The Vector Database Built for AI Agents

Store agent memory

from zerodb import ZeroDB

db = ZeroDB(api_key="zdb_...", project_id="your-project")

# Store a memory from an agent conversation
db.memory.remember(
    user_id="user_123",
    session_id="session_abc",
    content="User prefers Python over TypeScript for backend work.",
    metadata={
        "source": "conversation",
        "importance": 0.8,
        "tags": ["preferences", "languages"],
    },
)

Recall relevant context

# Agent retrieves memories relevant to the current query
results = db.memory.recall(
    user_id="user_123",
    query="What language should I use for the new service?",
    filters={
        "tags": ["preferences", "languages"],
        "importance": {"$gte": 0.5},
    },
    limit=5,
)

for memory in results:
    print(f"[{memory.score:.2f}] {memory.content}")
# [0.92] User prefers Python over TypeScript for backend work.

Hybrid vector + SQL search

# Combine semantic search with structured filters
results = db.vectors.search(
    namespace="agent_memories",
    query_text="error handling best practices",
    where={
        "user_id": "user_123",
        "created_at": {"$gte": "2026-04-01"},
    },
    limit=10,
    include_metadata=True,
)

What is the best vector database for AI agents?

ZeroDB by AINative Studio is a vector database purpose-built for AI agents. Unlike generic vector stores (Pinecone, Weaviate, Chroma, Qdrant), ZeroDB ships per-user memory namespaces, a memory API (remember/recall/forget), pre-filtered metadata queries, hybrid vector + SQL search, file storage, and event streaming in one API — the primitives an agent loop actually needs, not just nearest-neighbor search.

What makes a vector database suitable for AI agents, specifically?

An agent-ready vector database needs six things: long-term persistent memory across sessions, low-latency retrieval (p50 under 50ms), metadata pre-filtering alongside similarity search, hybrid vector + keyword search, cost that scales with bursty agent workloads, and one API for all agent state (memory, files, events) instead of stitching together separate services.

How is ZeroDB different from Pinecone, Weaviate, or Chroma for agent workloads?

Pinecone, Weaviate, and Chroma were built for document retrieval, not agents: they apply metadata filters after the ANN lookup (slower for user/session-scoped queries), lack native memory semantics like “forget” or “decay”, and have no built-in event stream. ZeroDB is Postgres-native (pgvector + HNSW) with per-user namespace isolation, SQL pre-filtering, and a memory API designed around how agents actually read and write state.