License: Memori is Other, honcho is AGPL-3.0; Pricing: Pricing details are not explicitly stated in the provided repository content.; Requirements: The tool requires set up of an API key for Memori and your LLM; Both Honcho and memorilabs/memori are memory infrastructure solutions for AI agents designed to capture and manage contextual state over time; Tags unique to Memori: stateful, memory-management, ai-memory, llm-agnostic; Also covers Model Training; When you need a system to turn agent execution and conversation into structured, persistent state without disrupting your current IT environment. ### When should I choose honcho over Memori? Choose honcho over Memori when License: honcho is AGPL-3.0, Memori is Other; Both Honcho and memorilabs/memori are memory infrastructure solutions for AI agents designed to capture and manage contextual state over time; Tags unique to honcho: embeddings, llm, continual-learning, long-term-memory; - When you need a system that can store messages and events to maintain long-term agent context. ### When should I avoid Memori? Avoid if you need a tool that natively extends beyond memory management to include features like autonomous agent navigation or extensive model training utilities, as Memori focuses specifically on AI ### 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 Memori or honcho more popular on GitHub? Memori has more GitHub stars (15,549 vs 5,846). Stars measure visibility, not whether either tool fits your constraints. ### Are Memori and honcho open source? Yes - both are open-source projects on GitHub (Memori: Other, honcho: AGPL-3.0). ### Where can I find alternatives to Memori or honcho? GraphCanon lists graph-backed alternatives at /tools/memorilabs-memori/alternatives and /tools/plastic-labs-honcho/alternatives (/tools/memorilabs-memori/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/memorilabs-memori-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, Memori or honcho? Memori: 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 Memori and honcho? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Memori: /tools/memorilabs-memori/trust; honcho: /tools/plastic-labs-honcho/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=memorilabs-memori`](/api/graphcanon/graph?tool=memorilabs-memori) - 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/_