Scaling OpenAI Agents SDK

Less is more. With its lightweight architecture, powerful primitives like agents, handoffs, and guardrails, OpenAI Agents SDK has become the go-to framework for creating sophisticated multi-agent workflows. At least for me :)

But there's one challenge that every developer faces when moving from prototype to production: session management at scale.

The SQLite Wall

When I started building my latest agentic application using the OpenAI Agents SDK and FastAPI, everything worked beautifully in development. The SDK's built-in SQLite session management handled conversation history seamlessly, automatically maintaining context across agent runs without any manual state handling.

But as I prepared to deploy across multiple instances, reality hit. SQLite, while perfect for prototyping, becomes a bottleneck when you need to:

• Share sessions across multiple application instances

• Survive container restarts and deployments

• Scale horizontally with load balancers

• Maintain session consistency in distributed environments

The problem wasn't unique to my application. The OpenAI Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the need to manually handle state between turns, but this session management is tied to SQLite's single-process limitations.

Enter openai-agents-redis

That's when I decided to build openai-agents-redis – a drop-in replacement for the SDK's session management that uses Redis as the persistence layer instead of SQLite.

Key Features

🔄 Drop-in Replacement: Same API as the original session management, so your existing code works unchanged.
⚡ Redis-Powered: Lightning-fast caching and persistent storage that scales horizontally.
🔗 Connection Pooling: Automatic connection management and pooling for optimal performance.
🧹 Automatic Cleanup: Handles serialization, deserialization, and session cleanup automatically.
🚀 Production Ready: Built for distributed deployments and high-availability scenarios.

How It Works

The implementation is surprisingly elegant. Instead of fighting with the SDK's architecture, openai-agents-redis works with it by implementing the same session interface while swapping out the storage backend.

Installation

Getting started is as simple as:

# Using uv (recommended)
uv add openai-agents-redis

# Using pip  
pip install openai-agents-redis

Usage

The beauty of this approach is in its simplicity. Here's how you use it:

from agents_redis.session import RedisSession
from agents import Agent, Runner

# Create a Redis-backed session
session = RedisSession(
    session_id=session_id,  # Use your own logic to generate session_id
    redis_url="redis://localhost:6379", # For local testing only
)

# Your existing agent code remains unchanged
agent = Agent(
    name="Assistant", 
    instructions="You are a helpful assistant"
)

# Start the runner with Redis session management
result = Runner.run_streamed(
    starting_agent=agent, 
    input=user_input, 
    context=context, 
    session=session  # Now backed by Redis!
)

That's it. Your agent conversations are now stored in Redis, shared across all your application instances, and will survive restarts.

The Architecture

Under the hood, openai-agents-redis handles several critical aspects:

Serialization: Converts complex agent conversation objects into Redis-compatible formats while preserving all necessary context and metadata.
Connection Management: Maintains efficient connection pools to Redis, handling reconnections and failures gracefully.
Session Lifecycle: Automatically manages session creation, updates, and cleanup without requiring manual intervention.
Compatibility: Ensures full compatibility with the OpenAI Agents SDK's session interface, so existing code works without modification.

Real-World Impact

The difference in production is night and day:

Before (SQLite): Each container had its own isolated session storage. Users lost conversation context when load balancers switched them between instances.
After (Redis): Sessions persist across the entire application cluster. Users maintain context regardless of which instance handles their request.
Performance: Redis's in-memory architecture provides significantly faster session retrieval and updates compared to SQLite disk I/O.
Reliability: Sessions survive individual container failures and deployments, providing a much more robust user experience.

See it in action 🚀

Future Enhancements

The current implementation focuses on core session management, but there are exciting possibilities on the roadmap:

• Full-text search capabilities for conversation history

• Vector similarity search for semantic conversation lookup

• Hybrid search combining text and semantic search

• Built-in monitoring dashboard for session analytics

• Advanced session analytics and conversation insights

Getting Started

Ready to scale your OpenAI Agents SDK application? Here's what you need:

Prerequisites

• 🐳 Docker (for Redis)

• ⚡️ uv package manager (recommended)

• 🦾 OpenAI Agents SDK

• 🔑 OpenAI API Key

Quick Start

1. Install the package:

    uv add openai-agents-redis
   

2. Start Redis (if you don't have it running):

    docker run -d -p 6379:6379 redis:alpine
   

3. Update your code to use RedisSession instead of the default session management.

4. Deploy with confidence knowing your sessions will scale with your application.

Testing

The package includes comprehensive tests to ensure reliability:

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov

Why This Matters

As AI applications move from experimental to production, session management becomes crucial. Users expect their conversations to be persistent, consistent, and available regardless of backend architecture decisions.

openai-agents-redis solves this problem by providing enterprise-grade session management that doesn't require you to rewrite your application. It's the missing piece that transforms your prototype into a production-ready system.

The OpenAI Agents SDK gives us the tools to build sophisticated AI agents. Now openai-agents-redis gives us the infrastructure to run them at scale.

Try It Today

• GitHub: https://github.com/rafaelpierre/openai-agents-redis

• PyPI: https://pypi.org/project/openai-agents-redis/

• Sample Repo: https://github.com/rafaelpierre/openai-agents-redis-example

If you're building agentic applications with the OpenAI Agents SDK and hitting SQLite's limitations, give openai-agents-redis a try. It's designed to be the session management solution you wish existed when you first hit the scaling wall.

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Have questions or feedback? I'd love to hear about your experience scaling agentic applications. Feel free to open an issue on GitHub or reach out with your thoughts!