--- title: "search vs langchain_semantic_search" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelindar-search-vs-venuv-langchain-semantic-search" tools: ["kelindar-search", "venuv-langchain-semantic-search"] --- # search vs langchain_semantic_search *GraphCanon updated Sep 20, 2026* ## Verdict Pick search if search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework; pick langchain_semantic_search if builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. [search](https://github.com/kelindar/search) reports 559 GitHub stars, 24 forks, and 5 open issues, last pushed Sep 19, 2026. [langchain_semantic_search](https://github.com/venuv/langchain_semantic_search) has 44 stars, 8 forks, and 0 open issues, last pushed Feb 7, 2023. Figures are from public GitHub metadata via [search's repository](https://github.com/kelindar/search) and [langchain_semantic_search's repository](https://github.com/venuv/langchain_semantic_search). | | [search](/tools/kelindar-search.md) | [langchain_semantic_search](/tools/venuv-langchain-semantic-search.md) | | --- | --- | --- | | Tagline | Go library for embedded vector search and semantic embeddings with llamacpp | Semantic search for Google Drive files using GPT3, LangChain, and Python | | Stars | 559 | 44 | | Forks | 24 | 8 | | Open issues | 5 | 0 | | Language | Go | Jupyter Notebook | | Adopt for | search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. | Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [search](/tools/kelindar-search.md) | [langchain_semantic_search](/tools/venuv-langchain-semantic-search.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1320d | | Open issues (now) | 5 | 0 | | Stars delta | +4 (30d) | 0 (30d) | | Full report | [trust report](/tools/kelindar-search/trust.md) | [trust report](/tools/venuv-langchain-semantic-search/trust.md) | ## Decision facts: search - **Adopt for:** search is a Go library enabling embedded vector search and semantic embeddings via the llama.cpp framework. ## Decision facts: langchain_semantic_search - **Adopt for:** Builds a FAISS index for semantic search over Google Drive files using LangChain, GPT3, Jupyter Notebook. ## Choose when ### Choose search if… - search is primarily Go; langchain_semantic_search is Jupyter Notebook. - Tags unique to search: ai, bert, embeddings, gguf. - Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go ### Choose langchain_semantic_search if… - langchain_semantic_search is primarily Jupyter Notebook; search is Go. - Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain. - Need semantic search capabilities specifically for your own documents in Google Drive ## When NOT to use search - Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities - Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ## When NOT to use langchain_semantic_search - Seeking a solution that supports large-scale, real-time or non-Google Drive document collections - Require a fully integrated end-to-end service without configuration for drive paths ## Common questions ### What is the difference between search and langchain_semantic_search? search: Go library for embedded vector search and semantic embeddings with llamacpp. langchain_semantic_search: Semantic search for Google Drive files using GPT3, LangChain, and Python. See the comparison table for live GitHub stats and shared categories. ### When should I choose search over langchain_semantic_search? Choose search over langchain_semantic_search when search is primarily Go; langchain_semantic_search is Jupyter Notebook; Tags unique to search: ai, bert, embeddings, gguf; Use for projects needing a lightweight, fast integration of semantic search capabilities within applications written in Go. ### When should I choose langchain_semantic_search over search? Choose langchain_semantic_search over search when langchain_semantic_search is primarily Jupyter Notebook; search is Go; Tags unique to langchain_semantic_search: faiss, google drive, gpt3, langchain; Need semantic search capabilities specifically for your own documents in Google Drive. ### When should I avoid search? Avoid if relying on out-of-the-box support beyond Go or requiring heavy customization that is not supported directly by llama.cpp's capabilities Not suitable when a more comprehensive database service with extensive querying and integration features is desired over an embedded solution ### When should I avoid langchain_semantic_search? Seeking a solution that supports large-scale, real-time or non-Google Drive document collections Require a fully integrated end-to-end service without configuration for drive paths ### Is search or langchain_semantic_search more popular on GitHub? search has more GitHub stars (559 vs 44). Stars measure visibility, not whether either tool fits your constraints. ### Are search and langchain_semantic_search open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to search or langchain_semantic_search? GraphCanon lists graph-backed alternatives at [search alternatives](/tools/kelindar-search/alternatives) and [langchain_semantic_search alternatives](/tools/venuv-langchain-semantic-search/alternatives) ([search markdown twin](/tools/kelindar-search/alternatives.md), [langchain_semantic_search markdown twin](/tools/venuv-langchain-semantic-search/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/kelindar-search-vs-venuv-langchain-semantic-search.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, search or langchain_semantic_search? search: Very active. langchain_semantic_search: Dormant. 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 search and langchain_semantic_search? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [search trust report](/tools/kelindar-search/trust); [langchain_semantic_search trust report](/tools/venuv-langchain-semantic-search/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelindar-search`](/api/graphcanon/graph?tool=kelindar-search) - 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/_