--- title: "honcho vs memsearch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/plastic-labs-honcho-vs-zilliztech-memsearch" tools: ["plastic-labs-honcho", "zilliztech-memsearch"] --- # honcho vs memsearch *GraphCanon updated Jul 12, 2026* ## Verdict Pick honcho if a Python memory library designed for building stateful AI agents with a focus on long-term and contextual memory management; pick memsearch if memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. [honcho](https://docs.honcho.dev) reports 5.9k GitHub stars, 707 forks, and 161 open issues, last pushed Jul 10, 2026. [memsearch](https://zilliztech.github.io/memsearch/) has 2.2k stars, 194 forks, and 224 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [honcho's repository](https://github.com/plastic-labs/honcho) and [memsearch's repository](https://github.com/zilliztech/memsearch). | | [honcho](/tools/plastic-labs-honcho.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Tagline | Memory library for building stateful agents | A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus. | | Stars | 5,902 | 2,228 | | Forks | 707 | 194 | | Open issues | 161 | 224 | | Language | Python | Python | | Adopt for | A Python memory library designed for building stateful AI agents with a focus on long-term and contextual memory management. | memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0: The software is free to use, distribute, and modify but requires that derivative works be similarly distributed as AGPL-3.0 under the same license. | MIT | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [honcho](/tools/plastic-labs-honcho.md) | [memsearch](/tools/zilliztech-memsearch.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 161 | 224 | | Full report | [trust report](/tools/plastic-labs-honcho/trust.md) | [trust report](/tools/zilliztech-memsearch/trust.md) | ## Decision facts: honcho - **Hosting:** self hosted - Honcho can be run locally using Docker, allowing for full control over the environment where it operates. - **Requirements:** - Requires setting up LLM API keys (Gemini, Anthropic, OpenAI) for certain functionalities.; - Python SDK needs to point to `http://localhost:8000` after Docker setup. - **Adopt for:** A Python memory library designed for building stateful AI agents with a focus on long-term and contextual memory management. - **License detail:** AGPL-3.0: The software is free to use, distribute, and modify but requires that derivative works be similarly distributed as AGPL-3.0 under the same license. ## Decision facts: memsearch - **Adopt for:** memsearch is a hybrid memory management solution for AI agents with Markdown and Milvus backing, ideal for rich semantic search and long-term data storage. ## Choose when ### Choose honcho if… - License: honcho is AGPL-3.0, memsearch is MIT. - Honcho can be run locally using Docker, allowing for full control over the environment where it operates. - Requirements: - Requires setting up LLM API keys (Gemini, Anthropic, OpenAI) for certain functionalities.; - Python SDK needs to point to `http://localhost:8000` after Docker setup.. - Tags unique to honcho: ai, ai-agents, ai-memory, anthropic. - Also covers LLM Frameworks. - honcho ships Docker support for self-hosted deployment. - - You are developing stateful AI agents that require robust, contextual, and long-term memory capabilities. ### Choose memsearch if… - License: memsearch is MIT, honcho is AGPL-3.0. - Tags unique to memsearch: long-term-memory, milvus, semantic-search. - Also covers Data & Retrieval. - When you need robust integration with AI agents like Claude Code or Codex ## When NOT to use honcho - - If your use case does not require long-term or contextual memory management, as honcho might introduce unnecessary complexity. - - In scenarios where a proprietary license is required, given honcho's AGPL-3.0 license may have implications for open-sourcing modifications. ## When NOT to use memsearch - If your application doesn't require integration with specific AI agents like Claude Code - In cases where only simple text data storage without semantic search is needed ## Common questions ### What is the difference between honcho and memsearch? honcho: Memory library for building stateful agents. memsearch: A persistent, unified memory layer for all your AI agents backed by Markdown and Milvus.. See the comparison table for live GitHub stats and shared categories. ### When should I choose honcho over memsearch? Choose honcho over memsearch when License: honcho is AGPL-3.0, memsearch is MIT; Honcho can be run locally using Docker, allowing for full control over the environment where it operates; Requirements: - Requires setting up LLM API keys (Gemini, Anthropic, OpenAI) for certain functionalities.; - Python SDK needs to point to `http://localhost:8000` after Docker setup.; Tags unique to honcho: ai, ai-agents, ai-memory, anthropic; Also covers LLM Frameworks; honcho ships Docker support for self-hosted deployment; - You are developing stateful AI agents that require robust, contextual, and long-term memory capabilities. ### When should I choose memsearch over honcho? Choose memsearch over honcho when License: memsearch is MIT, honcho is AGPL-3.0; Tags unique to memsearch: long-term-memory, milvus, semantic-search; Also covers Data & Retrieval; When you need robust integration with AI agents like Claude Code or Codex. ### When should I avoid honcho? - If your use case does not require long-term or contextual memory management, as honcho might introduce unnecessary complexity. - In scenarios where a proprietary license is required, given honcho's AGPL-3.0 license may have implications for open-sourcing modifications. ### When should I avoid memsearch? If your application doesn't require integration with specific AI agents like Claude Code In cases where only simple text data storage without semantic search is needed ### Is honcho or memsearch more popular on GitHub? honcho has more GitHub stars (5,902 vs 2,228). Stars measure visibility, not whether either tool fits your constraints. ### Are honcho and memsearch open source? Yes - both are open-source projects on GitHub (honcho: AGPL-3.0, memsearch: MIT). ### Where can I find alternatives to honcho or memsearch? GraphCanon lists graph-backed alternatives at [honcho alternatives](/tools/plastic-labs-honcho/alternatives) and [memsearch alternatives](/tools/zilliztech-memsearch/alternatives) ([honcho markdown twin](/tools/plastic-labs-honcho/alternatives.md), [memsearch markdown twin](/tools/zilliztech-memsearch/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/plastic-labs-honcho-vs-zilliztech-memsearch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, honcho or memsearch? honcho: Very active. memsearch: 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 honcho and memsearch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [honcho trust report](/tools/plastic-labs-honcho/trust); [memsearch trust report](/tools/zilliztech-memsearch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=plastic-labs-honcho`](/api/graphcanon/graph?tool=plastic-labs-honcho) - 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/_