pgvector vs Ragas for AI agents: Which Should You Use?

pgvector and Ragas solve different problems, and confusing them is where teams waste time. pgvector is a vector storage and retrieval extension for PostgreSQL; Ragas is an evaluation framework for measuring how good your RAG pipeline or agent behavior actually is. For AI agents, use pgvector for retrieval infrastructure and Ragas for quality checks and regression testing.

Quick Comparison

When pgvector Wins

Use pgvector when retrieval needs to live inside your existing transactional system. If your app already runs on PostgreSQL, adding vector columns and querying with <->, <=>, or <#> is cleaner than introducing another service.

It wins hard in these cases:

• •

  You need one database for app data + embeddings

  • •Store customer records, ticket history, policies, and embeddings in the same Postgres instance.
  • •Join semantic results with business data using normal SQL instead of stitching together two systems.
• •

  You need metadata filtering at query time

  • •pgvector plays well with WHERE tenant_id = ..., AND status = 'active', or date filters.
  • •That matters for agents that must respect tenant boundaries or only search approved documents.
• •

  You want predictable ops

  • •Postgres backups, replication, access control, observability, and migrations are already solved.
  • •You are not adding a separate vector database just to support semantic search.
• •

  You need hybrid retrieval

  • •Combine full-text search with embeddings in the same database.
  • •For agent systems that route between keyword lookup and semantic similarity, this is the sane default.

A typical setup looks like this:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE knowledge_base (
  id bigserial PRIMARY KEY,
  tenant_id bigint NOT NULL,
  content text NOT NULL,
  embedding vector(1536)
);

CREATE INDEX ON knowledge_base USING hnsw (embedding vector_cosine_ops);

Then your agent can retrieve candidates directly:

SELECT id, content
FROM knowledge_base
WHERE tenant_id = $1
ORDER BY embedding <=> $2
LIMIT 5;

That is production-friendly. It is also easy to reason about during incident response.

When Ragas Wins

Use Ragas when the question is not “where do I store vectors?” but “is my agent actually doing a good job?” That is a different layer entirely.

It wins in these cases:

• •

  You need automated evaluation of RAG quality

  • •Measure whether answers are grounded in retrieved context.
  • •Track metrics like faithfulness, answer_relevancy, context_precision, and context_recall.
• •

  You are comparing prompts, retrievers, or models

  • •Run experiments against multiple configurations.
  • •Use the same test set to see which version reduces hallucinations or improves groundedness.
• •

  You need regression testing before deployment

  • •Build a benchmark set from real queries.
  • •Catch quality drops when someone changes chunking logic, embedding models, or prompt templates.
• •

  You care about agent behavior beyond retrieval

  • •For multi-step agents, you need to know whether the final output matches the evidence chain.
  • •Ragas gives you a structured way to score outputs instead of relying on manual review.

Ragas fits naturally into Python evaluation code:

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

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

That tells you whether your agent is producing answers that are supported by retrieved context. If you are shipping AI agents into regulated environments like banking or insurance, that matters more than raw similarity scores.

For AI agents Specifically

Use both, but do not confuse their roles. pgvector powers the memory layer: retrieval over policies, customer notes, claim history, internal docs. Ragas powers the QA layer: proving that your agent’s answers stay grounded and do not degrade after changes.

If I had to pick one for an AI agent stack today: choose pgvector first if you still need retrieval infrastructure. Choose Ragas second once you have real traffic or test cases and need to prove quality before rollout.

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.