How to Use the pgvector (Vector Database) MCP in OpenAI Agents SDK

Use OpenAI Agents SDK to run vector searches directly in your PostgreSQL database with full tool discovery.

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Connect pgvector (Vector Database) MCP to OpenAI Agents SDK

Create your Vinkius account to connect pgvector (Vector Database) to OpenAI Agents SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup pgvector (Vector Database) with OpenAI Agents SDK

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Perplexity

Manage vector tables for OpenAI Agents SDK

Define your schema using `create_table` to store embeddings right where your application data lives. This keeps your architecture clean without extra sync layers. Once the table is ready, use `create_index` to handle HNSW or IVFFlat indexing. Your agent manages these operations while you monitor performance in your dashboard.

Execute similarity searches in your agent

Run `search_vectors` to retrieve relevant context for your agent during a conversation. It pulls the most accurate matches from your database in milliseconds. Your OpenAI Agents SDK implementation validates these results immediately. You get reliable data flow into your prompts without manual parsing.

Handle vector lifecycle with MCP Server

Update your memory store using `insert_vector` whenever your agent generates new insights. The server handles the connection to PostgreSQL while you focus on agent logic. Use `delete_vector` to purge stale context or `list_tables` to keep track of your active collections. It is a simple way to maintain a tidy vector workspace.

Setup guide

Set up pgvector (Vector Database) MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all pgvector (Vector Database) tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives pgvector (Vector Database) tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate pgvector (Vector Database) tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="pgvector (Vector Database) Agent",
            instructions="You have access to pgvector (Vector Database) tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about pgvector (Vector Database) MCP in OpenAI Agents SDK

Yes. The tools support HNSW indexing which handles millions of vectors efficiently. Just ensure your PostgreSQL instance has enough memory to keep the index resident.

The server exposes its tools via MCP, which your agent picks up automatically upon connection. You do not need to write custom wrappers to call `search_vectors`.

It does. Configure the MCPServerStreamableHttp endpoint in your agent setup and it connects directly. This keeps your infrastructure decoupled and portable.

The server uses standard PostgreSQL authentication. Your embeddings never leave your database cluster during processing.

The MCP protocol returns a structured error. Your agent catches this exception and can retry or alert you immediately.

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