How to Use the Redis Vector MCP in OpenAI Agents SDK

Q: Can OpenAI Agents SDK manage multiple indexes using this Redis Vector server?

Yes. You can use listindexes to discover every available index and switch between them dynamically during task execution.

Q: Is it possible to automate index creation in my OpenAI Agents SDK pipeline?

You can trigger createvectorindex whenever your agent determines a new category of data requires a dedicated search space.

Directly manage your Redis Vector embeddings inside OpenAI Agents SDK for production-grade agentic memory.

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Connect Redis Vector MCP to OpenAI Agents SDK

Create your Vinkius account to connect Redis Vector 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 Redis Vector with OpenAI Agents SDK

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Native index management for OpenAI Agents SDK

Your agent creates and inspects vector structures directly within your Redis stack. Call `create_vector_index` to define dimensions without leaving your Python code. `list_indexes` and `get_index_info` keep your agent aware of available memory partitions. It prevents stale data by verifying index states before running operations.

High-speed similarity search for agents

Run KNN similarity searches using `search_vectors` to find relevant context for your model. It interprets JSON float arrays to return exact matches from your Redis store. This MCP server handles the vector math so your agent focuses on reasoning. You get fast access to your data without writing custom serialization logic.

Safe vector updates in your production pipeline

Use `upsert_vector` to push new embeddings into your Redis hashes instantly. It keeps your agent's knowledge base current as new information flows into your system. `delete_vector` removes obsolete records to keep your index size manageable. Every action runs through your agent's safety guardrails to ensure data integrity.

Setup guide

Set up Redis Vector 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 Redis Vector tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Redis Vector 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="Redis Vector Agent",
            instructions="You have access to Redis Vector 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 Redis Vector MCP in OpenAI Agents SDK

Import your server into the agent constructor and pass the query as a float array to the `search_vectors` tool. The agent automatically executes the search and retrieves the top results.

Yes. You can use `list_indexes` to discover every available index and switch between them dynamically during task execution.

Absolutely. Every tool call, including vector upserts, goes through your configured agent validation layers before hitting the database.

You can trigger `create_vector_index` whenever your agent determines a new category of data requires a dedicated search space.

Vinkius handles authentication via endpoint tokens, keeping your Redis vector embeddings isolated within a secure sandbox. No raw data leaves your environment without explicit authorization.

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