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Memory & Cognition Layer for AI Agents

Pinecone. Mem0. Qdrant. Weaviate. LlamaIndex. Long-term memory, semantic search, and contextual awareness - connected, governed, and production-ready.

Curated by the Vinkius team — 5 production-ready MCP servers reviewed, tested, and ready to connect to your AI agents. Create your free account and start connecting in seconds — no infrastructure to manage, no code to write. Just plug in and let your agents work.

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Pinecone MCP Server

01MCP Server

Pinecone MCP Server

The #1 managed vector database - sub-10ms queries at billion-vector scale.

Pinecone is the industry standard for production vector search. This MCP Server connects your agent to serverless vector indexes with sub-10ms query latency, hybrid sparse-dense retrieval, and built-in metadata filtering. From semantic search to real-time RAG grounding - your agent gets instant access to billions of embeddings without managing a single shard.

Serverless vector indexing & search

Hybrid sparse-dense retrieval

Metadata filtering & namespaces

Connect your agent

Chroma

Chroma

Zero-config open-source embedding database

Milvus

Milvus

GPU-accelerated vector DB for billions of vectors

pgvector

pgvector

Vector similarity search inside PostgreSQL

LanceDB

LanceDB

Serverless vector DB with zero infrastructure

Mem0 MCP Server

02MCP Server

Mem0 MCP Server

The memory layer for AI agents - persistent recall across every session.

LLMs forget everything between sessions. Mem0 fixes that. This MCP Server gives your agent persistent, intelligent memory - it automatically extracts facts, preferences, and context from conversations and stores them across user, session, and agent scopes. Your agent remembers who it's talking to, what they care about, and what happened last time. No prompt stuffing, no token waste.

User, session & agent memory scopes

Automatic fact extraction & consolidation

Intelligent forgetting & memory decay

Connect your agent

Vectara

Vectara

End-to-end RAG with hallucination detection

R2R

R2R

Full-stack RAG engine with cited answers

Haystack

Haystack

Production RAG pipelines by deepset

LlamaCloud

LlamaCloud

Managed RAG parsing & data ingestion

Qdrant MCP Server

03MCP Server

Qdrant MCP Server

High-performance Rust-built vector engine - 97% memory reduction with binary quantization.

Built in Rust for raw speed, Qdrant is the vector database engineers choose when milliseconds matter. This MCP Server gives your agent access to HNSW-powered similarity search with advanced quantization, payload-based filtering, and multi-vector indexing. Qdrant reduces memory usage by up to 97% with binary quantization while maintaining search quality - critical for agents operating at enterprise scale.

HNSW similarity search with quantization

Payload-based metadata filtering

Multi-vector & multimodal indexing

Connect your agent

Supabase Vector

Supabase Vector

pgvector search inside the Supabase ecosystem

Redis Vector

Redis Vector

KNN vector search with sub-ms latency

MongoDB Atlas Vector

MongoDB Atlas Vector

Native vector search in your existing MongoDB

Elasticsearch Vector

Elasticsearch Vector

Dense vector kNN search at scale

Weaviate MCP Server

04MCP Server

Weaviate MCP Server

AI-native vector database - hybrid BM25 + vector search in a single query.

Weaviate combines vector and keyword search in a single query - and that matters more than benchmarks. This MCP Server gives your agent hybrid retrieval that blends BM25 keyword matching with dense vector similarity, built-in vectorization modules for text and images, and GraphQL-powered exploration. When your agent needs to find documents that match both meaning and exact terms, Weaviate delivers.

Hybrid BM25 + vector search

Built-in vectorization modules

GraphQL-powered data exploration

Connect your agent

Amazon Bedrock KB

Amazon Bedrock KB

AWS managed RAG & vector knowledge bases

Azure AI Search

Azure AI Search

Enterprise vector & semantic search on Azure

Cognee

Cognee

Knowledge graphs from unstructured data

Unstructured

Unstructured

Transform complex data into AI-ready inputs

LlamaIndex MCP Server

05MCP Server

LlamaIndex MCP Server

The #1 framework for RAG applications - ingest, index, and query any data source.

LlamaIndex is the connective tissue between your data and your LLM. This MCP Server lets your agent ingest documents from any source - PDFs, APIs, databases, wikis - and query them with structured or semantic retrieval. It handles chunking, embedding, indexing, and query planning so your agent doesn't have to. If you're building RAG, LlamaIndex is where you start.

Multi-source document ingestion

Structured & semantic query engines

Automatic chunking & index management

Connect your agent

Jina AI

Jina AI

Embeddings, reranking & semantic web search

Cohere Embed & Rerank

Cohere Embed & Rerank

Enterprise-grade embeddings & document reranking

Exa

Exa

Neural semantic search engine for AI agents

Linkup

Real-time AI search & RAG-ready content

Vector Search. RAG. Memory. Context. Ready for AI Agents.

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