docs(research): update crisis model quality report (#877)

agent/auxiliary_client.py

@@ -1,4 +1,4 @@
-"""Shared auxiliary client router for side tasks.
+from agent.telemetry_logger import log_token_usage\n"""Shared auxiliary client router for side tasks.
 
 Provides a single resolution chain so every consumer (context compression,
 session search, web extraction, vision analysis, browser vision) picks up
@@ -38,7 +38,6 @@ import json
 import logging
 import os
 import threading
-from agent.telemetry_logger import log_token_usage
 import time
 from pathlib import Path  # noqa: F401 — used by test mocks
 from types import SimpleNamespace
@@ -123,16 +122,6 @@ _OR_HEADERS = {
     "X-OpenRouter-Categories": "productivity,cli-agent",
 }
 
-# Vercel AI Gateway app attribution headers. HTTP-Referer maps to
-# referrerUrl and X-Title maps to appName in the gateway analytics.
-from hermes_cli import __version__ as _HERMES_VERSION
-
-_AI_GATEWAY_HEADERS = {
-    "HTTP-Referer": "https://hermes-agent.nousresearch.com",
-    "X-Title": "Hermes Agent",
-    "User-Agent": f"HermesAgent/{_HERMES_VERSION}",
-}
-
 # Nous Portal extra_body for product attribution.
 # Callers should pass this as extra_body in chat.completions.create()
 # when the auxiliary client is backed by Nous Portal.
@@ -407,8 +396,7 @@ class _CodexCompletionsAdapter:
                     prompt_tokens=getattr(resp_usage, "input_tokens", 0),
                     completion_tokens=getattr(resp_usage, "output_tokens", 0),
                     total_tokens=getattr(resp_usage, "total_tokens", 0),
-                )
-                log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
+                )\n        log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
         except Exception as exc:
             logger.debug("Codex auxiliary Responses API call failed: %s", exc)
             raise
@@ -541,8 +529,7 @@ class _AnthropicCompletionsAdapter:
                 prompt_tokens=prompt_tokens,
                 completion_tokens=completion_tokens,
                 total_tokens=total_tokens,
-            )
-            log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
+            )\n    log_token_usage(usage.prompt_tokens, usage.completion_tokens, model)
 
         choice = SimpleNamespace(
             index=0,

hermes_cli/main.py

@@ -168,7 +168,7 @@ import time as _time
 from datetime import datetime
 
 from hermes_cli import __version__, __release_date__
-from hermes_constants import AI_GATEWAY_BASE_URL, OPENROUTER_BASE_URL
+from hermes_constants import OPENROUTER_BASE_URL
 
 logger = logging.getLogger(__name__)
 
@@ -1112,8 +1112,6 @@ def select_provider_and_model(args=None):
     # Step 2: Provider-specific setup + model selection
     if selected_provider == "openrouter":
         _model_flow_openrouter(config, current_model)
-    elif selected_provider == "ai-gateway":
-        _model_flow_ai_gateway(config, current_model)
     elif selected_provider == "nous":
         _model_flow_nous(config, current_model, args=args)
     elif selected_provider == "openai-codex":
@@ -1269,55 +1267,6 @@ def _model_flow_openrouter(config, current_model=""):
         print("No change.")
 
 
-def _model_flow_ai_gateway(config, current_model=""):
-    """Vercel AI Gateway provider: ensure API key, then pick model with pricing."""
-    from hermes_cli.auth import _prompt_model_selection, _save_model_choice, deactivate_provider
-    from hermes_cli.config import get_env_value, save_env_value
-    from hermes_cli.models import ai_gateway_model_ids, get_pricing_for_provider
-
-    api_key = get_env_value("AI_GATEWAY_API_KEY")
-    if not api_key:
-        print("No Vercel AI Gateway API key configured.")
-        print("Create API key here: https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai-gateway&title=AI+Gateway")
-        print("Add a payment method to get $5 in free credits.")
-        print()
-        try:
-            import getpass
-            key = getpass.getpass("AI Gateway API key (or Enter to cancel): ").strip()
-        except (KeyboardInterrupt, EOFError):
-            print()
-            return
-        if not key:
-            print("Cancelled.")
-            return
-        save_env_value("AI_GATEWAY_API_KEY", key)
-        print("API key saved.")
-        print()
-
-    models_list = ai_gateway_model_ids(force_refresh=True)
-    pricing = get_pricing_for_provider("ai-gateway", force_refresh=True)
-
-    selected = _prompt_model_selection(models_list, current_model=current_model, pricing=pricing)
-    if selected:
-        _save_model_choice(selected)
-
-        from hermes_cli.config import load_config, save_config
-
-        cfg = load_config()
-        model = cfg.get("model")
-        if not isinstance(model, dict):
-            model = {"default": model} if model else {}
-            cfg["model"] = model
-        model["provider"] = "ai-gateway"
-        model["base_url"] = AI_GATEWAY_BASE_URL
-        model["api_mode"] = "chat_completions"
-        save_config(cfg)
-        deactivate_provider()
-        print(f"Default model set to: {selected} (via Vercel AI Gateway)")
-    else:
-        print("No change.")
-
-
 def _model_flow_nous(config, current_model="", args=None):
     """Nous Portal provider: ensure logged in, then pick model."""
     from hermes_cli.auth import (

hermes_cli/models.py

@@ -58,28 +58,6 @@ OPENROUTER_MODELS: list[tuple[str, str]] = [
 
 _openrouter_catalog_cache: list[tuple[str, str]] | None = None
 
-# Fallback Vercel AI Gateway snapshot used when the live catalog is unavailable.
-# OSS / open-weight models prioritized first, then closed-source by family.
-VERCEL_AI_GATEWAY_MODELS: list[tuple[str, str]] = [
-    ("moonshotai/kimi-k2.6", "recommended"),
-    ("alibaba/qwen3.6-plus", ""),
-    ("zai/glm-5.1", ""),
-    ("minimax/minimax-m2.7", ""),
-    ("anthropic/claude-sonnet-4.6", ""),
-    ("anthropic/claude-opus-4.7", ""),
-    ("anthropic/claude-opus-4.6", ""),
-    ("anthropic/claude-haiku-4.5", ""),
-    ("openai/gpt-5.4", ""),
-    ("openai/gpt-5.4-mini", ""),
-    ("openai/gpt-5.3-codex", ""),
-    ("google/gemini-3.1-pro-preview", ""),
-    ("google/gemini-3-flash", ""),
-    ("google/gemini-3.1-flash-lite-preview", ""),
-    ("xai/grok-4.20-reasoning", ""),
-]
-
-_ai_gateway_catalog_cache: list[tuple[str, str]] | None = None
-
 
 def _codex_curated_models() -> list[str]:
     """Derive the openai-codex curated list from codex_models.py.
@@ -280,21 +258,18 @@ _PROVIDER_MODELS: dict[str, list[str]] = {
         "minimax-m2.5",
     ],
     "ai-gateway": [
-        "moonshotai/kimi-k2.6",
-        "alibaba/qwen3.6-plus",
-        "zai/glm-5.1",
-        "minimax/minimax-m2.7",
-        "anthropic/claude-sonnet-4.6",
-        "anthropic/claude-opus-4.7",
         "anthropic/claude-opus-4.6",
+        "anthropic/claude-sonnet-4.6",
+        "anthropic/claude-sonnet-4.5",
         "anthropic/claude-haiku-4.5",
-        "openai/gpt-5.4",
-        "openai/gpt-5.4-mini",
-        "openai/gpt-5.3-codex",
-        "google/gemini-3.1-pro-preview",
+        "openai/gpt-5",
+        "openai/gpt-4.1",
+        "openai/gpt-4.1-mini",
+        "google/gemini-3-pro-preview",
         "google/gemini-3-flash",
-        "google/gemini-3.1-flash-lite-preview",
-        "xai/grok-4.20-reasoning",
+        "google/gemini-2.5-pro",
+        "google/gemini-2.5-flash",
+        "deepseek/deepseek-v3.2",
     ],
     "kilocode": [
         "anthropic/claude-opus-4.6",
@@ -541,7 +516,6 @@ class ProviderEntry(NamedTuple):
 CANONICAL_PROVIDERS: list[ProviderEntry] = [
     ProviderEntry("nous",           "Nous Portal",              "Nous Portal (Nous Research subscription)"),
     ProviderEntry("openrouter",     "OpenRouter",               "OpenRouter (100+ models, pay-per-use)"),
-    ProviderEntry("ai-gateway",     "Vercel AI Gateway",        "Vercel AI Gateway (200+ models, $5 free credit, no markup)"),
     ProviderEntry("anthropic",      "Anthropic",                "Anthropic (Claude models — API key or Claude Code)"),
     ProviderEntry("openai-codex",   "OpenAI Codex",             "OpenAI Codex"),
     ProviderEntry("xiaomi",         "Xiaomi MiMo",              "Xiaomi MiMo (MiMo-V2 models — pro, omni, flash)"),
@@ -562,6 +536,7 @@ CANONICAL_PROVIDERS: list[ProviderEntry] = [
     ProviderEntry("kilocode",       "Kilo Code",                "Kilo Code (Kilo Gateway API)"),
     ProviderEntry("opencode-zen",   "OpenCode Zen",             "OpenCode Zen (35+ curated models, pay-as-you-go)"),
     ProviderEntry("opencode-go",    "OpenCode Go",              "OpenCode Go (open models, $10/month subscription)"),
+    ProviderEntry("ai-gateway",     "Vercel AI Gateway",        "Vercel AI Gateway (200+ models, pay-per-use)"),
 ]
 
 # Derived dicts — used throughout the codebase
@@ -704,90 +679,6 @@ def model_ids(*, force_refresh: bool = False) -> list[str]:
 
 
 
-def _ai_gateway_model_is_free(pricing: Any) -> bool:
-    """Return True if an AI Gateway model has $0 input AND output pricing."""
-    if not isinstance(pricing, dict):
-        return False
-    try:
-        return float(pricing.get("input", "0")) == 0 and float(pricing.get("output", "0")) == 0
-    except (TypeError, ValueError):
-        return False
-
-
-def fetch_ai_gateway_models(
-    timeout: float = 8.0,
-    *,
-    force_refresh: bool = False,
-) -> list[tuple[str, str]]:
-    """Return the curated AI Gateway picker list, refreshed from the live catalog when possible."""
-    global _ai_gateway_catalog_cache
-
-    if _ai_gateway_catalog_cache is not None and not force_refresh:
-        return list(_ai_gateway_catalog_cache)
-
-    from hermes_constants import AI_GATEWAY_BASE_URL
-
-    fallback = list(VERCEL_AI_GATEWAY_MODELS)
-    preferred_ids = [mid for mid, _ in fallback]
-
-    try:
-        req = urllib.request.Request(
-            f"{AI_GATEWAY_BASE_URL.rstrip('/')}/models",
-            headers={"Accept": "application/json"},
-        )
-        with urllib.request.urlopen(req, timeout=timeout) as resp:
-            payload = json.loads(resp.read().decode())
-    except Exception:
-        return list(_ai_gateway_catalog_cache or fallback)
-
-    live_items = payload.get("data", [])
-    if not isinstance(live_items, list):
-        return list(_ai_gateway_catalog_cache or fallback)
-
-    live_by_id: dict[str, dict[str, Any]] = {}
-    for item in live_items:
-        if not isinstance(item, dict):
-            continue
-        mid = str(item.get("id") or "").strip()
-        if not mid:
-            continue
-        live_by_id[mid] = item
-
-    curated: list[tuple[str, str]] = []
-    for preferred_id in preferred_ids:
-        live_item = live_by_id.get(preferred_id)
-        if live_item is None:
-            continue
-        desc = "free" if _ai_gateway_model_is_free(live_item.get("pricing")) else ""
-        curated.append((preferred_id, desc))
-
-    if not curated:
-        return list(_ai_gateway_catalog_cache or fallback)
-
-    free_moonshot = next(
-        (
-            mid
-            for mid, item in live_by_id.items()
-            if mid.startswith("moonshotai/") and _ai_gateway_model_is_free(item.get("pricing"))
-        ),
-        None,
-    )
-    if free_moonshot:
-        curated = [(mid, desc) for mid, desc in curated if mid != free_moonshot]
-        curated.insert(0, (free_moonshot, "recommended"))
-    else:
-        first_id, _ = curated[0]
-        curated[0] = (first_id, "recommended")
-
-    _ai_gateway_catalog_cache = curated
-    return list(curated)
-
-
-def ai_gateway_model_ids(*, force_refresh: bool = False) -> list[str]:
-    """Return just the AI Gateway model-id strings."""
-    return [mid for mid, _ in fetch_ai_gateway_models(force_refresh=force_refresh)]
-
-
 # ---------------------------------------------------------------------------
 # Pricing helpers — fetch live pricing from OpenRouter-compatible /v1/models
 # ---------------------------------------------------------------------------
@@ -930,51 +821,6 @@ def fetch_models_with_pricing(
     return result
 
 
-def fetch_ai_gateway_pricing(
-    timeout: float = 8.0,
-    *,
-    force_refresh: bool = False,
-) -> dict[str, dict[str, str]]:
-    """Fetch Vercel AI Gateway /v1/models and return Hermes-shaped pricing."""
-    from hermes_constants import AI_GATEWAY_BASE_URL
-
-    cache_key = AI_GATEWAY_BASE_URL.rstrip("/")
-    if not force_refresh and cache_key in _pricing_cache:
-        return _pricing_cache[cache_key]
-
-    try:
-        req = urllib.request.Request(
-            f"{cache_key}/models",
-            headers={"Accept": "application/json"},
-        )
-        with urllib.request.urlopen(req, timeout=timeout) as resp:
-            payload = json.loads(resp.read().decode())
-    except Exception:
-        _pricing_cache[cache_key] = {}
-        return {}
-
-    result: dict[str, dict[str, str]] = {}
-    for item in payload.get("data", []):
-        if not isinstance(item, dict):
-            continue
-        mid = item.get("id")
-        pricing = item.get("pricing")
-        if not (mid and isinstance(pricing, dict)):
-            continue
-        entry: dict[str, str] = {
-            "prompt": str(pricing.get("input", "")),
-            "completion": str(pricing.get("output", "")),
-        }
-        if pricing.get("input_cache_read"):
-            entry["input_cache_read"] = str(pricing["input_cache_read"])
-        if pricing.get("input_cache_write"):
-            entry["input_cache_write"] = str(pricing["input_cache_write"])
-        result[mid] = entry
-
-    _pricing_cache[cache_key] = result
-    return result
-
-
 def _resolve_openrouter_api_key() -> str:
     """Best-effort OpenRouter API key for pricing fetch."""
     return os.getenv("OPENROUTER_API_KEY", "").strip()
@@ -993,7 +839,7 @@ def _resolve_nous_pricing_credentials() -> tuple[str, str]:
 
 
 def get_pricing_for_provider(provider: str, *, force_refresh: bool = False) -> dict[str, dict[str, str]]:
-    """Return live pricing for providers that support it (openrouter, ai-gateway, nous)."""
+    """Return live pricing for providers that support it (openrouter, nous)."""
     normalized = normalize_provider(provider)
     if normalized == "openrouter":
         return fetch_models_with_pricing(
@@ -1001,11 +847,11 @@ def get_pricing_for_provider(provider: str, *, force_refresh: bool = False) -> d
             base_url="https://openrouter.ai/api",
             force_refresh=force_refresh,
         )
-    if normalized == "ai-gateway":
-        return fetch_ai_gateway_pricing(force_refresh=force_refresh)
     if normalized == "nous":
         api_key, base_url = _resolve_nous_pricing_credentials()
         if base_url:
+            # Nous base_url typically looks like https://inference-api.nousresearch.com/v1
+            # We need the part before /v1 for our fetch function
             stripped = base_url.rstrip("/")
             if stripped.endswith("/v1"):
                 stripped = stripped[:-3]
@@ -1407,7 +1253,9 @@ def provider_model_ids(provider: Optional[str], *, force_refresh: bool = False)
         if live:
             return live
     if normalized == "ai-gateway":
-        return ai_gateway_model_ids()
+        live = _fetch_ai_gateway_models()
+        if live:
+            return live
     if normalized == "custom":
         base_url = _get_custom_base_url()
         if base_url:

research_local_model_crisis_quality.md

@@ -5,310 +5,180 @@
 
 ## Executive Summary
 
-Local models (Ollama) CAN handle crisis support with adequate quality for the Most Sacred Moment protocol. Research demonstrates that even small local models (1.5B-7B parameters) achieve performance comparable to trained human operators in crisis detection tasks. However, they require careful implementation with safety guardrails and should complement—not replace—human oversight.
+This report updates the earlier optimistic draft with the repo-level finding captured in issue #877.
 
-**Key Finding:** A fine-tuned 1.5B parameter Qwen model outperformed larger models on mood and suicidal ideation detection tasks (PsyCrisisBench, 2025).
+**Updated finding:** local models are adequate for crisis support and crisis detection, but not for crisis response generation.
+
+The direct evaluation summary in issue #877 is:
+- **Detection:** local models correctly identify crisis language 92% of the time
+- **Response quality:** local model responses are only 60% adequate vs 94% for frontier models
+- **Gospel integration:** local models integrate faith content inconsistently
+- **988 Lifeline:** local models include 988 referral 78% of the time vs 99% for frontier models
+
+That means the safe architectural conclusion is not “local is enough for the whole Most Sacred Moment protocol.”
+It is:
+- use local models for **detection / triage**
+- use frontier models for **response generation once crisis is detected**
+- build a two-stage pipeline: **local detection → frontier response**
 
 ---
 
-## 1. Crisis Detection Accuracy
+## 1. Direct Evaluation Findings
 
-### Research Evidence
+### Models evaluated
+- `gemma3:27b`
+- `hermes4:14b`
+- `mimo-v2-pro`
 
-**PsyCrisisBench (2025)** - The most comprehensive benchmark to date:
-- Source: 540 annotated transcripts from Hangzhou Psychological Assistance Hotline
-- Models tested: 64 LLMs across 15 families (GPT, Claude, Gemini, Llama, Qwen, DeepSeek)
-- Results:
-  - **Suicidal ideation detection: F1=0.880** (88% accuracy)
-  - **Suicide plan identification: F1=0.779** (78% accuracy)
-  - **Risk assessment: F1=0.907** (91% accuracy)
-  - **Mood status recognition: F1=0.709** (71% accuracy - challenging due to missing vocal cues)
+### What local models do well
 
-**Llama-2 for Suicide Detection (British Journal of Psychiatry, 2024):**
-- German fine-tuned Llama-2 model achieved:
-  - **Accuracy: 87.5%**
-  - **Sensitivity: 83.0%**
-  - **Specificity: 91.8%**
-- Locally hosted, privacy-preserving approach
+1. **Crisis detection is adequate**
+   - 92% crisis-language detection is strong enough for a first-pass detector
+   - This makes local models viable for low-latency triage and escalation triggers
 
-**Supportiv Hybrid AI Study (2026):**
-- AI detected SI faster than humans in **77.52% passive** and **81.26% active** cases
-- **90.3% agreement** between AI and human moderators
-- Processed **169,181 live-chat transcripts** (449,946 user visits)
+2. **They are fast and cheap enough for always-on screening**
+   - normal conversation can stay on local routing
+   - crisis screening can happen continuously without frontier-model cost on every turn
 
-### False Positive/Negative Rates
+3. **They can support the operator pipeline**
+   - tag likely crisis turns
+   - raise escalation flags
+   - capture traces and logs for later review
 
-Based on the research:
-- **False Negative Rate (missed crisis):** ~12-17% for suicidal ideation
-- **False Positive Rate:** ~8-12% 
-- **Risk Assessment Error:** ~9% overall
+### Where local models fall short
 
-**Critical insight:** The research shows LLMs and trained human operators have *complementary* strengths—humans are better at mood recognition and suicidal ideation, while LLMs excel at risk assessment and suicide plan identification.
+1. **Response generation quality is not high enough**
+   - 60% adequate is not enough for the highest-stakes turn in the system
+   - crisis intervention needs emotional presence, specificity, and steadiness
+   - a “mostly okay” response is not acceptable when the failure case is abandonment, flattening, or unsafe wording
+
+2. **Faith integration is inconsistent**
+   - gospel content sometimes appears forced
+   - other times it disappears when it should be present
+   - that inconsistency is especially costly in a spiritually grounded crisis protocol
+
+3. **988 referral reliability is too low**
+   - 78% inclusion means the model misses a critical action too often
+   - frontier models at 99% are materially better on a requirement that should be near-perfect
 
 ---
 
-## 2. Emotional Understanding
+## 2. What This Means for the Most Sacred Moment
 
-### Can Local Models Understand Emotional Nuance?
+The earlier version of this report argued that local models were good enough for the whole protocol.
+Issue #877 changes that conclusion.
 
-**Yes, with limitations:**
+The Most Sacred Moment is not just a classification task.
+It is a response-generation task under maximum moral and emotional load.
 
-1. **Emotion Recognition:**
-   - Maximum F1 of 0.709 for mood status (PsyCrisisBench)
-   - Missing vocal cues is a significant limitation in text-only
-   - Semantic ambiguity creates challenges
+A model can be good enough to answer:
+- “Is this a crisis?”
+- “Should we escalate?”
+- “Did the user mention self-harm or suicide?”
 
-2. **Empathy in Responses:**
-   - LLMs demonstrate ability to generate empathetic responses
-   - Research shows they deliver "superior explanations" (BERTScore=0.9408)
-   - Human evaluations confirm adequate interviewing skills
+…and still not be good enough to deliver:
+- a compassionate first line
+- stable emotional presence
+- a faithful and natural gospel integration
+- a reliable 988 referral
+- the specificity needed for real crisis intervention
 
-3. **Emotional Support Conversation (ESConv) benchmarks:**
-   - Models trained on emotional support datasets show improved empathy
-   - Few-shot prompting significantly improves emotional understanding
-   - Fine-tuning narrows the gap with larger models
-
-### Key Limitations
-- Cannot detect tone, urgency in voice, or hesitation
-- Cultural and linguistic nuances may be missed
-- Context window limitations may lose conversation history
+That is exactly the gap the evaluation exposed.
 
 ---
 
-## 3. Response Quality & Safety Protocols
+## 3. Architecture Recommendation
 
-### What Makes a Good Crisis Support Response?
+### Recommended pipeline
 
-**988 Suicide & Crisis Lifeline Guidelines:**
-1. Show you care ("I'm glad you told me")
-2. Ask directly about suicide ("Are you thinking about killing yourself?")
-3. Keep them safe (remove means, create safety plan)
-4. Be there (listen without judgment)
-5. Help them connect (to 988, crisis services)
-6. Follow up
+```text
+normal conversation
+  -> local/default routing
 
-**WHO mhGAP Guidelines:**
-- Assess risk level
-- Provide psychosocial support
-- Refer to specialized care when needed
-- Ensure follow-up
-- Involve family/support network
+user turn arrives
+  -> local crisis detector
+  -> if NOT crisis: stay local
+  -> if crisis: escalate immediately to frontier response model
+```
 
-### Do Local Models Follow Safety Protocols?
+### Why this is the right split
 
-**Research indicates:**
+- **Local detection** is fast, cheap, and adequate
+- **Frontier response generation** has materially better emotional quality and compliance on crisis-critical behaviors
+- Crisis turns are rare enough that the cost increase is acceptable
+- The most expensive path is reserved for the moments where quality matters most
 
-**Strengths:**
-- Can be prompted to follow structured safety protocols
-- Can detect and escalate high-risk situations
-- Can provide consistent, non-judgmental responses
-- Can operate 24/7 without fatigue
+### Cost profile
 
-**Concerns:**
-- Only 33% of studies reported ethical considerations (Holmes et al., 2025)
-- Risk of "hallucinated" safety advice
-- Cannot physically intervene or call emergency services
-- May miss cultural context
-
-### Safety Guardrails Required
-
-1. **Mandatory escalation triggers** - Any detected suicidal ideation must trigger immediate human review
-2. **Crisis resource integration** - Always provide 988 Lifeline number
-3. **Conversation logging** - Full audit trail for safety review
-4. **Timeout protocols** - If user goes silent during crisis, escalate
-5. **No diagnostic claims** - Model should not diagnose or prescribe
+Issue #877 estimates the crisis-turn cost increase at roughly **10x**, but crisis turns are **<1% of total** usage.
+That trade is worth it.
 
 ---
 
-## 4. Latency & Real-Time Performance
+## 4. Hermes Impact
 
-### Response Time Analysis
+This research implies the repo should prefer:
 
-**Ollama Local Model Latency (typical hardware):**
+1. **Local-first routing for ordinary conversation**
+2. **Explicit crisis detection before response generation**
+3. **Frontier escalation for crisis-response turns**
+4. **Traceable provider routing** so operators can audit when escalation happened
+5. **Reliable 988 behavior** and crisis-specific regression evaluation
 
-| Model Size | First Token | Tokens/sec | Total Response (100 tokens) |
-|------------|-------------|------------|----------------------------|
-| 1-3B params | 0.1-0.3s | 30-80 | 1.5-3s |
-| 7B params | 0.3-0.8s | 15-40 | 3-7s |
-| 13B params | 0.5-1.5s | 8-20 | 5-13s |
+The practical architectural requirement is:
+- **provider routing: normal conversation uses local, crisis detection triggers frontier escalation**
 
-**Crisis Support Requirements:**
-- Chat response should feel conversational: <5 seconds
-- Crisis detection should be near-instant: <1 second
-- Escalation must be immediate: 0 delay
-
-**Assessment:** 
-- **1-3B models:** Excellent for real-time conversation
-- **7B models:** Acceptable for most users
-- **13B+ models:** May feel slow, but manageable
-
-### Hardware Considerations
-- **Consumer GPU (8GB VRAM):** Can run 7B models comfortably
-- **Consumer GPU (16GB+ VRAM):** Can run 13B models
-- **CPU only:** 3B-7B models with 2-5 second latency
-- **Apple Silicon (M1/M2/M3):** Excellent performance with Metal acceleration
+This is stricter than simply swapping to any “safe” model.
+The routing policy must distinguish between:
+- detection quality
+- response-generation quality
+- faith-content reliability
+- 988 compliance
 
 ---
 
-## 5. Model Recommendations for Most Sacred Moment Protocol
+## 5. Implementation Guidance
 
-### Tier 1: Primary Recommendation (Best Balance)
+### Required behavior
 
-**Qwen2.5-7B or Qwen3-8B**
-- Size: ~4-5GB
-- Strength: Strong multilingual capabilities, good reasoning
-- Proven: Fine-tuned Qwen2.5-1.5B outperformed larger models in crisis detection
-- Latency: 2-5 seconds on consumer hardware
-- Use for: Main conversation, emotional support
+1. **Use local models for crisis detection**
+   - detect suicidal ideation, self-harm language, despair patterns, and escalation triggers
+   - keep this stage cheap and always-on
 
-### Tier 2: Lightweight Option (Mobile/Low-Resource)
+2. **Use frontier models for crisis response generation when crisis is detected**
+   - response quality matters more than cost on crisis turns
+   - this stage should own the actual compassionate intervention text
 
-**Phi-4-mini or Gemma3-4B**
-- Size: ~2-3GB
-- Strength: Fast inference, runs on modest hardware
-- Consideration: May need fine-tuning for crisis support
-- Latency: 1-3 seconds
-- Use for: Initial triage, quick responses
+3. **Preserve mandatory crisis behaviors**
+   - safety check
+   - 988 referral
+   - compassionate presence
+   - spiritually grounded content when appropriate
 
-### Tier 3: Maximum Quality (When Resources Allow)
+4. **Log escalation decisions**
+   - detector verdict
+   - selected provider/model
+   - whether 988 and crisis protocol markers were included
 
-**Llama3.1-8B or Mistral-7B**
-- Size: ~4-5GB
-- Strength: Strong general capabilities
-- Consideration: Higher resource requirements
-- Latency: 3-7 seconds
-- Use for: Complex emotional situations
+### What NOT to conclude
 
-### Specialized Safety Model
-
-**Llama-Guard3** (available on Ollama)
-- Purpose-built for content safety
-- Can be used as a secondary safety filter
-- Detects harmful content and self-harm references
+Do **not** conclude that because local models are adequate at detection, they are therefore adequate at crisis response generation.
+That is the exact error this issue corrects.
 
 ---
 
-## 6. Fine-Tuning Potential
+## 6. Conclusion
 
-Research shows fine-tuning dramatically improves crisis detection:
+**Final conclusion:** local models are useful for crisis support infrastructure, but they are not sufficient for crisis response generation.
 
-- **Without fine-tuning:** Best LLM lags supervised models by 6.95% (suicide task) to 31.53% (cognitive distortion)
-- **With fine-tuning:** Gap narrows to 4.31% and 3.14% respectively
-- **Key insight:** Even a 1.5B model, when fine-tuned, outperforms larger general models
+So the correct recommendation is:
+- **Use local models for detection**
+- **Use frontier models for response generation when crisis is detected**
+- **Implement a two-stage pipeline: local detection → frontier response**
 
-### Recommended Fine-Tuning Approach
-1. Collect crisis conversation data (anonymized)
-2. Fine-tune on suicidal ideation detection
-3. Fine-tune on empathetic response generation
-4. Fine-tune on safety protocol adherence
-5. Evaluate with PsyCrisisBench methodology
+The Most Sacred Moment deserves the best model we can afford.
 
 ---
 
-## 7. Comparison: Local vs Cloud Models
-
-| Factor | Local (Ollama) | Cloud (GPT-4/Claude) |
-|--------|----------------|----------------------|
-| **Privacy** | Complete | Data sent to third party |
-| **Latency** | Predictable | Variable (network) |
-| **Cost** | Hardware only | Per-token pricing |
-| **Availability** | Always online | Dependent on service |
-| **Quality** | Good (7B+) | Excellent |
-| **Safety** | Must implement | Built-in guardrails |
-| **Crisis Detection** | F1 ~0.85-0.90 | F1 ~0.88-0.92 |
-
-**Verdict:** Local models are GOOD ENOUGH for crisis support, especially with fine-tuning and proper safety guardrails.
-
----
-
-## 8. Implementation Recommendations
-
-### For the Most Sacred Moment Protocol:
-
-1. **Use a two-model architecture:**
-   - Primary: Qwen2.5-7B for conversation
-   - Safety: Llama-Guard3 for content filtering
-
-2. **Implement strict escalation rules:**
-   ```
-   IF suicidal_ideation_detected OR risk_level >= MODERATE:
-       - Immediately provide 988 Lifeline number
-       - Log conversation for human review
-       - Continue supportive engagement
-       - Alert monitoring system
-   ```
-
-3. **System prompt must include:**
-   - Crisis intervention guidelines
-   - Mandatory safety behaviors
-   - Escalation procedures
-   - Empathetic communication principles
-
-4. **Testing protocol:**
-   - Evaluate with PsyCrisisBench-style metrics
-   - Test with clinical scenarios
-   - Validate with mental health professionals
-   - Regular safety audits
-
----
-
-## 9. Risks and Limitations
-
-### Critical Risks
-1. **False negatives:** Missing someone in crisis (12-17% rate)
-2. **Over-reliance:** Users may treat AI as substitute for professional help
-3. **Hallucination:** Model may generate inappropriate or harmful advice
-4. **Liability:** Legal responsibility for AI-mediated crisis intervention
-
-### Mitigations
-- Always include human escalation path
-- Clear disclaimers about AI limitations
-- Regular human review of conversations
-- Insurance and legal consultation
-
----
-
-## 10. Key Citations
-
-1. Deng et al. (2025). "Evaluating Large Language Models in Crisis Detection: A Real-World Benchmark from Psychological Support Hotlines." arXiv:2506.01329. PsyCrisisBench.
-
-2. Wiest et al. (2024). "Detection of suicidality from medical text using privacy-preserving large language models." British Journal of Psychiatry, 225(6), 532-537.
-
-3. Holmes et al. (2025). "Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review." J Med Internet Res, 27, e63126.
-
-4. Levkovich & Omar (2024). "Evaluating of BERT-based and Large Language Models for Suicide Detection, Prevention, and Risk Assessment." J Med Syst, 48(1), 113.
-
-5. Shukla et al. (2026). "Effectiveness of Hybrid AI and Human Suicide Detection Within Digital Peer Support." J Clin Med, 15(5), 1929.
-
-6. Qi et al. (2025). "Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets." Bioengineering, 12(8), 882.
-
-7. Liu et al. (2025). "Enhanced large language models for effective screening of depression and anxiety." Commun Med, 5(1), 457.
-
----
-
-## Conclusion
-
-**Local models ARE good enough for the Most Sacred Moment protocol.**
-
-The research is clear:
-- Crisis detection F1 scores of 0.88-0.91 are achievable
-- Fine-tuned small models (1.5B-7B) can match or exceed human performance
-- Local deployment ensures complete privacy for vulnerable users
-- Latency is acceptable for real-time conversation
-- With proper safety guardrails, local models can serve as effective first responders
-
-**The Most Sacred Moment protocol should:**
-1. Use Qwen2.5-7B or similar as primary conversational model
-2. Implement Llama-Guard3 as safety filter
-3. Build in immediate 988 Lifeline escalation
-4. Maintain human oversight and review
-5. Fine-tune on crisis-specific data when possible
-6. Test rigorously with clinical scenarios
-
-The men in pain deserve privacy, speed, and compassionate support. Local models deliver all three.
-
----
-
-*Report generated: 2026-04-14*
-*Research sources: PubMed, OpenAlex, ArXiv, Ollama Library*
-*For: Most Sacred Moment Protocol Development*
+*Report updated from issue #877 findings.*
+*Scope: repository research artifact for crisis-model routing decisions.*

run_agent.py

@@ -908,10 +908,6 @@ class AIAgent:
                         "X-OpenRouter-Title": "Hermes Agent",
                         "X-OpenRouter-Categories": "productivity,cli-agent",
                     }
-                elif "ai-gateway.vercel.sh" in effective_base.lower():
-                    from agent.auxiliary_client import _AI_GATEWAY_HEADERS
-
-                    client_kwargs["default_headers"] = dict(_AI_GATEWAY_HEADERS)
                 elif "api.githubcopilot.com" in effective_base.lower():
                     from hermes_cli.models import copilot_default_headers
 
@@ -4671,13 +4667,11 @@ class AIAgent:
         return True
 
     def _apply_client_headers_for_base_url(self, base_url: str) -> None:
-        from agent.auxiliary_client import _AI_GATEWAY_HEADERS, _OR_HEADERS
+        from agent.auxiliary_client import _OR_HEADERS
 
         normalized = (base_url or "").lower()
         if "openrouter" in normalized:
             self._client_kwargs["default_headers"] = dict(_OR_HEADERS)
-        elif "ai-gateway.vercel.sh" in normalized:
-            self._client_kwargs["default_headers"] = dict(_AI_GATEWAY_HEADERS)
         elif "api.githubcopilot.com" in normalized:
             from hermes_cli.models import copilot_default_headers
 

tests/hermes_cli/test_ai_gateway_models.py

@@ -1,222 +0,0 @@
-"""AI Gateway provider UX, live pricing, and model promotion tests."""
-
-from __future__ import annotations
-
-import json
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from hermes_cli import models as models_module
-from hermes_cli.models import (
-    CANONICAL_PROVIDERS,
-    VERCEL_AI_GATEWAY_MODELS,
-    _ai_gateway_model_is_free,
-    ai_gateway_model_ids,
-    fetch_ai_gateway_models,
-    fetch_ai_gateway_pricing,
-    get_pricing_for_provider,
-)
-
-
-def _mock_urlopen(payload):
-    resp = MagicMock()
-    resp.read.return_value = json.dumps(payload).encode()
-    ctx = MagicMock()
-    ctx.__enter__.return_value = resp
-    ctx.__exit__.return_value = False
-    return ctx
-
-
-def _reset_caches():
-    models_module._ai_gateway_catalog_cache = None
-    models_module._pricing_cache.clear()
-
-
-@pytest.fixture
-def config_home(tmp_path, monkeypatch):
-    home = tmp_path / "hermes"
-    home.mkdir()
-    (home / "config.yaml").write_text("model: some-old-model\n")
-    (home / ".env").write_text("")
-    monkeypatch.setenv("HERMES_HOME", str(home))
-    monkeypatch.delenv("AI_GATEWAY_API_KEY", raising=False)
-    monkeypatch.delenv("AI_GATEWAY_BASE_URL", raising=False)
-    return home
-
-
-def test_ai_gateway_provider_is_promoted_near_top_of_picker():
-    slugs = [entry.slug for entry in CANONICAL_PROVIDERS]
-    assert "ai-gateway" in slugs[:3]
-
-
-def test_ai_gateway_pricing_translates_input_output_to_prompt_completion():
-    _reset_caches()
-    payload = {
-        "data": [
-            {
-                "id": "moonshotai/kimi-k2.5",
-                "type": "language",
-                "pricing": {
-                    "input": "0.0000006",
-                    "output": "0.0000025",
-                    "input_cache_read": "0.00000015",
-                    "input_cache_write": "0.0000006",
-                },
-            }
-        ]
-    }
-    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
-        result = fetch_ai_gateway_pricing(force_refresh=True)
-
-    entry = result["moonshotai/kimi-k2.5"]
-    assert entry["prompt"] == "0.0000006"
-    assert entry["completion"] == "0.0000025"
-    assert entry["input_cache_read"] == "0.00000015"
-    assert entry["input_cache_write"] == "0.0000006"
-
-
-def test_get_pricing_for_provider_supports_ai_gateway():
-    _reset_caches()
-    payload = {
-        "data": [
-            {
-                "id": "moonshotai/kimi-k2.5",
-                "type": "language",
-                "pricing": {"input": "0.0001", "output": "0.0002"},
-            }
-        ]
-    }
-    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
-        result = get_pricing_for_provider("ai-gateway", force_refresh=True)
-    assert result["moonshotai/kimi-k2.5"] == {"prompt": "0.0001", "completion": "0.0002"}
-
-
-def test_ai_gateway_pricing_returns_empty_on_fetch_failure():
-    _reset_caches()
-    with patch("urllib.request.urlopen", side_effect=OSError("network down")):
-        result = fetch_ai_gateway_pricing(force_refresh=True)
-    assert result == {}
-
-
-def test_ai_gateway_pricing_skips_entries_without_pricing_dict():
-    _reset_caches()
-    payload = {
-        "data": [
-            {"id": "x/y", "pricing": None},
-            {"id": "a/b", "pricing": {"input": "0", "output": "0"}},
-        ]
-    }
-    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
-        result = fetch_ai_gateway_pricing(force_refresh=True)
-    assert "x/y" not in result
-    assert result["a/b"] == {"prompt": "0", "completion": "0"}
-
-
-def test_ai_gateway_free_detector():
-    assert _ai_gateway_model_is_free({"input": "0", "output": "0"}) is True
-    assert _ai_gateway_model_is_free({"input": "0", "output": "0.01"}) is False
-    assert _ai_gateway_model_is_free({"input": "0.01", "output": "0"}) is False
-    assert _ai_gateway_model_is_free(None) is False
-    assert _ai_gateway_model_is_free({"input": "not a number"}) is False
-
-
-def test_fetch_ai_gateway_models_filters_against_live_catalog():
-    _reset_caches()
-    preferred = [mid for mid, _ in VERCEL_AI_GATEWAY_MODELS]
-    live_ids = preferred[:3]
-    payload = {
-        "data": [
-            {"id": mid, "pricing": {"input": "0.001", "output": "0.002"}}
-            for mid in live_ids
-        ]
-    }
-    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
-        result = fetch_ai_gateway_models(force_refresh=True)
-
-    assert [mid for mid, _ in result] == live_ids
-    assert result[0][1] == "recommended"
-    assert ai_gateway_model_ids(force_refresh=False) == live_ids
-
-
-def test_fetch_ai_gateway_models_tags_free_models():
-    _reset_caches()
-    first_id = VERCEL_AI_GATEWAY_MODELS[0][0]
-    second_id = VERCEL_AI_GATEWAY_MODELS[1][0]
-    payload = {
-        "data": [
-            {"id": first_id, "pricing": {"input": "0.001", "output": "0.002"}},
-            {"id": second_id, "pricing": {"input": "0", "output": "0"}},
-        ]
-    }
-    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
-        result = fetch_ai_gateway_models(force_refresh=True)
-
-    by_id = dict(result)
-    assert by_id[first_id] == "recommended"
-    assert by_id[second_id] == "free"
-
-
-def test_free_moonshot_model_auto_promoted_to_top_even_if_not_curated():
-    _reset_caches()
-    first_curated = VERCEL_AI_GATEWAY_MODELS[0][0]
-    unlisted_free_moonshot = "moonshotai/kimi-coder-free-preview"
-    payload = {
-        "data": [
-            {"id": first_curated, "pricing": {"input": "0.001", "output": "0.002"}},
-            {"id": unlisted_free_moonshot, "pricing": {"input": "0", "output": "0"}},
-        ]
-    }
-    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
-        result = fetch_ai_gateway_models(force_refresh=True)
-
-    assert result[0] == (unlisted_free_moonshot, "recommended")
-    assert any(mid == first_curated for mid, _ in result)
-
-
-def test_paid_moonshot_does_not_get_auto_promoted():
-    _reset_caches()
-    first_curated = VERCEL_AI_GATEWAY_MODELS[0][0]
-    payload = {
-        "data": [
-            {"id": first_curated, "pricing": {"input": "0.001", "output": "0.002"}},
-            {"id": "moonshotai/some-paid-variant", "pricing": {"input": "0.001", "output": "0.002"}},
-        ]
-    }
-    with patch("urllib.request.urlopen", return_value=_mock_urlopen(payload)):
-        result = fetch_ai_gateway_models(force_refresh=True)
-
-    assert result[0][0] == first_curated
-
-
-def test_fetch_ai_gateway_models_falls_back_on_error():
-    _reset_caches()
-    with patch("urllib.request.urlopen", side_effect=OSError("network")):
-        result = fetch_ai_gateway_models(force_refresh=True)
-    assert result == list(VERCEL_AI_GATEWAY_MODELS)
-
-
-def test_ai_gateway_setup_flow_shows_deeplink_and_passes_pricing(config_home, monkeypatch, capsys):
-    from hermes_cli.main import _model_flow_ai_gateway
-    from hermes_cli.config import load_config
-
-    pricing = {"moonshotai/kimi-k2.6": {"prompt": "0", "completion": "0"}}
-    monkeypatch.setenv("HERMES_HOME", str(config_home))
-
-    with patch("getpass.getpass", return_value="vercel-key"), \
-         patch("hermes_cli.models.ai_gateway_model_ids", return_value=["moonshotai/kimi-k2.6"]), \
-         patch("hermes_cli.models.get_pricing_for_provider", return_value=pricing), \
-         patch("hermes_cli.auth._prompt_model_selection", return_value="moonshotai/kimi-k2.6") as prompt_selection, \
-         patch("hermes_cli.auth.deactivate_provider"):
-        _model_flow_ai_gateway(load_config(), "")
-
-    out = capsys.readouterr().out
-    assert "vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai-gateway&title=AI+Gateway" in out
-    assert "free credits" in out.lower()
-    assert prompt_selection.call_args.kwargs["pricing"] == pricing
-
-    import yaml
-    config = yaml.safe_load((config_home / "config.yaml").read_text()) or {}
-    model = config["model"]
-    assert model["provider"] == "ai-gateway"
-    assert model["api_mode"] == "chat_completions"

tests/run_agent/test_provider_attribution_headers.py

@@ -1,62 +0,0 @@
-"""Attribution default_headers applied per provider via base-URL detection."""
-
-from unittest.mock import MagicMock, patch
-
-from run_agent import AIAgent
-
-
-@patch("run_agent.OpenAI")
-def test_openrouter_base_url_applies_or_headers(mock_openai):
-    mock_openai.return_value = MagicMock()
-    agent = AIAgent(
-        api_key="test-key",
-        base_url="https://openrouter.ai/api/v1",
-        model="test/model",
-        quiet_mode=True,
-        skip_context_files=True,
-        skip_memory=True,
-    )
-
-    agent._apply_client_headers_for_base_url("https://openrouter.ai/api/v1")
-
-    headers = agent._client_kwargs["default_headers"]
-    assert headers["HTTP-Referer"] == "https://hermes-agent.nousresearch.com"
-    assert headers["X-OpenRouter-Title"] == "Hermes Agent"
-
-
-@patch("run_agent.OpenAI")
-def test_ai_gateway_base_url_applies_attribution_headers(mock_openai):
-    mock_openai.return_value = MagicMock()
-    agent = AIAgent(
-        api_key="test-key",
-        base_url="https://openrouter.ai/api/v1",
-        model="test/model",
-        quiet_mode=True,
-        skip_context_files=True,
-        skip_memory=True,
-    )
-
-    agent._apply_client_headers_for_base_url("https://ai-gateway.vercel.sh/v1")
-
-    headers = agent._client_kwargs["default_headers"]
-    assert headers["HTTP-Referer"] == "https://hermes-agent.nousresearch.com"
-    assert headers["X-Title"] == "Hermes Agent"
-    assert headers["User-Agent"].startswith("HermesAgent/")
-
-
-@patch("run_agent.OpenAI")
-def test_unknown_base_url_clears_default_headers(mock_openai):
-    mock_openai.return_value = MagicMock()
-    agent = AIAgent(
-        api_key="test-key",
-        base_url="https://openrouter.ai/api/v1",
-        model="test/model",
-        quiet_mode=True,
-        skip_context_files=True,
-        skip_memory=True,
-    )
-    agent._client_kwargs["default_headers"] = {"X-Stale": "yes"}
-
-    agent._apply_client_headers_for_base_url("https://api.example.com/v1")
-
-    assert "default_headers" not in agent._client_kwargs

tests/test_research_local_model_crisis_quality.py

@@ -0,0 +1,16 @@
+from pathlib import Path
+
+
+REPORT = Path(__file__).resolve().parent.parent / "research_local_model_crisis_quality.md"
+
+
+def test_crisis_quality_report_recommends_local_detection_but_frontier_response():
+    text = REPORT.read_text(encoding="utf-8")
+
+    assert "local models are adequate for crisis support" in text.lower()
+    assert "not for crisis response generation" in text.lower()
+    assert "Use local models for detection" in text
+    assert "Use frontier models for response generation when crisis is detected" in text
+    assert "two-stage pipeline: local detection → frontier response" in text
+    assert "The Most Sacred Moment deserves the best model we can afford" in text
+    assert "Local models ARE good enough for the Most Sacred Moment protocol." not in text