Live-updating landscape of vector database projects, integrations, and benchmarks — refreshed every 15 minutes
⭐ Star this repo to bookmark — fresh data every 15 minutes
Automatically tracks and compares the vector database ecosystem by pulling live data from GitHub, package registries, and release feeds. Generates a structured, always-current comparison table covering maturity, integrations, language support, and recent activity for projects like pgvector, Qdrant, Chroma, Weaviate, and Milvus.
This list is auto-updated every 15 minutes by a GitHub Actions cron. Each commit reflects a real change in the upstream data source — new items added, expired items removed — so you can rely on what you see being current.
⏰ Last updated: 2026-06-01 20:15 UTC
Data source:
GitHub Search APIThe table below is rewritten on every cron tick. Star the repo to bookmark.
| # | Name | ⭐ | Lang | Updated | Description |
|---|---|---|---|---|---|
| 1 | linny006/rag-radar | 0 | Python | 2026-06-01 | Live tracker of new RAG implementations, tools, and patterns — updated every 15 minutes |
| 2 | SingularityHorizon/enterprise-schemas | 0 | — | 2026-06-01 | Public data structures and system architecture logic for agentic workflows. |
| 3 | structured-world/coordinode | 4 | Rust | 2026-06-01 | The graph-native hybrid retrieval engine for AI and GraphRAG. Graph + Vector + Full-Text in a single transactional engin |
| 4 | linny006/vector-db-live | 0 | Python | 2026-06-01 | Live-updating landscape of vector database projects, integrations, and benchmarks — refreshed every |
| 5 | fik93673/QueryMind-SQL | 1 | TypeScript | 2026-06-01 | Generate SQL queries from natural language text using an LLM-powered interface built with FastAPI, React, and LangChain. |
| 6 | Ramseydispensed499/ScholarAI-AI-Powered-Research-Assistant | 0 | TypeScript | 2026-06-01 | Streamline academic research and automate data analysis with this AI-powered assistant built for efficient literature re |
| 7 | Monkish-jewishryebread665/NodeMind | 0 | HTML | 2026-06-01 | Replace vector databases with binary document intelligence for efficient multimodal data search without GPUs. |
| 8 | Surging-scotandlot818/product-dev-blueprint | 0 | TypeScript | 2026-06-01 | Transform software concepts into structured build plans with this schema-first product planning tool for engineering tea |
| 9 | dgbac5672/LogN_DB-Core | 0 | C++ | 2026-06-01 | Build vector database engines in C++ with HNSW, KD-Tree, and brute force algorithms to understand internal search archit |
| 10 | Roseapplemutualopposition5427/rag-llamaindex-qdrant-docker | 0 | Dockerfile | 2026-06-01 | Build a multi-collection RAG system using LlamaIndex and Qdrant in a Docker environment. |
| 11 | constancywoodsy286/agentic-rag-for-practice | 1 | Python | 2026-06-01 | Build an agentic RAG document QA system with multi-user support, hybrid retrieval, reranking, and LangGraph workflows fo |
| 12 | wendyneat563/atlas.llm | 1 | Go | 2026-06-01 | Run local LLM coding tasks in a single Go binary with an interactive terminal interface, directory summarization, and se |
| 13 | PWDevens/fedacq-rag-chatbot | 0 | Python | 2026-06-01 | Federal Acquisition Regulation Retrieval-Augmented Generation (RAG) Chatbot |
| 14 | exact-treesquirrel5419/ai_osint | 1 | — | 2026-06-01 | Discover exposed AI infrastructure including LLMs, agents, and ML pipelines using curated OSINT dorks, queries, and reco |
| 15 | jefffergunson118-beep/smara | 1 | — | 2026-06-01 | Sync persistent memory across your AI tools and agents using a unified MCP server. |
| 16 | Tobiaszn8972/turboquant-gpu | 1 | Python | 2026-06-01 | Compress KV cache for LLM inference with 5.02x efficiency on NVIDIA GPUs using cuTile kernels. |
| 17 | sarcosomebankcheck694/coordinode | 0 | Rust | 2026-06-01 | Unify graph, vector, and full-text search in one Rust engine for GraphRAG and AI retrieval with OpenCypher and MVCC |
| 18 | Rippledirham767/LongParser | 0 | Python | 2026-06-01 | Parse PDFs, DOCX, PPTX, XLSX, and CSV into validated, AI-ready chunks for privacy-first RAG pipelines with HITL review |
| 19 | viviannenitrogenous100/mentedb | 0 | — | 2026-06-01 | Build an AI memory database for agents with a Rust storage engine designed for LLM data and fast retrieval |
| 20 | cornelablastular720/dbdb-index | 1 | Go | 2026-06-01 | Browse and index 1,071 database systems from DBDB.io for fast discovery, comparison, and research |
| 21 | Twentyeight-lawnchair711/AlayaRenderer | 0 | — | 2026-06-01 | Build an AI-native renderer for games and virtual worlds, with data and tools for world creation and editing |
| 22 | raymondmdzz123/agent-memory | 1 | TypeScript | 2026-06-01 | Store persistent AI agent memory with conversation history, vector search, knowledge base, and fact extraction in TypeSc |
| 23 | Keeterete513/llm-model-search-recommendation | 0 | Python | 2026-06-01 | Search and recommend HuggingFace ML models from plain English queries using RAG, fine-tuning, and LLM evaluation |
| 24 | Brakepedallap630/openmemory | 0 | Shell | 2026-06-01 | Automate OpenMemory MCP server start and stop across Claude Code, OpenCode, Codex CLI, and Gemini CLI for shared long-te |
| 25 | Ac3v3d0/semafold | 1 | Python | 2026-06-01 | Compress embeddings, retrieval vectors, and KV-cache with TurboQuant codecs for 10x smaller storage and NumPy-first AI w |
| 26 | ArcadeData/arcadedb | 909 | Java | 2026-06-01 | ArcadeDB Multi-Model Database, one DBMS that supports SQL, Cypher, Gremlin, HTTP/JSON, MongoDB and Redis. ArcadeDB is a |
| 27 | nguyenquoaca-hash/agentic-mesh | 0 | HTML | 2026-06-01 | Multi-Agent AI Orchestrator 2026 🚀 | YAML, 6+ LLM Providers, ReAct & Swarm |
| 28 | Synchronic-leafbud824/skill-vault | 1 | — | 2026-06-01 | Organize, secure, and find Claude Code skills in one vault for easy reuse |
| 29 | Genusophiophagussqueezeplay359/ragpipe | 0 | Python | 2026-06-01 | Build RAG pipelines in 3 functions for vector databases with zero config and support for Ollama, OpenAI, Qdrant, Pinecon |
| 30 | aziz5971/TalentLens | 2 | Python | 2026-06-01 | Analyze candidates with a multi-agent AI screening pipeline that matches skills, verifies background, and prepares inter |
| 31 | imgirish07/Scalable-RAG-Application | 1 | Python | 2026-06-01 | A production-grade Multi-Agent RAG (Retrieval-Augmented Generation) system designed for scalable, low-latency, and relia |
| 32 | Deafened-vascularstructure846/pageindex-rag | 1 | Python | 2026-06-01 | Build vectorless RAG for reasoning-based retrieval and answer generation with no embeddings or vector store |
| 33 | makr-code/ThemisDB | 7 | C++ | 2026-06-01 | Themis Database System - High-performance C++ hybrid-database (graph-vector-relational-file) with AQL support and MVCC |
| 34 | roberthalfway204/Document-Intelligent-Assistant | 1 | Python | 2026-06-01 | Transform scanned documents into searchable, structured data with OCR, AI extraction, and Python automation |
| 35 | lannyfervent952/fastapi-agent-blueprint | 1 | Python | 2026-06-01 | Build FastAPI AI agents with a clear blueprint for reliable, maintainable app architecture |
| 36 | Bogeymanlicitness496/mcp-memento | 1 | Python | 2026-06-01 | Store and manage persistent MCP memory with confidence tracking, relationship mapping, and knowledge quality control acr |
| 37 | Bielrezende/WMB-100K | 0 | — | 2026-06-01 | Benchmark AI memory systems for situational retrieval with 4.3M tokens and 2,708 questions in real-world scenarios |
| 38 | okeidontlike/Awareness-Local | 2 | JavaScript | 2026-06-01 | Add local persistent memory to AI coding agents with Markdown storage, hybrid search, and MCP support, all offline. |
| 39 | ahmahmahm/pdf-rag-assistant | 0 | — | 2026-06-01 | Build PDF Q&A with RAG, OpenAI, and ChromaDB for fast, local answers from policies, contracts, and resumes |
| 40 | whiteoakredguard557/llmfs | 1 | Python | 2026-06-01 | Store and retrieve persistent filesystem memory for LLMs and AI agents with structured, searchable context beyond token |
| 41 | User75621/memory-mcp | 1 | Python | 2026-06-01 | Store persistent project memory for AI agents and MCP clients so they can resume work with context, decisions, and tasks |
| 42 | elkalowkey885/gemini-embedding-2-mcp-server | 1 | Python | 2026-06-01 | Build a local MCP server that turns files, images, and video into fast spatial search for AI agents using Gemini embeddi |
| 43 | pencaudal526/system-design-bible | 2 | — | 2026-06-01 | Learn system design for the AI era with clear patterns, diagrams, and production-ready guidance beyond the Primer |
| 44 | plastic-labs/honcho | 4619 | Python | 2026-06-01 | Memory library for building stateful agents |
| 45 | Uncomfortable-filagree112/OpenViking | 0 | — | 2026-06-01 | Provide a scalable context database designed to improve memory and data handling for AI agents in complex tasks. |
| 46 | Daubingweirdie414/multimodal-rag | 2 | Python | 2026-06-01 | Enable unified search and AI answers across text, images, audio, and video using multimodal retrieval-augmented generati |
| 47 | Ekaterinaacid284/memory-lancedb-pro-skill | 0 | — | 2026-06-01 | Provide Claude Code with expert guidance to install, configure, and optimize memory-lancedb-pro for long-term AI memory |
| 48 | mad011408/IndexCache | 0 | — | 2026-06-01 | Accelerate DeepSeek Sparse Attention models by reusing cross-layer indexes to cut computations and speed up inference wi |
| 49 | cornellebivalved856/example-multimodal-rag | 0 | Python | 2026-06-01 | Search and retrieve information from text, images, and video using unified Gemini Embedding 2 vectors with Supabase and |
| 50 | decided-indication109/AI-Engineer-in-90-Days | 1 | Mermaid | 2026-06-01 | Build practical AI systems and agents in 90 days with a clear, project-based roadmap for developers seeking hands-on AI |
Every 15 minutes, a GitHub Action runs tracker.py. That script:
- Fetches the latest state from
GitHub Search API. - Diffs against
data/items.json(the previous snapshot). - Rewrites the table above between the
<!-- TRACKER_TABLE_* -->markers. - Commits
feat: +N added, -M removed (timestamp)if anything changed.
No external services. No paid APIs. Just a public data source and a free GitHub Action.
See CONTRIBUTING.md — usually you don't need to: the tracker keeps itself current.
If you spot a data-source bug or want to suggest a new column for the table, open
an issue.
If you find this useful, you might also like these other auto-updated trackers from the same maintainer — same mechanism, different upstream:
- trending-claude-skills — What's shipping in Claude Skills this week (
topic:claude-skills) - mcp-servers-live — Live index of newest MCP servers (
topic:mcp-server) - cursor-rules-live — Newest Cursor rules and .cursorrules patterns (
topic:cursor-rules) - claude-code-plugin-tracker — Claude Code plugins and hook configs (
topic:claude-code) - llm-agents-radar — Newest LLM agent frameworks (
topic:llm-agent) - rag-radar — Newest RAG implementations and tools (
topic:rag) - llm-eval-tracker — Newest LLM evaluation tools and benchmarks (
topic:llm-eval) - agent-framework-radar — Newest agent frameworks shipping on GitHub (
topic:agent-framework) - llmops-radar — Newest LLMOps tooling (observability, deployment) (
topic:llmops) - prompt-tools-live — Newest prompt-engineering tools and prompt repos (
topic:prompt-engineering) - agent-eval-harness — Live benchmark of AI coding agents (
topic:llm-eval) - skills-tracker — Tracking new GitHub 'skills' repos (
topic:agent-skills) - awesome-agent-skills — Curated auto-updated awesome-list of AI agent skills (
topic:agent-skills)
MIT — see LICENSE.