--- title: "nanoclaw vs learn-claude-code" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nanocoai-nanoclaw-vs-shareai-lab-learn-claude-code" tools: ["nanocoai-nanoclaw", "shareai-lab-learn-claude-code"] --- # nanoclaw vs learn-claude-code Neutral, constraint-first comparison with live GitHub stats. | | [nanoclaw](/tools/nanocoai-nanoclaw.md) | [learn-claude-code](/tools/shareai-lab-learn-claude-code.md) | | --- | --- | --- | | Tagline | A lightweight alternative to OpenClaw for secure agent execution | A nano claude code–like 「agent harness」, built from 0 to 1 | | Stars | 30,157 | 70,293 | | Forks | 12,892 | 11,457 | | Open issues | 828 | 54 | | Language | TypeScript | Python | | Adopt for | NanoClaw is a lightweight alternative to OpenClaw, designed specifically to run agents securely in isolated containers and support multiple messaging platforms. | The repository focuses on developing a minimalistic agent harness using Python and emphasizes that agency comes from model training rather than external code orchestration. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [nanoclaw](/tools/nanocoai-nanoclaw.md) | [learn-claude-code](/tools/shareai-lab-learn-claude-code.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 11d | | Open issues (now) | 828 | 54 | | Security scan | 2 low (2 low) | 1 low (1 low) | | Full report | [trust report](/tools/nanocoai-nanoclaw/trust.md) | [trust report](/tools/shareai-lab-learn-claude-code/trust.md) | **Typed relationship:** nanoclaw _(successor)_ learn-claude-code NanoClaw can be seen as a more evolved version of learn-claude-code with enhancements towards security and lightweight design. Coexists - Both tools coexist in the ecosystem, offering different levels of complexity for developing AI agents. ## Decision facts: nanoclaw - **Adopt for:** NanoClaw is a lightweight alternative to OpenClaw, designed specifically to run agents securely in isolated containers and support multiple messaging platforms. ## Decision facts: learn-claude-code - **Pricing:** freemium - The repository is available under the MIT License, which permits free use and modification of the code. There are no direct cost-incurring components mentioned in the provided data. - **Requirements:** Min 1 GB RAM; The repository highlights that Bash is all you need to start, implying a minimal requirement for system specifications. No Docker or complex setup needed. - **Adopt for:** The repository focuses on developing a minimalistic agent harness using Python and emphasizes that agency comes from model training rather than external code orchestration. ## Choose when ### Choose nanoclaw if… - nanoclaw is primarily TypeScript; learn-claude-code is Python. - NanoClaw can be seen as a more evolved version of learn-claude-code with enhancements towards security and lightweight design. - Tags unique to nanoclaw: claude-skills, openclaw, ai-assistant, agents-sdk. - - When you need a secure execution environment for AI agents that runs in OS-level isolated containers rather than with shared memory. ### Choose learn-claude-code if… - learn-claude-code is primarily Python; nanoclaw is TypeScript. - Pricing: The repository is available under the MIT License, which permits free use and modification of the code. There are no direct cost-incurring components mentioned in the provided data.. - Requirements: Min 1 GB RAM; The repository highlights that Bash is all you need to start, implying a minimal requirement for system specifications. No Docker or complex setup needed.. - NanoClaw can be seen as a more evolved version of learn-claude-code with enhancements towards security and lightweight design. - Tags unique to learn-claude-code: agent-development, llm, python, educational. - Also covers Developer Tools. - - Prefer 'learn-claude-code' if you are looking for an educational tool to understand how to build an AI agent harness with a focus on simplicity. It teaches the principle of minimizing external code. ## When NOT to use nanoclaw - - If your project requires advanced features or configurations not supported by NanoClaw’s lightweight design. - - If you are uncomfortable with setting up Docker containers for each agent and prefer a more integrated solution without isolation at the OS level. ## When NOT to use learn-claude-code - - Avoid 'learn-claude-code' if your requirement is for an out-of-the-box, more feature-rich or production-ready AI agent development framework. It might be too simplistic for complex projects. - - If you need a comprehensive set of built-in functionalities and sophisticated orchestration features in your agent development project, 'learn-claude-code' may not be the best fit due to its minimal - - This tool isn't suitable if you require an advanced or heavily optimized AI harness that requires deep integration with external systems, as it focuses on a minimalist approach. ## Common questions ### What is the difference between nanoclaw and learn-claude-code? nanoclaw: A lightweight alternative to OpenClaw for secure agent execution. learn-claude-code: A nano claude code–like 「agent harness」, built from 0 to 1. See the comparison table for live GitHub stats and shared categories. ### When should I choose nanoclaw over learn-claude-code? Choose nanoclaw over learn-claude-code when nanoclaw is primarily TypeScript; learn-claude-code is Python; NanoClaw can be seen as a more evolved version of learn-claude-code with enhancements towards security and lightweight design; Tags unique to nanoclaw: claude-skills, openclaw, ai-assistant, agents-sdk; - When you need a secure execution environment for AI agents that runs in OS-level isolated containers rather than with shared memory. ### When should I choose learn-claude-code over nanoclaw? Choose learn-claude-code over nanoclaw when learn-claude-code is primarily Python; nanoclaw is TypeScript; Pricing: The repository is available under the MIT License, which permits free use and modification of the code. There are no direct cost-incurring components mentioned in the provided data.; Requirements: Min 1 GB RAM; The repository highlights that Bash is all you need to start, implying a minimal requirement for system specifications. No Docker or complex setup needed.; NanoClaw can be seen as a more evolved version of learn-claude-code with enhancements towards security and lightweight design; Tags unique to learn-claude-code: agent-development, llm, python, educational; Also covers Developer Tools; - Prefer 'learn-claude-code' if you are looking for an educational tool to understand how to build an AI agent harness with a focus on simplicity. It teaches the principle of minimizing external code. ### When should I avoid nanoclaw? - If your project requires advanced features or configurations not supported by NanoClaw’s lightweight design. - If you are uncomfortable with setting up Docker containers for each agent and prefer a more integrated solution without isolation at the OS level. ### When should I avoid learn-claude-code? - Avoid 'learn-claude-code' if your requirement is for an out-of-the-box, more feature-rich or production-ready AI agent development framework. It might be too simplistic for complex projects. - If you need a comprehensive set of built-in functionalities and sophisticated orchestration features in your agent development project, 'learn-claude-code' may not be the best fit due to its minimal - This tool isn't suitable if you require an advanced or heavily optimized AI harness that requires deep integration with external systems, as it focuses on a minimalist approach. ### Is nanoclaw or learn-claude-code more popular on GitHub? learn-claude-code has more GitHub stars (70,293 vs 30,157). Stars measure visibility, not whether either tool fits your constraints. ### Are nanoclaw and learn-claude-code open source? Yes - both are open-source projects on GitHub (nanoclaw: MIT, learn-claude-code: MIT). ### Where can I find alternatives to nanoclaw or learn-claude-code? GraphCanon lists graph-backed alternatives at /tools/nanocoai-nanoclaw/alternatives and /tools/shareai-lab-learn-claude-code/alternatives (/tools/nanocoai-nanoclaw/alternatives.md, /tools/shareai-lab-learn-claude-code/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 /compare/nanocoai-nanoclaw-vs-shareai-lab-learn-claude-code.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, nanoclaw or learn-claude-code? nanoclaw: Very active. learn-claude-code: 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 nanoclaw and learn-claude-code? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: nanoclaw: /tools/nanocoai-nanoclaw/trust; learn-claude-code: /tools/shareai-lab-learn-claude-code/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nanocoai-nanoclaw`](/api/graphcanon/graph?tool=nanocoai-nanoclaw) - 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/_