--- title: "mengram vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaizhanov-mengram-vs-datawhalechina-hello-agents" tools: ["alibaizhanov-mengram", "datawhalechina-hello-agents"] --- # mengram vs hello-agents *GraphCanon updated Jul 12, 2026* ## Verdict Pick mengram if mengram offers memory functionalities tailored for AI agents, including semantic, episodic, and procedural capabilities with integrations into platforms like LangChain, CrewAI, and OpenClaw; 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. [mengram](https://mengram.io) reports 183 GitHub stars, 26 forks, and 20 open issues, last pushed Jun 17, 2026. [hello-agents](https://hello-agents.datawhale.cc) has 65k stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [mengram's repository](https://github.com/alibaizhanov/mengram) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [mengram](/tools/alibaizhanov-mengram.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | Human-like memory for AI agents — semantic, episodic & procedural. | Course on building intelligent agents from scratch | | Stars | 183 | 65,432 | | Forks | 26 | 8,109 | | Open issues | 20 | 144 | | Language | Python | Python | | Adopt for | Mengram offers memory functionalities tailored for AI agents, including semantic, episodic, and procedural capabilities with integrations into platforms like LangChain, CrewAI, and OpenClaw. | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | AI Agents, Evaluation & Observability | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mengram](/tools/alibaizhanov-mengram.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 24d | 0d | | Open issues (now) | 20 | 144 | | Owner type | User | Organization | | Security scan | 23 low (23 low) | No lockfile | | Full report | [trust report](/tools/alibaizhanov-mengram/trust.md) | [trust report](/tools/datawhalechina-hello-agents/trust.md) | ## Decision facts: mengram - **Adopt for:** Mengram offers memory functionalities tailored for AI agents, including semantic, episodic, and procedural capabilities with integrations into platforms like LangChain, CrewAI, and OpenClaw. ## 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. ## Choose when ### Choose mengram if… - License: mengram is Apache-2.0, hello-agents is Other. - Tags unique to mengram: agent-memory, ai-agents, ai-memory, cognitive-architecture. - Also covers Evaluation & Observability. - mengram ships Docker support for self-hosted deployment. - Use Mengram if your project requires a comprehensive suite of human-like memory capabilities (semantic, episodic, procedural) for AI agents. ### Choose hello-agents if… - License: hello-agents is Other, mengram is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, rag, tutorial. - 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 NOT to use mengram - Avoid Mengram if your project focuses solely on a specific type of memory (e.g., only semantic) and requires more specialized functionality not provided by Mengram. - Mengram might be less appealing if direct terminal access is preferred over the provided one-prompt setup method, which some users might deem as more complex or cumbersome. ## 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. ## Common questions ### What is the difference between mengram and hello-agents? mengram: Human-like memory for AI agents — semantic, episodic & procedural.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose mengram over hello-agents? Choose mengram over hello-agents when License: mengram is Apache-2.0, hello-agents is Other; Tags unique to mengram: agent-memory, ai-agents, ai-memory, cognitive-architecture; Also covers Evaluation & Observability; mengram ships Docker support for self-hosted deployment; Use Mengram if your project requires a comprehensive suite of human-like memory capabilities (semantic, episodic, procedural) for AI agents. ### When should I choose hello-agents over mengram? Choose hello-agents over mengram when License: hello-agents is Other, mengram is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, rag, tutorial; 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 avoid mengram? Avoid Mengram if your project focuses solely on a specific type of memory (e.g., only semantic) and requires more specialized functionality not provided by Mengram. Mengram might be less appealing if direct terminal access is preferred over the provided one-prompt setup method, which some users might deem as more complex or cumbersome. ### 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. ### Is mengram or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 183). Stars measure visibility, not whether either tool fits your constraints. ### Are mengram and hello-agents open source? Yes - both are open-source projects on GitHub (mengram: Apache-2.0, hello-agents: Other). ### Where can I find alternatives to mengram or hello-agents? GraphCanon lists graph-backed alternatives at [mengram alternatives](/tools/alibaizhanov-mengram/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([mengram markdown twin](/tools/alibaizhanov-mengram/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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/alibaizhanov-mengram-vs-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mengram or hello-agents? mengram: Active. hello-agents: 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 mengram and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mengram trust report](/tools/alibaizhanov-mengram/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaizhanov-mengram`](/api/graphcanon/graph?tool=alibaizhanov-mengram) - 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/_