backend/logic_evaluator.py

@@ -0,0 +1,268 @@
+"""
+Logic Evaluator (Phase 3)
+
+Rein deterministischer boolescher Evaluator für Workflow-Bedingungen.
+Keine KI, keine Interpretation - nur strikte Logik-Auswertung.
+
+Unterstützt:
+- Vergleichsoperatoren: EQ, NEQ, IN, NOT_IN, GT, LT, GTE, LTE, CONTAINS
+- Logische Operatoren: AND, OR, NOT
+- Verschachtelung via operands
+"""
+
+from typing import Dict, Any, Optional, Tuple, List
+import logging
+from workflow_models import (
+    LogicExpression,
+    LogicOperator,
+    NormalizedSignal,
+    SignalStatus
+)
+
+logger = logging.getLogger(__name__)
+
+
+def evaluate_logic_expression(
+    expression: LogicExpression,
+    context: Dict[str, Any]
+) -> Tuple[bool, Optional[str]]:
+    """
+    Evaluiert LogicExpression gegen context.
+
+    Args:
+        expression: Die auszuwertende Bedingung
+        context: Execution context mit node_results
+
+    Returns:
+        (result: bool, error: Optional[str])
+        - result: True/False basierend auf Auswertung
+        - error: Fehlermeldung wenn Evaluation fehlschlägt
+
+    Beispiel:
+        expression = LogicExpression(
+            operator=LogicOperator.EQ,
+            ref="body.relevanz",
+            value="decrease"
+        )
+        result, error = evaluate_logic_expression(expression, context)
+    """
+    try:
+        operator = expression.operator
+
+        # Logische Operatoren (verschachtelt)
+        if operator == LogicOperator.AND:
+            return _evaluate_and(expression, context)
+        elif operator == LogicOperator.OR:
+            return _evaluate_or(expression, context)
+        elif operator == LogicOperator.NOT:
+            return _evaluate_not(expression, context)
+
+        # Vergleichsoperatoren (benötigen ref)
+        elif operator in [
+            LogicOperator.EQ, LogicOperator.NEQ,
+            LogicOperator.IN, LogicOperator.NOT_IN,
+            LogicOperator.GT, LogicOperator.LT,
+            LogicOperator.GTE, LogicOperator.LTE,
+            LogicOperator.CONTAINS
+        ]:
+            return _evaluate_comparison(expression, context)
+
+        else:
+            return False, f"Unknown operator: {operator}"
+
+    except Exception as e:
+        logger.error(f"Logic evaluation failed: {e}", exc_info=True)
+        return False, str(e)
+
+
+def _evaluate_and(
+    expression: LogicExpression,
+    context: Dict[str, Any]
+) -> Tuple[bool, Optional[str]]:
+    """Evaluiert AND - alle operands müssen True sein."""
+    if not expression.operands:
+        return False, "AND requires operands"
+
+    for operand in expression.operands:
+        result, error = evaluate_logic_expression(operand, context)
+        if error:
+            return False, f"AND operand failed: {error}"
+        if not result:
+            return False, None  # Short-circuit: einer False → gesamt False
+
+    return True, None
+
+
+def _evaluate_or(
+    expression: LogicExpression,
+    context: Dict[str, Any]
+) -> Tuple[bool, Optional[str]]:
+    """Evaluiert OR - mindestens ein operand muss True sein."""
+    if not expression.operands:
+        return False, "OR requires operands"
+
+    errors = []
+    for operand in expression.operands:
+        result, error = evaluate_logic_expression(operand, context)
+        if error:
+            errors.append(error)
+            continue
+        if result:
+            return True, None  # Short-circuit: einer True → gesamt True
+
+    # Alle False oder Fehler
+    if errors:
+        return False, f"All OR operands failed: {', '.join(errors)}"
+    return False, None
+
+
+def _evaluate_not(
+    expression: LogicExpression,
+    context: Dict[str, Any]
+) -> Tuple[bool, Optional[str]]:
+    """Evaluiert NOT - negiert operand."""
+    if not expression.operands or len(expression.operands) != 1:
+        return False, "NOT requires exactly one operand"
+
+    result, error = evaluate_logic_expression(expression.operands[0], context)
+    if error:
+        return False, f"NOT operand failed: {error}"
+
+    return not result, None
+
+
+def _evaluate_comparison(
+    expression: LogicExpression,
+    context: Dict[str, Any]
+) -> Tuple[bool, Optional[str]]:
+    """Evaluiert Vergleichsoperator."""
+    if not expression.ref:
+        return False, f"{expression.operator} requires ref"
+
+    # Signal auflösen
+    signal, error = resolve_signal_reference(expression.ref, context)
+    if error:
+        return False, error
+    if signal is None:
+        return False, f"Signal not found: {expression.ref}"
+
+    # Prüfe Signal-Status
+    if signal.status in [SignalStatus.UNCLEAR, SignalStatus.INVALID, SignalStatus.NOT_DECIDABLE]:
+        return False, f"Signal '{expression.ref}' has status {signal.status} - cannot evaluate"
+
+    # Vergleich durchführen
+    left_value = signal.normalized_value or signal.raw_value
+    right_value = expression.value
+
+    return compare_values(expression.operator, left_value, right_value)
+
+
+def resolve_signal_reference(
+    ref: str,
+    context: Dict[str, Any]
+) -> Tuple[Optional[NormalizedSignal], Optional[str]]:
+    """
+    Löst Referenz wie "node_1.relevanz" auf.
+
+    Args:
+        ref: Signal-Referenz im Format "node_id.question_type"
+        context: Execution context mit node_results
+
+    Returns:
+        (signal, error_message)
+
+    Error wenn:
+    - Node existiert nicht in context
+    - Signal-Typ existiert nicht für diesen Node
+    """
+    # Parse reference
+    parts = ref.split(".")
+    if len(parts) != 2:
+        return None, f"Invalid reference format: '{ref}' (expected 'node_id.question_type')"
+
+    node_id, question_type = parts
+
+    # Hole Node-Results aus context
+    node_results = context.get("node_results", {})
+    if node_id not in node_results:
+        return None, f"Node '{node_id}' not found in context"
+
+    node_state = node_results[node_id]
+
+    # Suche Signal
+    for signal in node_state.normalized_signals:
+        if signal.question_type == question_type:
+            return signal, None
+
+    return None, f"Signal '{question_type}' not found in node '{node_id}'"
+
+
+def compare_values(
+    operator: LogicOperator,
+    left: Any,
+    right: Any
+) -> Tuple[bool, Optional[str]]:
+    """
+    Führt Vergleichsoperation durch.
+
+    Args:
+        operator: Vergleichsoperator
+        left: Linker Wert (aus Signal)
+        right: Rechter Wert (aus Expression)
+
+    Returns:
+        (result: bool, error: Optional[str])
+    """
+    try:
+        if operator == LogicOperator.EQ:
+            return left == right, None
+
+        elif operator == LogicOperator.NEQ:
+            return left != right, None
+
+        elif operator == LogicOperator.IN:
+            if not isinstance(right, (list, tuple, set)):
+                return False, f"IN operator requires list, got {type(right)}"
+            return left in right, None
+
+        elif operator == LogicOperator.NOT_IN:
+            if not isinstance(right, (list, tuple, set)):
+                return False, f"NOT_IN operator requires list, got {type(right)}"
+            return left not in right, None
+
+        elif operator == LogicOperator.CONTAINS:
+            # left enthält right (z.B. String-Suche)
+            if isinstance(left, str) and isinstance(right, str):
+                return right in left, None
+            elif isinstance(left, (list, tuple, set)):
+                return right in left, None
+            else:
+                return False, f"CONTAINS not supported for types {type(left)}/{type(right)}"
+
+        elif operator == LogicOperator.GT:
+            return _numeric_compare(left, right, lambda a, b: a > b)
+
+        elif operator == LogicOperator.LT:
+            return _numeric_compare(left, right, lambda a, b: a < b)
+
+        elif operator == LogicOperator.GTE:
+            return _numeric_compare(left, right, lambda a, b: a >= b)
+
+        elif operator == LogicOperator.LTE:
+            return _numeric_compare(left, right, lambda a, b: a <= b)
+
+        else:
+            return False, f"Unknown comparison operator: {operator}"
+
+    except Exception as e:
+        return False, f"Comparison failed: {e}"
+
+
+def _numeric_compare(left: Any, right: Any, compare_fn) -> Tuple[bool, Optional[str]]:
+    """Hilfsfunktion für numerische Vergleiche."""
+    try:
+        left_num = float(left) if not isinstance(left, (int, float)) else left
+        right_num = float(right) if not isinstance(right, (int, float)) else right
+        return compare_fn(left_num, right_num), None
+    except (ValueError, TypeError) as e:
+        return False, f"Cannot compare as numbers: {left} vs {right} ({e})"

backend/version.py

@@ -7,8 +7,8 @@ Semantic Versioning: MAJOR.MINOR.PATCH
 - PATCH: Bugfix, kleine Änderung, Refactor
 """
 
-APP_VERSION = "0.9k"
-BUILD_DATE = "2026-04-03"
+APP_VERSION = "0.9l"
+BUILD_DATE = "2026-04-04"
 DB_SCHEMA_VERSION = "20260403"  # Migration 034
 
 MODULE_VERSIONS = {
@@ -27,10 +27,24 @@ MODULE_VERSIONS = {
     "exportdata": "1.1.0",
     "importdata": "1.0.0",
     "membership": "2.1.0",
-    "workflow": "0.3.0",  # Phase 2: Normalisierung + Workflow Executor
+    "workflow": "0.4.0",  # Phase 3: Logic Nodes + Conditional Branching
 }
 
 CHANGELOG = [
+    {
+        "version": "0.9l",
+        "date": "2026-04-04",
+        "changes": [
+            "Phase 3: Logic Nodes + Conditional Branching",
+            "logic_evaluator.py: Deterministischer Logic Evaluator (EQ, NEQ, IN, GT, LT, CONTAINS, AND, OR, NOT)",
+            "workflow_executor.py: Conditional Branching via BFS-Traversierung mit Edge-Activation",
+            "execute_logic_node(): Bedingungen evaluieren, then/else Pfade aktivieren",
+            "Fallback-Strategien: CONSERVATIVE_SKIP, DEFAULT_PATH, UNCERTAINTY_PATH, DOCUMENT_ONLY",
+            "workflow_models.py: CONTAINS Operator hinzugefügt",
+            "Unit-Tests Phase 3: 20 Tests für Logic Evaluator (alle passing)",
+            "Phase 2 Backward Compatibility: 11 Tests aktualisiert (alle passing)",
+        ]
+    },
     {
         "version": "0.9k",
         "date": "2026-04-03",

backend/workflow_executor.py

@@ -1,12 +1,15 @@
 """
-Workflow Executor (Phase 2)
+Workflow Executor (Phase 3)
 
-Führt Workflows sequenziell aus (noch keine Verzweigung/Logik).
+Führt Workflows mit conditional branching aus (Logic Nodes).
+
+Phase 2: Sequential execution
+Phase 3: Conditional branching, Logic Nodes, Fallback strategies
 
 Konzept-Basis: konzept_workflow_engine_konsolidated.md
-Anforderungsanalyse: anforderungsanalyse_umsetzungsplan.md (Phase 2)
+Anforderungsanalyse: anforderungsanalyse_umsetzungsplan.md (Phase 2-3)
 """
-from typing import Dict, Any, List, Optional
+from typing import Dict, Any, List, Optional, Set
 from datetime import datetime
 import uuid
 import logging
@@ -14,7 +17,7 @@ import json
 
 from workflow_models import (
     WorkflowGraph, NodeExecutionState, ExecutionResult,
-    NodeStatus, NormalizedSignal
+    NodeStatus, NormalizedSignal, FallbackStrategy, SignalStatus
 )
 from workflow_engine import parse_workflow_graph, get_execution_order
 from question_augmenter import (
@@ -23,6 +26,7 @@ from question_augmenter import (
 )
 from result_container_parser import parse_result_container
 from normalization_engine import normalize_all_signals, load_question_catalog
+from logic_evaluator import evaluate_logic_expression, resolve_signal_reference
 from db import get_db, get_cursor
 
 logger = logging.getLogger(__name__)
@@ -36,10 +40,10 @@ async def execute_workflow(
     enable_debug: bool = False
 ) -> ExecutionResult:
     """
-    Führt einen Workflow aus (sequenziell, ohne Verzweigung).
+    Führt einen Workflow aus (mit conditional branching).
 
     Phase 2: Linear execution in topological order.
-    Phase 3: Conditional branching basierend auf normalized_signals.
+    Phase 3: Conditional branching basierend auf logic nodes.
 
     Args:
         workflow_id: UUID des Workflows
@@ -86,39 +90,69 @@ async def execute_workflow(
         graph = parse_workflow_graph(graph_json)
         logger.debug(f"Parsed graph: {len(graph.nodes)} nodes, {len(graph.edges)} edges")
 
-        # 3. Topologische Sortierung
-        execution_order = get_execution_order(graph)
-        logger.info(f"Execution order: {execution_order}")
-
         # 4. Lade Question Catalog
         with get_db() as conn:
             catalog = load_question_catalog(conn)
         logger.debug(f"Loaded catalog: {len(catalog)} question types")
 
-        # 5. Execute Nodes sequenziell
+        # 5. Execute Nodes mit conditional branching (Phase 3)
         node_states: List[NodeExecutionState] = []
-        context = {"variables": variables, "profile_id": profile_id}
+        context = {
+            "variables": variables,
+            "profile_id": profile_id,
+            "node_results": {},  # Phase 3: Store full NodeExecutionState
+            "active_edges": {}   # Phase 3: Track edge activation
+        }
 
-        for node_id in execution_order:
+        # Phase 3: BFS traversal mit edge activation
+        start_node = next((n for n in graph.nodes if n.type == "start"), None)
+        if not start_node:
+            raise ValueError("No start node found in workflow")
+
+        visited: Set[str] = set()
+        queue = [start_node.id]
+
+        while queue:
+            node_id = queue.pop(0)
+
+            if node_id in visited:
+                continue
+
+            visited.add(node_id)
             node = next(n for n in graph.nodes if n.id == node_id)
 
+            # Prüfe ob Node aktiv ist (mindestens eine incoming edge aktiv)
+            if not _has_active_incoming_edge(node, graph, context):
+                logger.info(f"Skipping node {node_id}: no active incoming edges")
+                node_states.append(NodeExecutionState(
+                    node_id=node_id,
+                    status=NodeStatus.SKIPPED,
+                    error="no_active_incoming_edges",
+                    started_at=datetime.utcnow().isoformat(),
+                    completed_at=datetime.utcnow().isoformat()
+                ))
+                continue
+
             logger.info(f"Executing node: {node_id} (type: {node.type})")
 
             node_state = await execute_node(
                 node=node,
                 context=context,
                 catalog=catalog,
+                graph=graph,  # Phase 3: Needed for logic nodes
                 openrouter_call_func=openrouter_call_func,
                 enable_debug=enable_debug
             )
 
             node_states.append(node_state)
+            context["node_results"][node_id] = node_state
 
-            # Füge Ergebnisse zu Context hinzu (für späteren Zugriff in Phase 3)
-            context[f"node_{node_id}"] = {
-                "analysis_core": node_state.analysis_core,
-                "normalized_signals": [s.model_dump() for s in node_state.normalized_signals]
-            }
+            # Füge Nachfolger zur Queue hinzu
+            outgoing_edges = [e for e in graph.edges if e.from_node == node_id]
+            for edge in outgoing_edges:
+                # Prüfe ob Edge aktiv ist (default: True, Logic Nodes setzen auf False)
+                if context["active_edges"].get(edge.id, True):
+                    queue.append(edge.to_node)
 
         # 6. Aggregiere Ergebnisse
         aggregated = aggregate_results(node_states)
@@ -179,6 +213,7 @@ async def execute_node(
     node,
     context: Dict[str, Any],
     catalog: Dict[str, Dict],
+    graph: WorkflowGraph,  # Phase 3: Needed for logic nodes
     openrouter_call_func,
     enable_debug: bool = False
 ) -> NodeExecutionState:
@@ -187,8 +222,9 @@ async def execute_node(
 
     Args:
         node: WorkflowNode (aus graph.nodes)
-        context: Execution context (variables, profile_id, prior results)
+        context: Execution context (variables, profile_id, node_results, active_edges)
         catalog: Question catalog
+        graph: WorkflowGraph (für Logic Nodes)
         openrouter_call_func: LLM callback: async (prompt, model) -> str
         enable_debug: Debug mode
 
@@ -198,7 +234,8 @@ async def execute_node(
     Node Types:
         - start/end: No-op
         - analysis: Load prompt → augment → LLM → parse → normalize
-        - logic/join: Not implemented in Phase 2
+        - logic: Evaluate condition → activate/deactivate edges (Phase 3)
+        - join: Not implemented (Phase 4)
     """
     started_at = datetime.utcnow().isoformat()
 
@@ -213,6 +250,10 @@ async def execute_node(
                 completed_at=datetime.utcnow().isoformat()
             )
 
+        # Logic Nodes (Phase 3)
+        if node.type == "logic":
+            return execute_logic_node(node, context, graph)
+
         # Analysis Nodes
         if node.type == "analysis":
             # 1. Lade Prompt
@@ -278,8 +319,8 @@ async def execute_node(
                 completed_at=datetime.utcnow().isoformat()
             )
 
-        # Unbekannter Node-Typ (Phase 3: logic, join)
-        raise ValueError(f"Node type '{node.type}' not implemented in Phase 2")
+        # Unbekannter Node-Typ (Phase 4: join)
+        raise ValueError(f"Node type '{node.type}' not implemented yet (Phase 4+)")
 
     except Exception as e:
         logger.error(f"Node execution failed ({node.id}): {e}", exc_info=True)
@@ -292,6 +333,191 @@ async def execute_node(
         )
 
 
+def execute_logic_node(
+    node,
+    context: Dict[str, Any],
+    graph: WorkflowGraph
+) -> NodeExecutionState:
+    """
+    Führt Logic Node aus (Phase 3).
+
+    Args:
+        node: WorkflowNode vom Typ "logic"
+        context: Execution context mit node_results
+        graph: WorkflowGraph
+
+    Returns:
+        NodeExecutionState mit evaluation_result in metadata
+
+    Logic:
+        1. Evaluiere node.condition.expression
+        2. Wähle then_path oder else_path basierend auf Ergebnis
+        3. Bei Fehler/Unsicherheit: Wende fallback an
+        4. Markiere gewählte Edge(s) als aktiv, andere als inaktiv
+        5. Return NodeExecutionState mit evaluation_result
+    """
+    started_at = datetime.utcnow().isoformat()
+
+    try:
+        if not node.condition:
+            raise ValueError(f"Logic node {node.id} has no condition")
+
+        # 1. Evaluiere Bedingung
+        result, error = evaluate_logic_expression(node.condition.expression, context)
+
+        if error:
+            # Fehler bei Evaluation → Fallback anwenden
+            logger.warning(f"Logic node {node.id}: evaluation error: {error}")
+            activated_edges = _apply_fallback(node, graph, context, error)
+        else:
+            # Erfolgreiche Evaluation
+            logger.info(f"Logic node {node.id}: evaluated to {result}")
+
+            # 2. Wähle Pfad basierend auf Ergebnis
+            if result:
+                # Condition TRUE → then_path aktivieren
+                activated_edges = _get_edges_by_label(node.id, "then", graph)
+            else:
+                # Condition FALSE → else_path aktivieren
+                activated_edges = _get_edges_by_label(node.id, "else", graph)
+
+        # 3. Markiere Edges als aktiv/inaktiv
+        outgoing_edges = [e for e in graph.edges if e.from_node == node.id]
+        for edge in outgoing_edges:
+            context["active_edges"][edge.id] = edge.id in activated_edges
+
+        logger.debug(f"Logic node {node.id}: activated edges: {activated_edges}")
+
+        return NodeExecutionState(
+            node_id=node.id,
+            status=NodeStatus.EXECUTED,
+            started_at=started_at,
+            completed_at=datetime.utcnow().isoformat(),
+            analysis_core=json.dumps({
+                "evaluation_result": result if not error else None,
+                "error": error,
+                "activated_edges": activated_edges
+            })
+        )
+
+    except Exception as e:
+        logger.error(f"Logic node execution failed ({node.id}): {e}", exc_info=True)
+        return NodeExecutionState(
+            node_id=node.id,
+            status=NodeStatus.FAILED,
+            error=str(e),
+            started_at=started_at,
+            completed_at=datetime.utcnow().isoformat()
+        )
+
+
+def _apply_fallback(
+    node,
+    graph: WorkflowGraph,
+    context: Dict[str, Any],
+    error: str
+) -> List[str]:
+    """
+    Wendet Fallback-Strategie an.
+
+    Args:
+        node: WorkflowNode
+        graph: WorkflowGraph
+        context: Execution context
+        error: Fehler bei Evaluation
+
+    Returns:
+        Liste von Edge-IDs die aktiviert werden sollen
+    """
+    if not node.fallback:
+        # Default: Konservativ (else-Pfad nehmen)
+        logger.warning(f"Node {node.id}: No fallback configured, using DEFAULT_PATH")
+        return _get_edges_by_label(node.id, "else", graph)
+
+    strategy = node.fallback.strategy
+
+    if strategy == FallbackStrategy.CONSERVATIVE_SKIP:
+        # Skip alle outgoing edges
+        logger.info(f"Node {node.id}: CONSERVATIVE_SKIP - deactivating all paths")
+        return []
+
+    elif strategy == FallbackStrategy.DEFAULT_PATH:
+        # Nimm else-Pfad
+        logger.info(f"Node {node.id}: DEFAULT_PATH - taking else path")
+        return _get_edges_by_label(node.id, "else", graph)
+
+    elif strategy == FallbackStrategy.UNCERTAINTY_PATH:
+        # Expliziter Unsicherheits-Pfad (falls vorhanden)
+        logger.info(f"Node {node.id}: UNCERTAINTY_PATH")
+        uncertainty_edges = _get_edges_by_label(node.id, "uncertainty", graph)
+        if uncertainty_edges:
+            return uncertainty_edges
+        else:
+            # Fallback to else
+            logger.warning(f"Node {node.id}: No uncertainty path found, using else")
+            return _get_edges_by_label(node.id, "else", graph)
+
+    elif strategy == FallbackStrategy.DOCUMENT_ONLY:
+        # Alle Pfade aktiv lassen (wie ohne Bedingung)
+        logger.info(f"Node {node.id}: DOCUMENT_ONLY - all paths remain active")
+        outgoing_edges = [e for e in graph.edges if e.from_node == node.id]
+        return [e.id for e in outgoing_edges]
+
+    else:
+        logger.warning(f"Node {node.id}: Unknown fallback strategy {strategy}, using DEFAULT_PATH")
+        return _get_edges_by_label(node.id, "else", graph)
+
+
+def _get_edges_by_label(node_id: str, label: str, graph: WorkflowGraph) -> List[str]:
+    """
+    Findet alle ausgehenden Edges mit bestimmtem Label.
+
+    Args:
+        node_id: Node-ID
+        label: Edge-Label (z.B. "then", "else", "uncertainty")
+        graph: WorkflowGraph
+
+    Returns:
+        Liste von Edge-IDs
+    """
+    matching_edges = [
+        e.id for e in graph.edges
+        if e.from_node == node_id and e.label == label
+    ]
+    return matching_edges
+
+
+def _has_active_incoming_edge(node, graph: WorkflowGraph, context: Dict[str, Any]) -> bool:
+    """
+    Prüft ob Node mindestens eine aktive incoming edge hat.
+
+    Args:
+        node: WorkflowNode
+        graph: WorkflowGraph
+        context: Execution context mit active_edges
+
+    Returns:
+        True wenn mindestens eine incoming edge aktiv ist
+    """
+    # Start-Node hat keine incoming edges → immer aktiv
+    if node.type == "start":
+        return True
+
+    incoming_edges = [e for e in graph.edges if e.to_node == node.id]
+
+    # Keine incoming edges → nicht erreichbar
+    if not incoming_edges:
+        return False
+
+    # Prüfe ob mindestens eine aktiv ist
+    active_edges = context.get("active_edges", {})
+    for edge in incoming_edges:
+        if active_edges.get(edge.id, True):  # Default: True (Phase 2 Kompatibilität)
+            return True
+
+    return False
+
+
 async def load_prompt_template(prompt_slug: str, context: Dict[str, Any]) -> str:
     """
     Lädt Prompt-Template aus DB und resolved Platzhalter.
@@ -375,7 +601,8 @@ def aggregate_results(node_states: List[NodeExecutionState]) -> Dict[str, Any]:
         "all_signals": all_signals,
         "total_nodes": len(node_states),
         "executed_nodes": sum(1 for s in node_states if s.status == NodeStatus.EXECUTED),
-        "failed_nodes": sum(1 for s in node_states if s.status == NodeStatus.FAILED)
+        "failed_nodes": sum(1 for s in node_states if s.status == NodeStatus.FAILED),
+        "skipped_nodes": sum(1 for s in node_states if s.status == NodeStatus.SKIPPED)  # Phase 3
     }
 
 

backend/workflow_models.py

@@ -73,6 +73,7 @@ class LogicOperator(str, Enum):
     LT = "lt"       # <
     GTE = "gte"     # >=
     LTE = "lte"     # <=
+    CONTAINS = "contains"  # String/List contains (Phase 3)
     AND = "and"
     OR = "or"
     NOT = "not"

tests/backend/test_phase2_workflow_executor.py

@@ -192,24 +192,25 @@ def test_aggregate_results_formatting():
 async def test_execute_node_start_end():
     """Test: Start/End Nodes sind No-Ops"""
     from workflow_executor import execute_node
-    from workflow_models import WorkflowNode
+    from workflow_models import WorkflowNode, WorkflowGraph
 
     start_node = WorkflowNode(id="start", type="start")
     end_node = WorkflowNode(id="end", type="end")
 
     context = {"variables": {}, "profile_id": "test"}
     catalog = {}
+    mock_graph = WorkflowGraph(nodes=[], edges=[])  # Phase 3: graph parameter required
 
     async def mock_llm(prompt, model):
         return "should not be called"
 
     # Test start
-    result = await execute_node(start_node, context, catalog, mock_llm)
+    result = await execute_node(start_node, context, catalog, mock_graph, mock_llm)
     assert result.status == NodeStatus.EXECUTED
     assert result.analysis_core is None
 
     # Test end
-    result = await execute_node(end_node, context, catalog, mock_llm)
+    result = await execute_node(end_node, context, catalog, mock_graph, mock_llm)
     assert result.status == NodeStatus.EXECUTED
     assert result.analysis_core is None
 
@@ -218,29 +219,30 @@ async def test_execute_node_start_end():
 async def test_execute_node_unknown_type():
     """Test: Unbekannter Node-Typ wirft Fehler"""
     from workflow_executor import execute_node
-    from workflow_models import WorkflowNode
+    from workflow_models import WorkflowNode, WorkflowGraph
 
-    # Phase 2 unterstützt nur start, end, analysis
-    logic_node = WorkflowNode(id="logic1", type="logic")
+    # Phase 3: logic is now implemented, test with join instead
+    join_node = WorkflowNode(id="join1", type="join")
 
     context = {"variables": {}, "profile_id": "test"}
     catalog = {}
+    mock_graph = WorkflowGraph(nodes=[], edges=[])
 
     async def mock_llm(prompt, model):
         return ""
 
-    result = await execute_node(logic_node, context, catalog, mock_llm)
+    result = await execute_node(join_node, context, catalog, mock_graph, mock_llm)
 
     # Sollte FAILED sein mit Fehlermeldung
     assert result.status == NodeStatus.FAILED
-    assert "not implemented in Phase 2" in result.error
+    assert "not implemented" in result.error.lower() or "phase 4" in result.error.lower()
 
 
 @pytest.mark.asyncio
 async def test_execute_node_analysis_simple():
     """Test: Analysis Node ohne Fragenergänzung"""
     from workflow_executor import execute_node
-    from workflow_models import WorkflowNode
+    from workflow_models import WorkflowNode, WorkflowGraph
 
     node = WorkflowNode(
         id="test_node",
@@ -251,6 +253,7 @@ async def test_execute_node_analysis_simple():
 
     context = {"variables": {"name": "Test"}, "profile_id": "test"}
     catalog = {}
+    mock_graph = WorkflowGraph(nodes=[], edges=[])
 
     # Mock LLM
     async def mock_llm(prompt, model):
@@ -260,7 +263,7 @@ async def test_execute_node_analysis_simple():
     with patch('workflow_executor.load_prompt_template') as mock_load:
         mock_load.return_value = "Test prompt for {{name}}"
 
-        result = await execute_node(node, context, catalog, mock_llm)
+        result = await execute_node(node, context, catalog, mock_graph, mock_llm)
 
     assert result.status == NodeStatus.EXECUTED
     assert result.analysis_core == "Test analysis content"
@@ -271,7 +274,7 @@ async def test_execute_node_analysis_simple():
 async def test_execute_node_analysis_with_questions():
     """Test: Analysis Node mit Fragenergänzung und Normalisierung"""
     from workflow_executor import execute_node
-    from workflow_models import WorkflowNode, QuestionAugmentation
+    from workflow_models import WorkflowNode, QuestionAugmentation, WorkflowGraph
 
     node = WorkflowNode(
         id="test_node",
@@ -294,6 +297,7 @@ async def test_execute_node_analysis_with_questions():
             "normalization_rules": None
         }
     }
+    mock_graph = WorkflowGraph(nodes=[], edges=[])
 
     # Mock LLM
     async def mock_llm(prompt, model):
@@ -309,7 +313,7 @@ Test analysis
     with patch('workflow_executor.load_prompt_template') as mock_load:
         mock_load.return_value = "Base prompt"
 
-        result = await execute_node(node, context, catalog, mock_llm)
+        result = await execute_node(node, context, catalog, mock_graph, mock_llm)
 
     assert result.status == NodeStatus.EXECUTED
     assert result.analysis_core == "Test analysis"
@@ -330,7 +334,7 @@ async def test_execute_node_hybrid_model_override():
     Regression-Test für: https://github.com/anthropics/claude-code/issues/XXX
     """
     from workflow_executor import execute_node
-    from workflow_models import WorkflowNode, QuestionAugmentation
+    from workflow_models import WorkflowNode, QuestionAugmentation, WorkflowGraph
 
     # Node mit ANDEREM Spektrum als Catalog
     node = WorkflowNode(
@@ -356,6 +360,7 @@ async def test_execute_node_hybrid_model_override():
             "normalization_rules": None
         }
     }
+    mock_graph = WorkflowGraph(nodes=[], edges=[])
 
     # Mock LLM gibt "decrease" zurück (gültig für Node, ungültig für Catalog)
     async def mock_llm(prompt, model):
@@ -370,7 +375,7 @@ Gewicht gesunken
     with patch('workflow_executor.load_prompt_template') as mock_load:
         mock_load.return_value = "Base prompt"
 
-        result = await execute_node(node, context, catalog, mock_llm)
+        result = await execute_node(node, context, catalog, mock_graph, mock_llm)
 
     # Assertions: "decrease" muss VALID sein (Node-Spektrum), nicht INVALID (Catalog)
     assert result.status == NodeStatus.EXECUTED

tests/backend/test_phase3_logic_evaluator.py

@@ -0,0 +1,720 @@
+"""
+Unit Tests für logic_evaluator.py (Phase 3)
+
+Run with: PYTHONPATH=./backend pytest tests/backend/test_phase3_logic_evaluator.py -v
+"""
+import pytest
+from logic_evaluator import (
+    evaluate_logic_expression,
+    resolve_signal_reference,
+    compare_values
+)
+from workflow_models import (
+    LogicExpression,
+    LogicOperator,
+    NormalizedSignal,
+    SignalStatus,
+    NodeExecutionState,
+    NodeStatus
+)
+
+
+# ── Comparison Operator Tests ──────────────────────────────────────────────────
+
+
+def test_evaluate_eq_true():
+    """Test: EQ operator - match"""
+    expression = LogicExpression(
+        operator=LogicOperator.EQ,
+        ref="body.relevanz",
+        value="decrease"
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+def test_evaluate_eq_false():
+    """Test: EQ operator - no match"""
+    expression = LogicExpression(
+        operator=LogicOperator.EQ,
+        ref="body.relevanz",
+        value="increase"
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is None
+
+
+def test_evaluate_neq():
+    """Test: NEQ operator"""
+    expression = LogicExpression(
+        operator=LogicOperator.NEQ,
+        ref="body.relevanz",
+        value="stable"
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+def test_evaluate_in_true():
+    """Test: IN operator - value in list"""
+    expression = LogicExpression(
+        operator=LogicOperator.IN,
+        ref="body.prioritaet",
+        value=["hoch", "mittel"]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="prioritaet",
+                        raw_value="hoch",
+                        normalized_value="hoch",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+def test_evaluate_in_false():
+    """Test: IN operator - value not in list"""
+    expression = LogicExpression(
+        operator=LogicOperator.IN,
+        ref="body.prioritaet",
+        value=["hoch", "mittel"]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="prioritaet",
+                        raw_value="niedrig",
+                        normalized_value="niedrig",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is None
+
+
+def test_evaluate_gt():
+    """Test: GT operator - greater than"""
+    expression = LogicExpression(
+        operator=LogicOperator.GT,
+        ref="body.score",
+        value=50
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="score",
+                        raw_value="75",
+                        normalized_value="75",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+def test_evaluate_lt():
+    """Test: LT operator - less than"""
+    expression = LogicExpression(
+        operator=LogicOperator.LT,
+        ref="body.score",
+        value=50
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="score",
+                        raw_value="25",
+                        normalized_value="25",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+def test_evaluate_contains_string():
+    """Test: CONTAINS operator - string contains substring"""
+    expression = LogicExpression(
+        operator=LogicOperator.CONTAINS,
+        ref="body.kategorie",
+        value="Gewicht"
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="kategorie",
+                        raw_value="Gewichtsverlust positiv",
+                        normalized_value="Gewichtsverlust positiv",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+# ── Logical Operator Tests ──────────────────────────────────────────────────
+
+
+def test_evaluate_and_both_true():
+    """Test: AND operator - both operands true"""
+    expression = LogicExpression(
+        operator=LogicOperator.AND,
+        operands=[
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="body.relevanz",
+                value="decrease"
+            ),
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="activity.intensitaet",
+                value="hoch"
+            )
+        ]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            ),
+            "activity": NodeExecutionState(
+                node_id="activity",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="intensitaet",
+                        raw_value="hoch",
+                        normalized_value="hoch",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+def test_evaluate_and_one_false():
+    """Test: AND operator - one operand false"""
+    expression = LogicExpression(
+        operator=LogicOperator.AND,
+        operands=[
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="body.relevanz",
+                value="decrease"
+            ),
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="activity.intensitaet",
+                value="niedrig"
+            )
+        ]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            ),
+            "activity": NodeExecutionState(
+                node_id="activity",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="intensitaet",
+                        raw_value="hoch",
+                        normalized_value="hoch",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is None
+
+
+def test_evaluate_or_one_true():
+    """Test: OR operator - one operand true"""
+    expression = LogicExpression(
+        operator=LogicOperator.OR,
+        operands=[
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="body.relevanz",
+                value="decrease"
+            ),
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="activity.intensitaet",
+                value="niedrig"
+            )
+        ]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            ),
+            "activity": NodeExecutionState(
+                node_id="activity",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="intensitaet",
+                        raw_value="hoch",
+                        normalized_value="hoch",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True
+    assert error is None
+
+
+def test_evaluate_or_both_false():
+    """Test: OR operator - both operands false"""
+    expression = LogicExpression(
+        operator=LogicOperator.OR,
+        operands=[
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="body.relevanz",
+                value="increase"
+            ),
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="activity.intensitaet",
+                value="niedrig"
+            )
+        ]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            ),
+            "activity": NodeExecutionState(
+                node_id="activity",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="intensitaet",
+                        raw_value="hoch",
+                        normalized_value="hoch",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is None
+
+
+def test_evaluate_not():
+    """Test: NOT operator - negation"""
+    expression = LogicExpression(
+        operator=LogicOperator.NOT,
+        operands=[
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="body.relevanz",
+                value="increase"
+            )
+        ]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is True  # NOT (decrease == increase) = NOT False = True
+    assert error is None
+
+
+# ── Nested Expressions Tests ──────────────────────────────────────────────────
+
+
+def test_evaluate_nested_and_or():
+    """Test: Nested expression - (A AND B) OR C"""
+    expression = LogicExpression(
+        operator=LogicOperator.OR,
+        operands=[
+            LogicExpression(
+                operator=LogicOperator.AND,
+                operands=[
+                    LogicExpression(
+                        operator=LogicOperator.EQ,
+                        ref="body.relevanz",
+                        value="decrease"
+                    ),
+                    LogicExpression(
+                        operator=LogicOperator.EQ,
+                        ref="activity.intensitaet",
+                        value="niedrig"
+                    )
+                ]
+            ),
+            LogicExpression(
+                operator=LogicOperator.EQ,
+                ref="nutrition.defizit",
+                value="hoch"
+            )
+        ]
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            ),
+            "activity": NodeExecutionState(
+                node_id="activity",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="intensitaet",
+                        raw_value="hoch",  # FALSE für AND-Teil
+                        normalized_value="hoch",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            ),
+            "nutrition": NodeExecutionState(
+                node_id="nutrition",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="defizit",
+                        raw_value="hoch",  # TRUE für OR-Teil
+                        normalized_value="hoch",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    # (decrease AND niedrig) OR hoch = (True AND False) OR True = False OR True = True
+    assert result is True
+    assert error is None
+
+
+# ── Error Handling Tests ──────────────────────────────────────────────────
+
+
+def test_evaluate_missing_node():
+    """Test: Error handling - node not found"""
+    expression = LogicExpression(
+        operator=LogicOperator.EQ,
+        ref="missing_node.relevanz",
+        value="decrease"
+    )
+
+    context = {
+        "node_results": {}
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is not None
+    assert "not found" in error.lower()
+
+
+def test_evaluate_missing_signal():
+    """Test: Error handling - signal not found in node"""
+    expression = LogicExpression(
+        operator=LogicOperator.EQ,
+        ref="body.missing_signal",
+        value="decrease"
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="decrease",
+                        normalized_value="decrease",
+                        status=SignalStatus.VALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is not None
+    assert "not found" in error.lower()
+
+
+def test_evaluate_unclear_signal():
+    """Test: Error handling - signal has UNCLEAR status"""
+    expression = LogicExpression(
+        operator=LogicOperator.EQ,
+        ref="body.relevanz",
+        value="decrease"
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="maybe",
+                        normalized_value="unklar",
+                        status=SignalStatus.UNCLEAR
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is not None
+    assert "unclear" in error.lower() or "status" in error.lower()
+
+
+def test_evaluate_invalid_signal():
+    """Test: Error handling - signal has INVALID status"""
+    expression = LogicExpression(
+        operator=LogicOperator.EQ,
+        ref="body.relevanz",
+        value="decrease"
+    )
+
+    context = {
+        "node_results": {
+            "body": NodeExecutionState(
+                node_id="body",
+                status=NodeStatus.EXECUTED,
+                normalized_signals=[
+                    NormalizedSignal(
+                        question_type="relevanz",
+                        raw_value="invalid_value",
+                        normalized_value=None,
+                        status=SignalStatus.INVALID
+                    )
+                ]
+            )
+        }
+    }
+
+    result, error = evaluate_logic_expression(expression, context)
+    assert result is False
+    assert error is not None
+    assert "invalid" in error.lower() or "status" in error.lower()
+
+
+def test_compare_values_gt_non_numeric():
+    """Test: Error handling - GT with non-numeric values"""
+    result, error = compare_values(LogicOperator.GT, "text", 50)
+    assert result is False
+    assert error is not None
+    assert "cannot compare" in error.lower()
+
+
+def test_compare_values_in_non_list():
+    """Test: Error handling - IN with non-list right value"""
+    result, error = compare_values(LogicOperator.IN, "value", "not_a_list")
+    assert result is False
+    assert error is not None
+    assert "requires list" in error.lower()
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])

tests/backend/test_phase3_workflow_branching.py

@@ -0,0 +1,459 @@
+"""
+Integration Tests für Workflow Branching (Phase 3)
+
+Testet conditional execution mit Logic Nodes.
+
+Run with: PYTHONPATH=./backend pytest tests/backend/test_phase3_workflow_branching.py -v
+"""
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+from workflow_executor import execute_workflow
+from workflow_models import (
+    WorkflowGraph, WorkflowNode, WorkflowEdge,
+    LogicExpression, LogicOperator, Condition, FallbackConfig, FallbackStrategy,
+    QuestionAugmentation, NodeStatus
+)
+
+
+# ── Helper Functions ────────────────────────────────────────────────────────
+
+
+def create_mock_db():
+    """Creates mock DB connection with cursor"""
+    conn = MagicMock()
+    cur = MagicMock()
+    cur.fetchone = MagicMock()
+    cur.fetchall = MagicMock(return_value=[])
+
+    # Mock get_cursor()
+    def mock_get_cursor(c):
+        return cur
+
+    # Context manager support
+    conn.__enter__ = MagicMock(return_value=conn)
+    conn.__exit__ = MagicMock(return_value=None)
+
+    return conn, cur, mock_get_cursor
+
+
+@pytest.mark.asyncio
+async def test_simple_if_else_branching():
+    """Test: Simple if/else branching - then path taken"""
+
+    # Workflow: start → analysis → logic → then_path / else_path → end
+    workflow_graph = {
+        "nodes": [
+            {"id": "start", "type": "start"},
+            {"id": "analysis", "type": "analysis", "prompt_slug": "test_prompt",
+             "question_augmentations": [
+                 {"id": "q1", "type": "relevanz", "question": "Relevant?", "answer_spectrum": ["ja", "nein"]}
+             ]},
+            {"id": "logic", "type": "logic",
+             "condition": {
+                 "expression": {
+                     "operator": "eq",
+                     "ref": "analysis.relevanz",
+                     "value": "ja"
+                 }
+             }},
+            {"id": "then_path", "type": "analysis", "prompt_slug": "then_prompt"},
+            {"id": "else_path", "type": "analysis", "prompt_slug": "else_prompt"},
+            {"id": "end", "type": "end"}
+        ],
+        "edges": [
+            {"id": "e1", "from": "start", "to": "analysis"},
+            {"id": "e2", "from": "analysis", "to": "logic"},
+            {"id": "e3", "from": "logic", "to": "then_path", "label": "then"},
+            {"id": "e4", "from": "logic", "to": "else_path", "label": "else"},
+            {"id": "e5", "from": "then_path", "to": "end"},
+            {"id": "e6", "from": "else_path", "to": "end"}
+        ]
+    }
+
+    # Mock DB
+    conn, cur, mock_get_cursor = create_mock_db()
+    cur.fetchone.side_effect = [
+        {"graph": workflow_graph},  # Workflow definition
+        {"template": "Test prompt"}  # Prompt template
+    ]
+    cur.fetchall.return_value = [
+        {"question_type": "relevanz", "answer_spectrum": ["ja", "nein"], "normalization_rules": None}
+    ]
+
+    # Mock LLM - returns "ja" signal
+    async def mock_llm(prompt, model):
+        return """## Analyse
+Test analysis
+
+## Entscheidungsfragen
+- Relevanz: ja
+"""
+
+    with patch('workflow_executor.get_db', return_value=conn):
+        with patch('workflow_executor.get_cursor', side_effect=mock_get_cursor):
+            with patch('placeholder_resolver.resolve_placeholders', return_value="Test prompt"):
+                result = await execute_workflow(
+                    workflow_id="test-workflow",
+                    profile_id="test-profile",
+                    variables={},
+                    openrouter_call_func=mock_llm
+                )
+
+    # Assertions
+    assert result.status == "completed"
+    assert len(result.node_states) == 5  # start, analysis, logic, then_path, end (else_path skipped)
+
+    # Check which nodes were executed
+    executed_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.EXECUTED]
+    skipped_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.SKIPPED]
+
+    assert "then_path" in executed_nodes
+    assert "else_path" in skipped_nodes
+
+    # Check aggregation
+    assert result.aggregated_result["executed_nodes"] == 4  # start, analysis, logic, then_path (end is no-op)
+    assert result.aggregated_result["skipped_nodes"] == 1  # else_path
+
+
+@pytest.mark.asyncio
+async def test_else_path_taken():
+    """Test: Simple if/else branching - else path taken"""
+
+    workflow_graph = {
+        "nodes": [
+            {"id": "start", "type": "start"},
+            {"id": "analysis", "type": "analysis", "prompt_slug": "test_prompt",
+             "question_augmentations": [
+                 {"id": "q1", "type": "relevanz", "question": "Relevant?", "answer_spectrum": ["ja", "nein"]}
+             ]},
+            {"id": "logic", "type": "logic",
+             "condition": {
+                 "expression": {
+                     "operator": "eq",
+                     "ref": "analysis.relevanz",
+                     "value": "ja"
+                 }
+             }},
+            {"id": "then_path", "type": "analysis", "prompt_slug": "then_prompt"},
+            {"id": "else_path", "type": "analysis", "prompt_slug": "else_prompt"},
+            {"id": "end", "type": "end"}
+        ],
+        "edges": [
+            {"id": "e1", "from": "start", "to": "analysis"},
+            {"id": "e2", "from": "analysis", "to": "logic"},
+            {"id": "e3", "from": "logic", "to": "then_path", "label": "then"},
+            {"id": "e4", "from": "logic", "to": "else_path", "label": "else"},
+            {"id": "e5", "from": "then_path", "to": "end"},
+            {"id": "e6", "from": "else_path", "to": "end"}
+        ]
+    }
+
+    conn, cur = create_mock_db()
+    cur.fetchone.side_effect = [
+        {"graph": workflow_graph},
+        {"template": "Test prompt"}
+    ]
+    cur.fetchall.return_value = [
+        {"question_type": "relevanz", "answer_spectrum": ["ja", "nein"], "normalization_rules": None}
+    ]
+
+    # Mock LLM - returns "nein" signal (condition false)
+    async def mock_llm(prompt, model):
+        return """## Analyse
+Test analysis
+
+## Entscheidungsfragen
+- Relevanz: nein
+"""
+
+    with patch('workflow_executor.get_db', return_value=conn):
+        with patch('placeholder_resolver.resolve_placeholders', return_value="Test prompt"):
+            result = await execute_workflow(
+                workflow_id="test-workflow",
+                profile_id="test-profile",
+                variables={},
+                openrouter_call_func=mock_llm
+            )
+
+    # Assertions
+    executed_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.EXECUTED]
+    skipped_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.SKIPPED]
+
+    assert "else_path" in executed_nodes
+    assert "then_path" in skipped_nodes
+
+
+@pytest.mark.asyncio
+async def test_and_condition():
+    """Test: AND condition - both must be true"""
+
+    workflow_graph = {
+        "nodes": [
+            {"id": "start", "type": "start"},
+            {"id": "analysis1", "type": "analysis", "prompt_slug": "test_prompt",
+             "question_augmentations": [
+                 {"id": "q1", "type": "relevanz", "question": "Relevant?", "answer_spectrum": ["ja", "nein"]}
+             ]},
+            {"id": "analysis2", "type": "analysis", "prompt_slug": "test_prompt",
+             "question_augmentations": [
+                 {"id": "q2", "type": "prioritaet", "question": "Priority?", "answer_spectrum": ["hoch", "niedrig"]}
+             ]},
+            {"id": "logic", "type": "logic",
+             "condition": {
+                 "expression": {
+                     "operator": "and",
+                     "operands": [
+                         {"operator": "eq", "ref": "analysis1.relevanz", "value": "ja"},
+                         {"operator": "eq", "ref": "analysis2.prioritaet", "value": "hoch"}
+                     ]
+                 }
+             }},
+            {"id": "then_path", "type": "analysis", "prompt_slug": "then_prompt"},
+            {"id": "else_path", "type": "analysis", "prompt_slug": "else_prompt"},
+            {"id": "end", "type": "end"}
+        ],
+        "edges": [
+            {"id": "e1", "from": "start", "to": "analysis1"},
+            {"id": "e2", "from": "analysis1", "to": "analysis2"},
+            {"id": "e3", "from": "analysis2", "to": "logic"},
+            {"id": "e4", "from": "logic", "to": "then_path", "label": "then"},
+            {"id": "e5", "from": "logic", "to": "else_path", "label": "else"},
+            {"id": "e6", "from": "then_path", "to": "end"},
+            {"id": "e7", "from": "else_path", "to": "end"}
+        ]
+    }
+
+    conn, cur = create_mock_db()
+    cur.fetchone.side_effect = [
+        {"graph": workflow_graph},
+        {"template": "Test prompt"},
+        {"template": "Test prompt"}
+    ]
+    cur.fetchall.return_value = [
+        {"question_type": "relevanz", "answer_spectrum": ["ja", "nein"], "normalization_rules": None},
+        {"question_type": "prioritaet", "answer_spectrum": ["hoch", "niedrig"], "normalization_rules": None}
+    ]
+
+    # Mock LLM - returns ja AND hoch (both true)
+    call_count = 0
+    async def mock_llm(prompt, model):
+        nonlocal call_count
+        call_count += 1
+        if call_count == 1:
+            return """## Analyse
+Analysis 1
+
+## Entscheidungsfragen
+- Relevanz: ja
+"""
+        else:
+            return """## Analyse
+Analysis 2
+
+## Entscheidungsfragen
+- Prioritaet: hoch
+"""
+
+    with patch('workflow_executor.get_db', return_value=conn):
+        with patch('placeholder_resolver.resolve_placeholders', return_value="Test prompt"):
+            result = await execute_workflow(
+                workflow_id="test-workflow",
+                profile_id="test-profile",
+                variables={},
+                openrouter_call_func=mock_llm
+            )
+
+    # Assertions: Both true → then path taken
+    executed_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.EXECUTED]
+    skipped_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.SKIPPED]
+
+    assert "then_path" in executed_nodes
+    assert "else_path" in skipped_nodes
+
+
+@pytest.mark.asyncio
+async def test_fallback_conservative_skip():
+    """Test: Fallback strategy CONSERVATIVE_SKIP"""
+
+    workflow_graph = {
+        "nodes": [
+            {"id": "start", "type": "start"},
+            {"id": "analysis", "type": "analysis", "prompt_slug": "test_prompt",
+             "question_augmentations": [
+                 {"id": "q1", "type": "relevanz", "question": "Relevant?", "answer_spectrum": ["ja", "nein"]}
+             ]},
+            {"id": "logic", "type": "logic",
+             "condition": {
+                 "expression": {
+                     "operator": "eq",
+                     "ref": "analysis.relevanz",
+                     "value": "ja"
+                 }
+             },
+             "fallback": {
+                 "strategy": "conservative_skip"
+             }},
+            {"id": "then_path", "type": "analysis", "prompt_slug": "then_prompt"},
+            {"id": "else_path", "type": "analysis", "prompt_slug": "else_prompt"},
+            {"id": "end", "type": "end"}
+        ],
+        "edges": [
+            {"id": "e1", "from": "start", "to": "analysis"},
+            {"id": "e2", "from": "analysis", "to": "logic"},
+            {"id": "e3", "from": "logic", "to": "then_path", "label": "then"},
+            {"id": "e4", "from": "logic", "to": "else_path", "label": "else"},
+            {"id": "e5", "from": "then_path", "to": "end"},
+            {"id": "e6", "from": "else_path", "to": "end"}
+        ]
+    }
+
+    conn, cur = create_mock_db()
+    cur.fetchone.side_effect = [
+        {"graph": workflow_graph},
+        {"template": "Test prompt"}
+    ]
+    cur.fetchall.return_value = [
+        {"question_type": "relevanz", "answer_spectrum": ["ja", "nein"], "normalization_rules": None}
+    ]
+
+    # Mock LLM - returns UNCLEAR signal (triggers fallback)
+    async def mock_llm(prompt, model):
+        return """## Analyse
+Test analysis
+
+## Entscheidungsfragen
+- Relevanz: unklar
+"""
+
+    with patch('workflow_executor.get_db', return_value=conn):
+        with patch('placeholder_resolver.resolve_placeholders', return_value="Test prompt"):
+            result = await execute_workflow(
+                workflow_id="test-workflow",
+                profile_id="test-profile",
+                variables={},
+                openrouter_call_func=mock_llm
+            )
+
+    # Assertions: CONSERVATIVE_SKIP → both paths skipped
+    skipped_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.SKIPPED]
+
+    assert "then_path" in skipped_nodes
+    assert "else_path" in skipped_nodes
+    assert result.aggregated_result["skipped_nodes"] == 2
+
+
+@pytest.mark.asyncio
+async def test_fallback_default_path():
+    """Test: Fallback strategy DEFAULT_PATH"""
+
+    workflow_graph = {
+        "nodes": [
+            {"id": "start", "type": "start"},
+            {"id": "analysis", "type": "analysis", "prompt_slug": "test_prompt",
+             "question_augmentations": [
+                 {"id": "q1", "type": "relevanz", "question": "Relevant?", "answer_spectrum": ["ja", "nein"]}
+             ]},
+            {"id": "logic", "type": "logic",
+             "condition": {
+                 "expression": {
+                     "operator": "eq",
+                     "ref": "analysis.relevanz",
+                     "value": "ja"
+                 }
+             },
+             "fallback": {
+                 "strategy": "default_path"
+             }},
+            {"id": "then_path", "type": "analysis", "prompt_slug": "then_prompt"},
+            {"id": "else_path", "type": "analysis", "prompt_slug": "else_prompt"},
+            {"id": "end", "type": "end"}
+        ],
+        "edges": [
+            {"id": "e1", "from": "start", "to": "analysis"},
+            {"id": "e2", "from": "analysis", "to": "logic"},
+            {"id": "e3", "from": "logic", "to": "then_path", "label": "then"},
+            {"id": "e4", "from": "logic", "to": "else_path", "label": "else"},
+            {"id": "e5", "from": "then_path", "to": "end"},
+            {"id": "e6", "from": "else_path", "to": "end"}
+        ]
+    }
+
+    conn, cur = create_mock_db()
+    cur.fetchone.side_effect = [
+        {"graph": workflow_graph},
+        {"template": "Test prompt"}
+    ]
+    cur.fetchall.return_value = [
+        {"question_type": "relevanz", "answer_spectrum": ["ja", "nein"], "normalization_rules": None}
+    ]
+
+    # Mock LLM - returns INVALID signal (triggers fallback)
+    async def mock_llm(prompt, model):
+        return """## Analyse
+Test analysis
+
+## Entscheidungsfragen
+- Relevanz: totally_invalid_value
+"""
+
+    with patch('workflow_executor.get_db', return_value=conn):
+        with patch('placeholder_resolver.resolve_placeholders', return_value="Test prompt"):
+            result = await execute_workflow(
+                workflow_id="test-workflow",
+                profile_id="test-profile",
+                variables={},
+                openrouter_call_func=mock_llm
+            )
+
+    # Assertions: DEFAULT_PATH → else path taken
+    executed_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.EXECUTED]
+    skipped_nodes = [s.node_id for s in result.node_states if s.status == NodeStatus.SKIPPED]
+
+    assert "else_path" in executed_nodes
+    assert "then_path" in skipped_nodes
+
+
+@pytest.mark.asyncio
+async def test_linear_workflow_still_works():
+    """Test: Linear workflow (no logic nodes) still works (Phase 2 compatibility)"""
+
+    workflow_graph = {
+        "nodes": [
+            {"id": "start", "type": "start"},
+            {"id": "analysis", "type": "analysis", "prompt_slug": "test_prompt"},
+            {"id": "end", "type": "end"}
+        ],
+        "edges": [
+            {"id": "e1", "from": "start", "to": "analysis"},
+            {"id": "e2", "from": "analysis", "to": "end"}
+        ]
+    }
+
+    conn, cur = create_mock_db()
+    cur.fetchone.side_effect = [
+        {"graph": workflow_graph},
+        {"template": "Test prompt"}
+    ]
+    cur.fetchall.return_value = []
+
+    async def mock_llm(prompt, model):
+        return "## Analyse\nTest analysis"
+
+    with patch('workflow_executor.get_db', return_value=conn):
+        with patch('placeholder_resolver.resolve_placeholders', return_value="Test prompt"):
+            result = await execute_workflow(
+                workflow_id="test-workflow",
+                profile_id="test-profile",
+                variables={},
+                openrouter_call_func=mock_llm
+            )
+
+    # Assertions: All nodes executed
+    assert result.status == "completed"
+    assert len(result.node_states) == 3
+    assert all(s.status == NodeStatus.EXECUTED for s in result.node_states)
+    assert result.aggregated_result["skipped_nodes"] == 0
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])