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

pgvector and DeepEval solve different problems, and treating them as substitutes is how teams waste time. pgvector is a Postgres extension for storing and searching embeddings with SQL; DeepEval is a testing and evaluation framework for LLM apps, including multi-agent workflows. For multi-agent systems, use pgvector for retrieval state and DeepEval for evaluation.

Quick Comparison

When pgvector Wins

• •

  You need agent memory inside your operational database

  If your agents need to remember customer context, policy history, case notes, or prior tool outputs, keep that data in Postgres with pgvector.

  You can store embeddings next to business records and query them with normal SQL:

  CREATE EXTENSION IF NOT EXISTS vector;
  
  CREATE TABLE agent_memory (
    id bigserial PRIMARY KEY,
    tenant_id uuid NOT NULL,
    role text NOT NULL,
    content text NOT NULL,
    embedding vector(1536)
  );
  
  CREATE INDEX ON agent_memory USING hnsw (embedding vector_cosine_ops);
  

  That matters when retrieval must respect tenant filters, audit constraints, or row-level security.

• •

  You need joins between vectors and relational data

  Multi-agent systems rarely work on embeddings alone. You usually need metadata like case ID, assigned agent, workflow stage, jurisdiction, or risk score.

  pgvector lets you do semantic search plus SQL filtering in one query instead of pushing data through a separate vector store.

• •

  You want one production system instead of two

  If your stack already runs on Postgres, adding pgvector is simpler than introducing another persistence layer.

  Fewer moving parts means easier backups, easier access control, easier observability, and fewer failure modes when agents are under load.

• •

  You need transactional behavior

  Agent workflows often write state after tool calls: extracted entities, selected actions, human-review flags.

  With pgvector in Postgres, you get ACID semantics around those writes instead of bolting consistency onto an external store.

When DeepEval Wins

• •

  You need to know whether the agents are actually good

  Storage does not tell you if your multi-agent orchestration works.

  DeepEval exists to score outputs using metrics like:

  • •AnswerRelevancyMetric
  • •FaithfulnessMetric
  • •ContextualPrecisionMetric
  • •GEval

  That is the right tool when you want to catch regressions before shipping a broken planner or supervisor agent.

• •

  You are comparing prompts, models, or orchestration strategies

  Multi-agent systems fail in subtle ways: one agent overcalls tools; another hallucinates handoffs; a router picks the wrong specialist.

  DeepEval gives you a repeatable harness to compare versions against test cases instead of arguing from anecdotes.

• •

  You need automated evaluation in CI

  A serious agent stack needs tests that fail when behavior drifts.

  DeepEval fits directly into Python test workflows so you can run evals on pull requests and block merges when quality drops below threshold.

• •

  You care about LLM-as-judge scoring

  For tasks where exact-match metrics are useless — summarization quality, policy reasoning, multi-step delegation — DeepEval’s judge-based metrics are the right hammer.

  It is built for subjective-but-repeatable evaluation where deterministic assertions do not capture reality.

For multi-agent systems Specifically

Use pgvector as the shared memory layer and DeepEval as the quality gate. Multi-agent systems need both retrieval infrastructure and behavioral validation; pgvector handles the former cleanly inside Postgres, while DeepEval tells you whether the orchestration actually produces correct outcomes.

If I had to pick only one for a multi-agent project starting today: choose pgvector if you are building the system itself; choose DeepEval if the system already exists and your job is to prove it works reliably.

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.