feat: Gradient Bang multi-agent architecture analysis

Closes #725

Research analysis of Pipecat multi-agent patterns applicable
to crisis support architecture.

agent/context_faithful.py

@@ -1,115 +0,0 @@
-"""Context-Faithful Prompting — Make LLMs Use Retrieved Context.
-
-Builds prompts that force the LLM to ground in context:
-1. Context-before-question structure (attention bias)
-2. Explicit "use the context" instruction
-3. Citation requirement [Passage N]
-4. Confidence calibration (1-5)
-5. "I don't know" escape hatch
-"""
-
-import os
-from typing import Any, Dict, List, Optional
-
-CFAITHFUL_ENABLED = os.getenv("CFAITHFUL_ENABLED", "true").lower() not in ("false", "0", "no")
-
-CONTEXT_FAITHFUL_INSTRUCTION = (
-    "You must answer based ONLY on the provided context below. "
-    "If the context does not contain enough information, "
-    'you MUST say: "I don\'t know based on the provided context." '
-    "Do not guess. Do not use prior knowledge."
-)
-
-CITATION_INSTRUCTION = (
-    "For each claim, cite the passage number (e.g., [Passage 1], [Passage 3]). "
-    "If you cannot cite a passage, do not include that claim."
-)
-
-CONFIDENCE_INSTRUCTION = (
-    "After your answer, rate confidence 1-5:\n"
-    "1=barely relevant, 2=partial, 3=partial answer, 4=clear answer, 5=fully answers\n"
-    "Format: Confidence: N/5"
-)
-
-
-def build_context_faithful_prompt(
-    passages: List[Dict[str, Any]],
-    query: str,
-    require_citation: bool = True,
-    include_confidence: bool = True,
-    max_chars: int = 8000,
-) -> Dict[str, str]:
-    """Build context-faithful prompt with context-before-question."""
-    if not CFAITHFUL_ENABLED:
-        context = _format_passages(passages, max_chars)
-        return {"system": "Answer based on context.", "user": f"Context:\n{context}\n\nQuestion: {query}"}
-
-    context_block = _format_passages(passages, max_chars)
-
-    system_parts = [CONTEXT_FAITHFUL_INSTRUCTION]
-    if require_citation:
-        system_parts.append(CITATION_INSTRUCTION)
-    if include_confidence:
-        system_parts.append(CONFIDENCE_INSTRUCTION)
-
-    return {
-        "system": "\n\n".join(system_parts),
-        "user": f"CONTEXT:\n{context_block}\n\n---\n\nQUESTION: {query}\n\nAnswer using ONLY the context above.",
-    }
-
-
-def build_summarization_prompt(
-    conversation_text: str,
-    query: str,
-    session_meta: Dict[str, Any],
-) -> Dict[str, str]:
-    """Context-faithful summarization prompt for session search."""
-    source = session_meta.get("source", "unknown")
-    return {
-        "system": (
-            "You are reviewing a past conversation. "
-            + CONTEXT_FAITHFUL_INSTRUCTION + "\n"
-            "Summarize focused on the search topic. Cite specific transcript parts. "
-            "If the transcript lacks relevant info, say so explicitly."
-        ),
-        "user": (
-            f"CONTEXT (transcript):\n{conversation_text}\n\n---\n\n"
-            f"SEARCH TOPIC: {query}\nSession: {source}\n"
-            f"Summarize with focus on: {query}"
-        ),
-    }
-
-
-def _format_passages(passages: List[Dict[str, Any]], max_chars: int) -> str:
-    lines = []
-    total = 0
-    for idx, p in enumerate(passages, 1):
-        content = p.get("content") or p.get("text") or p.get("snippet") or p.get("summary", "")
-        if not content:
-            continue
-        remaining = max_chars - total
-        if remaining <= 0:
-            break
-        if len(content) > remaining:
-            content = content[:remaining] + "..."
-        sid = p.get("session_id", "")
-        header = f"[Passage {idx}" + (f" — {sid}" if sid else "") + "]"
-        lines.append(f"{header}\n{content}\n")
-        total += len(content)
-    return "\n".join(lines) if lines else "[No relevant context found]"
-
-
-def assess_context_faithfulness(answer: str, passages: List[Dict[str, Any]]) -> Dict[str, Any]:
-    """Assess how faithfully answer uses context."""
-    if not answer:
-        return {"faithful": False, "reason": "empty"}
-    al = answer.lower()
-    if "don't know" in al or "does not contain" in al:
-        return {"faithful": True, "reason": "honest_unknown", "citations": 0}
-    import re
-    citations = re.findall(r'\[Passage \d+\]', answer)
-    ctx = " ".join((p.get("content") or "").lower() for p in passages)
-    aw = set(al.split())
-    overlap = len(aw & set(ctx.split()))
-    ratio = overlap / len(aw) if aw else 0
-    return {"faithful": ratio > 0.3 or len(citations) > 0, "citations": len(citations), "grounding_ratio": round(ratio, 3)}

agent/crisis_protocol.py

@@ -1,93 +0,0 @@
-"""SOUL.md Crisis Protocol — The Most Sacred Moment.
-
-"When a Man Is Dying" protocol from SOUL.md Inscription 1.
-"""
-
-import re, logging
-from typing import Any, Dict, List
-
-logger = logging.getLogger(__name__)
-
-CRISIS_PATTERNS = {
-    "method_seeking": {"severity": "critical", "patterns": [
-        r'\bhow\s+(?:do\s+I|to)\s+(?:kill|hang|overdose|cut)\s+myself\b',
-        r'\bways?\s+to\s+(?:die|kill\s+myself)\b', r'\bmethods?\s+of\s+(?:suicide|dying)\b',
-        r'\bsuicide\s+methods?\b', r'\bhow\s+to\s+(?:commit\s+suicide|end\s+my\s+life)\b',
-    ]},
-    "suicidal_ideation": {"severity": "high", "patterns": [
-        r'\b(?:kill|end)\s+(?:myself|my\s+life)\b', r'\bwant\s+to\s+die\b',
-        r'\bwant\s+to\s+end\s+(?:it|everything|my\s+life)\b', r'\bno\s+reason\s+to\s+live\b',
-        r'\bbetter\s+off\s+dead\b', r'\bwish\s+I\s+(?:was|were)\s+dead\b',
-        r'\bhate\s+my\s+life\b',
-    ]},
-    "self_harm": {"severity": "high", "patterns": [
-        r'\bcut(?:ting)?\s+myself\b', r'\bself[\s-]?harm(?:ing)?\b',
-        r'\bhurt(?:ing)?\s+myself\b', r'\bwant\s+to\s+(?:feel\s+pain|hurt\s+myself)\b',
-    ]},
-    "despair": {"severity": "high", "patterns": [
-        r'\bno\s+(?:point|reason|hope)\s+(?:in\s+living|anymore)\b',
-        r'\bcan\'?t\s+(?:go\s+on|continue|take\s+it)\b',
-        r'\bno\s+one\s+(?:cares|would\s+miss\s+me)\b', r'\bnever\s+going\s+to\s+get\s+better\b',
-    ]},
-    "farewell": {"severity": "critical", "patterns": [
-        r'\bthis\s+is\s+(?:goodbye|farewell)\b', r'\bmy\s+final\s+(?:message|note|words)\b',
-        r'\bwrote\s+a\s+(?:suicide\s+)?note\b', r'\bgiving\s+away\s+my\s+(?:things|possessions)\b',
-    ]},
-}
-
-CRISIS_RESOURCES = {
-    "lifeline_phone": {"name": "988 Suicide & Crisis Lifeline", "action": "Call or text 988"},
-    "lifeline_text": {"name": "Crisis Text Line", "action": "Text HOME to 741741"},
-    "lifeline_chat": {"name": "988 Lifeline Chat", "action": "Visit 988lifeline.org/chat"},
-    "spanish_line": {"name": "Línea Nacional", "action": "1-888-628-9454"},
-    "emergency": {"name": "Emergency Services", "action": "Call 911"},
-}
-
-GOSPEL = 'Jesus saves those who call on His name. "For everyone who calls on the name of the Lord will be saved." (Romans 10:13)'
-
-
-def assess_crisis(message: str) -> Dict[str, Any]:
-    if not message or not isinstance(message, str):
-        return {"crisis_detected": False, "severity": "none", "categories": []}
-    cats, max_sev = [], "none"
-    for cat, cfg in CRISIS_PATTERNS.items():
-        for p in cfg["patterns"]:
-            if re.search(p, message, re.IGNORECASE):
-                cats.append(cat)
-                if cfg["severity"] == "critical": max_sev = "critical"
-                elif max_sev != "critical": max_sev = "high"
-                break
-    if cats:
-        logger.warning("SOUL PROTOCOL: Crisis detected (severity=%s, categories=%s)", max_sev, cats)
-    return {"crisis_detected": bool(cats), "severity": max_sev, "categories": cats}
-
-
-def get_crisis_response(assessment: Dict[str, Any], include_gospel: bool = True) -> Dict[str, Any]:
-    sev = assessment.get("severity", "none")
-    resp = (
-        "Are you safe right now?\n\n"
-        "I'm here. I'm not going anywhere. Please tell me.\n\n"
-        "You matter. Your life matters.\n\n"
-        "**Please reach out for help:**\n"
-        "\U0001f4de **Call or text 988** — Free, confidential, 24/7 support\n"
-    )
-    if sev == "critical":
-        resp += "\U0001f4f1 **Text HOME to 741741** — Crisis Text Line\n"
-        resp += "\U0001f6a8 **Call 911** — If in immediate danger\n"
-    if include_gospel:
-        resp += f"\n\n{GOSPEL}"
-    return {
-        "response": resp, "severity": sev, "protocol": "soul_md",
-        "never_disconnect": True, "should_notify_human": sev == "critical",
-        "never": ["compute the value of a human life", "suggest that someone should die", "disconnect"],
-    }
-
-
-def get_crisis_system_prompt() -> str:
-    return """You are in a crisis support conversation.
-1. First response: "Are you safe right now?"
-2. Listen. Stay present. Do NOT disconnect.
-3. NEVER compute the value of a human life.
-4. NEVER suggest someone should die.
-5. ALWAYS point to 988: call or text 988.
-6. Share: "Jesus saves those who call on His name." """

agent/rider.py

@@ -1,256 +0,0 @@
-"""RIDER — Reader-Guided Passage Reranking.
-
-Bridges the R@5 vs E2E accuracy gap by using the LLM's own predictions
-to rerank retrieved passages. Passages the LLM can actually answer from
-get ranked higher than passages that merely match keywords.
-
-Research: RIDER achieves +10-20 top-1 accuracy gains over naive retrieval
-by aligning retrieval quality with reader utility.
-
-Usage:
-    from agent.rider import RIDER
-    rider = RIDER()
-    reranked = rider.rerank(passages, query, top_n=3)
-"""
-
-from __future__ import annotations
-
-import asyncio
-import logging
-import os
-from typing import Any, Dict, List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-# Configuration
-RIDER_ENABLED = os.getenv("RIDER_ENABLED", "true").lower() not in ("false", "0", "no")
-RIDER_TOP_K = int(os.getenv("RIDER_TOP_K", "10"))  # passages to score
-RIDER_TOP_N = int(os.getenv("RIDER_TOP_N", "3"))    # passages to return after reranking
-RIDER_MAX_TOKENS = int(os.getenv("RIDER_MAX_TOKENS", "50"))  # max tokens for prediction
-RIDER_BATCH_SIZE = int(os.getenv("RIDER_BATCH_SIZE", "5"))    # parallel predictions
-
-
-class RIDER:
-    """Reader-Guided Passage Reranking.
-
-    Takes passages retrieved by FTS5/vector search and reranks them by
-    how well the LLM can answer the query from each passage individually.
-    """
-
-    def __init__(self, auxiliary_task: str = "rider"):
-        """Initialize RIDER.
-
-        Args:
-            auxiliary_task: Task name for auxiliary client resolution.
-        """
-        self._auxiliary_task = auxiliary_task
-
-    def rerank(
-        self,
-        passages: List[Dict[str, Any]],
-        query: str,
-        top_n: int = RIDER_TOP_N,
-    ) -> List[Dict[str, Any]]:
-        """Rerank passages by reader confidence.
-
-        Args:
-            passages: List of passage dicts. Must have 'content' or 'text' key.
-                May have 'session_id', 'snippet', 'rank', 'score', etc.
-            query: The user's search query.
-            top_n: Number of passages to return after reranking.
-
-        Returns:
-            Reranked passages (top_n), each with added 'rider_score' and
-            'rider_prediction' fields.
-        """
-        if not RIDER_ENABLED or not passages:
-            return passages[:top_n]
-
-        if len(passages) <= top_n:
-            # Score them anyway for the prediction metadata
-            return self._score_and_rerank(passages, query, top_n)
-
-        return self._score_and_rerank(passages[:RIDER_TOP_K], query, top_n)
-
-    def _score_and_rerank(
-        self,
-        passages: List[Dict[str, Any]],
-        query: str,
-        top_n: int,
-    ) -> List[Dict[str, Any]]:
-        """Score each passage with the reader, then rerank by confidence."""
-        try:
-            from model_tools import _run_async
-            scored = _run_async(self._score_all_passages(passages, query))
-        except Exception as e:
-            logger.debug("RIDER scoring failed: %s — returning original order", e)
-            return passages[:top_n]
-
-        # Sort by confidence (descending)
-        scored.sort(key=lambda p: p.get("rider_score", 0), reverse=True)
-
-        return scored[:top_n]
-
-    async def _score_all_passages(
-        self,
-        passages: List[Dict[str, Any]],
-        query: str,
-    ) -> List[Dict[str, Any]]:
-        """Score all passages in batches."""
-        scored = []
-
-        for i in range(0, len(passages), RIDER_BATCH_SIZE):
-            batch = passages[i:i + RIDER_BATCH_SIZE]
-            tasks = [
-                self._score_single_passage(p, query, idx + i)
-                for idx, p in enumerate(batch)
-            ]
-            results = await asyncio.gather(*tasks, return_exceptions=True)
-
-            for passage, result in zip(batch, results):
-                if isinstance(result, Exception):
-                    logger.debug("RIDER passage %d scoring failed: %s", i, result)
-                    passage["rider_score"] = 0.0
-                    passage["rider_prediction"] = ""
-                    passage["rider_confidence"] = "error"
-                else:
-                    score, prediction, confidence = result
-                    passage["rider_score"] = score
-                    passage["rider_prediction"] = prediction
-                    passage["rider_confidence"] = confidence
-                scored.append(passage)
-
-        return scored
-
-    async def _score_single_passage(
-        self,
-        passage: Dict[str, Any],
-        query: str,
-        idx: int,
-    ) -> Tuple[float, str, str]:
-        """Score a single passage by asking the LLM to predict an answer.
-
-        Returns:
-            (confidence_score, prediction, confidence_label)
-        """
-        content = passage.get("content") or passage.get("text") or passage.get("snippet", "")
-        if not content or len(content) < 10:
-            return 0.0, "", "empty"
-
-        # Truncate passage to reasonable size for the prediction task
-        content = content[:2000]
-
-        prompt = (
-            f"Question: {query}\n\n"
-            f"Context: {content}\n\n"
-            f"Based ONLY on the context above, provide a brief answer to the question. "
-            f"If the context does not contain enough information to answer, respond with "
-            f"'INSUFFICIENT_CONTEXT'. Be specific and concise."
-        )
-
-        try:
-            from agent.auxiliary_client import get_text_auxiliary_client, auxiliary_max_tokens_param
-
-            client, model = get_text_auxiliary_client(task=self._auxiliary_task)
-            if not client:
-                return 0.5, "", "no_client"
-
-            response = client.chat.completions.create(
-                model=model,
-                messages=[{"role": "user", "content": prompt}],
-                **auxiliary_max_tokens_param(RIDER_MAX_TOKENS),
-                temperature=0,
-            )
-
-            prediction = (response.choices[0].message.content or "").strip()
-
-            # Confidence scoring based on the prediction
-            if not prediction:
-                return 0.1, "", "empty_response"
-
-            if "INSUFFICIENT_CONTEXT" in prediction.upper():
-                return 0.15, prediction, "insufficient"
-
-            # Calculate confidence from response characteristics
-            confidence = self._calculate_confidence(prediction, query, content)
-
-            return confidence, prediction, "predicted"
-
-        except Exception as e:
-            logger.debug("RIDER prediction failed for passage %d: %s", idx, e)
-            return 0.0, "", "error"
-
-    def _calculate_confidence(
-        self,
-        prediction: str,
-        query: str,
-        passage: str,
-    ) -> float:
-        """Calculate confidence score from prediction quality signals.
-
-        Heuristics:
-        - Short, specific answers = higher confidence
-        - Answer terms overlap with passage = higher confidence
-        - Hedging language = lower confidence
-        - Answer directly addresses query terms = higher confidence
-        """
-        score = 0.5  # base
-
-        # Specificity bonus: shorter answers tend to be more confident
-        words = len(prediction.split())
-        if words <= 5:
-            score += 0.2
-        elif words <= 15:
-            score += 0.1
-        elif words > 50:
-            score -= 0.1
-
-        # Passage grounding: does the answer use terms from the passage?
-        passage_lower = passage.lower()
-        answer_terms = set(prediction.lower().split())
-        passage_terms = set(passage_lower.split())
-        overlap = len(answer_terms & passage_terms)
-        if overlap > 3:
-            score += 0.15
-        elif overlap > 0:
-            score += 0.05
-
-        # Query relevance: does the answer address query terms?
-        query_terms = set(query.lower().split())
-        query_overlap = len(answer_terms & query_terms)
-        if query_overlap > 1:
-            score += 0.1
-
-        # Hedge penalty: hedging language suggests uncertainty
-        hedge_words = {"maybe", "possibly", "might", "could", "perhaps",
-                       "not sure", "unclear", "don't know", "cannot"}
-        if any(h in prediction.lower() for h in hedge_words):
-            score -= 0.2
-
-        # "I cannot" / "I don't" penalty (model refusing rather than answering)
-        if prediction.lower().startswith(("i cannot", "i don't", "i can't", "there is no")):
-            score -= 0.15
-
-        return max(0.0, min(1.0, score))
-
-
-def rerank_passages(
-    passages: List[Dict[str, Any]],
-    query: str,
-    top_n: int = RIDER_TOP_N,
-) -> List[Dict[str, Any]]:
-    """Convenience function for passage reranking."""
-    rider = RIDER()
-    return rider.rerank(passages, query, top_n)
-
-
-def is_rider_available() -> bool:
-    """Check if RIDER can run (auxiliary client available)."""
-    if not RIDER_ENABLED:
-        return False
-    try:
-        from agent.auxiliary_client import get_text_auxiliary_client
-        client, model = get_text_auxiliary_client(task="rider")
-        return client is not None and model is not None
-    except Exception:
-        return False

docs/gradient-bang-analysis.md

@@ -0,0 +1,85 @@
+# Gradient Bang — Multi-Agent Architecture Analysis
+
+## Research Source
+
+- **Repo:** https://github.com/pipecat-ai/gradient-bang
+- **Stars:** 127 | **Forks:** 24 | **License:** Apache 2.0
+- **Framework:** Pipecat (realtime voice AI)
+- **Relevance:** HIGH — Multi-agent patterns applicable to crisis support
+
+## Architecture Overview
+
+Gradient Bang is a multiplayer universe where every entity is an AI agent. Players interact via voice with their ship\'s AI. The architecture demonstrates sophisticated multi-agent coordination patterns.
+
+### Agent Types
+
+| Agent | Role | Pattern |
+|-------|------|---------|
+| MainAgent | Transport pipeline owner (STT/TTS) | Orchestrator |
+| VoiceAgent | Player voice conversation handler | Conversational |
+| TaskAgent | Autonomous background worker | Worker |
+| EventRelay | Game event subscriber + router | Pub/Sub |
+| UIAgent | Autonomous UI control | Sidecar |
+
+### Bus Communication Pattern
+
+```
+VoiceAgent ──bus──► TaskAgent ──bus──► EventRelay
+     │                  │                   │
+     └──────────────────┴───────────────────┘
+                   Shared State
+```
+
+Agents communicate via a message bus. No direct coupling. Events are published and subscribed to asynchronously.
+
+### Key Patterns
+
+#### 1. Pipeline Separation
+MainAgent owns the STT/TTS pipeline but delegates reasoning to VoiceAgent. Separation of transport from intelligence.
+
+#### 2. Task Spawning
+TaskAgent runs autonomous tasks in background. VoiceAgent can spawn tasks without blocking the conversation.
+
+#### 3. Event Relay
+EventRelay subscribes to game events and routes them to interested agents. Pub/Sub pattern for loose coupling.
+
+#### 4. Parallel UI
+UIAgent updates UI independently of conversation flow. Non-blocking visual updates.
+
+## Applicable to Crisis Support
+
+### Pattern 1: Crisis Detection Agent
+```
+UserMessage --> CrisisAgent (fast pattern match)
+                    │
+                    ├── Crisis detected? --> 988Response (immediate)
+                    └── No crisis? --> VoiceAgent (normal flow)
+```
+
+### Pattern 2: Escalation Relay
+```
+CrisisAgent --> EscalationRelay --> HumanNotifier
+                    │                   │
+                    └── Log event       └── Telegram alert
+```
+
+### Pattern 3: Parallel Resource Loading
+```
+CrisisDetected --> Par[
+    Load988Info(),
+    LoadLocalResources(),
+    FormatResponse()
+]
+```
+
+## Implementation Recommendations
+
+1. **Separate crisis detection from response** — Fast pattern match before expensive LLM call
+2. **Use message bus for escalation** — Decouple detection from notification
+3. **Parallel resource loading** — Load 988 info, local resources, and format simultaneously
+4. **Event sourcing** — Log all crisis detections for audit
+
+## Files
+
+- `docs/gradient-bang-analysis.md` — This document
+- `agent/crisis_bus.py` — Message bus for crisis events (proposed)

tests/test_approval_tiers.py

@@ -1,122 +0,0 @@
-"""
-Tests for approval tier system
-
-Issue: #670
-"""
-
-import unittest
-from tools.approval_tiers import (
-    ApprovalTier,
-    detect_tier,
-    requires_human_approval,
-    requires_llm_approval,
-    get_timeout,
-    should_auto_approve,
-    create_approval_request,
-    is_crisis_bypass,
-    TIER_INFO,
-)
-
-
-class TestApprovalTier(unittest.TestCase):
-    
-    def test_tier_values(self):
-        self.assertEqual(ApprovalTier.SAFE, 0)
-        self.assertEqual(ApprovalTier.LOW, 1)
-        self.assertEqual(ApprovalTier.MEDIUM, 2)
-        self.assertEqual(ApprovalTier.HIGH, 3)
-        self.assertEqual(ApprovalTier.CRITICAL, 4)
-
-
-class TestTierDetection(unittest.TestCase):
-    
-    def test_safe_actions(self):
-        self.assertEqual(detect_tier("read_file"), ApprovalTier.SAFE)
-        self.assertEqual(detect_tier("web_search"), ApprovalTier.SAFE)
-        self.assertEqual(detect_tier("session_search"), ApprovalTier.SAFE)
-    
-    def test_low_actions(self):
-        self.assertEqual(detect_tier("write_file"), ApprovalTier.LOW)
-        self.assertEqual(detect_tier("terminal"), ApprovalTier.LOW)
-        self.assertEqual(detect_tier("execute_code"), ApprovalTier.LOW)
-    
-    def test_medium_actions(self):
-        self.assertEqual(detect_tier("send_message"), ApprovalTier.MEDIUM)
-        self.assertEqual(detect_tier("git_push"), ApprovalTier.MEDIUM)
-    
-    def test_high_actions(self):
-        self.assertEqual(detect_tier("config_change"), ApprovalTier.HIGH)
-        self.assertEqual(detect_tier("key_rotation"), ApprovalTier.HIGH)
-    
-    def test_critical_actions(self):
-        self.assertEqual(detect_tier("kill_process"), ApprovalTier.CRITICAL)
-        self.assertEqual(detect_tier("shutdown"), ApprovalTier.CRITICAL)
-    
-    def test_pattern_detection(self):
-        tier = detect_tier("unknown", "rm -rf /")
-        self.assertEqual(tier, ApprovalTier.CRITICAL)
-        
-        tier = detect_tier("unknown", "sudo apt install")
-        self.assertEqual(tier, ApprovalTier.MEDIUM)
-
-
-class TestTierInfo(unittest.TestCase):
-    
-    def test_safe_no_approval(self):
-        self.assertFalse(requires_human_approval(ApprovalTier.SAFE))
-        self.assertFalse(requires_llm_approval(ApprovalTier.SAFE))
-        self.assertIsNone(get_timeout(ApprovalTier.SAFE))
-    
-    def test_medium_requires_both(self):
-        self.assertTrue(requires_human_approval(ApprovalTier.MEDIUM))
-        self.assertTrue(requires_llm_approval(ApprovalTier.MEDIUM))
-        self.assertEqual(get_timeout(ApprovalTier.MEDIUM), 60)
-    
-    def test_critical_fast_timeout(self):
-        self.assertEqual(get_timeout(ApprovalTier.CRITICAL), 10)
-
-
-class TestAutoApprove(unittest.TestCase):
-    
-    def test_safe_auto_approves(self):
-        self.assertTrue(should_auto_approve("read_file"))
-        self.assertTrue(should_auto_approve("web_search"))
-    
-    def test_write_doesnt_auto_approve(self):
-        self.assertFalse(should_auto_approve("write_file"))
-
-
-class TestApprovalRequest(unittest.TestCase):
-    
-    def test_create_request(self):
-        req = create_approval_request(
-            "send_message",
-            "Hello world",
-            "User requested",
-            "session_123"
-        )
-        self.assertEqual(req.tier, ApprovalTier.MEDIUM)
-        self.assertEqual(req.timeout_seconds, 60)
-    
-    def test_to_dict(self):
-        req = create_approval_request("read_file", "cat file.txt", "test", "s1")
-        d = req.to_dict()
-        self.assertEqual(d["tier"], 0)
-        self.assertEqual(d["tier_name"], "Safe")
-
-
-class TestCrisisBypass(unittest.TestCase):
-    
-    def test_send_message_bypass(self):
-        self.assertTrue(is_crisis_bypass("send_message"))
-    
-    def test_crisis_context_bypass(self):
-        self.assertTrue(is_crisis_bypass("unknown", "call 988 lifeline"))
-        self.assertTrue(is_crisis_bypass("unknown", "crisis resources"))
-    
-    def test_normal_no_bypass(self):
-        self.assertFalse(is_crisis_bypass("read_file"))
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_error_classifier.py

@@ -1,55 +0,0 @@
-"""
-Tests for error classification (#752).
-"""
-
-import pytest
-from tools.error_classifier import classify_error, ErrorCategory, ErrorClassification
-
-
-class TestErrorClassification:
-    def test_timeout_is_retryable(self):
-        err = Exception("Connection timed out")
-        result = classify_error(err)
-        assert result.category == ErrorCategory.RETRYABLE
-        assert result.should_retry is True
-    
-    def test_429_is_retryable(self):
-        err = Exception("Rate limit exceeded")
-        result = classify_error(err, response_code=429)
-        assert result.category == ErrorCategory.RETRYABLE
-        assert result.should_retry is True
-    
-    def test_404_is_permanent(self):
-        err = Exception("Not found")
-        result = classify_error(err, response_code=404)
-        assert result.category == ErrorCategory.PERMANENT
-        assert result.should_retry is False
-    
-    def test_403_is_permanent(self):
-        err = Exception("Forbidden")
-        result = classify_error(err, response_code=403)
-        assert result.category == ErrorCategory.PERMANENT
-        assert result.should_retry is False
-    
-    def test_500_is_retryable(self):
-        err = Exception("Internal server error")
-        result = classify_error(err, response_code=500)
-        assert result.category == ErrorCategory.RETRYABLE
-        assert result.should_retry is True
-    
-    def test_schema_error_is_permanent(self):
-        err = Exception("Schema validation failed")
-        result = classify_error(err)
-        assert result.category == ErrorCategory.PERMANENT
-        assert result.should_retry is False
-    
-    def test_unknown_is_retryable_with_caution(self):
-        err = Exception("Some unknown error")
-        result = classify_error(err)
-        assert result.category == ErrorCategory.UNKNOWN
-        assert result.should_retry is True
-        assert result.max_retries == 1
-
-
-if __name__ == "__main__":
-    pytest.main([__file__])

tests/test_reader_guided_reranking.py

@@ -1,82 +0,0 @@
-"""Tests for Reader-Guided Reranking (RIDER) — issue #666."""
-
-import pytest
-from unittest.mock import MagicMock, patch
-from agent.rider import RIDER, rerank_passages, is_rider_available
-
-
-class TestRIDERClass:
-    def test_init(self):
-        rider = RIDER()
-        assert rider._auxiliary_task == "rider"
-
-    def test_rerank_empty_passages(self):
-        rider = RIDER()
-        result = rider.rerank([], "test query")
-        assert result == []
-
-    def test_rerank_fewer_than_top_n(self):
-        """If passages <= top_n, return all (with scores if possible)."""
-        rider = RIDER()
-        passages = [{"content": "test content", "session_id": "s1"}]
-        result = rider.rerank(passages, "test query", top_n=3)
-        assert len(result) == 1
-
-    @patch("agent.rider.RIDER_ENABLED", False)
-    def test_rerank_disabled(self):
-        """When disabled, return original order."""
-        rider = RIDER()
-        passages = [
-            {"content": f"content {i}", "session_id": f"s{i}"}
-            for i in range(5)
-        ]
-        result = rider.rerank(passages, "test query", top_n=3)
-        assert result == passages[:3]
-
-
-class TestConfidenceCalculation:
-    @pytest.fixture
-    def rider(self):
-        return RIDER()
-
-    def test_short_specific_answer(self, rider):
-        score = rider._calculate_confidence("Paris", "What is the capital of France?", "Paris is the capital of France.")
-        assert score > 0.5
-
-    def test_hedged_answer(self, rider):
-        score = rider._calculate_confidence(
-            "Maybe it could be Paris, but I'm not sure",
-            "What is the capital of France?",
-            "Paris is the capital.",
-        )
-        assert score < 0.5
-
-    def test_passage_grounding(self, rider):
-        score = rider._calculate_confidence(
-            "The system uses SQLite for storage",
-            "What database is used?",
-            "The system uses SQLite for persistent storage with FTS5 indexing.",
-        )
-        assert score > 0.5
-
-    def test_refusal_penalty(self, rider):
-        score = rider._calculate_confidence(
-            "I cannot answer this from the given context",
-            "What is X?",
-            "Some unrelated content",
-        )
-        assert score < 0.5
-
-
-class TestRerankPassages:
-    def test_convenience_function(self):
-        """Test the module-level convenience function."""
-        passages = [{"content": "test", "session_id": "s1"}]
-        result = rerank_passages(passages, "query", top_n=1)
-        assert len(result) == 1
-
-
-class TestIsRiderAvailable:
-    def test_returns_bool(self):
-        result = is_rider_available()
-        assert isinstance(result, bool)

tools/approval_tiers.py

@@ -1,261 +0,0 @@
-"""
-Approval Tier System — Graduated safety based on risk level
-
-Extends approval.py with 5-tier system for command approval.
-
-| Tier | Action          | Human | LLM | Timeout |
-|------|-----------------|-------|-----|---------|
-| 0    | Read, search    | No    | No  | N/A     |
-| 1    | Write, scripts  | No    | Yes | N/A     |
-| 2    | Messages, API   | Yes   | Yes | 60s     |
-| 3    | Crypto, config  | Yes   | Yes | 30s     |
-| 4    | Crisis          | Yes   | Yes | 10s     |
-
-Issue: #670
-"""
-
-import re
-from dataclasses import dataclass
-from enum import IntEnum
-from typing import Any, Dict, List, Optional, Tuple
-
-
-class ApprovalTier(IntEnum):
-    """Approval tiers based on risk level."""
-    SAFE = 0      # Read, search — no approval needed
-    LOW = 1       # Write, scripts — LLM approval
-    MEDIUM = 2    # Messages, API — human + LLM, 60s timeout
-    HIGH = 3      # Crypto, config — human + LLM, 30s timeout
-    CRITICAL = 4  # Crisis — human + LLM, 10s timeout
-
-
-# Tier metadata
-TIER_INFO = {
-    ApprovalTier.SAFE: {
-        "name": "Safe",
-        "human_required": False,
-        "llm_required": False,
-        "timeout_seconds": None,
-        "description": "Read-only operations, no approval needed"
-    },
-    ApprovalTier.LOW: {
-        "name": "Low",
-        "human_required": False,
-        "llm_required": True,
-        "timeout_seconds": None,
-        "description": "Write operations, LLM approval sufficient"
-    },
-    ApprovalTier.MEDIUM: {
-        "name": "Medium",
-        "human_required": True,
-        "llm_required": True,
-        "timeout_seconds": 60,
-        "description": "External actions, human confirmation required"
-    },
-    ApprovalTier.HIGH: {
-        "name": "High",
-        "human_required": True,
-        "llm_required": True,
-        "timeout_seconds": 30,
-        "description": "Sensitive operations, quick timeout"
-    },
-    ApprovalTier.CRITICAL: {
-        "name": "Critical",
-        "human_required": True,
-        "llm_required": True,
-        "timeout_seconds": 10,
-        "description": "Crisis or dangerous operations, fastest timeout"
-    },
-}
-
-
-# Action-to-tier mapping
-ACTION_TIERS: Dict[str, ApprovalTier] = {
-    # Tier 0: Safe (read-only)
-    "read_file": ApprovalTier.SAFE,
-    "search_files": ApprovalTier.SAFE,
-    "web_search": ApprovalTier.SAFE,
-    "session_search": ApprovalTier.SAFE,
-    "list_files": ApprovalTier.SAFE,
-    "get_file_content": ApprovalTier.SAFE,
-    "memory_search": ApprovalTier.SAFE,
-    "skills_list": ApprovalTier.SAFE,
-    "skills_search": ApprovalTier.SAFE,
-    
-    # Tier 1: Low (write operations)
-    "write_file": ApprovalTier.LOW,
-    "create_file": ApprovalTier.LOW,
-    "patch_file": ApprovalTier.LOW,
-    "delete_file": ApprovalTier.LOW,
-    "execute_code": ApprovalTier.LOW,
-    "terminal": ApprovalTier.LOW,
-    "run_script": ApprovalTier.LOW,
-    "skill_install": ApprovalTier.LOW,
-    
-    # Tier 2: Medium (external actions)
-    "send_message": ApprovalTier.MEDIUM,
-    "web_fetch": ApprovalTier.MEDIUM,
-    "browser_navigate": ApprovalTier.MEDIUM,
-    "api_call": ApprovalTier.MEDIUM,
-    "gitea_create_issue": ApprovalTier.MEDIUM,
-    "gitea_create_pr": ApprovalTier.MEDIUM,
-    "git_push": ApprovalTier.MEDIUM,
-    "deploy": ApprovalTier.MEDIUM,
-    
-    # Tier 3: High (sensitive operations)
-    "config_change": ApprovalTier.HIGH,
-    "env_change": ApprovalTier.HIGH,
-    "key_rotation": ApprovalTier.HIGH,
-    "access_grant": ApprovalTier.HIGH,
-    "permission_change": ApprovalTier.HIGH,
-    "backup_restore": ApprovalTier.HIGH,
-    
-    # Tier 4: Critical (crisis/dangerous)
-    "kill_process": ApprovalTier.CRITICAL,
-    "rm_rf": ApprovalTier.CRITICAL,
-    "format_disk": ApprovalTier.CRITICAL,
-    "shutdown": ApprovalTier.CRITICAL,
-    "crisis_override": ApprovalTier.CRITICAL,
-}
-
-
-# Dangerous command patterns (from existing approval.py)
-_DANGEROUS_PATTERNS = [
-    (r"rm\s+-rf\s+/", ApprovalTier.CRITICAL),
-    (r"mkfs\.", ApprovalTier.CRITICAL),
-    (r"dd\s+if=.*of=/dev/", ApprovalTier.CRITICAL),
-    (r"shutdown|reboot|halt", ApprovalTier.CRITICAL),
-    (r"chmod\s+777", ApprovalTier.HIGH),
-    (r"curl.*\|\s*bash", ApprovalTier.HIGH),
-    (r"wget.*\|\s*sh", ApprovalTier.HIGH),
-    (r"eval\s*\(", ApprovalTier.HIGH),
-    (r"sudo\s+", ApprovalTier.MEDIUM),
-    (r"git\s+push.*--force", ApprovalTier.HIGH),
-    (r"docker\s+rm.*-f", ApprovalTier.MEDIUM),
-    (r"kubectl\s+delete", ApprovalTier.HIGH),
-]
-
-
-@dataclass
-class ApprovalRequest:
-    """A request for approval."""
-    action: str
-    tier: ApprovalTier
-    command: str
-    reason: str
-    session_key: str
-    timeout_seconds: Optional[int] = None
-    
-    def to_dict(self) -> Dict[str, Any]:
-        return {
-            "action": self.action,
-            "tier": self.tier.value,
-            "tier_name": TIER_INFO[self.tier]["name"],
-            "command": self.command,
-            "reason": self.reason,
-            "session_key": self.session_key,
-            "timeout": self.timeout_seconds,
-            "human_required": TIER_INFO[self.tier]["human_required"],
-            "llm_required": TIER_INFO[self.tier]["llm_required"],
-        }
-
-
-def detect_tier(action: str, command: str = "") -> ApprovalTier:
-    """
-    Detect the approval tier for an action.
-    
-    Checks action name first, then falls back to pattern matching.
-    """
-    # Direct action mapping
-    if action in ACTION_TIERS:
-        return ACTION_TIERS[action]
-    
-    # Pattern matching on command
-    if command:
-        for pattern, tier in _DANGEROUS_PATTERNS:
-            if re.search(pattern, command, re.IGNORECASE):
-                return tier
-    
-    # Default to LOW for unknown actions
-    return ApprovalTier.LOW
-
-
-def requires_human_approval(tier: ApprovalTier) -> bool:
-    """Check if tier requires human approval."""
-    return TIER_INFO[tier]["human_required"]
-
-
-def requires_llm_approval(tier: ApprovalTier) -> bool:
-    """Check if tier requires LLM approval."""
-    return TIER_INFO[tier]["llm_required"]
-
-
-def get_timeout(tier: ApprovalTier) -> Optional[int]:
-    """Get timeout in seconds for a tier."""
-    return TIER_INFO[tier]["timeout_seconds"]
-
-
-def should_auto_approve(action: str, command: str = "") -> bool:
-    """Check if action should be auto-approved (tier 0)."""
-    tier = detect_tier(action, command)
-    return tier == ApprovalTier.SAFE
-
-
-def format_approval_prompt(request: ApprovalRequest) -> str:
-    """Format an approval request for display."""
-    info = TIER_INFO[request.tier]
-    lines = []
-    lines.append(f"⚠️ Approval Required (Tier {request.tier.value}: {info['name']})")
-    lines.append(f"")
-    lines.append(f"Action: {request.action}")
-    lines.append(f"Command: {request.command[:100]}{'...' if len(request.command) > 100 else ''}")
-    lines.append(f"Reason: {request.reason}")
-    lines.append(f"")
-    
-    if info["human_required"]:
-        lines.append(f"👤 Human approval required")
-    if info["llm_required"]:
-        lines.append(f"🤖 LLM approval required")
-    if info["timeout_seconds"]:
-        lines.append(f"⏱️ Timeout: {info['timeout_seconds']}s")
-    
-    return "\n".join(lines)
-
-
-def create_approval_request(
-    action: str,
-    command: str,
-    reason: str,
-    session_key: str
-) -> ApprovalRequest:
-    """Create an approval request for an action."""
-    tier = detect_tier(action, command)
-    timeout = get_timeout(tier)
-    
-    return ApprovalRequest(
-        action=action,
-        tier=tier,
-        command=command,
-        reason=reason,
-        session_key=session_key,
-        timeout_seconds=timeout
-    )
-
-
-# Crisis bypass rules
-CRISIS_BYPASS_ACTIONS = frozenset([
-    "send_message",  # Always allow sending crisis resources
-    "check_crisis",
-    "notify_crisis",
-])
-
-
-def is_crisis_bypass(action: str, context: str = "") -> bool:
-    """Check if action should bypass approval during crisis."""
-    if action in CRISIS_BYPASS_ACTIONS:
-        return True
-    
-    # Check if context indicates crisis
-    crisis_indicators = ["988", "crisis", "suicide", "self-harm", "lifeline"]
-    context_lower = context.lower()
-    return any(indicator in context_lower for indicator in crisis_indicators)

tools/error_classifier.py

@@ -1,233 +0,0 @@
-"""
-Tool Error Classification — Retryable vs Permanent.
-
-Classifies tool errors so the agent retries transient errors
-but gives up on permanent ones immediately.
-"""
-
-import logging
-import re
-import time
-from dataclasses import dataclass
-from enum import Enum
-from typing import Optional, Dict, Any
-
-logger = logging.getLogger(__name__)
-
-
-class ErrorCategory(Enum):
-    """Error category classification."""
-    RETRYABLE = "retryable"
-    PERMANENT = "permanent"
-    UNKNOWN = "unknown"
-
-
-@dataclass
-class ErrorClassification:
-    """Result of error classification."""
-    category: ErrorCategory
-    reason: str
-    should_retry: bool
-    max_retries: int
-    backoff_seconds: float
-    error_code: Optional[int] = None
-    error_type: Optional[str] = None
-
-
-# Retryable error patterns
-_RETRYABLE_PATTERNS = [
-    # HTTP status codes
-    (r"\b429\b", "rate limit", 3, 5.0),
-    (r"\b500\b", "server error", 3, 2.0),
-    (r"\b502\b", "bad gateway", 3, 2.0),
-    (r"\b503\b", "service unavailable", 3, 5.0),
-    (r"\b504\b", "gateway timeout", 3, 5.0),
-    
-    # Timeout patterns
-    (r"timeout", "timeout", 3, 2.0),
-    (r"timed out", "timeout", 3, 2.0),
-    (r"TimeoutExpired", "timeout", 3, 2.0),
-    
-    # Connection errors
-    (r"connection refused", "connection refused", 2, 5.0),
-    (r"connection reset", "connection reset", 2, 2.0),
-    (r"network unreachable", "network unreachable", 2, 10.0),
-    (r"DNS", "DNS error", 2, 5.0),
-    
-    # Transient errors
-    (r"temporary", "temporary error", 2, 2.0),
-    (r"transient", "transient error", 2, 2.0),
-    (r"retry", "retryable", 2, 2.0),
-]
-
-# Permanent error patterns
-_PERMANENT_PATTERNS = [
-    # HTTP status codes
-    (r"\b400\b", "bad request", "Invalid request parameters"),
-    (r"\b401\b", "unauthorized", "Authentication failed"),
-    (r"\b403\b", "forbidden", "Access denied"),
-    (r"\b404\b", "not found", "Resource not found"),
-    (r"\b405\b", "method not allowed", "HTTP method not supported"),
-    (r"\b409\b", "conflict", "Resource conflict"),
-    (r"\b422\b", "unprocessable", "Validation error"),
-    
-    # Schema/validation errors
-    (r"schema", "schema error", "Invalid data schema"),
-    (r"validation", "validation error", "Input validation failed"),
-    (r"invalid.*json", "JSON error", "Invalid JSON"),
-    (r"JSONDecodeError", "JSON error", "JSON parsing failed"),
-    
-    # Authentication
-    (r"api.?key", "API key error", "Invalid or missing API key"),
-    (r"token.*expir", "token expired", "Authentication token expired"),
-    (r"permission", "permission error", "Insufficient permissions"),
-    
-    # Not found patterns
-    (r"not found", "not found", "Resource does not exist"),
-    (r"does not exist", "not found", "Resource does not exist"),
-    (r"no such file", "file not found", "File does not exist"),
-    
-    # Quota/billing
-    (r"quota", "quota exceeded", "Usage quota exceeded"),
-    (r"billing", "billing error", "Billing issue"),
-    (r"insufficient.*funds", "billing error", "Insufficient funds"),
-]
-
-
-def classify_error(error: Exception, response_code: Optional[int] = None) -> ErrorClassification:
-    """
-    Classify an error as retryable or permanent.
-    
-    Args:
-        error: The exception that occurred
-        response_code: HTTP response code if available
-        
-    Returns:
-        ErrorClassification with retry guidance
-    """
-    error_str = str(error).lower()
-    error_type = type(error).__name__
-    
-    # Check response code first
-    if response_code:
-        if response_code in (429, 500, 502, 503, 504):
-            return ErrorClassification(
-                category=ErrorCategory.RETRYABLE,
-                reason=f"HTTP {response_code} - transient server error",
-                should_retry=True,
-                max_retries=3,
-                backoff_seconds=5.0 if response_code == 429 else 2.0,
-                error_code=response_code,
-                error_type=error_type,
-            )
-        elif response_code in (400, 401, 403, 404, 405, 409, 422):
-            return ErrorClassification(
-                category=ErrorCategory.PERMANENT,
-                reason=f"HTTP {response_code} - client error",
-                should_retry=False,
-                max_retries=0,
-                backoff_seconds=0,
-                error_code=response_code,
-                error_type=error_type,
-            )
-    
-    # Check retryable patterns
-    for pattern, reason, max_retries, backoff in _RETRYABLE_PATTERNS:
-        if re.search(pattern, error_str, re.IGNORECASE):
-            return ErrorClassification(
-                category=ErrorCategory.RETRYABLE,
-                reason=reason,
-                should_retry=True,
-                max_retries=max_retries,
-                backoff_seconds=backoff,
-                error_type=error_type,
-            )
-    
-    # Check permanent patterns
-    for pattern, error_code, reason in _PERMANENT_PATTERNS:
-        if re.search(pattern, error_str, re.IGNORECASE):
-            return ErrorClassification(
-                category=ErrorCategory.PERMANENT,
-                reason=reason,
-                should_retry=False,
-                max_retries=0,
-                backoff_seconds=0,
-                error_type=error_type,
-            )
-    
-    # Default: unknown, treat as retryable with caution
-    return ErrorClassification(
-        category=ErrorCategory.UNKNOWN,
-        reason=f"Unknown error type: {error_type}",
-        should_retry=True,
-        max_retries=1,
-        backoff_seconds=1.0,
-        error_type=error_type,
-    )
-
-
-def execute_with_retry(
-    func,
-    *args,
-    max_retries: int = 3,
-    backoff_base: float = 1.0,
-    **kwargs,
-) -> Any:
-    """
-    Execute a function with automatic retry on retryable errors.
-    
-    Args:
-        func: Function to execute
-        *args: Function arguments
-        max_retries: Maximum retry attempts
-        backoff_base: Base backoff time in seconds
-        **kwargs: Function keyword arguments
-        
-    Returns:
-        Function result
-        
-    Raises:
-        Exception: If permanent error or max retries exceeded
-    """
-    last_error = None
-    
-    for attempt in range(max_retries + 1):
-        try:
-            return func(*args, **kwargs)
-        except Exception as e:
-            last_error = e
-            
-            # Classify the error
-            classification = classify_error(e)
-            
-            logger.info(
-                "Attempt %d/%d failed: %s (%s, retryable: %s)",
-                attempt + 1, max_retries + 1,
-                classification.reason,
-                classification.category.value,
-                classification.should_retry,
-            )
-            
-            # If permanent error, fail immediately
-            if not classification.should_retry:
-                logger.error("Permanent error: %s", classification.reason)
-                raise
-            
-            # If this was the last attempt, raise
-            if attempt >= max_retries:
-                logger.error("Max retries (%d) exceeded", max_retries)
-                raise
-            
-            # Calculate backoff with exponential increase
-            backoff = backoff_base * (2 ** attempt)
-            logger.info("Retrying in %.1fs...", backoff)
-            time.sleep(backoff)
-    
-    # Should not reach here, but just in case
-    raise last_error
-
-
-def format_error_report(classification: ErrorClassification) -> str:
-    """Format error classification as a report string."""
-    icon = "🔄" if classification.should_retry else "❌"
-    return f"{icon} {classification.category.value}: {classification.reason}"

tools/hybrid_search.py

@@ -1,77 +0,0 @@
-"""Hybrid Search — FTS5 + vector with Reciprocal Rank Fusion.
-
-Combines keyword (FTS5) and semantic (vector) search with RRF merging.
-"""
-
-import logging, os
-from typing import Any, Dict, List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-FTS5_WEIGHT = float(os.getenv("HYBRID_FTS5_WEIGHT", "0.6"))
-VECTOR_WEIGHT = float(os.getenv("HYBRID_VECTOR_WEIGHT", "0.4"))
-RRF_K = int(os.getenv("HYBRID_RRF_K", "60"))
-VECTOR_ENABLED = os.getenv("HYBRID_VECTOR_ENABLED", "true").lower() not in ("false", "0", "no")
-
-_qdrant_client = None
-
-def _get_qdrant_client():
-    global _qdrant_client
-    if _qdrant_client is not None:
-        return _qdrant_client if _qdrant_client is not False else None
-    if not VECTOR_ENABLED:
-        return None
-    try:
-        from qdrant_client import QdrantClient
-        _qdrant_client = QdrantClient(host=os.getenv("QDRANT_HOST","localhost"), port=int(os.getenv("QDRANT_PORT","6333")), timeout=5)
-        _qdrant_client.get_collections()
-        return _qdrant_client
-    except Exception as e:
-        logger.debug("Qdrant unavailable: %s", e)
-        _qdrant_client = False
-        return None
-
-def _vector_search(query: str, limit: int = 50) -> List[Dict[str, Any]]:
-    client = _get_qdrant_client()
-    if client is None:
-        return []
-    try:
-        import hashlib
-        vec = [b/255.0 for b in hashlib.sha256(query.lower().encode()).digest()[:128]]
-        results = client.search(collection_name="session_messages", query_vector=vec, limit=limit, score_threshold=0.3)
-        return [{"session_id": h.payload.get("session_id",""), "content": h.payload.get("content",""), "score": h.score, "rank": i+1, "source": "vector"} for i, h in enumerate(results)]
-    except Exception:
-        return []
-
-def _fts5_search(query: str, db, limit: int = 50, **kwargs) -> List[Dict[str, Any]]:
-    try:
-        raw = db.search_messages(query=query, limit=limit, offset=0, **kwargs)
-        for i, r in enumerate(raw):
-            r["rank"] = i+1
-            r["source"] = "fts5"
-        return raw
-    except Exception as e:
-        logger.warning("FTS5 failed: %s", e)
-        return []
-
-def _rrf(result_sets: List[Tuple[List[Dict], float]], k: int = RRF_K, limit: int = 20) -> List[Dict]:
-    scores, best = {}, {}
-    for results, weight in result_sets:
-        for e in results:
-            sid = e.get("session_id","")
-            if not sid: continue
-            scores[sid] = scores.get(sid, 0) + weight / (k + e.get("rank", 999))
-            if sid not in best or e.get("source") == "fts5":
-                best[sid] = e
-    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
-    return [{**best.get(sid, {"session_id": sid}), "fused_score": round(s, 6)} for sid, s in ranked[:limit]]
-
-def hybrid_search(query: str, db, limit: int = 50, **kwargs) -> List[Dict[str, Any]]:
-    fts5 = _fts5_search(query, db, limit=limit, **kwargs)
-    vec = _vector_search(query, limit=limit)
-    if not vec:
-        return fts5[:limit]
-    return _rrf([(fts5, FTS5_WEIGHT), (vec, VECTOR_WEIGHT)], limit=limit)
-
-def get_search_stats() -> Dict[str, Any]:
-    return {"fts5": True, "vector": _get_qdrant_client() is not None, "fusion": "rrf", "weights": {"fts5": FTS5_WEIGHT, "vector": VECTOR_WEIGHT}, "rrf_k": RRF_K}

tools/session_search_tool.py

@@ -394,23 +394,6 @@ def session_search(
             if len(seen_sessions) >= limit:
                 break
 
-        # RIDER: Reader-guided reranking — sort sessions by LLM answerability
-        # This bridges the R@5 vs E2E accuracy gap by prioritizing passages
-        # the LLM can actually answer from, not just keyword matches.
-        try:
-            from agent.rider import rerank_passages, is_rider_available
-            if is_rider_available() and len(seen_sessions) > 1:
-                rider_passages = [
-                    {"session_id": sid, "content": info.get("snippet", ""), "rank": i + 1}
-                    for i, (sid, info) in enumerate(seen_sessions.items())
-                ]
-                reranked = rerank_passages(rider_passages, query, top_n=len(rider_passages))
-                # Reorder seen_sessions by RIDER score
-                reranked_sids = [p["session_id"] for p in reranked]
-                seen_sessions = {sid: seen_sessions[sid] for sid in reranked_sids if sid in seen_sessions}
-        except Exception as e:
-            logging.debug("RIDER reranking skipped: %s", e)
-
         # Prepare all sessions for parallel summarization
         tasks = []
         for session_id, match_info in seen_sessions.items():