--- title: "semble vs learn-claude-code" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minishlab-semble-vs-shareai-lab-learn-claude-code" tools: ["minishlab-semble", "shareai-lab-learn-claude-code"] --- # semble vs learn-claude-code *GraphCanon updated Jul 12, 2026* ## Verdict Pick semble if semble is a Python-based tool that facilitates fast and accurate code search for AI agents with up to 98% fewer tokens compared to traditional grep+read methods; pick learn-claude-code if learn-Claude-Code is a minimalistic development tool leveraging Bash and Python to build an agent harness inspired by Claude coding concepts. [semble](https://minish.ai/packages/semble/introduction/) reports 5.6k GitHub stars, 236 forks, and 8 open issues, last pushed Jul 8, 2026. [learn-claude-code](https://learn.shareai.run) has 71k stars, 12k forks, and 62 open issues, last pushed Jun 26, 2026. Figures are from public GitHub metadata via [semble's repository](https://github.com/MinishLab/semble) and [learn-claude-code's repository](https://github.com/shareAI-lab/learn-claude-code). | | [semble](/tools/minishlab-semble.md) | [learn-claude-code](/tools/shareai-lab-learn-claude-code.md) | | --- | --- | --- | | Tagline | Fast and Accurate Code Search for Agents | Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1 | | Stars | 5,581 | 70,653 | | Forks | 236 | 11,506 | | Open issues | 8 | 62 | | Language | Python | Python | | Adopt for | Semble is a Python-based tool that facilitates fast and accurate code search for AI agents with up to 98% fewer tokens compared to traditional grep+read methods. | Learn-Claude-Code is a minimalistic development tool leveraging Bash and Python to build an agent harness inspired by Claude coding concepts. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | AI Agents, Data & Retrieval | AI Agents, Developer Tools | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [semble](/tools/minishlab-semble.md) | [learn-claude-code](/tools/shareai-lab-learn-claude-code.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 3d | 14d | | Open issues (now) | 8 | 62 | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/minishlab-semble/trust.md) | [trust report](/tools/shareai-lab-learn-claude-code/trust.md) | ## Decision facts: semble - **Requirements:** Operating with Python, Semble does not require Docker for its operation. - **Adopt for:** Semble is a Python-based tool that facilitates fast and accurate code search for AI agents with up to 98% fewer tokens compared to traditional grep+read methods. ## Decision facts: learn-claude-code - **Requirements:** Min 1 GB RAM - **Adopt for:** Learn-Claude-Code is a minimalistic development tool leveraging Bash and Python to build an agent harness inspired by Claude coding concepts. - **License detail:** MIT License ## Choose when ### Choose semble if… - Requirements: Operating with Python, Semble does not require Docker for its operation.. - Tags unique to semble: embeddings, agents, retrieval, mcp. - Also covers Data & Retrieval. - - Use Semble when you are specifically working with AI agents or models and require efficient, token-economical code search operations. ### Choose learn-claude-code if… - Requirements: Min 1 GB RAM. - Tags unique to learn-claude-code: agent-development, llm, python, educational. - Also covers Developer Tools. - When you prefer leveraging both Bash scripting and Python for developing AI agents. ## When NOT to use semble - - Avoid using Semble if your use case does not involve AI agents or the model-context-protocol (MCP). Competitor tools might offer better features tailored to non-agent-based code search. - - Not recommended in scenarios where token efficiency is not a concern, as competitors may provide more versatile functionalities without focusing on token reduction. ## When NOT to use learn-claude-code - For projects requiring extensive front-end integration with complex UI frameworks as Learn-Claude-Code focuses on backend scripting and Python. - If your project needs a fully-fledged development suite; Learn-Claude-Code offers a more streamlined, educational approach rather than comprehensive feature-rich suites. ## Common questions ### What is the difference between semble and learn-claude-code? semble: Fast and Accurate Code Search for Agents. learn-claude-code: Bash is all you need - 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 semble over learn-claude-code? Choose semble over learn-claude-code when Requirements: Operating with Python, Semble does not require Docker for its operation.; Tags unique to semble: embeddings, agents, retrieval, mcp; Also covers Data & Retrieval; - Use Semble when you are specifically working with AI agents or models and require efficient, token-economical code search operations. ### When should I choose learn-claude-code over semble? Choose learn-claude-code over semble when Requirements: Min 1 GB RAM; Tags unique to learn-claude-code: agent-development, llm, python, educational; Also covers Developer Tools; When you prefer leveraging both Bash scripting and Python for developing AI agents. ### When should I avoid semble? - Avoid using Semble if your use case does not involve AI agents or the model-context-protocol (MCP). Competitor tools might offer better features tailored to non-agent-based code search. - Not recommended in scenarios where token efficiency is not a concern, as competitors may provide more versatile functionalities without focusing on token reduction. ### When should I avoid learn-claude-code? For projects requiring extensive front-end integration with complex UI frameworks as Learn-Claude-Code focuses on backend scripting and Python. If your project needs a fully-fledged development suite; Learn-Claude-Code offers a more streamlined, educational approach rather than comprehensive feature-rich suites. ### Is semble or learn-claude-code more popular on GitHub? learn-claude-code has more GitHub stars (70,653 vs 5,581). Stars measure visibility, not whether either tool fits your constraints. ### Are semble and learn-claude-code open source? Yes - both are open-source projects on GitHub (semble: MIT, learn-claude-code: MIT). ### Where can I find alternatives to semble or learn-claude-code? GraphCanon lists graph-backed alternatives at [semble alternatives](/tools/minishlab-semble/alternatives) and [learn-claude-code alternatives](/tools/shareai-lab-learn-claude-code/alternatives) ([semble markdown twin](/tools/minishlab-semble/alternatives.md), [learn-claude-code markdown twin](/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 [this comparison](/compare/minishlab-semble-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, semble or learn-claude-code? semble: 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 semble and learn-claude-code? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [semble trust report](/tools/minishlab-semble/trust); [learn-claude-code trust report](/tools/shareai-lab-learn-claude-code/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=minishlab-semble`](/api/graphcanon/graph?tool=minishlab-semble) - 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/_