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

pgvector is a Postgres extension for vector similarity search. MongoDB is a general-purpose document database with built-in vector search through Atlas Vector Search, plus the rest of the document model around it. For AI agents, my default pick is pgvector unless you already run MongoDB for your core app and need vector search inside the same stack.

Quick Comparison

When pgvector Wins

• •

  Your agent needs tight joins between embeddings and business data

  If your agent retrieves customer records, policy data, tickets, or account history alongside embeddings, Postgres wins immediately. You can store vectors in one table and join them to normalized data with standard SQL.

• •

  You care about transactional correctness

  AI agents often write memory after tool calls or workflow steps. With Postgres you get ACID transactions, so you can update agent state, embeddings metadata, and audit logs together without inventing consistency glue.

• •

  You want simpler ops

  If your stack already runs on Postgres, adding pgvector is a small change:

  CREATE EXTENSION IF NOT EXISTS vector;
  
  CREATE TABLE agent_memory (
    id bigserial PRIMARY KEY,
    tenant_id uuid 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 is hard to beat for speed of implementation.

• •

  You need strong filtering before similarity search

  Agent retrieval usually needs constraints like tenant_id, user_id, document_type, or created_at. Postgres handles this cleanly with SQL predicates before or after vector matching.

When MongoDB Wins

• •

  Your application data is already in MongoDB

  If your product team stores everything as documents in MongoDB Atlas, forcing a second database just for vectors adds friction. Use $vectorSearch inside the same document model and keep the pipeline simple.

• •

  Your schema changes constantly

  Agent payloads are messy: tool outputs differ by provider, memory objects evolve fast, and new fields appear every sprint. MongoDB handles that without migration churn.

• •

  You want document-first retrieval

  For agents that store conversation turns, tool traces, web page snapshots, or nested JSON blobs, MongoDB fits naturally. You can keep raw context as documents and attach embeddings to those same records.

• •

  You are already committed to Atlas services

  If you use Atlas Search, triggers, sync pipelines, or managed scaling across multiple environments, adding vector search into that platform is straightforward:

  db.agentMemory.aggregate([
    {
      $vectorSearch: {
        index: "agent_memory_index",
        path: "embedding",
        queryVector: queryEmbedding,
        numCandidates: 100,
        limit: 5
      }
    }
  ])
  

  That keeps retrieval inside one managed ecosystem.

For AI agents Specifically

For most AI agents, I recommend pgvector. Agents usually need retrieval plus filters plus transactional writes more than they need exotic document flexibility, and Postgres gives you all three without splitting your system across multiple databases.

Pick MongoDB only when your entire application is already document-native in Atlas or when your agent memory is mostly unstructured JSON that changes constantly. Otherwise pgvector is the cleaner engineering choice and the safer production default.

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.