--- title: "octocode vs semble" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bgauryy-octocode-vs-minishlab-semble" tools: ["bgauryy-octocode", "minishlab-semble"] --- # octocode vs semble *GraphCanon updated Aug 22, 2026* ## Verdict Pick octocode if octocode is an MCP server that uses LLM patterns for semantic code research and context generation in real-time; 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. [octocode](https://octocode.ai/) reports 900 GitHub stars, 77 forks, and 2 open issues, last pushed Jul 25, 2026. [semble](https://minish.ai/packages/semble/introduction/) has 5.9k stars, 256 forks, and 3 open issues, last pushed Aug 12, 2026. Figures are from public GitHub metadata via [octocode's repository](https://github.com/bgauryy/octocode) and [semble's repository](https://github.com/MinishLab/semble). | | [octocode](/tools/bgauryy-octocode.md) | [semble](/tools/minishlab-semble.md) | | --- | --- | --- | | Tagline | MCP server for semantic code research with LLM patterns | Fast and Accurate Code Search for Agents | | Stars | 900 | 5,927 | | Forks | 77 | 256 | | Open issues | 2 | 3 | | Language | TypeScript | Python | | Adopt for | Octocode is an MCP server that uses LLM patterns for semantic code research and context generation in real-time. | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Data & Retrieval, LLM Frameworks | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [octocode](/tools/bgauryy-octocode.md) | [semble](/tools/minishlab-semble.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 1d | 10d | | Open issues (now) | 2 | 3 | | Stars delta | Unknown | +247 (30d) | | Open issues delta | Unknown | -4 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/bgauryy-octocode/trust.md) | [trust report](/tools/minishlab-semble/trust.md) | ## Shared compatibility - **Cursor**: [octocode](/tools/bgauryy-octocode.md) - Works with Cursor; [semble](/tools/minishlab-semble.md) - Works with Cursor - **VS Code**: [octocode](/tools/bgauryy-octocode.md) - Works with VS Code; [semble](/tools/minishlab-semble.md) - Works with VS Code ## Decision facts: octocode - **Adopt for:** Octocode is an MCP server that uses LLM patterns for semantic code research and context generation in real-time. ## 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. ## Choose when ### Choose octocode if… - octocode is primarily TypeScript; semble is Python. - Tags unique to octocode: agent, ai-tools, code-intelligence, context. - Also covers LLM Frameworks. - When you need to search across both public and private repositories based on your user permissions. ### Choose semble if… - semble is primarily Python; octocode is TypeScript. - Requirements: Operating with Python, Semble does not require Docker for its operation.. - Tags unique to semble: agents, embeddings, mcp, retrieval. - - Use Semble when you are specifically working with AI agents or models and require efficient, token-economical code search operations. ## When NOT to use octocode - If your use case does not require real-time semantic context generation from large language models (LLMs). - When you have no need for integrating with public and private repositories via their permissions system. ## 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. ## Common questions ### What is the difference between octocode and semble? octocode: MCP server for semantic code research with LLM patterns. semble: Fast and Accurate Code Search for Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose octocode over semble? Choose octocode over semble when octocode is primarily TypeScript; semble is Python; Tags unique to octocode: agent, ai-tools, code-intelligence, context; Also covers LLM Frameworks; When you need to search across both public and private repositories based on your user permissions. ### When should I choose semble over octocode? Choose semble over octocode when semble is primarily Python; octocode is TypeScript; Requirements: Operating with Python, Semble does not require Docker for its operation.; Tags unique to semble: agents, embeddings, mcp, retrieval; - Use Semble when you are specifically working with AI agents or models and require efficient, token-economical code search operations. ### When should I avoid octocode? If your use case does not require real-time semantic context generation from large language models (LLMs). When you have no need for integrating with public and private repositories via their permissions system. ### 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. ### Is octocode or semble more popular on GitHub? semble has more GitHub stars (5,927 vs 900). Stars measure visibility, not whether either tool fits your constraints. ### Are octocode and semble open source? Yes - both are open-source projects on GitHub (octocode: MIT, semble: MIT). ### Where can I find alternatives to octocode or semble? GraphCanon lists graph-backed alternatives at [octocode alternatives](/tools/bgauryy-octocode/alternatives) and [semble alternatives](/tools/minishlab-semble/alternatives) ([octocode markdown twin](/tools/bgauryy-octocode/alternatives.md), [semble markdown twin](/tools/minishlab-semble/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/bgauryy-octocode-vs-minishlab-semble.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, octocode or semble? octocode: Very active. semble: 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 octocode and semble? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [octocode trust report](/tools/bgauryy-octocode/trust); [semble trust report](/tools/minishlab-semble/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bgauryy-octocode`](/api/graphcanon/graph?tool=bgauryy-octocode) - 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/_