CATEGORY INTELLIGENCE / WORLDWIDE
Vector databases
The retrieval infrastructure behind AI products.
Needs validation · Preliminary recommendation
Mixed search signals
The time windows or related search terms disagree. Narrow the use case and compare the original curves before making a market claim.
Report dated · Method 1.2.0 · low evidence confidence
- 663 active projects match the published GitHub search scope.
- The keyword “vector database” has 104 complete weeks; 0% are reported as zero.
Quadrant not yet established
What to do next
- Inspect the leading projects and their unresolved issues to identify a specific user problem.
- Ask potential users how they solve that problem today and what would make them switch.
- Repeat the scan with a familiar search phrase and compare the evidence before committing.
- Matching active GitHub projects
- 663
- Search-interest growth
- -18% · Last 8 complete weeks vs previous 8
- Search term and region
- vector database · Worldwide
- Complete weekly observations
- 104
Why this classification
- Search attention is cooling recently but remains above the same period last year. A pullback is not a long-term decline.
- Median weekly search interest fell 18% across two consecutive eight-week windows.
- 663 matching active repositories; this is search coverage, not a count of direct competitors.
- Four-week search change: -35%; thirteen-week change: 19%. These windows check the direction of the eight-week comparison.
Source evidence
GitHub repository search · topic:vector-database fork:false archived:false stars:>=5 pushed:>=2026-03-20
Collected 2026-09-16T04:46:21.995Z
Google Trends search interest · Collected 2026-09-16T04:46:21.995Z
Leading repositories
| Repository | Stars | Description |
|---|---|---|
| Mintplex-Labs/anything-llm | 66,083 | Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience |
| meilisearch/meilisearch | 59,301 | A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications. |
| pathwaycom/llm-app | 58,927 | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more. |
| run-llama/llama_index | 52,177 | LlamaIndex is the document processing platform for AI |
| milvus-io/milvus | 46,123 | Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search |
| VectifyAI/PageIndex | 35,654 | 📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG |
| qdrant/qdrant | 34,585 | Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/ |
| topoteretes/cognee | 30,710 | Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine. |
| NirDiamant/RAG_Techniques | 29,503 | This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial. |
| weaviate/weaviate | 16,814 | Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database. |
Limits of this result
- Google Trends measures relative search attention, not customers, revenue or willingness to pay.
- Supply counts active repositories matching the displayed GitHub topic and phrase queries; unmatched and closed-source competitors are outside this coverage.
- Short and longer search windows or related terms do not agree. A single market label would overstate the evidence.
- Repository density and search attention are separate observations. Neither proves commercial competition or demand.
Search interest measures attention, not paying customers. Classification thresholds are published heuristics and still require empirical calibration.
Use and share the evidence
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