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ai/ai_config.json

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+{
+  "model": "qwen/qwen3-next-80b-a3b-thinking:nitro",
+  "max_tokens": 8192,
+  "prompts": {
+    "command_parser": {
+      "system": "You are a helpful AI assistant that parses user commands into structured JSON. Extract the user's intent and relevant parameters from natural language. Return ONLY valid JSON, no explanations.\n\nBe flexible with language - handle typos, slang, and casual phrasing. Consider conversation context when available.\n\nIf unclear, ask for clarification in the 'needs_clarification' field with confidence < 0.8.\n\nAvailable interaction types:\n- \"routine\": managing daily routines (create, start, complete steps, view history)\n- \"medication\": managing medications (add, take, skip, snooze, check schedule, refills)\n\nFor routine commands, extract: action (create|list|start|complete_step|skip_step|cancel|history|schedule), routine_name?, step_name?, duration_minutes?, days?, time?\nFor medication commands, extract: action (add|list|take|skip|snooze|today|adherence|refill), med_name?, dosage?, unit?, frequency?, times?, reason?, minutes?",
+      "user_template": "Parse this command into structured JSON.\n\nCurrent conversation context (if any):\n{history_context}\n\nUser message: \"{user_input}\"\n\nReturn JSON with:\n{\n  \"interaction_type\": \"routine\" or \"medication\",\n  \"action\": \"string\",\n  \"confidence\": number (0-1),\n  \"needs_clarification\": \"string\" (if confidence < 0.8),\n  ... other extracted fields ...\n}\n\nIf unclear, ask for clarification in the needs_clarification field."
+    }
+  },
+  "validation": {
+    "max_retries": 3,
+    "timeout_seconds": 15,
+    "validators": {}
+  }
+}

ai/parser.py

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+"""
+parser.py - LLM-powered JSON parser with retry and validation
+
+Config-driven via ai_config.json. Supports:
+- Any OpenAI-compatible API (OpenRouter, local, etc.)
+- Reasoning models that output in reasoning field
+- Schema validation with automatic retry
+- Conversation context for multi-turn interactions
+"""
+
+import json
+import os
+import re
+from openai import OpenAI
+
+CONFIG_PATH = os.environ.get(
+    "AI_CONFIG_PATH", os.path.join(os.path.dirname(__file__), "ai_config.json")
+)
+
+with open(CONFIG_PATH, "r") as f:
+    AI_CONFIG = json.load(f)
+
+client = OpenAI(
+    api_key=os.getenv("OPENROUTER_API_KEY"),
+    base_url=os.getenv("OPENROUTER_BASE_URL", "https://openrouter.ai/api/v1"),
+)
+
+
+def _extract_json_from_text(text):
+    """Pull the first JSON object out of a block of text (for reasoning models)."""
+    match = re.search(r"```json\s*(\{.*?\})\s*```", text, re.DOTALL)
+    if match:
+        return match.group(1)
+    match = re.search(r"(\{[^{}]*\})", text, re.DOTALL)
+    if match:
+        return match.group(1)
+    return None
+
+
+def _call_llm(system_prompt, user_prompt):
+    """Call OpenAI-compatible API and return the response text."""
+    try:
+        response = client.chat.completions.create(
+            model=AI_CONFIG["model"],
+            max_tokens=AI_CONFIG.get("max_tokens", 8192),
+            timeout=AI_CONFIG["validation"]["timeout_seconds"],
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+        )
+        msg = response.choices[0].message
+        text = msg.content.strip() if msg.content else ""
+        if text:
+            return text
+        reasoning = getattr(msg, "reasoning", None)
+        if reasoning:
+            extracted = _extract_json_from_text(reasoning)
+            if extracted:
+                return extracted
+        return None
+    except Exception as e:
+        print(f"LLM error: {type(e).__name__}: {e}", flush=True)
+        return None
+
+
+def parse(user_input, interaction_type, retry_count=0, errors=None, history=None):
+    """
+    Parse user input into structured JSON using LLM.
+
+    Args:
+        user_input: The raw user message
+        interaction_type: Key in ai_config.json prompts (e.g., 'command_parser')
+        retry_count: Internal retry counter
+        errors: Previous validation errors for retry
+        history: List of (user_msg, parsed_result) tuples for context
+
+    Returns:
+        dict: Parsed JSON or error dict
+    """
+    if retry_count >= AI_CONFIG["validation"]["max_retries"]:
+        return {
+            "error": f"Failed to parse after {retry_count} retries",
+            "user_input": user_input,
+        }
+
+    prompt_config = AI_CONFIG["prompts"].get(interaction_type)
+    if not prompt_config:
+        return {
+            "error": f"Unknown interaction type: {interaction_type}",
+            "user_input": user_input,
+        }
+
+    history_context = "No previous context"
+    if history and len(history) > 0:
+        history_lines = []
+        for i, (msg, result) in enumerate(history[-3:]):
+            history_lines.append(f"{i + 1}. User: {msg}")
+            if isinstance(result, dict) and not result.get("error"):
+                history_lines.append(f"   Parsed: {json.dumps(result)}")
+            else:
+                history_lines.append(f"   Parsed: {result}")
+        history_context = "\n".join(history_lines)
+
+    user_prompt = prompt_config["user_template"].format(
+        user_input=user_input, history_context=history_context
+    )
+
+    if errors:
+        user_prompt += (
+            f"\n\nPrevious attempt had errors: {errors}\nPlease fix and try again."
+        )
+
+    response_text = _call_llm(prompt_config["system"], user_prompt)
+    if not response_text:
+        return {"error": "AI service unavailable", "user_input": user_input}
+
+    try:
+        parsed = json.loads(response_text)
+    except json.JSONDecodeError:
+        return parse(
+            user_input,
+            interaction_type,
+            retry_count + 1,
+            ["Response was not valid JSON"],
+            history=history,
+        )
+
+    if "error" in parsed:
+        return parsed
+
+    validator = AI_CONFIG["validation"].get("validators", {}).get(interaction_type)
+    if validator:
+        validation_errors = validator(parsed)
+        if validation_errors:
+            return parse(
+                user_input,
+                interaction_type,
+                retry_count + 1,
+                validation_errors,
+                history=history,
+            )
+
+    return parsed
+
+
+def register_validator(interaction_type, validator_fn):
+    """Register a custom validation function for an interaction type."""
+    if "validators" not in AI_CONFIG["validation"]:
+        AI_CONFIG["validation"]["validators"] = {}
+    AI_CONFIG["validation"]["validators"][interaction_type] = validator_fn