Agent Integrations

• 1: OpenAI Agents
• 1.1: Agent Sessions

1 - OpenAI Agents

What is the Dapr OpenAI Agents integration?

Dapr provides OpenAI agents first class integrations that range from agent session management to connecting agents via pub/sub and orchestrating agentic workflows. The Dapr OpenAI integration is an extension in the OpenAI Python SDK that developers can use to augment OpenAI agents with the various Dapr APIs.

1.1 - Agent Sessions

Overview

By using Dapr to manage the state and session data for OpenAI agents, users can store agent state in all databases supported by Dapr, including key/value stores, caches and SQL databases. Developers also get built-in tracing, metrics and resiliency policies that make agent session data operate reliably in production.

Getting Started

Initialize Dapr locally to set up a self-hosted environment for development. This process fetches and installs the Dapr sidecar binaries, runs essential services as Docker containers, and prepares a default components folder for your application. For detailed steps, see the official guide on initializing Dapr locally.

To initialize the Dapr control plane containers and create a default configuration file, run:

dapr init

Verify you have container instances with daprio/dapr, openzipkin/zipkin, and redis images running:

docker ps

Install Python

Install Dependencies

pip install openai-agents dapr

Create an OpenAI Agent

Let’s create a simple OpenAI agent. Put the following in a file named openai_agent.py:

import asyncio
from agents import Agent, Runner
from agents.extensions.memory.dapr_session import DaprSession

async def main():
    agent = Agent(
        name="Assistant",
        instructions="Reply very concisely.",
    )

    session = DaprSession.from_address(
        session_id="123",
        state_store_name="statestore"
    )

    result = await Runner.run(agent, "What city is the Golden Gate Bridge in?", session=session)
    print(result.final_output)

    result = await Runner.run(agent, "What state is it in?", session=session)
    print(result.final_output)

    result = await Runner.run(agent, "What's the population?", session=session)
    print(result.final_output)

asyncio.run(main())

Set an OpenAI API key

export OPENAI_API_KEY=sk-...

Create a Python venv

python -m venv .venv                                                                                                                                                                      
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Create the database component

The component file is how Dapr connects to your databae. The full list of supported databases can be found here. Create a components directory and this file in it:

statestore.yaml:

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: statestore
spec:
  type: state.redis
  version: v1
  metadata:
  - name: redisHost
    value: localhost:6379
  - name: redisPassword
    value: ""

Run The Agent

Now run the local Dapr process and your Python script using the Dapr CLI.

dapr run --app-id openaisessions --dapr-grpc-port 50001 --resources-path ./components -- python3 ./openai_agent.py

Open http://localhost:9411 to view your the traces and dependency graph.

You can see the session data stored in Redis with the following command

hgetall "123:messages" 

Next Steps

Now that you have an OpenAI agent using Dapr to manage the agent sessions, explore more you can do with the State API and how to enable resiliency policies for enhanced reliability.

Read more about OpenAI agent sessions and Dapr here.