--- title: "semble vs agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/minishlab-semble-vs-wshobson-agents" tools: ["minishlab-semble", "wshobson-agents"] --- # semble vs agents *GraphCanon updated Aug 22, 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 agents if the agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode. [semble](https://minish.ai/packages/semble/introduction/) reports 5.9k GitHub stars, 256 forks, and 3 open issues, last pushed Aug 12, 2026. [agents](https://sethhobson.com) has 39k stars, 4.1k forks, and 5 open issues, last pushed Aug 18, 2026. Figures are from public GitHub metadata via [semble's repository](https://github.com/MinishLab/semble) and [agents's repository](https://github.com/wshobson/agents). | | [semble](/tools/minishlab-semble.md) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Tagline | Fast and Accurate Code Search for Agents | Multi-harness agentic plugin marketplace for various AI agents | | Stars | 5,927 | 38,928 | | Forks | 256 | 4,145 | | Open issues | 3 | 5 | | 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. | The agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | 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) | [agents](/tools/wshobson-agents.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 10d | 1d | | Open issues (now) | 3 | 5 | | Stars delta | +247 (30d) | +860 (30d) | | Open issues delta | -4 (30d) | +2 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/minishlab-semble/trust.md) | [trust report](/tools/wshobson-agents/trust.md) | ## Shared compatibility - **Cursor**: [semble](/tools/minishlab-semble.md) - Works with Cursor; [agents](/tools/wshobson-agents.md) - Works with Cursor ## 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: agents - **Adopt for:** The agents tool is a marketplace for plugins that enhances multiple AI agents, offering integration and management capabilities across several platforms, including Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot ## Choose when ### Choose semble if… - Requirements: Operating with Python, Semble does not require Docker for its operation.. - Tags unique to semble: agents, code-search, embeddings, 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 agents if… - Tags unique to agents: agent-skills, agentic-ai, automation, prompt-engineering. - Also covers Developer Tools. - You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments ## 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 agents - You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support - Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem ## Common questions ### What is the difference between semble and agents? semble: Fast and Accurate Code Search for Agents. agents: Multi-harness agentic plugin marketplace for various AI agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose semble over agents? Choose semble over agents when Requirements: Operating with Python, Semble does not require Docker for its operation.; Tags unique to semble: agents, code-search, embeddings, 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 agents over semble? Choose agents over semble when Tags unique to agents: agent-skills, agentic-ai, automation, prompt-engineering; Also covers Developer Tools; You are working specifically within the ecosystems of Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, or Gemini CLI, as it provides tailored plugins for these environments. ### 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 agents? You are working solely within a niche environment that isn't one of the supported platforms (like Claude Code, Codex CLI, etc.) because it may not offer compatible plugins or extensive support Your project requirements do not include interoperability between multiple AI agents and you only need to leverage functionalities from a single AI agent with a robust in-built plugin ecosystem ### Is semble or agents more popular on GitHub? agents has more GitHub stars (38,928 vs 5,927). Stars measure visibility, not whether either tool fits your constraints. ### Are semble and agents open source? Yes - both are open-source projects on GitHub (semble: MIT, agents: MIT). ### Where can I find alternatives to semble or agents? GraphCanon lists graph-backed alternatives at [semble alternatives](/tools/minishlab-semble/alternatives) and [agents alternatives](/tools/wshobson-agents/alternatives) ([semble markdown twin](/tools/minishlab-semble/alternatives.md), [agents markdown twin](/tools/wshobson-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/minishlab-semble-vs-wshobson-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, semble or agents? semble: Active. 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 semble and agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [semble trust report](/tools/minishlab-semble/trust); [agents trust report](/tools/wshobson-agents/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/_