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

pgvector and Ragas solve different problems, and that matters a lot in multi-agent systems. pgvector is a PostgreSQL extension for storing and querying embeddings with vector, ivfflat, and hnsw; Ragas is an evaluation framework for measuring retrieval and LLM system quality with metrics like faithfulness, answer_relevancy, and context_precision.

For multi-agent systems, use pgvector as your data layer and Ragas as your evaluation layer. If you force one to do the other’s job, you’ll build a brittle system.

Quick Comparison

When pgvector Wins

• •

  You need shared memory across agents

  Multi-agent systems usually need a common store for facts, prior decisions, retrieved documents, and conversation state. pgvector gives you one Postgres database where each agent can write embeddings and query them with SQL.

• •

  You already run PostgreSQL in production

  If your system already has Postgres for users, transactions, audit logs, or workflow state, adding pgvector is the cleanest move. You avoid introducing a separate vector database just to support semantic lookup.

• •

  You need transactional consistency

  Agents often write state while also retrieving context. With pgvector inside Postgres, you can wrap writes to agent memory and metadata updates in the same transaction.

• •

  You need hybrid filtering

  Multi-agent systems rarely search by similarity alone. pgvector works well when you combine vector similarity with filters like tenant ID, account type, region, workflow stage, or agent ownership.

A typical pattern looks like this:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE agent_memory (
  id bigserial PRIMARY KEY,
  agent_id text NOT NULL,
  tenant_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);

SELECT content
FROM agent_memory
WHERE tenant_id = 'bank-123'
ORDER BY embedding <=> $1
LIMIT 5;

That is production-grade behavior: one store for state plus retrieval.

When Ragas Wins

• •

  You need to know whether your agents are actually good

  A multi-agent system can look impressive while producing garbage answers or missing context repeatedly. Ragas tells you that with metrics like faithfulness, context_recall, answer_correctness, and context_entity_recall.

• •

  You are comparing prompts, tools, or agent orchestration strategies

  If you’re testing whether Agent A should call Agent B first, or whether planner-executor beats supervisor-worker for your workload, Ragas gives you repeatable evaluation harnesses instead of vibes.

• •

  You need regression testing before deployment

  Multi-agent systems drift fast when prompts change or tools get updated. Ragas lets you build test datasets and score runs so you can catch quality drops before they hit users.

• •

  Your bottleneck is quality measurement

  If the team keeps asking “is this better?” and nobody has a hard answer, Ragas is the right tool. It turns subjective review into measurable output.

A useful workflow is to evaluate retrieved context separately from final answers:

from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy

result = evaluate(
    dataset=testset,
    metrics=[faithfulness, answer_relevancy],
)
print(result)

That matters because multi-agent failures are often hidden in intermediate steps: bad retrieval, poor tool selection, weak handoffs between agents.

For multi-agent systems Specifically

Use pgvector to store shared memory and retrieve context across agents. Use Ragas to score whether the whole system is producing grounded answers and using context correctly.

If I had to pick one first for a real multi-agent build: pgvector. It solves the runtime problem; Ragas solves the evaluation problem later. Without reliable shared memory your agents will thrash; without Ragas you won’t know they’re thrashing until users complain.

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.