--- title: "pmb vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/oleksiijko-pmb-vs-sindresorhus-awesome" tools: ["oleksiijko-pmb", "sindresorhus-awesome"] --- # pmb vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick pmb when license: pmb is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, pmb is Apache-2.0. [pmb](https://pypi.org/project/pmb-ai/) reports 300 GitHub stars, 15 forks, and 5 open issues, last pushed Jul 11, 2026. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [pmb's repository](https://github.com/oleksiijko/pmb) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [pmb](/tools/oleksiijko-pmb.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Local-first persistent memory for AI coding agents with offline and multilingual capabilities. | 😎 Curated list of awesome topics including hardware resources | | Stars | 300 | 484,026 | | Forks | 15 | 35,799 | | Open issues | 5 | 92 | | Language | Python | - | | Adopt for | pmb - Local-first persistent memory for AI coding agents | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | AI Agents, Data & Retrieval | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pmb](/tools/oleksiijko-pmb.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 11d | | Open issues (now) | 5 | 92 | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/oleksiijko-pmb/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Decision facts: pmb - **Requirements:** No explicit requirements listed in repository data, but likely Python and compatible SQL database setup. - **Adopt for:** pmb - Local-first persistent memory for AI coding agents - **License detail:** Apache-2.0 ## Choose when ### Choose pmb if… - License: pmb is Apache-2.0, awesome is CC0-1.0. - Requirements: No explicit requirements listed in repository data, but likely Python and compatible SQL database setup.. - Tags unique to pmb: codex, ai-memory, knowledge-graph, claude-code. - Also covers AI Agents, Data & Retrieval. - - When you need a solution that integrates directly with popular AI coding agents such as Claude Code, Cursor, and Codex. ### Choose awesome if… - License: awesome is CC0-1.0, pmb is Apache-2.0. - Tags unique to awesome: resources, awesome-list. - Also covers LLM Frameworks. ## When NOT to use pmb - - In scenarios where real-time data retrieval is essential since pmb focuses on local storage rather than cloud-based synchronization. - - If your project requires heavy reliance on online services or if offline functionality doesn't provide a necessary advantage. - - When the specific use case does not benefit from having multilingual capabilities for AI coding tasks. ## When NOT to use awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between pmb and awesome? pmb: Local-first persistent memory for AI coding agents with offline and multilingual capabilities.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose pmb over awesome? Choose pmb over awesome when License: pmb is Apache-2.0, awesome is CC0-1.0; Requirements: No explicit requirements listed in repository data, but likely Python and compatible SQL database setup.; Tags unique to pmb: codex, ai-memory, knowledge-graph, claude-code; Also covers AI Agents, Data & Retrieval; - When you need a solution that integrates directly with popular AI coding agents such as Claude Code, Cursor, and Codex. ### When should I choose awesome over pmb? Choose awesome over pmb when License: awesome is CC0-1.0, pmb is Apache-2.0; Tags unique to awesome: resources, awesome-list; Also covers LLM Frameworks. ### When should I avoid pmb? - In scenarios where real-time data retrieval is essential since pmb focuses on local storage rather than cloud-based synchronization. - If your project requires heavy reliance on online services or if offline functionality doesn't provide a necessary advantage. - When the specific use case does not benefit from having multilingual capabilities for AI coding tasks. ### When should I avoid awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is pmb or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 300). Stars measure visibility, not whether either tool fits your constraints. ### Are pmb and awesome open source? Yes - both are open-source projects on GitHub (pmb: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to pmb or awesome? GraphCanon lists graph-backed alternatives at [pmb alternatives](/tools/oleksiijko-pmb/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([pmb markdown twin](/tools/oleksiijko-pmb/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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/oleksiijko-pmb-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, pmb or awesome? pmb: Very active. awesome: 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 pmb and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pmb trust report](/tools/oleksiijko-pmb/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=oleksiijko-pmb`](/api/graphcanon/graph?tool=oleksiijko-pmb) - 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/_