Redis Vector MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Redis Vector through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Redis Vector Assistant",
instructions=(
"You help users interact with Redis Vector. "
"You have access to 6 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Redis Vector"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Redis Vector MCP Server
Connect your Redis database (equipped with the RediSearch module) to your AI agent, turning it into an advanced Vector Database administrator. Activating this integration grants your conversational interface the power to interact directly with your semantic search engine, enabling tasks like querying mathematical embeddings for similar records, configuring fresh vector indexes, and managing geometric data structures without needing dedicated external database clients.
The OpenAI Agents SDK auto-discovers all 6 tools from Redis Vector through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Redis Vector, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
What you can do
- Similarity Vector Search (KNN) — Let the AI perform rapid native vector comparisons (
search_vectors). Provide an embedding array via prompt or code, and retrieve the absolute nearesttop_kneighbors securely cached in your infrastructure. - Index Management — Actively discover all loaded RediSearch vector indexes, investigate their configured dimensions (
get_index_info), or command the AI to instantiate new KNN indexes (create_vector_index) tailored for fresh AI workloads. - Embedding Administration — Inject and modify geometric vector components associated with a document key (
upsert_vector), or purge legacy embeddings efficiently (delete_vector) to keep semantic records clean and operational.
The Redis Vector MCP Server exposes 6 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Redis Vector to OpenAI Agents SDK via MCP
Follow these steps to integrate the Redis Vector MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 6 tools from Redis Vector
Why Use OpenAI Agents SDK with the Redis Vector MCP Server
OpenAI Agents SDK provides unique advantages when paired with Redis Vector through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Redis Vector + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Redis Vector MCP Server delivers measurable value.
Automated workflows: build agents that query Redis Vector, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Redis Vector, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Redis Vector tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Redis Vector to resolve tickets, look up records, and update statuses without human intervention
Redis Vector MCP Tools for OpenAI Agents SDK (6)
These 6 tools become available when you connect Redis Vector to OpenAI Agents SDK via MCP:
create_vector_index
Specify the name and vector dimensions. Creates a new RediSearch vector index
delete_vector
Deletes a vector document from Redis
get_index_info
Retrieves details for a specific vector index
list_indexes
Lists all RediSearch vector indexes
search_vectors
Provide the query vector as a JSON array of floats. Performs a KNN similarity search in a vector index
upsert_vector
Specify the document key and the vector as a JSON array. Inserts or updates a vector in a Redis hash
Example Prompts for Redis Vector in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Redis Vector immediately.
"Search the index 'customer-support-vector' for the top 3 similar records to this embedding vector: [0.12, -0.45, 0.08, 0.99...]"
"Insert a new embedding into the database with the key 'user:439:preference' containing the vector `[0.2, -0.1...]`."
"Retrieve the index information logic and schema mapping for 'docs-semantic-index'."
Troubleshooting Redis Vector MCP Server with OpenAI Agents SDK
Common issues when connecting Redis Vector to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Redis Vector + OpenAI Agents SDK FAQ
Common questions about integrating Redis Vector MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Redis Vector with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Redis Vector to OpenAI Agents SDK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.