pgvector vs Qdrant for multi-agent systems: Which Should You Use?

pgvector is not a vector database product in the same sense as Qdrant. It is a PostgreSQL extension that adds vector search to your existing relational stack, while Qdrant is a purpose-built vector database with filtering, payload indexing, and distributed search as first-class features.

For multi-agent systems, use Qdrant unless your agents already live inside Postgres and your retrieval needs are small and simple.

Quick Comparison

When pgvector Wins

Use pgvector when your multi-agent system is already built around PostgreSQL. If your agents persist task state, conversation history, tool outputs, and embeddings in the same database, keeping everything in one place reduces complexity fast.

It also wins when your retrieval layer is simple. If you only need cosine or L2 similarity over a few hundred thousand rows and basic metadata filtering via SQL WHERE clauses, pgvector is enough.

It is the right call when you need transactional consistency between agent state and embeddings. A common pattern is storing an agent’s memory record and its embedding in the same transaction so you never end up with dangling references or partially written state.

It also fits teams that want zero new infrastructure. If your platform team already standardizes on Postgres backups, monitoring, access control, replication, and failover, adding the vector extension is operationally cleaner than introducing another datastore.

Typical pgvector patterns look like this:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE agent_memory (
  id bigserial PRIMARY KEY,
  agent_id text NOT NULL,
  content text NOT NULL,
  embedding vector(1536),
  created_at timestamptz DEFAULT now()
);

CREATE INDEX ON agent_memory USING hnsw (embedding vector_cosine_ops);

That setup works well for:

• •agent scratchpads
• •conversation recall
• •lightweight semantic search
• •SQL-first teams that want one datastore

When Qdrant Wins

Use Qdrant when retrieval is the core of the system. Multi-agent systems tend to generate lots of intermediate artifacts: plans, observations, tool outputs, summaries, entity graphs. Qdrant handles those as collections with payloads and filtering built in.

It wins hard on filtering. In multi-agent workflows you rarely search by vector alone; you search by agent_id, tenant ID, workflow stage, document type, timestamp range, or permission scope. Qdrant’s payload indexing and filter model are built for that.

It also wins when scale matters. If multiple agents are querying memory concurrently while other agents are writing new context constantly, a dedicated vector store gives you better isolation than overloading Postgres with ANN workloads.

Qdrant is also the better fit when you want clean programmatic APIs instead of SQL gymnastics. Its REST and gRPC interfaces make it straightforward to integrate into services that are not database-centric.

A typical Qdrant collection setup looks like this:

from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance

client = QdrantClient(url="http://localhost:6333")

client.create_collection(
    collection_name="agent_memory",
    vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)

And filtered search is exactly what multi-agent systems need:

results = client.search(
    collection_name="agent_memory",
    query_vector=query_embedding,
    limit=5,
    query_filter={
        "must": [
            {"key": "agent_id", "match": {"value": "planner-1"}},
            {"key": "tenant_id", "match": {"value": "bank-a"}}
        ]
    }
)

That kind of filter-first retrieval is where Qdrant earns its keep.

Qdrant is the right choice for:

• •shared memory across many agents
• •tenant-isolated retrieval
• •semantic routing before tool execution
• •large-scale RAG pipelines
• •payload-heavy search where metadata matters as much as similarity

For multi-agent systems Specifically

Pick Qdrant. Multi-agent systems are not just “vector search with more threads”; they need fast filtered retrieval over messy operational context: who produced this memory item, which workflow owns it, whether it belongs to this tenant, whether it is stale. Qdrant handles those access patterns natively instead of forcing you to bolt them onto SQL queries.

Use pgvector only if your system is already Postgres-first and your agent memory layer stays small. The moment retrieval becomes central to coordination between agents — planning, delegation checksums of context — Qdrant is the better tool.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.