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

Multi-agent systems need two things: fast vector retrieval and a place to keep state, tools, and conversation history. pgvector gives you vector search inside Postgres; MongoDB gives you a document database with vector search layered on top.

For most multi-agent systems, use MongoDB if your agents are state-heavy and event-driven, and pgvector if your stack is already Postgres-first and retrieval is the core problem.

Quick Comparison

When pgvector Wins

• •

  Your source of truth is already in Postgres

  If your agents work off customers, policies, claims, tickets, or transactions stored in relational tables, pgvector is the cleanest option. You can join embeddings to business data in one query instead of syncing between systems.

• •

  You need tight SQL + vector filtering

  Multi-agent systems often need “find the top 20 similar items where tenant_id = X and status = active.” With pgvector, that’s just SQL:

  SELECT id
  FROM chunks
  WHERE tenant_id = $1 AND status = 'active'
  ORDER BY embedding <-> $2
  LIMIT 10;
  

  That pattern is hard to beat when agents need retrieval plus deterministic filters.

• •

  You want fewer infrastructure pieces

  A lot of agent stacks fail because people add a vector DB before they need one. If you already run Postgres for app state, auth metadata, audit logs, and queues like pgmq or LISTEN/NOTIFY, pgvector keeps the architecture simple.

• •

  Your team knows SQL better than MongoDB search

  This matters more than people admit. A team that can tune EXPLAIN ANALYZE, build partial indexes, and reason about transactions will ship faster with pgvector than learning a second datastore model.

What pgvector looks like in practice

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 when each agent stores memory chunks alongside structured metadata like agent_id, tenant_id, or workflow_id.

When MongoDB Wins

• •

  Your agents are document-native

  Multi-agent systems generate messy state: tool results, intermediate plans, messages from other agents, retries, errors, approvals. MongoDB handles that shape naturally because each record can evolve without migrations.

• •

  You need built-in operational memory

  Agents need more than vectors. They need conversation history, scratchpads, task graphs, tool outputs, and status transitions. MongoDB’s document model makes it easy to store all of that in one record per thread or workflow.

• •

  You want hybrid retrieval over rich documents

  Atlas Vector Search lets you combine semantic search with metadata filters inside the same platform using $vectorSearch. That’s useful when an agent needs “similar incidents from this customer segment in the last 30 days” without building custom plumbing.

• •

  You expect schema churn

  Agent systems change constantly: new tools appear, message formats evolve, new metadata gets added every week. MongoDB tolerates that churn better than rigid relational schemas.

What MongoDB looks like in practice

db.agentMemory.aggregate([
  {
    $vectorSearch: {
      index: "memory_index",
      path: "embedding",
      queryVector: queryEmbedding,
      numCandidates: 100,
      limit: 10,
      filter: { tenantId: "acme", type: "tool_result" }
    }
  }
]);

That pattern is strong when the agent needs semantically relevant memory plus flexible document fields in one pipeline.

For multi-agent systems Specifically

Use MongoDB if your agents coordinate through shared state, logs of actions, tool outputs, task graphs, and evolving message schemas. Use pgvector only if retrieval sits on top of an existing Postgres system and your “memory” is really just embedded rows with some metadata attached.

My recommendation is blunt: for multi-agent systems as a category, MongoDB is the better default because agents are document machines first and vector machines second. If your system is mostly RAG against structured enterprise data inside Postgres, then pgvector is the better choice — otherwise MongoDB will fit the shape of the problem better from day one.

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.