The Vector Database Built for AI Agents
Persistent memory, low-latency retrieval, metadata filtering, and hybrid search — all in one API, priced for agent workloads. ZeroDB is the vector database designed around how AI agents actually work.
What makes a vector database suitable for AI agents?
AI agents have fundamentally different requirements than a generic RAG pipeline. They run in loops, accumulate state, coordinate with other agents, and need to retrieve memories keyed to users and sessions. Here are the six requirements every agent-ready vector database must meet.
Long-term persistent memory
Agents need to remember across sessions — not just within a single context window. A vector DB for agents must store, index, and return memories keyed to users, threads, or workflows.
Low-latency retrieval
Every agent loop includes at least one retrieval call. p50 latency under 50ms and p95 under 200ms is the difference between a snappy agent and one that stalls.
Metadata filtering
Agents query memories scoped by user, session, tool, time window, or importance. Pure vector similarity is not enough — you need structured filters alongside ANN search.
Hybrid search (vector + keyword)
Semantic search misses exact names, error codes, and rare tokens. Production agents combine dense vectors with BM25 or SQL predicates to avoid hallucinated retrievals.
Cost predictability
Agent workloads are bursty. A vector database priced per-vector or per-dimension gets expensive fast. You want pay-per-query or flat-tier pricing that scales with your active agents.
One API for all agent state
Agents juggle memories, files, events, tools, and structured data. Stitching together 4 services (vector store + S3 + Kafka + Postgres) is operational overhead you do not need.
Why generic vector stores break down for agent workloads
Pinecone, Weaviate, and Chroma were designed for document retrieval — not agents. Here is where they start to crack when you actually build production agent systems.
No per-user namespace isolation
Generic vector stores dump everything into one index. Agent platforms serving multiple users need hard isolation between memory stores.
No native memory semantics
You end up rebuilding "important", "summarize", "forget", "decay" on top of raw vector CRUD. ZeroDB ships these as first-class operations.
Expensive metadata filters
Pinecone, Weaviate, and others apply metadata filters after ANN lookup. For agents filtering by user+session+time, this kills recall and latency.
No event stream
Agent workflows are event-driven — "tool called", "memory updated", "goal completed". Generic vector DBs force you to bolt on Kafka or Redis streams.
How ZeroDB fits the agent memory + retrieval workflow
ZeroDB is built from the ground up for agent state. Every API is designed around what agents actually do: remember things, recall them later, filter by context, and act on the results. Here is the full agent loop, powered by a single API.
1. Remember
Agent stores an experience, observation, or tool result as a memory with semantic embedding + metadata.
2. Recall
Agent queries by intent — ZeroDB returns the most relevant memories filtered by user, session, and recency.
3. Reflect
Agent consolidates memories into summaries, updates importance scores, and forgets stale data.
ZeroDB vs generic vector databases
A side-by-side of what you get with ZeroDB versus what you would have to build on top of a generic vector store.
| Feature | ZeroDB | Generic Vector DB |
|---|---|---|
| Vector search (HNSW) | ✓ | ✓ |
| Per-user namespaces | ✓ built-in | Manual index sharding |
| Memory API (remember/recall/forget) | ✓ | ✗ |
| Metadata pre-filtering | ✓ SQL predicates | Post-filter (slow) |
| Hybrid search (vector + SQL) | ✓ | ✗ |
| File storage (S3-compatible) | ✓ | ✗ |
| Event streaming | ✓ | ✗ |
| Knowledge graph | ✓ | ✗ |
| Free tier | ✓ 100k vectors | Varies |
| MCP native | ✓ | ✗ |
Code examples
Storing agent memory and retrieving relevant context in a few lines of Python. ZeroDB handles embeddings, indexing, and metadata filtering — you just call the API.
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,
)Ready to build agents with persistent memory?
ZeroDB gives you the vector database, memory API, metadata filtering, and event stream your agents need — in one free-tier-friendly API.