--- title: "hello-agents vs pmb" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-oleksiijko-pmb" tools: ["datawhalechina-hello-agents", "oleksiijko-pmb"] --- # hello-agents vs pmb *GraphCanon updated Jul 12, 2026* ## Verdict Pick hello-agents if hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods; pick pmb if pmb - Local-first persistent memory for AI coding agents. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [pmb](https://pypi.org/project/pmb-ai/) has 300 stars, 15 forks, and 5 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [pmb's repository](https://github.com/oleksiijko/pmb). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [pmb](/tools/oleksiijko-pmb.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Local-first persistent memory for AI coding agents with offline and multilingual capabilities. | | Stars | 65,432 | 300 | | Forks | 8,109 | 15 | | Open issues | 144 | 5 | | Language | Python | Python | | Adopt for | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | pmb - Local-first persistent memory for AI coding agents | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | Apache-2.0 | | Categories | LLM Frameworks, AI Agents | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [pmb](/tools/oleksiijko-pmb.md) | | --- | --- | --- | | Open issues (now) | 144 | 5 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/oleksiijko-pmb/trust.md) | ## Decision facts: hello-agents - **Requirements:** Min 4 GB RAM; Python knowledge assumed - **Adopt for:** hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. - **License detail:** hello-agents is covered under an unconventional license which may require further review before usage. ## 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 hello-agents if… - License: hello-agents is Other, pmb is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: llm, rag, tutorial, agent. - Also covers LLM Frameworks. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### Choose pmb if… - License: pmb is Apache-2.0, hello-agents is Other. - 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 Data & Retrieval. - - When you need a solution that integrates directly with popular AI coding agents such as Claude Code, Cursor, and Codex. ## When NOT to use hello-agents - Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. - Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ## 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. ## Common questions ### What is the difference between hello-agents and pmb? hello-agents: Course on building intelligent agents from scratch. pmb: Local-first persistent memory for AI coding agents with offline and multilingual capabilities.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over pmb? Choose hello-agents over pmb when License: hello-agents is Other, pmb is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: llm, rag, tutorial, agent; Also covers LLM Frameworks; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### When should I choose pmb over hello-agents? Choose pmb over hello-agents when License: pmb is Apache-2.0, hello-agents is Other; 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 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 avoid hello-agents? Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ### 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. ### Is hello-agents or pmb more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 300). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and pmb open source? Yes - both are open-source projects on GitHub (hello-agents: Other, pmb: Apache-2.0). ### Where can I find alternatives to hello-agents or pmb? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [pmb alternatives](/tools/oleksiijko-pmb/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [pmb markdown twin](/tools/oleksiijko-pmb/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/datawhalechina-hello-agents-vs-oleksiijko-pmb.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or pmb? hello-agents: Very active. pmb: 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 hello-agents and pmb? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [pmb trust report](/tools/oleksiijko-pmb/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - 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/_