# Useful but Little-Known Features of OpenAI Agent SDK 🌍 Ever wanted to mix multiple LLM providers within a single agent system? `MultiProvider` automatically routes requests to the right provider based on model name prefixes. 📌 Title: Prefix Routing with MultiProvider 🔗 URL: 🧩 Overview `MultiProvider` routes requests to the appropriate provider based on model name prefixes (e.g., `openai/gpt-4.1`). Setting `openai_prefix_mode="model_id"` treats `openai/...` as a literal model ID, while `unknown_prefix_mode="model_id"` routes unknown prefixes as model IDs too. Enable `openai_use_responses_websocket=True` for WebSocket transport on supported providers. 🛠 How to use it ```python from agents import Agent, MultiProvider, RunConfig, Runner provider = MultiProvider( openai_base_url="", openai_api_key="...", openai_use_responses_websocket=True, openai_prefix_mode="model_id", unknown_prefix_mode="model_id", ) agent = Agent( name="Assistant", instructions="Be concise.", model="openai/gpt-4.1", ) result = await agent, "Hello", run_config=RunConfig(model_provider=provider), ) ``` 🏗 Building it into production ・Assign different provider models to each agent based on cost and latency requirements ・Combine with gateway services like OpenRouter using `openai_prefix_mode="model_id"` to pass prefixed model names through ・Switch providers at runtime via `RunConfig(model_provider=provider)` for A/B testing ・Enable WebSocket for improved streaming performance on supported providers 💡 Use cases 🔀 Routing GPT-4.1 vs GPT-5.5 based on task difficulty 🌐 Unified access to multiple providers through OpenRouter 💰 Hybrid operation mixing high-cost and low-cost models 🧪 Quality comparison testing across different models ⚠️ Watch out By default, `openai/...` aliases to the OpenAI provider, and unknown prefixes raise `UserError`. When using external gateways like OpenRouter, always set both `openai_prefix_mode` and `unknown_prefix_mode` to `"model_id"`. Note that feature support (tool calling, structured output, etc.) varies across providers. ✨ With MultiProvider, build agent systems that freely combine the best models from any provider. #OpenAIAgentSDK# #AIAgent#

Using transaction data from long-term contracts, @OrnnExchange has modeled a price surface for GPU rentals as a function of order size and contract length. The data shows a positive correlation between price and GPU capacity size, and an inverse correlation between price and contract length. Now, what's the underlying reasons behind these trends? 1/ Small orders are filled from leftover capacity. As a result, sellers may discount it to maximize utilization. 2/ Big buyers are buying scarcity. A request for >1k H100s, etc clustered together has only a few vendors to choose from. See the numbers at and rent your capacity at

US President Donald Trump and Chinese leader Xi Jinping met for less than 48 hours on their first White House summit to stabilize ties. Here’s a summary of the statements released by both sides.