Vector DB Tools for AI Agents
Vector databases for semantic search and RAG. Store and retrieve embeddings for agent knowledge retrieval.
9 tools in this category
ActiveloopCLI
Deep Lake database for multimodal AI and agent memory
HelixDBCLI
Graph-vector database and knowledge infrastructure for agents
LanceDBCLI
Open-source multimodal lakehouse and vector database
Lantern
Open-source Postgres vector search for AI applications
PineconeMCP
Managed vector database for agent knowledge, retrieval, and memory
QdrantMCP
Open-source vector database and semantic memory for AI agents
Weaviate CloudMCP
Managed vector database with hybrid search and native MCP
ZeroEntropy
High-accuracy retrieval, reranking, and search APIs for agents
Zilliz CloudMCP
Managed Milvus vector database for scalable agent retrieval
How to compare Vector DB tools
Vector databases for semantic search and RAG. Store and retrieve embeddings for agent knowledge retrieval. Start by writing down the action the agent must complete, the data it may access, and the evidence an operator needs after the run. Those requirements are more useful than comparing long feature lists without a workload.
Test two or three providers with the same input set. Compare output quality, latency, error behavior, authentication, SDK fit, usage limits, and total cost at the expected volume. Verify whether retries are safe and whether the service exposes enough logs or identifiers to investigate a failed agent run.
4agent does not treat inclusion as an endorsement. Listings summarize public integration details and link to each provider so you can confirm current documentation, pricing, regional availability, data handling, and service terms before adoption.
Sources and verification
Use these primary sources to verify current behavior before making an implementation or purchasing decision. Product capabilities, limits, and prices can change after a 4agent listing is reviewed.