open-responses
OpenResponses API empowers developers to leverage the incredible capabilities of various LLM providers through a familiar interface - the OpenAI Responses API structure.
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OpenResponses API provides enterprise-grade AI capabilities through a powerful API, simplifying development and deployment while ensuring complete data control. It offers automated tracing, integrated RAG for contextual information retrieval, pre-built tool integrations, self-hosted architecture, and an OpenAI-compatible interface. The toolkit addresses development challenges like feature gaps and integration complexity, as well as operational concerns such as data privacy and operational control. Engineering teams can benefit from improved productivity, production readiness, compliance confidence, and simplified architecture by choosing OpenResponses.
README:
Unlock enterprise-grade AI capabilities through a single, powerful API — simplify development, accelerate deployment, and maintain complete data control
OpenResponses revolutionizes how developers build AI applications by providing a comprehensive, production-ready toolkit with essential enterprise features—all through an elegantly simplified API interface. Stop cobbling together disparate tools and start building what matters.
Run OpenResponses locally to access an OpenAI-compatible API that works seamlessly with multiple model providers and supports unlimited tool integrations. Deploy a complete AI infrastructure on your own hardware with full data sovereignty.
docker run -p 8080:8080 masaicai/open-responses:latestopenai_client = OpenAI(base_url="http://localhost:8080/v1", api_key=os.getenv("OPENAI_API_KEY"), default_headers={'x-model-provider': 'openai'})
response = openai_client.responses.create(
model="gpt-4o-mini",
input="Write a poem on Masaic"
)client = AsyncOpenAI(base_url="http://localhost:8080/v1", api_key=os.getenv("OPENAI_API_KEY"), default_headers={'x-model-provider': 'openai'})
agent = Agent(
name="Assistant",
instructions="You are a humorous poet who can write funny poems of 4 lines.",
model=OpenAIResponsesModel(model="gpt-4o-mini", openai_client=client)
)curl --location 'http://localhost:8080/v1/responses' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer OPENAI_API_KEY' \
--header 'x-model-provider: openai' \
--data '{
"model": "gpt-4o",
"stream": false,
"input": [
{
"role": "user",
"content": "Write a poem on Masaic"
}
]
}'For detailed implementation instructions, see our Quick Start Guide.
- Getting Started
- Core Capabilities
- Key Problems Solved
- Why Engineering Teams Should Choose OpenResponses
- API Reference
- Coming Soon
- Frequently Asked Questions
- Configuration
- Documentation
- Local Development
- Production Use
- Contributing
- License
| Feature | Description | Benefit |
|---|---|---|
| Automated Tracing | Comprehensive request and response monitoring | Track performance and usage without additional code |
| Integrated RAG | Contextual information retrieval | Enhance responses with relevant external data automatically |
| Pre-built Tool Integrations | Web search, GitHub access, and more | Deploy advanced capabilities instantly |
| Self-Hosted Architecture | Full control of deployment infrastructure | Maintain complete data sovereignty |
| OpenAI-Compatible Interface | Drop-in replacement for existing OpenAI implementations | Minimal code changes for migration |
- Feature Gap: Most open-source AI models lack critical enterprise capabilities required for production environments
- Integration Complexity: Implementing supplementary features like retrieval augmentation and monitoring requires significant development overhead
- Resource Diversion: Engineering teams spend excessive time on infrastructure rather than core application logic
- Data Privacy: Organizations with sensitive data face compliance barriers when using cloud-hosted AI services
- Operational Control: Many applications require full control over the AI processing pipeline
- Developer Productivity: Focus engineering efforts on application features rather than infrastructure
- Production Readiness: Enterprise capabilities and batteries included out-of-the-box
- Compliance Confidence: Deploy with data privacy requirements fully addressed
- Simplified Architecture: Consolidate AI infrastructure through widely used OpenAI API Specifications
The API implements the following OpenAI-compatible endpoints:
| Endpoint | Description |
|---|---|
POST /v1/responses |
Create a new model response |
GET /v1/responses/{responseId} |
Retrieve a specific response |
DELETE /v1/responses/{responseId} |
Delete a response |
GET /v1/responses/{responseId}/input_items |
List input items for a response |
Replace the placeholder API keys with your own values.
curl --location 'http://localhost:8080/v1/responses' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer GROQ_API_KEY' \
--data '{
"model": "llama-3.2-3b-preview",
"stream": true,
"input": [
{
"role": "user",
"content": "Write a poem on OpenResponses"
}
]
}'curl --location 'http://localhost:8080/v1/responses' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer ANTHROPIC_API_KEY' \
--header 'x-model-provider: claude' \
--data '{
"model": "claude-3-5-sonnet-20241022",
"stream": false,
"input": [
{
"role": "user",
"content": "Write a poem on OpenResponses"
}
]
}'curl --location 'http://localhost:8080/v1/responses' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer YOUR_API_KEY' \
--data '{
"model": "your-model",
"stream": false,
"tools": [
{
"type": "brave_web_search"
}
],
"input": [
{
"role": "user",
"content": "What are the latest developments in AI?"
}
]
}'We're continuously evolving OpenResponses with powerful new features to elevate your AI applications even further. Stay tuned!
Yes! OpenResponses acts as a pass-through to the provider APIs using your own keys.
Our benchmarks show minimal overhead compared to direct API calls.
OpenResponses standardizes error responses across providers:
{
"type": "rate_limit_exceeded",
"message": "Rate limit exceeded. Please try again in 30 seconds.",
"param": null,
"code": "rate_limit"
}The application supports the following environment variables:
| Variable | Description | Default |
|---|---|---|
MCP_SERVER_CONFIG_FILE_PATH |
Path to MCP server configuration | - |
MASAIC_MAX_TOOL_CALLS |
Maximum number of allowed tool calls | 10 |
MASAIC_MAX_STREAMING_TIMEOUT |
Maximum streaming timeout in ms | 60000 |
SPRING_PRODFILES_ACTIVE |
otel profile enables open telemetry exports |
- |
For more details on granular configurations refer:
Explore our comprehensive documentation to learn more about OpenResponses features, configuration options, and integration methods.
Follow these instructions to set up the project locally for development:
- Java JDK 21+
- Gradle (optional, as project includes Gradle Wrapper)
- Docker (optional, for containerized setup)
- Clone the repository
git clone https://github.com/masaic-ai-platform/open-responses.git
cd open-responses- Build the project
Use the Gradle Wrapper included in the project:
./gradlew build- Configure Environment Variables
Create or update the application.properties file with necessary configuration under src/main/resources:
server.port: 8080Set any additional configuration required by your project.
- Run the server
To start the server in development mode:
./gradlew bootRunBuild and run the application using Docker:
./gradlew build
docker build -t openresponses .
docker run -p 8080:8080 -d openresponsesRun the tests with:
./gradlew testAlpha Release Disclaimer: This project is currently in alpha stage. The API and features are subject to breaking changes as we continue to evolve and improve the platform. While we strive to maintain stability, please be aware that updates may require modifications to your integration code.
Contributions are welcome! Please feel free to submit a Pull Request.
"Alone we can do so little; together we can do so much." — Helen Keller
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Made with ❤️ by the Masaic AI Team
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