--- title: "honcho vs unbody" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/plastic-labs-honcho-vs-unbody-io-unbody" tools: ["plastic-labs-honcho", "unbody-io-unbody"] --- # honcho vs unbody *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 unbody if unbody is positioned as a modular, open-source backend for AI-native applications emphasizing dynamic knowledge processing. [honcho](https://docs.honcho.dev) reports 5.9k GitHub stars, 707 forks, and 161 open issues, last pushed Jul 10, 2026. [unbody](https://unbody.io) has 527 stars, 49 forks, and 3 open issues, last pushed Apr 14, 2026. Figures are from public GitHub metadata via [honcho's repository](https://github.com/plastic-labs/honcho) and [unbody's repository](https://github.com/unbody-io/unbody). | | [honcho](/tools/plastic-labs-honcho.md) | [unbody](/tools/unbody-io-unbody.md) | | --- | --- | --- | | Tagline | Memory library for building stateful agents | The Supabase of AI era. A modular, open-source backend for building AI-native software — designed for knowledge, not static data. | | Stars | 5,902 | 527 | | Forks | 707 | 49 | | Open issues | 161 | 3 | | Language | Python | TypeScript | | Adopt for | A Python memory library designed for building stateful AI agents with a focus on long-term and contextual memory management. | unbody is positioned as a modular, open-source backend for AI-native applications emphasizing dynamic knowledge processing. | | 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. | Apache-2.0 | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, Data & Retrieval, Developer Tools, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [honcho](/tools/plastic-labs-honcho.md) | [unbody](/tools/unbody-io-unbody.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 88d | | Open issues (now) | 161 | 3 | | Full report | [trust report](/tools/plastic-labs-honcho/trust.md) | [trust report](/tools/unbody-io-unbody/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: unbody - **Adopt for:** unbody is positioned as a modular, open-source backend for AI-native applications emphasizing dynamic knowledge processing. ## Choose when ### Choose honcho if… - honcho is primarily Python; unbody is TypeScript. - License: honcho is AGPL-3.0, unbody is Apache-2.0. - 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: agent-memory, ai, ai-agents, ai-memory. - Also covers LLM Frameworks. - - You are developing stateful AI agents that require robust, contextual, and long-term memory capabilities. ### Choose unbody if… - unbody is primarily TypeScript; honcho is Python. - License: unbody is Apache-2.0, honcho is AGPL-3.0. - Tags unique to unbody: agentic-ai, ai-native, backend, chatbot. - Also covers Data & Retrieval, Developer Tools. - You need to build an application that requires continuous learning and updating from new data in real-time. ## 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 unbody - If your requirement is for managing static datasets where the information does not evolve over time, like historical sales data analysis. - For projects that do not need advanced integration with AI agents and require only traditional backend functionalities without sophisticated knowledge processing capabilities. ## Common questions ### What is the difference between honcho and unbody? honcho: Memory library for building stateful agents. unbody: The Supabase of AI era. A modular, open-source backend for building AI-native software — designed for knowledge, not static data.. See the comparison table for live GitHub stats and shared categories. ### When should I choose honcho over unbody? Choose honcho over unbody when honcho is primarily Python; unbody is TypeScript; License: honcho is AGPL-3.0, unbody is Apache-2.0; 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: agent-memory, ai, ai-agents, ai-memory; Also covers LLM Frameworks; - You are developing stateful AI agents that require robust, contextual, and long-term memory capabilities. ### When should I choose unbody over honcho? Choose unbody over honcho when unbody is primarily TypeScript; honcho is Python; License: unbody is Apache-2.0, honcho is AGPL-3.0; Tags unique to unbody: agentic-ai, ai-native, backend, chatbot; Also covers Data & Retrieval, Developer Tools; You need to build an application that requires continuous learning and updating from new data in real-time. ### 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 unbody? If your requirement is for managing static datasets where the information does not evolve over time, like historical sales data analysis. For projects that do not need advanced integration with AI agents and require only traditional backend functionalities without sophisticated knowledge processing capabilities. ### Is honcho or unbody more popular on GitHub? honcho has more GitHub stars (5,902 vs 527). Stars measure visibility, not whether either tool fits your constraints. ### Are honcho and unbody open source? Yes - both are open-source projects on GitHub (honcho: AGPL-3.0, unbody: Apache-2.0). ### Where can I find alternatives to honcho or unbody? GraphCanon lists graph-backed alternatives at [honcho alternatives](/tools/plastic-labs-honcho/alternatives) and [unbody alternatives](/tools/unbody-io-unbody/alternatives) ([honcho markdown twin](/tools/plastic-labs-honcho/alternatives.md), [unbody markdown twin](/tools/unbody-io-unbody/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-unbody-io-unbody.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, honcho or unbody? honcho: Very active. unbody: Steady. 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 unbody? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [honcho trust report](/tools/plastic-labs-honcho/trust); [unbody trust report](/tools/unbody-io-unbody/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/_