python-sdk

What This Skill Does

The Python SDK skill allows users to seamlessly integrate the inference.sh platform directly into their Python applications. By acting as a programmatic interface, it enables the execution of a vast library of over 150+ AI models, the creation of stateful agents, and the construction of complex RAG (Retrieval-Augmented Generation) pipelines. The SDK supports both synchronous and asynchronous execution, streaming responses, and automatic file handling, making it a robust choice for developers looking to scale AI capabilities within their software stack.

Installation

To integrate this skill, use the command: clawhub install openclaw/skills/skills/okaris/python-sdk. Once installed, you will need to install the library via pip: pip install inferencesh. Ensure your environment is configured for Python 3.8+ and that you have a valid API key from inference.sh stored in your environment variables as INFERENCE_API_KEY to ensure secure and efficient authentication during runtime.

Use Cases

• AI App Integration: Directly call hosted models like Flux or Veo from within your backend services.
• Agent Development: Build sophisticated, stateful agents using the SDK’s session management capabilities, keeping workers warm and maintaining context across multiple requests.
• Automated Workflows: Create data processing pipelines that handle file uploads, model inference, and output parsing without manual intervention.
• RAG Implementation: Facilitate complex retrieval flows by managing inputs and outputs between vector databases and the inference.sh model endpoints.

Example Prompts

1. "Use the inferencesh Python SDK to run the flux-schnell model and generate an image of a cybernetic cat from my prompt."
2. "Show me how to initialize a stateful session with a 5-minute timeout using the inferencesh client."
3. "Help me write a script that streams output logs while running a long-form video generation task on the Veo model."

Tips & Limitations

For production-grade applications, always prefer using environment variables over hardcoded API keys. When working with large files, leverage the upload_file utility for optimized data handling rather than manual binary stream manipulation. Note that while the SDK is highly versatile, execution is dependent on the availability of the target models on inference.sh. Ensure you monitor your session_timeout settings to balance performance and cost effectively when using stateful sessions.