AI Agent Databases: the data layer for agentic AI
AI agents generate and consume far more data than traditional software — state, conversation history, embeddings, documents and tool output. No single database handles every one of those workloads equally well, so production agent stacks usually combine several. This page surveys nine databases for AI agents and adds pgEdge Starfleet, a new Postgres platform that gives each coding agent its own database branch.
What an agent database has to get right
Five criteria to weigh before picking a database for an agent workload.
Data Model
Structured relational tables, flexible documents, vectors, or analytical columns — match the model to the data the agent actually produces.
Query & Retrieval
SQL, semantic search, metadata filtering, or hybrid keyword-plus-vector retrieval, depending on how the agent looks things up.
Scale & Performance
How the system copes with growing data volumes and many concurrent agents and users.
AI-Native Capabilities
Built-in vector search, embedding storage, MCP servers and integrations with agent frameworks.
Developer Experience
SDKs, documentation, branching and provisioning APIs, and how well it fits the team's existing ecosystem.
The AI agent database directory
PostgreSQL-based, document, in-memory and purpose-built vector databases — search or browse below.
Fully managed PostgreSQL integrated with the Databricks Data Intelligence Platform. Combines transactional storage with analytics and ML, offers semantic, keyword and hybrid retrieval through Lakebase Search, and is governed through Unity Catalog. Best for organizations already on Databricks that want agent state and analytics in one place.
Visit site →Serverless PostgreSQL that separates compute from storage, so each scales independently. Offers instant database branching for testing, pgvector for embeddings, and programmatic management through APIs and an MCP server — a natural home for transactional data and agent state.
Visit site →The mature open-source relational database with a vast extension ecosystem. With the pgvector extension it covers both the system of record (users, workflows, agent state) and moderate-scale vector search, and it is supported by every major cloud provider.
Visit site →A document database with flexible BSON schemas that suit evolving agent data such as conversation history and user-generated content. Atlas Vector Search combines similarity search with metadata filtering and aggregation, keeping operational and vector data in one system.
Visit site →An in-memory data platform with extremely low-latency reads and writes. Ideal for session state, conversation caching and frequently accessed data, with vector similarity search through the Redis Query Engine. Complements — rather than replaces — a durable system of record.
Visit site →A serverless vector database built for AI applications. Handles indexing, scaling and production infrastructure, with metadata filtering, hybrid search, integrated inference models and multitenancy for large-scale RAG and context retrieval.
Visit site →An open-source, AI-native vector database with a modular architecture: pluggable vectorizers, external embedding providers, keyword and hybrid search, reranking and multimodal retrieval. Available self-hosted or as a managed cloud service.
Visit site →An open-source vector database known for advanced payload filtering and indexing. Supports hybrid dense and sparse retrieval, multitenancy, distributed deployments and quantization for large collections, self-hosted or managed.
Visit site →An open-source vector database with a distributed architecture built for very large-scale similarity search. Offers multiple index types and metrics, metadata filtering, hybrid search and GPU acceleration, self-hosted or through Zilliz Cloud.
Visit site →A Postgres cloud platform launched 28 September 2026 that gives every coding agent its own copy-on-write database branch on standard community Postgres. Ships with the open-source Agentic AI Toolkit (MCP server with SafeSession, RAG API on pgvector, PostgREST API) and a path from hosted prototype to air-gapped production. From $25/month with a 14-day free trial.
Visit site →Choosing a database
Agents don't automatically need a vector database — many work fine on a relational system, and PostgreSQL with pgvector often covers both transactional and retrieval needs at moderate scale. Dedicated vector databases earn their place with very large embedding collections or specialized retrieval requirements.
| Choose | If you need… |
|---|---|
| Databricks Lakebase | Integrated operational, analytics and AI workloads on Databricks |
| Neon | Serverless PostgreSQL with branching and AI tooling |
| PostgreSQL | A mature relational database plus the pgvector extension |
| MongoDB | Flexible documents and vector search in one system |
| Redis | Low-latency caching and session state |
| Pinecone | Serverless vector database infrastructure |
| Weaviate | An open-source AI-native database with flexible hybrid search |
| Qdrant | Open-source vectors with advanced payload filtering |
| Milvus | Distributed, large-scale vector search |
| pgEdge Starfleet | An isolated Postgres branch per coding agent, with a route to on-prem or air-gapped production |
Many production AI applications use more than one database. A typical stack pairs PostgreSQL for transactional data, Redis for caching and session state, and a vector database such as Pinecone for semantic retrieval.