💡 What is Open Responses?

Open Responses lets you run a fully self-hosted version of OpenAI's Responses API. It works seamlessly with any large language model (LLM) provider—whether it's Claude, Qwen, Deepseek R1, Ollama, or others. It's a fully-compatible drop-in replacement for the official API. Swap out OpenAI without changing your existing Agents SDK code.

Just run npx -y open-responses init and then:

from openai import AsyncOpenAI
from agents import set_default_openai_client
set_default_openai_client(AsyncOpenAI(base_url="http://localhost:8080/"))

agent = Agent(name="Test Agent", ...)
# ...

🔥🔥🔥

What is Responses API?

From OpenAI docs on Responses API:

The Responses API is our newest core API and an agentic API primitive, combining the simplicity of Chat Completions with the ability to do more agentic tasks. > As model capabilities evolve, the Responses API is a flexible foundation for building action-oriented applications, with built-in tools:

• Web search
• File search
• Computer use

You can read about it in more detail on their announcement blog post.

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✨ Why use Open Responses?

• 🔄 Bring Your Own Model - Compatible with any LLM provider you prefer.
• 🔒 Privacy First - Fully self-hosted, giving you total control over your data.
• 🔌 Easy Switch - Drop-in replacement compatible with OpenAI’s official Agents SDK.
• 🚀 Fast Setup - Get started quickly with Docker or our straightforward CLI.
• 🛠️ Built-in Tools - Supports automatic tool calls like web searches using open-source alternatives.

🚀 Quick Start

One simple command to get going:

npx -y open-responses init
# or: uvx open-responses init

💻 Quick Examples

Using the Agents SDK

from openai import AsyncOpenAI
from agents import set_default_openai_client

# Create and configure the OpenAI client
custom_client = AsyncOpenAI(base_url="http://localhost:8080/", api_key="YOUR_RESPONSES_API_KEY")
set_default_openai_client(custom_client)


agent = Agent(
  name="Test Agent",
  instructions="You are a helpful assistant that provides concise responses."
  model="openrouter/deepseek/deepseek-r1"
)

result = await Runner.run(agent, "Hello! Are you working correctly?")

print(result.final_output)

More examples here.

Using the OpenAI SDK

javascript

import { OpenAI } from 'openai';

const client = new OpenAI({
  baseURL: 'http://localhost:8080/',
  apiKey: "RESPONSE_API_KEY"
});

const response = await client.responses.create({
  model: "gpt-4o-mini",
  input: "What's the population of the world today?"
});

console.log(response.output[0].content[0].text);

More examples here.

python

import os
from openai import OpenAI

client = OpenAI(
  base_url="http://localhost:8080/",
  api_key=os.getenv("RESPONSE_API_KEY")
)

response = client.responses.create(
  model="gpt-4o-mini",
  input="What's the population of the world today?"
)

print(response.output[0].content[0].text)

More examples here.

📦 Install Manually

We publish pre-built docker images which you can run using docker compose directly.

mkdir julep-responses-api
cd julep-responses-api
wget https://u.julep.ai/responses-env.example -O .env
wget https://u.julep.ai/responses-compose.yaml -O docker-compose.yml
docker compose up --watch

📚 Learn More

• 🚀 Quickstart Guide
• 🧠 API Concepts
• 📝 Code Examples
• 💻 CLI Documentation
• 🔮 Project Roadmap

📖 About Julep AI

Open Responses is proudly built by Julep AI—the open-source platform empowering data and ML teams to rapidly create, deploy, and manage stateful AI workflows and intelligent agents at scale.

🤝 Contributing

We’d love your contributions! Open Responses is licensed under Apache-2.0.

Check out our main project as well