Home Assistant & OpenRouter: Fixing Reasoning Details
Have you ever been working with Home Assistant and found that your conversations with AI models, especially those that rely on complex reasoning like Gemini 3, seem to be losing their thread? It's a frustrating experience when the AI forgets crucial context from previous turns, leading to incomplete or nonsensical responses. This issue often stems from how certain integrations handle the communication with these powerful language models. Specifically, when using the OpenRouter integration within Home Assistant, a bug has been identified where reasoning_details are not being preserved correctly. This is a critical component for models that need to recall and build upon previous reasoning steps, much like how a human would maintain a train of thought during a conversation. The problem lies in the rebuilding of model_args["messages"] between turns in the conversation. This process, as detailed in the Home Assistant Core codebase, inadvertently discards the vital reasoning_details. Without these details, the AI model is essentially starting fresh with each new message, unable to leverage its own prior analysis or the user's preceding context. This leads to a degradation in the quality of interactions, making advanced features reliant on deep contextual understanding less effective or entirely unusable. Imagine trying to solve a complex math problem, but each time you write down a step, the paper erases the previous one – that's the kind of struggle the AI faces here. The OpenRouter integration is designed to act as a bridge, allowing Home Assistant to interface with a variety of AI models, and ensuring that this bridge is robust enough to carry all necessary information, including the nuances of AI reasoning, is paramount for unlocking the full potential of AI within your smart home.
This loss of reasoning_details is particularly problematic for sophisticated AI models that go beyond simple question-and-answer formats. Models like Gemini 3 are designed to perform complex analytical tasks, generate creative content, and engage in multi-turn dialogues where the ability to refer back to and build upon previous reasoning is fundamental. When this context is lost, the AI's capability to perform these advanced functions is severely hampered. The Home Assistant Core's implementation of the OpenRouter integration, specifically the way it reconstructs the message history for API calls, is where the breakdown occurs. According to the linked issue in the Home Assistant Core repository, the code responsible for managing the conversation turns fails to carry over the reasoning_details when it rebuilds the model_args["messages"] object. This is not an inherent flaw in the AI model itself, but rather an implementation detail within the integration that prevents the model from receiving the necessary information to function optimally. The integration needs to be updated to recognize and pass along these reasoning_details, ensuring that the AI has a complete picture of the conversation's history and reasoning process. This is similar to how tool calls are currently handled, where specific arguments are preserved. The guidance provided by OpenRouter itself on preserving reasoning blocks offers a clear path forward. By implementing these best practices, developers can ensure that reasoning_details are correctly transmitted between turns, much like how tool call arguments are managed. This would allow Home Assistant to fully leverage the advanced reasoning capabilities of models like Gemini 3, leading to more intelligent, coherent, and context-aware interactions within the smart home environment. The goal is to create a seamless experience where the AI feels like a true assistant, capable of understanding and remembering the intricacies of your requests and its own thought processes.
Understanding the Core Problem: Message Reconstruction and Lost Context
The heart of the issue lies in how the OpenRouter integration in Home Assistant handles the conversational data passed to AI models. When you interact with an AI through Home Assistant, especially using models that generate intermediate reasoning steps (often referred to as reasoning_details), these steps need to be sent back and forth between Home Assistant and the AI model's API. Think of reasoning_details as the AI's scratchpad – it's where the model works out its thoughts, analyzes information, and plans its response before actually delivering the final answer. For complex tasks, this scratchpad is essential for the AI to maintain coherence and accuracy over multiple turns of a conversation. The problem arises because, during the process of rebuilding the model_args["messages"] object – which is essentially the structured data that gets sent to the AI API – the reasoning_details are being dropped. This happens specifically between conversational turns, meaning that for each new message you send, the AI doesn't have access to its own prior reasoning from the previous turn. This is a critical flaw for any AI that relies on a persistent understanding of its thought process. The developers at Home Assistant have identified that this occurs in a specific file (entity.py) within the core repository, at line 291, pointing to the exact location where this data loss happens during the message reconstruction phase. This is particularly relevant for newer, more powerful models like Gemini 3, which are designed to exhibit sophisticated reasoning capabilities. Without the preservation of reasoning_details, these advanced capabilities cannot be fully utilized through the OpenRouter integration.
Why Preserving reasoning_details Matters for AI in Home Assistant
In the context of Home Assistant, the ability for AI models to preserve reasoning details is not just a technical nicety; it's fundamental to delivering intelligent automation and assistance. Imagine you're asking your smart home AI to adjust the lighting based on a complex set of conditions, like
