docs: add docstrings to dated file handler

ref: N25B-401

.env.example

@@ -0,0 +1,20 @@
+# Example .env file. To use, make a copy, call it ".env" (i.e. removing the ".example" suffix), then you edit values.
+
+# The hostname of the Robot Interface. Change if the Control Backend and Robot Interface are running on different computers.
+RI_HOST="localhost"
+
+# URL for the local LLM API. Must be an API that implements the OpenAI Chat Completions API, but most do.
+LLM_SETTINGS__LOCAL_LLM_URL="http://localhost:1234/v1/chat/completions"
+
+# Name of the local LLM model to use.
+LLM_SETTINGS__LOCAL_LLM_MODEL="gpt-oss"
+
+# Number of non-speech chunks to wait before speech ended. A chunk is approximately 31 ms. Increasing this number allows longer pauses in speech, but also increases response time.
+BEHAVIOUR_SETTINGS__VAD_NON_SPEECH_PATIENCE_CHUNKS=15
+
+# Timeout in milliseconds for socket polling. Increase this number if network latency/jitter is high, often the case when using Wi-Fi. Perhaps 500 ms. A symptom of this issue is transcriptions getting cut off.
+BEHAVIOUR_SETTINGS__SOCKET_POLLER_TIMEOUT_MS=100
+
+
+
+# For an exhaustive list of options, see the control_backend.core.config module in the docs.

.gitignore

@@ -222,6 +222,10 @@ __marimo__/
 docs/*
 !docs/conf.py
 
+# Generated files
+agentspeak.asl
+experiment-*.log
+
 
 
 

.logging_config.yaml

@@ -1,36 +1,57 @@
 version: 1
 
 custom_levels:
-  OBSERVATION: 25
-  ACTION: 26
+  OBSERVATION: 24
+  ACTION: 25
+  CHAT: 26
   LLM: 9
 
 formatters:
   # Console output
   colored:
-    (): "colorlog.ColoredFormatter"
+    class: colorlog.ColoredFormatter
     format: "{log_color}{asctime}.{msecs:03.0f} | {levelname:11} | {name:70} | {message}"
     style: "{"
     datefmt: "%H:%M:%S"
 
   # User-facing UI (structured JSON)
-  json_experiment:
-    (): "pythonjsonlogger.jsonlogger.JsonFormatter"
+  json:
+    class: pythonjsonlogger.jsonlogger.JsonFormatter
     format: "{name} {levelname} {levelno} {message} {created} {relativeCreated}"
     style: "{"
 
+  # Experiment stream for console and file output, with optional `role` field
+  experiment:
+    class: control_backend.logging.OptionalFieldFormatter
+    format: "%(asctime)s %(levelname)s %(role?)s %(message)s"
+    defaults:
+      role: "-"
+
+filters:
+  # Filter out any log records that have the extra field "partial" set to True, indicating that they
+  #  will be replaced later.
+  partial:
+    (): control_backend.logging.PartialFilter
+
 handlers:
   console:
     class: logging.StreamHandler
     level: DEBUG
     formatter: colored
+    filters: [partial]
     stream: ext://sys.stdout
   ui:
     class: zmq.log.handlers.PUBHandler
     level: LLM
-    formatter: json_experiment
+    formatter: json
+  file:
+    class: control_backend.logging.DatedFileHandler
+    formatter: experiment
+    filters: [partial]
+    # Directory must match config.logging_settings.experiment_log_directory
+    file_prefix: experiment_logs/experiment
 
-# Level of external libraries
+# Level for external libraries
 root:
   level: WARN
   handlers: [console]
@@ -39,3 +60,6 @@ loggers:
   control_backend:
     level: LLM
     handlers: [ui]
+  experiment:  # This name must match config.logging_settings.experiment_logger_name
+    level: DEBUG
+    handlers: [ui, file]

README.md

@@ -27,6 +27,7 @@ This + part might differ based on what model you choose.
 copy the model name in the module loaded and replace local_llm_modelL. In settings.
 
 
+
 ## Running
 To run the project (development server), execute the following command (while inside the root repository):
 
@@ -34,6 +35,14 @@ To run the project (development server), execute the following command (while in
 uv run fastapi dev src/control_backend/main.py
 ```
 
+### Environment Variables
+
+You can use environment variables to change settings. Make a copy of the [`.env.example`](.env.example) file, name it `.env` and put it in the root directory. The file itself describes how to do the configuration.
+
+For an exhaustive list of environment options, see the `control_backend.core.config` module in the docs.
+
+
+
 ## Testing
 Testing happens automatically when opening a merge request to any branch. If you want to manually run the test suite, you can do so by running the following for unit tests:
 

pyproject.toml

@@ -7,6 +7,7 @@ requires-python = ">=3.13"
 dependencies = [
   "agentspeak>=0.2.2",
   "colorlog>=6.10.1",
+  "deepface>=0.0.96",
   "fastapi[all]>=0.115.6",
   "mlx-whisper>=0.4.3 ; sys_platform == 'darwin'",
   "numpy>=2.3.3",
@@ -21,6 +22,7 @@ dependencies = [
   "silero-vad>=6.0.0",
   "sphinx>=7.3.7",
   "sphinx-rtd-theme>=3.0.2",
+  "tf-keras>=2.20.1",
   "torch>=2.8.0",
   "uvicorn>=0.37.0",
 ]
@@ -48,6 +50,7 @@ test = [
   "pytest-asyncio>=1.2.0",
   "pytest-cov>=7.0.0",
   "pytest-mock>=3.15.1",
+  "python-slugify>=8.0.4",
   "pyyaml>=6.0.3",
   "pyzmq>=27.1.0",
   "soundfile>=0.13.1",

src/control_backend/agents/__init__.py

@@ -1 +1,5 @@
+"""
+This package contains all agent implementations for the PepperPlus Control Backend.
+"""
+
 from .base import BaseAgent as BaseAgent

src/control_backend/agents/actuation/__init__.py

@@ -1,2 +1,6 @@
+"""
+Agents responsible for controlling the robot's physical actions, such as speech and gestures.
+"""
+
 from .robot_gesture_agent import RobotGestureAgent as RobotGestureAgent
 from .robot_speech_agent import RobotSpeechAgent as RobotSpeechAgent

src/control_backend/agents/actuation/robot_gesture_agent.py

@@ -33,7 +33,7 @@ class RobotGestureAgent(BaseAgent):
     def __init__(
         self,
         name: str,
-        address=settings.zmq_settings.ri_command_address,
+        address: str,
         bind=False,
         gesture_data=None,
         single_gesture_data=None,

src/control_backend/agents/base.py

@@ -1,9 +1,10 @@
 import logging
+from abc import ABC
 
 from control_backend.core.agent_system import BaseAgent as CoreBaseAgent
 
 
-class BaseAgent(CoreBaseAgent):
+class BaseAgent(CoreBaseAgent, ABC):
     """
     The primary base class for all implementation agents.
 

src/control_backend/agents/bdi/__init__.py

@@ -1,8 +1,10 @@
+"""
+Agents and utilities for the BDI (Belief-Desire-Intention) reasoning system,
+implementing AgentSpeak(L) logic.
+"""
+
 from control_backend.agents.bdi.bdi_core_agent import BDICoreAgent as BDICoreAgent
 
-from .belief_collector_agent import (
-    BDIBeliefCollectorAgent as BDIBeliefCollectorAgent,
-)
 from .text_belief_extractor_agent import (
     TextBeliefExtractorAgent as TextBeliefExtractorAgent,
 )

src/control_backend/agents/bdi/agentspeak_ast.py

@@ -77,10 +77,10 @@ class AstTerm(AstExpression, ABC):
         return AstBinaryOp(self, BinaryOperatorType.NOT_EQUALS, _coalesce_expr(other))
 
 
-@dataclass
+@dataclass(eq=False)
 class AstAtom(AstTerm):
     """
-    Grounded expression in all lowercase.
+    Represents a grounded atom in AgentSpeak (e.g., lowercase constants).
     """
 
     value: str
@@ -89,10 +89,10 @@ class AstAtom(AstTerm):
         return self.value.lower()
 
 
-@dataclass
+@dataclass(eq=False)
 class AstVar(AstTerm):
     """
-    Ungrounded variable expression. First letter capitalized.
+    Represents an ungrounded variable in AgentSpeak (e.g., capitalized names).
     """
 
     name: str
@@ -101,24 +101,36 @@ class AstVar(AstTerm):
         return self.name.capitalize()
 
 
-@dataclass
+@dataclass(eq=False)
 class AstNumber(AstTerm):
+    """
+    Represents a numeric constant in AgentSpeak.
+    """
+
     value: int | float
 
     def _to_agentspeak(self) -> str:
         return str(self.value)
 
 
-@dataclass
+@dataclass(eq=False)
 class AstString(AstTerm):
+    """
+    Represents a string literal in AgentSpeak.
+    """
+
     value: str
 
     def _to_agentspeak(self) -> str:
         return f'"{self.value}"'
 
 
-@dataclass
+@dataclass(eq=False)
 class AstLiteral(AstTerm):
+    """
+    Represents a literal (functor and terms) in AgentSpeak.
+    """
+
     functor: str
     terms: list[AstTerm] = field(default_factory=list)
 
@@ -142,6 +154,10 @@ class BinaryOperatorType(StrEnum):
 
 @dataclass
 class AstBinaryOp(AstExpression):
+    """
+    Represents a binary logical or relational operation in AgentSpeak.
+    """
+
     left: AstExpression
     operator: BinaryOperatorType
     right: AstExpression
@@ -167,6 +183,10 @@ class AstBinaryOp(AstExpression):
 
 @dataclass
 class AstLogicalExpression(AstExpression):
+    """
+    Represents a logical expression, potentially negated, in AgentSpeak.
+    """
+
     expression: AstExpression
     negated: bool = False
 
@@ -208,6 +228,10 @@ class AstStatement(AstNode):
 
 @dataclass
 class AstRule(AstNode):
+    """
+    Represents an inference rule in AgentSpeak. If there is no condition, it always holds.
+    """
+
     result: AstExpression
     condition: AstExpression | None = None
 
@@ -231,6 +255,10 @@ class TriggerType(StrEnum):
 
 @dataclass
 class AstPlan(AstNode):
+    """
+    Represents a plan in AgentSpeak, consisting of a trigger, context, and body.
+    """
+
     type: TriggerType
     trigger_literal: AstExpression
     context: list[AstExpression]
@@ -260,6 +288,10 @@ class AstPlan(AstNode):
 
 @dataclass
 class AstProgram(AstNode):
+    """
+    Represents a full AgentSpeak program, consisting of rules and plans.
+    """
+
     rules: list[AstRule] = field(default_factory=list)
     plans: list[AstPlan] = field(default_factory=list)
 

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -3,9 +3,11 @@ from functools import singledispatchmethod
 from slugify import slugify
 
 from control_backend.agents.bdi.agentspeak_ast import (
+    AstAtom,
     AstBinaryOp,
     AstExpression,
     AstLiteral,
+    AstNumber,
     AstPlan,
     AstProgram,
     AstRule,
@@ -17,8 +19,10 @@ from control_backend.agents.bdi.agentspeak_ast import (
     TriggerType,
 )
 from control_backend.schemas.program import (
+    BaseGoal,
     BasicNorm,
     ConditionalNorm,
+    EmotionBelief,
     GestureAction,
     Goal,
     InferredBelief,
@@ -37,12 +41,32 @@ from control_backend.schemas.program import (
 
 
 class AgentSpeakGenerator:
+    """
+    Generator class that translates a high-level :class:`~control_backend.schemas.program.Program`
+    into AgentSpeak(L) source code.
+
+    It handles the conversion of phases, norms, goals, and triggers into AgentSpeak rules and plans,
+    ensuring the robot follows the defined behavioral logic.
+    """
+
     _asp: AstProgram
 
     def generate(self, program: Program) -> str:
+        """
+        Translates a Program object into an AgentSpeak source string.
+
+        :param program: The behavior program to translate.
+        :return: The generated AgentSpeak code as a string.
+        """
         self._asp = AstProgram()
 
-        self._asp.rules.append(AstRule(self._astify(program.phases[0])))
+        if program.phases:
+            self._asp.rules.append(AstRule(self._astify(program.phases[0])))
+        else:
+            self._asp.rules.append(AstRule(AstLiteral("phase", [AstString("end")])))
+
+        self._asp.rules.append(AstRule(AstLiteral("!notify_cycle")))
+
         self._add_keyword_inference()
         self._add_default_plans()
 
@@ -70,6 +94,7 @@ class AgentSpeakGenerator:
         self._add_reply_with_goal_plan()
         self._add_say_plan()
         self._add_reply_plan()
+        self._add_notify_cycle_plan()
 
     def _add_reply_with_goal_plan(self):
         self._asp.plans.append(
@@ -132,6 +157,29 @@ class AgentSpeakGenerator:
             )
         )
 
+    def _add_notify_cycle_plan(self):
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("notify_cycle"),
+                [],
+                [
+                    AstStatement(
+                        StatementType.DO_ACTION,
+                        AstLiteral(
+                            "findall",
+                            [AstVar("Norm"), AstLiteral("norm", [AstVar("Norm")]), AstVar("Norms")],
+                        ),
+                    ),
+                    AstStatement(
+                        StatementType.DO_ACTION, AstLiteral("notify_norms", [AstVar("Norms")])
+                    ),
+                    AstStatement(StatementType.DO_ACTION, AstLiteral("wait", [AstNumber(100)])),
+                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("notify_cycle")),
+                ],
+            )
+        )
+
     def _process_phases(self, phases: list[Phase]) -> None:
         for curr_phase, next_phase in zip([None] + phases, phases + [None], strict=True):
             if curr_phase:
@@ -145,7 +193,12 @@ class AgentSpeakGenerator:
                 type=TriggerType.ADDED_BELIEF,
                 trigger_literal=AstLiteral("user_said", [AstVar("Message")]),
                 context=[AstLiteral("phase", [AstString("end")])],
-                body=[AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("reply"))],
+                body=[
+                    AstStatement(
+                        StatementType.DO_ACTION, AstLiteral("notify_user_said", [AstVar("Message")])
+                    ),
+                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("reply")),
+                ],
             )
         )
 
@@ -157,7 +210,7 @@ class AgentSpeakGenerator:
 
         previous_goal = None
         for goal in phase.goals:
-            self._process_goal(goal, phase, previous_goal)
+            self._process_goal(goal, phase, previous_goal, main_goal=True)
             previous_goal = goal
 
         for trigger in phase.triggers:
@@ -171,29 +224,57 @@ class AgentSpeakGenerator:
             self._astify(to_phase) if to_phase else AstLiteral("phase", [AstString("end")])
         )
 
-        context = [from_phase_ast, ~AstLiteral("responded_this_turn")]
-        if from_phase and from_phase.goals:
-            context.append(self._astify(from_phase.goals[-1], achieved=True))
+        check_context = [from_phase_ast]
+        if from_phase:
+            for goal in from_phase.goals:
+                check_context.append(self._astify(goal, achieved=True))
+
+        force_context = [from_phase_ast]
 
         body = [
+            AstStatement(
+                StatementType.DO_ACTION,
+                AstLiteral(
+                    "notify_transition_phase",
+                    [
+                        AstString(str(from_phase.id)),
+                        AstString(str(to_phase.id) if to_phase else "end"),
+                    ],
+                ),
+            ),
             AstStatement(StatementType.REMOVE_BELIEF, from_phase_ast),
             AstStatement(StatementType.ADD_BELIEF, to_phase_ast),
         ]
 
-        if from_phase:
-            body.extend(
-                [
-                    AstStatement(
-                        StatementType.TEST_GOAL, AstLiteral("user_said", [AstVar("Message")])
-                    ),
-                    AstStatement(
-                        StatementType.REPLACE_BELIEF, AstLiteral("user_said", [AstVar("Message")])
-                    ),
-                ]
-            )
+        # if from_phase:
+        #     body.extend(
+        #         [
+        #             AstStatement(
+        #                 StatementType.TEST_GOAL, AstLiteral("user_said", [AstVar("Message")])
+        #             ),
+        #             AstStatement(
+        #                 StatementType.REPLACE_BELIEF, AstLiteral("user_said", [AstVar("Message")])
+        #             ),
+        #         ]
+        #     )
 
+        # Check
         self._asp.plans.append(
-            AstPlan(TriggerType.ADDED_GOAL, AstLiteral("transition_phase"), context, body)
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("transition_phase"),
+                check_context,
+                [
+                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("force_transition_phase")),
+                ],
+            )
+        )
+
+        # Force
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL, AstLiteral("force_transition_phase"), force_context, body
+            )
         )
 
     def _process_norm(self, norm: Norm, phase: Phase) -> None:
@@ -201,7 +282,11 @@ class AgentSpeakGenerator:
 
         match norm:
             case ConditionalNorm(condition=cond):
-                rule = AstRule(self._astify(norm), self._astify(phase) & self._astify(cond))
+                rule = AstRule(
+                    self._astify(norm),
+                    self._astify(phase) & self._astify(cond)
+                    | AstAtom(f"force_{self.slugify(norm)}"),
+                )
             case BasicNorm():
                 rule = AstRule(self._astify(norm), self._astify(phase))
 
@@ -213,6 +298,11 @@ class AgentSpeakGenerator:
     def _add_default_loop(self, phase: Phase) -> None:
         actions = []
 
+        actions.append(
+            AstStatement(
+                StatementType.DO_ACTION, AstLiteral("notify_user_said", [AstVar("Message")])
+            )
+        )
         actions.append(AstStatement(StatementType.REMOVE_BELIEF, AstLiteral("responded_this_turn")))
         actions.append(AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("check_triggers")))
 
@@ -236,6 +326,7 @@ class AgentSpeakGenerator:
         phase: Phase,
         previous_goal: Goal | None = None,
         continues_response: bool = False,
+        main_goal: bool = False,
     ) -> None:
         context: list[AstExpression] = [self._astify(phase)]
         context.append(~self._astify(goal, achieved=True))
@@ -245,6 +336,13 @@ class AgentSpeakGenerator:
             context.append(~AstLiteral("responded_this_turn"))
 
         body = []
+        if main_goal:  # UI only needs to know about the main goals
+            body.append(
+                AstStatement(
+                    StatementType.DO_ACTION,
+                    AstLiteral("notify_goal_start", [AstString(self.slugify(goal))]),
+                )
+            )
 
         subgoals = []
         for step in goal.plan.steps:
@@ -283,12 +381,28 @@ class AgentSpeakGenerator:
         body = []
         subgoals = []
 
+        body.append(
+            AstStatement(
+                StatementType.DO_ACTION,
+                AstLiteral("notify_trigger_start", [AstString(self.slugify(trigger))]),
+            )
+        )
         for step in trigger.plan.steps:
             body.append(self._step_to_statement(step))
             if isinstance(step, Goal):
                 step.can_fail = False  # triggers are continuous sequence
                 subgoals.append(step)
 
+        # Arbitrary wait for UI to display nicely
+        body.append(AstStatement(StatementType.DO_ACTION, AstLiteral("wait", [AstNumber(2000)])))
+
+        body.append(
+            AstStatement(
+                StatementType.DO_ACTION,
+                AstLiteral("notify_trigger_end", [AstString(self.slugify(trigger))]),
+            )
+        )
+
         self._asp.plans.append(
             AstPlan(
                 TriggerType.ADDED_GOAL,
@@ -298,6 +412,9 @@ class AgentSpeakGenerator:
             )
         )
 
+        # Force trigger (from UI)
+        self._asp.plans.append(AstPlan(TriggerType.ADDED_GOAL, self._astify(trigger), [], body))
+
         for subgoal in subgoals:
             self._process_goal(subgoal, phase, continues_response=True)
 
@@ -322,6 +439,16 @@ class AgentSpeakGenerator:
             )
         )
 
+        # Force phase transition fallback
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("force_transition_phase"),
+                [],
+                [AstStatement(StatementType.EMPTY, AstLiteral("true"))],
+            )
+        )
+
     @singledispatchmethod
     def _astify(self, element: ProgramElement) -> AstExpression:
         raise NotImplementedError(f"Cannot convert element {element} to an AgentSpeak expression.")
@@ -332,13 +459,11 @@ class AgentSpeakGenerator:
 
     @_astify.register
     def _(self, sb: SemanticBelief) -> AstExpression:
-        return AstLiteral(self.get_semantic_belief_slug(sb))
-
-    @staticmethod
-    def get_semantic_belief_slug(sb: SemanticBelief) -> str:
-        # If you need a method like this for other types, make a public slugify singledispatch for
-        #  all types.
-        return f"semantic_{AgentSpeakGenerator._slugify_str(sb.name)}"
+        return AstLiteral(self.slugify(sb))
+    
+    @_astify.register
+    def _(self, eb: EmotionBelief) -> AstExpression:
+        return AstLiteral("emotion_detected", [AstAtom(eb.emotion)])
 
     @_astify.register
     def _(self, ib: InferredBelief) -> AstExpression:
@@ -383,6 +508,11 @@ class AgentSpeakGenerator:
     def slugify(element: ProgramElement) -> str:
         raise NotImplementedError(f"Cannot convert element {element} to a slug.")
 
+    @slugify.register
+    @staticmethod
+    def _(n: Norm) -> str:
+        return f"norm_{AgentSpeakGenerator._slugify_str(n.norm)}"
+
     @slugify.register
     @staticmethod
     def _(sb: SemanticBelief) -> str:
@@ -390,7 +520,7 @@ class AgentSpeakGenerator:
 
     @slugify.register
     @staticmethod
-    def _(g: Goal) -> str:
+    def _(g: BaseGoal) -> str:
         return AgentSpeakGenerator._slugify_str(g.name)
 
     @slugify.register

src/control_backend/agents/bdi/asl_ast.py

@@ -1,203 +0,0 @@
-import typing
-from dataclasses import dataclass, field
-
-# --- Types ---
-
-
-@dataclass
-class BeliefLiteral:
-    """
-    Represents a literal or atom.
-    Example: phase(1), user_said("hello"), ~started
-    """
-
-    functor: str
-    args: list[str] = field(default_factory=list)
-    negated: bool = False
-
-    def __str__(self):
-        # In ASL, 'not' is usually for closed-world assumption (prolog style),
-        # '~' is for explicit negation in beliefs.
-        # For simplicity in behavior trees, we often use 'not' for conditions.
-        prefix = "not " if self.negated else ""
-        if not self.args:
-            return f"{prefix}{self.functor}"
-
-        # Clean args to ensure strings are quoted if they look like strings,
-        # but usually the converter handles the quoting of string literals.
-        args_str = ", ".join(self.args)
-        return f"{prefix}{self.functor}({args_str})"
-
-
-@dataclass
-class GoalLiteral:
-    name: str
-
-    def __str__(self):
-        return f"!{self.name}"
-
-
-@dataclass
-class ActionLiteral:
-    """
-    Represents a step in a plan body.
-    Example: .say("Hello") or !achieve_goal
-    """
-
-    code: str
-
-    def __str__(self):
-        return self.code
-
-
-@dataclass
-class BinaryOp:
-    """
-    Represents logical operations.
-    Example: (A & B) | C
-    """
-
-    left: "Expression | str"
-    operator: typing.Literal["&", "|"]
-    right: "Expression | str"
-
-    def __str__(self):
-        l_str = str(self.left)
-        r_str = str(self.right)
-
-        if isinstance(self.left, BinaryOp):
-            l_str = f"({l_str})"
-        if isinstance(self.right, BinaryOp):
-            r_str = f"({r_str})"
-
-        return f"{l_str} {self.operator} {r_str}"
-
-
-Literal = BeliefLiteral | GoalLiteral | ActionLiteral
-Expression = Literal | BinaryOp | str
-
-
-@dataclass
-class Rule:
-    """
-    Represents an inference rule.
-    Example: head :- body.
-    """
-
-    head: Expression
-    body: Expression | None = None
-
-    def __str__(self):
-        if not self.body:
-            return f"{self.head}."
-        return f"{self.head} :- {self.body}."
-
-
-@dataclass
-class PersistentRule:
-    """
-    Represents an inference rule, where the inferred belief is persistent when formed.
-    """
-
-    head: Expression
-    body: Expression
-
-    def __str__(self):
-        if not self.body:
-            raise Exception("Rule without body should not be persistent.")
-
-        lines = []
-
-        if isinstance(self.body, BinaryOp):
-            lines.append(f"+{self.body.left}")
-            if self.body.operator == "&":
-                lines.append(f"  :   {self.body.right}")
-            lines.append(f"  <-  +{self.head}.")
-            if self.body.operator == "|":
-                lines.append(f"+{self.body.right}")
-                lines.append(f"  <-  +{self.head}.")
-
-        return "\n".join(lines)
-
-
-@dataclass
-class Plan:
-    """
-    Represents a plan.
-    Syntax: +trigger : context <- body.
-    """
-
-    trigger: BeliefLiteral | GoalLiteral
-    context: list[Expression] = field(default_factory=list)
-    body: list[ActionLiteral] = field(default_factory=list)
-
-    def __str__(self):
-        # Indentation settings
-        INDENT = "      "
-        ARROW = "\n  <-  "
-        COLON = "\n  :   "
-
-        # Build Header
-        header = f"+{self.trigger}"
-        if self.context:
-            ctx_str = f" &\n{INDENT}".join(str(c) for c in self.context)
-            header += f"{COLON}{ctx_str}"
-
-        # Case 1: Empty body
-        if not self.body:
-            return f"{header}."
-
-        # Case 2: Short body (optional optimization, keeping it uniform usually better)
-        header += ARROW
-
-        lines = []
-        # We start the first action on the same line or next line.
-        # Let's put it on the next line for readability if there are multiple.
-
-        if len(self.body) == 1:
-            return f"{header}{self.body[0]}."
-
-        # First item
-        lines.append(f"{header}{self.body[0]};")
-        # Middle items
-        for item in self.body[1:-1]:
-            lines.append(f"{INDENT}{item};")
-        # Last item
-        lines.append(f"{INDENT}{self.body[-1]}.")
-
-        return "\n".join(lines)
-
-
-@dataclass
-class AgentSpeakFile:
-    """
-    Root element representing the entire generated file.
-    """
-
-    initial_beliefs: list[Rule] = field(default_factory=list)
-    inference_rules: list[Rule | PersistentRule] = field(default_factory=list)
-    plans: list[Plan] = field(default_factory=list)
-
-    def __str__(self):
-        sections = []
-
-        if self.initial_beliefs:
-            sections.append("// --- Initial Beliefs & Facts ---")
-            sections.extend(str(rule) for rule in self.initial_beliefs)
-            sections.append("")
-
-        if self.inference_rules:
-            sections.append("// --- Inference Rules ---")
-            sections.extend(str(rule) for rule in self.inference_rules if isinstance(rule, Rule))
-            sections.append("")
-            sections.extend(
-                str(rule) for rule in self.inference_rules if isinstance(rule, PersistentRule)
-            )
-            sections.append("")
-
-        if self.plans:
-            sections.append("// --- Plans ---")
-            # Separate plans by a newline for readability
-            sections.extend(str(plan) + "\n" for plan in self.plans)
-
-        return "\n".join(sections)

src/control_backend/agents/bdi/asl_gen.py

@@ -1,425 +0,0 @@
-import asyncio
-import time
-from functools import singledispatchmethod
-
-from slugify import slugify
-
-from control_backend.agents.bdi import BDICoreAgent
-from control_backend.agents.bdi.asl_ast import (
-    ActionLiteral,
-    AgentSpeakFile,
-    BeliefLiteral,
-    BinaryOp,
-    Expression,
-    GoalLiteral,
-    PersistentRule,
-    Plan,
-    Rule,
-)
-from control_backend.agents.bdi.bdi_program_manager import test_program
-from control_backend.schemas.program import (
-    BasicBelief,
-    Belief,
-    ConditionalNorm,
-    GestureAction,
-    Goal,
-    InferredBelief,
-    KeywordBelief,
-    LLMAction,
-    LogicalOperator,
-    Phase,
-    Program,
-    ProgramElement,
-    SemanticBelief,
-    SpeechAction,
-)
-
-
-async def do_things():
-    res = input("Wanna generate")
-    if res == "y":
-        program = AgentSpeakGenerator().generate(test_program)
-        filename = f"{int(time.time())}.asl"
-        with open(filename, "w") as f:
-            f.write(program)
-    else:
-        # filename = "0test.asl"
-        filename = "1766062491.asl"
-    bdi_agent = BDICoreAgent("BDICoreAgent", filename)
-    flag = asyncio.Event()
-    await bdi_agent.start()
-    await flag.wait()
-
-
-def do_other_things():
-    print(AgentSpeakGenerator().generate(test_program))
-
-
-class AgentSpeakGenerator:
-    """
-    Converts a Pydantic Program behavior model into an AgentSpeak(L) AST,
-    then renders it to a string.
-    """
-
-    def generate(self, program: Program) -> str:
-        asl = AgentSpeakFile()
-
-        self._generate_startup(program, asl)
-
-        for i, phase in enumerate(program.phases):
-            next_phase = program.phases[i + 1] if i < len(program.phases) - 1 else None
-
-            self._generate_phase_flow(phase, next_phase, asl)
-
-            self._generate_norms(phase, asl)
-
-            self._generate_goals(phase, asl)
-
-            self._generate_triggers(phase, asl)
-
-        self._generate_fallbacks(program, asl)
-
-        return str(asl)
-
-    # --- Section: Startup & Phase Management ---
-
-    def _generate_startup(self, program: Program, asl: AgentSpeakFile):
-        if not program.phases:
-            return
-
-        # Initial belief: phase(start).
-        asl.initial_beliefs.append(Rule(head=BeliefLiteral("phase", ['"start"'])))
-
-        # Startup plan: +started : phase(start) <- -phase(start); +phase(first_id).
-        asl.plans.append(
-            Plan(
-                trigger=BeliefLiteral("started"),
-                context=[BeliefLiteral("phase", ['"start"'])],
-                body=[
-                    ActionLiteral('-phase("start")'),
-                    ActionLiteral(f'+phase("{program.phases[0].id}")'),
-                ],
-            )
-        )
-
-        # Initial plans:
-        asl.plans.append(
-            Plan(
-                trigger=GoalLiteral("generate_response_with_goal(Goal)"),
-                context=[BeliefLiteral("user_said", ["Message"])],
-                body=[
-                    ActionLiteral("+responded_this_turn"),
-                    ActionLiteral(".findall(Norm, norm(Norm), Norms)"),
-                    ActionLiteral(".reply_with_goal(Message, Norms, Goal)"),
-                ],
-            )
-        )
-
-    def _generate_phase_flow(self, phase: Phase, next_phase: Phase | None, asl: AgentSpeakFile):
-        """Generates the main loop listener and the transition logic for this phase."""
-
-        # +user_said(Message) : phase(ID) <- !goal1; !goal2; !transition_phase.
-        goal_actions = [ActionLiteral("-responded_this_turn")]
-        goal_actions += [
-            ActionLiteral(f"!check_{self._slugify_str(keyword)}")
-            for keyword in self._get_keyword_conditionals(phase)
-        ]
-        goal_actions += [ActionLiteral(f"!{self._slugify(g)}") for g in phase.goals]
-        goal_actions.append(ActionLiteral("!transition_phase"))
-
-        asl.plans.append(
-            Plan(
-                trigger=BeliefLiteral("user_said", ["Message"]),
-                context=[BeliefLiteral("phase", [f'"{phase.id}"'])],
-                body=goal_actions,
-            )
-        )
-
-        # +!transition_phase : phase(ID) <- -phase(ID); +(NEXT_ID).
-        next_id = str(next_phase.id) if next_phase else "end"
-
-        transition_context = [BeliefLiteral("phase", [f'"{phase.id}"'])]
-        if phase.goals:
-            transition_context.append(BeliefLiteral(f"achieved_{self._slugify(phase.goals[-1])}"))
-
-        asl.plans.append(
-            Plan(
-                trigger=GoalLiteral("transition_phase"),
-                context=transition_context,
-                body=[
-                    ActionLiteral(f'-phase("{phase.id}")'),
-                    ActionLiteral(f'+phase("{next_id}")'),
-                    ActionLiteral("user_said(Anything)"),
-                    ActionLiteral("-+user_said(Anything)"),
-                ],
-            )
-        )
-
-    def _get_keyword_conditionals(self, phase: Phase) -> list[str]:
-        res = []
-        for belief in self._extract_basic_beliefs_from_phase(phase):
-            if isinstance(belief, KeywordBelief):
-                res.append(belief.keyword)
-
-        return res
-
-    # --- Section: Norms & Beliefs ---
-
-    def _generate_norms(self, phase: Phase, asl: AgentSpeakFile):
-        for norm in phase.norms:
-            norm_slug = f'"{norm.norm}"'
-            head = BeliefLiteral("norm", [norm_slug])
-
-            # Base context is the phase
-            phase_lit = BeliefLiteral("phase", [f'"{phase.id}"'])
-
-            if isinstance(norm, ConditionalNorm):
-                self._ensure_belief_inference(norm.condition, asl)
-
-                condition_expr = self._belief_to_expr(norm.condition)
-                body = BinaryOp(phase_lit, "&", condition_expr)
-            else:
-                body = phase_lit
-
-            asl.inference_rules.append(Rule(head=head, body=body))
-
-    def _ensure_belief_inference(self, belief: Belief, asl: AgentSpeakFile):
-        """
-        Recursively adds rules to infer beliefs.
-        Checks strictly to avoid duplicates if necessary,
-        though ASL engines often handle redefinition or we can use a set to track processed IDs.
-        """
-        if isinstance(belief, KeywordBelief):
-            pass
-            # # Rule: keyword_said("word") :- user_said(M) & .substring("word", M, P) & P >= 0.
-            # kwd_slug = f'"{belief.keyword}"'
-            # head = BeliefLiteral("keyword_said", [kwd_slug])
-            #
-            # # Avoid duplicates
-            # if any(str(r.head) == str(head) for r in asl.inference_rules):
-            #     return
-            #
-            # body = BinaryOp(
-            #     BeliefLiteral("user_said", ["Message"]),
-            #     "&",
-            #     BinaryOp(f".substring({kwd_slug}, Message, Pos)", "&", "Pos >= 0"),
-            # )
-            #
-            # asl.inference_rules.append(Rule(head=head, body=body))
-
-        elif isinstance(belief, InferredBelief):
-            self._ensure_belief_inference(belief.left, asl)
-            self._ensure_belief_inference(belief.right, asl)
-
-            slug = self._slugify(belief)
-            head = BeliefLiteral(slug)
-
-            if any(str(r.head) == str(head) for r in asl.inference_rules):
-                return
-
-            op_char = "&" if belief.operator == LogicalOperator.AND else "|"
-            body = BinaryOp(
-                self._belief_to_expr(belief.left), op_char, self._belief_to_expr(belief.right)
-            )
-            asl.inference_rules.append(PersistentRule(head=head, body=body))
-
-    def _belief_to_expr(self, belief: Belief) -> Expression:
-        if isinstance(belief, KeywordBelief):
-            return BeliefLiteral("keyword_said", [f'"{belief.keyword}"'])
-        else:
-            return BeliefLiteral(self._slugify(belief))
-
-    # --- Section: Goals ---
-
-    def _generate_goals(self, phase: Phase, asl: AgentSpeakFile):
-        previous_goal: Goal | None = None
-        for goal in phase.goals:
-            self._generate_goal_plan_recursive(goal, str(phase.id), previous_goal, asl)
-            previous_goal = goal
-
-    def _generate_goal_plan_recursive(
-        self,
-        goal: Goal,
-        phase_id: str,
-        previous_goal: Goal | None,
-        asl: AgentSpeakFile,
-        responded_needed: bool = True,
-        can_fail: bool = True,
-    ):
-        goal_slug = self._slugify(goal)
-
-        # phase(ID) & not responded_this_turn & not achieved_goal
-        context = [
-            BeliefLiteral("phase", [f'"{phase_id}"']),
-        ]
-
-        if responded_needed:
-            context.append(BeliefLiteral("responded_this_turn", negated=True))
-        if can_fail:
-            context.append(BeliefLiteral(f"achieved_{goal_slug}", negated=True))
-
-        if previous_goal:
-            prev_slug = self._slugify(previous_goal)
-            context.append(BeliefLiteral(f"achieved_{prev_slug}"))
-
-        body_actions = []
-        sub_goals_to_process = []
-
-        for step in goal.plan.steps:
-            if isinstance(step, Goal):
-                sub_slug = self._slugify(step)
-                body_actions.append(ActionLiteral(f"!{sub_slug}"))
-                sub_goals_to_process.append(step)
-            elif isinstance(step, SpeechAction):
-                body_actions.append(ActionLiteral(f'.say("{step.text}")'))
-            elif isinstance(step, GestureAction):
-                body_actions.append(ActionLiteral(f'.gesture("{step.gesture}")'))
-            elif isinstance(step, LLMAction):
-                body_actions.append(ActionLiteral(f'!generate_response_with_goal("{step.goal}")'))
-
-        # Mark achievement
-        if not goal.can_fail:
-            body_actions.append(ActionLiteral(f"+achieved_{goal_slug}"))
-
-        asl.plans.append(Plan(trigger=GoalLiteral(goal_slug), context=context, body=body_actions))
-        asl.plans.append(
-            Plan(trigger=GoalLiteral(goal_slug), context=[], body=[ActionLiteral("true")])
-        )
-
-        prev_sub = None
-        for sub_goal in sub_goals_to_process:
-            self._generate_goal_plan_recursive(sub_goal, phase_id, prev_sub, asl)
-            prev_sub = sub_goal
-
-    # --- Section: Triggers ---
-
-    def _generate_triggers(self, phase: Phase, asl: AgentSpeakFile):
-        for keyword in self._get_keyword_conditionals(phase):
-            asl.plans.append(
-                Plan(
-                    trigger=GoalLiteral(f"check_{self._slugify_str(keyword)}"),
-                    context=[
-                        ActionLiteral(
-                            f'user_said(Message) & .substring("{keyword}", Message, Pos) & Pos >= 0'
-                        )
-                    ],
-                    body=[
-                        ActionLiteral(f'+keyword_said("{keyword}")'),
-                        ActionLiteral(f'-keyword_said("{keyword}")'),
-                    ],
-                )
-            )
-            asl.plans.append(
-                Plan(
-                    trigger=GoalLiteral(f"check_{self._slugify_str(keyword)}"),
-                    body=[ActionLiteral("true")],
-                )
-            )
-
-        for trigger in phase.triggers:
-            self._ensure_belief_inference(trigger.condition, asl)
-
-            trigger_belief_slug = self._belief_to_expr(trigger.condition)
-
-            body_actions = []
-            sub_goals = []
-
-            for step in trigger.plan.steps:
-                if isinstance(step, Goal):
-                    sub_slug = self._slugify(step)
-                    body_actions.append(ActionLiteral(f"!{sub_slug}"))
-                    sub_goals.append(step)
-                elif isinstance(step, SpeechAction):
-                    body_actions.append(ActionLiteral(f'.say("{step.text}")'))
-                elif isinstance(step, GestureAction):
-                    body_actions.append(
-                        ActionLiteral(f'.gesture("{step.gesture.type}", "{step.gesture.name}")')
-                    )
-                elif isinstance(step, LLMAction):
-                    body_actions.append(
-                        ActionLiteral(f'!generate_response_with_goal("{step.goal}")')
-                    )
-
-            asl.plans.append(
-                Plan(
-                    trigger=BeliefLiteral(trigger_belief_slug),
-                    context=[BeliefLiteral("phase", [f'"{phase.id}"'])],
-                    body=body_actions,
-                )
-            )
-
-            # Recurse for triggered goals
-            prev_sub = None
-            for sub_goal in sub_goals:
-                self._generate_goal_plan_recursive(
-                    sub_goal, str(phase.id), prev_sub, asl, False, False
-                )
-                prev_sub = sub_goal
-
-    # --- Section: Fallbacks ---
-
-    def _generate_fallbacks(self, program: Program, asl: AgentSpeakFile):
-        asl.plans.append(
-            Plan(trigger=GoalLiteral("transition_phase"), context=[], body=[ActionLiteral("true")])
-        )
-
-    # --- Helpers ---
-
-    @singledispatchmethod
-    def _slugify(self, element: ProgramElement) -> str:
-        if element.name:
-            raise NotImplementedError("Cannot slugify this element.")
-        return self._slugify_str(element.name)
-
-    @_slugify.register
-    def _(self, goal: Goal) -> str:
-        if goal.name:
-            return self._slugify_str(goal.name)
-        return f"goal_{goal.id.hex}"
-
-    @_slugify.register
-    def _(self, kwb: KeywordBelief) -> str:
-        return f"keyword_said({kwb.keyword})"
-
-    @_slugify.register
-    def _(self, sb: SemanticBelief) -> str:
-        return self._slugify_str(sb.description)
-
-    @_slugify.register
-    def _(self, ib: InferredBelief) -> str:
-        return self._slugify_str(ib.name)
-
-    def _slugify_str(self, text: str) -> str:
-        return slugify(text, separator="_", stopwords=["a", "an", "the", "we", "you", "I"])
-
-    def _extract_basic_beliefs_from_program(self, program: Program) -> list[BasicBelief]:
-        beliefs = []
-
-        for phase in program.phases:
-            beliefs.extend(self._extract_basic_beliefs_from_phase(phase))
-
-        return beliefs
-
-    def _extract_basic_beliefs_from_phase(self, phase: Phase) -> list[BasicBelief]:
-        beliefs = []
-
-        for norm in phase.norms:
-            if isinstance(norm, ConditionalNorm):
-                beliefs += self._extract_basic_beliefs_from_belief(norm.condition)
-
-        for trigger in phase.triggers:
-            beliefs += self._extract_basic_beliefs_from_belief(trigger.condition)
-
-        return beliefs
-
-    def _extract_basic_beliefs_from_belief(self, belief: Belief) -> list[BasicBelief]:
-        if isinstance(belief, InferredBelief):
-            return self._extract_basic_beliefs_from_belief(
-                belief.left
-            ) + self._extract_basic_beliefs_from_belief(belief.right)
-        return [belief]
-
-
-if __name__ == "__main__":
-    asyncio.run(do_things())
-    # do_other_things()y

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -1,5 +1,6 @@
 import asyncio
 import copy
+import json
 import time
 from collections.abc import Iterable
 
@@ -13,7 +14,7 @@ from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.llm_prompt_message import LLMPromptMessage
-from control_backend.schemas.ri_message import SpeechCommand
+from control_backend.schemas.ri_message import GestureCommand, RIEndpoint, SpeechCommand
 
 DELIMITER = ";\n"  # TODO: temporary until we support lists in AgentSpeak
 
@@ -100,14 +101,12 @@ class BDICoreAgent(BaseAgent):
             maybe_more_work = True
             while maybe_more_work:
                 maybe_more_work = False
-                self.logger.debug("Stepping BDI.")
                 if self.bdi_agent.step():
                     maybe_more_work = True
 
                 if not maybe_more_work:
                     deadline = self.bdi_agent.shortest_deadline()
                     if deadline:
-                        self.logger.debug("Sleeping until %s", deadline)
                         await asyncio.sleep(deadline - time.time())
                         maybe_more_work = True
                     else:
@@ -155,6 +154,20 @@ class BDICoreAgent(BaseAgent):
                     body=cmd.model_dump_json(),
                 )
                 await self.send(out_msg)
+            case settings.agent_settings.user_interrupt_name:
+                self.logger.debug("Received user interruption: %s", msg)
+
+                match msg.thread:
+                    case "force_phase_transition":
+                        self._set_goal("transition_phase")
+                    case "force_trigger":
+                        self._force_trigger(msg.body)
+                    case "force_norm":
+                        self._force_norm(msg.body)
+                    case "force_next_phase":
+                        self._force_next_phase()
+                    case _:
+                        self.logger.warning("Received unknown user interruption: %s", msg)
 
     def _apply_belief_changes(self, belief_changes: BeliefMessage):
         """
@@ -201,16 +214,35 @@ class BDICoreAgent(BaseAgent):
             agentspeak.runtime.Intention(),
         )
 
+        # Check for transitions
+        self.bdi_agent.call(
+            agentspeak.Trigger.addition,
+            agentspeak.GoalType.achievement,
+            agentspeak.Literal("transition_phase"),
+            agentspeak.runtime.Intention(),
+        )
+
+        # Check triggers
+        self.bdi_agent.call(
+            agentspeak.Trigger.addition,
+            agentspeak.GoalType.achievement,
+            agentspeak.Literal("check_triggers"),
+            agentspeak.runtime.Intention(),
+        )
+
         self._wake_bdi_loop.set()
 
         self.logger.debug(f"Added belief {self.format_belief_string(name, args)}")
 
-    def _remove_belief(self, name: str, args: Iterable[str]):
+    def _remove_belief(self, name: str, args: Iterable[str] | None):
         """
         Removes a specific belief (with arguments), if it exists.
         """
-        new_args = (agentspeak.Literal(arg) for arg in args)
-        term = agentspeak.Literal(name, new_args)
+        if args is None:
+            term = agentspeak.Literal(name)
+        else:
+            new_args = (agentspeak.Literal(arg) for arg in args)
+            term = agentspeak.Literal(name, new_args)
 
         result = self.bdi_agent.call(
             agentspeak.Trigger.removal,
@@ -250,6 +282,43 @@ class BDICoreAgent(BaseAgent):
 
         self.logger.debug(f"Removed {removed_count} beliefs.")
 
+    def _set_goal(self, name: str, args: Iterable[str] | None = None):
+        args = args or []
+
+        if args:
+            merged_args = DELIMITER.join(arg for arg in args)
+            new_args = (agentspeak.Literal(merged_args),)
+            term = agentspeak.Literal(name, new_args)
+        else:
+            term = agentspeak.Literal(name)
+
+        self.bdi_agent.call(
+            agentspeak.Trigger.addition,
+            agentspeak.GoalType.achievement,
+            term,
+            agentspeak.runtime.Intention(),
+        )
+
+        self._wake_bdi_loop.set()
+
+        self.logger.debug(f"Set goal !{self.format_belief_string(name, args)}.")
+
+    def _force_trigger(self, name: str):
+        self._set_goal(name)
+
+        self.logger.info("Manually forced trigger %s.", name)
+
+    # TODO: make this compatible for critical norms
+    def _force_norm(self, name: str):
+        self._add_belief(f"force_{name}")
+
+        self.logger.info("Manually forced norm %s.", name)
+
+    def _force_next_phase(self):
+        self._set_goal("force_transition_phase")
+
+        self.logger.info("Manually forced phase transition.")
+
     def _add_custom_actions(self) -> None:
         """
         Add any custom actions here. Inside `@self.actions.add()`, the first argument is
@@ -258,21 +327,18 @@ class BDICoreAgent(BaseAgent):
         """
 
         @self.actions.add(".reply", 2)
-        def _reply(agent: "BDICoreAgent", term, intention):
+        def _reply(agent, term, intention):
             """
             Let the LLM generate a response to a user's utterance with the current norms and goals.
             """
             message_text = agentspeak.grounded(term.args[0], intention.scope)
             norms = agentspeak.grounded(term.args[1], intention.scope)
 
-            self.logger.debug("Norms: %s", norms)
-            self.logger.debug("User text: %s", message_text)
-
             self.add_behavior(self._send_to_llm(str(message_text), str(norms), ""))
             yield
 
         @self.actions.add(".reply_with_goal", 3)
-        def _reply_with_goal(agent: "BDICoreAgent", term, intention):
+        def _reply_with_goal(agent, term, intention):
             """
             Let the LLM generate a response to a user's utterance with the current norms and a
             specific goal.
@@ -280,18 +346,24 @@ class BDICoreAgent(BaseAgent):
             message_text = agentspeak.grounded(term.args[0], intention.scope)
             norms = agentspeak.grounded(term.args[1], intention.scope)
             goal = agentspeak.grounded(term.args[2], intention.scope)
-
-            self.logger.debug(
-                '"reply_with_goal" action called with message=%s, norms=%s, goal=%s',
-                message_text,
-                norms,
-                goal,
-            )
             self.add_behavior(self._send_to_llm(str(message_text), str(norms), str(goal)))
             yield
 
+        @self.actions.add(".notify_norms", 1)
+        def _notify_norms(agent, term, intention):
+            norms = agentspeak.grounded(term.args[0], intention.scope)
+
+            norm_update_message = InternalMessage(
+                to=settings.agent_settings.user_interrupt_name,
+                thread="active_norms_update",
+                body=str(norms),
+            )
+
+            self.add_behavior(self.send(norm_update_message, should_log=False))
+            yield
+
         @self.actions.add(".say", 1)
-        def _say(agent: "BDICoreAgent", term, intention):
+        def _say(agent, term, intention):
             """
             Make the robot say the given text instantly.
             """
@@ -305,12 +377,21 @@ class BDICoreAgent(BaseAgent):
                 sender=settings.agent_settings.bdi_core_name,
                 body=speech_command.model_dump_json(),
             )
-            # TODO: add to conversation history
+
             self.add_behavior(self.send(speech_message))
+
+            chat_history_message = InternalMessage(
+                to=settings.agent_settings.llm_name,
+                thread="assistant_message",
+                body=str(message_text),
+            )
+
+            self.add_behavior(self.send(chat_history_message))
+
             yield
 
         @self.actions.add(".gesture", 2)
-        def _gesture(agent: "BDICoreAgent", term, intention):
+        def _gesture(agent, term, intention):
             """
             Make the robot perform the given gesture instantly.
             """
@@ -323,13 +404,112 @@ class BDICoreAgent(BaseAgent):
                 gesture_name,
             )
 
-            # gesture = Gesture(type=gesture_type, name=gesture_name)
-            # gesture_message = InternalMessage(
-            #     to=settings.agent_settings.robot_gesture_name,
-            #     sender=settings.agent_settings.bdi_core_name,
-            #     body=gesture.model_dump_json(),
-            # )
-            # asyncio.create_task(agent.send(gesture_message))
+            if str(gesture_type) == "single":
+                endpoint = RIEndpoint.GESTURE_SINGLE
+            elif str(gesture_type) == "tag":
+                endpoint = RIEndpoint.GESTURE_TAG
+            else:
+                self.logger.warning("Gesture type %s could not be resolved.", gesture_type)
+                endpoint = RIEndpoint.GESTURE_SINGLE
+
+            gesture_command = GestureCommand(endpoint=endpoint, data=gesture_name)
+            gesture_message = InternalMessage(
+                to=settings.agent_settings.robot_gesture_name,
+                sender=settings.agent_settings.bdi_core_name,
+                body=gesture_command.model_dump_json(),
+            )
+            self.add_behavior(self.send(gesture_message))
+            yield
+
+        @self.actions.add(".notify_user_said", 1)
+        def _notify_user_said(agent, term, intention):
+            user_said = agentspeak.grounded(term.args[0], intention.scope)
+
+            msg = InternalMessage(
+                to=settings.agent_settings.llm_name, thread="user_message", body=str(user_said)
+            )
+
+            self.add_behavior(self.send(msg))
+
+            yield
+
+        @self.actions.add(".notify_trigger_start", 1)
+        def _notify_trigger_start(agent, term, intention):
+            """
+            Notify the UI about the trigger we just started doing.
+            """
+            trigger_name = agentspeak.grounded(term.args[0], intention.scope)
+
+            self.logger.debug("Started trigger %s", trigger_name)
+
+            msg = InternalMessage(
+                to=settings.agent_settings.user_interrupt_name,
+                sender=self.name,
+                thread="trigger_start",
+                body=str(trigger_name),
+            )
+
+            # TODO: check with Pim
+            self.add_behavior(self.send(msg))
+
+            yield
+
+        @self.actions.add(".notify_trigger_end", 1)
+        def _notify_trigger_end(agent, term, intention):
+            """
+            Notify the UI about the trigger we just started doing.
+            """
+            trigger_name = agentspeak.grounded(term.args[0], intention.scope)
+
+            self.logger.debug("Finished trigger %s", trigger_name)
+
+            msg = InternalMessage(
+                to=settings.agent_settings.user_interrupt_name,
+                sender=self.name,
+                thread="trigger_end",
+                body=str(trigger_name),
+            )
+
+            self.add_behavior(self.send(msg))
+
+            yield
+
+        @self.actions.add(".notify_goal_start", 1)
+        def _notify_goal_start(agent, term, intention):
+            """
+            Notify the UI about the goal we just started chasing.
+            """
+            goal_name = agentspeak.grounded(term.args[0], intention.scope)
+
+            self.logger.debug("Started chasing goal %s", goal_name)
+
+            msg = InternalMessage(
+                to=settings.agent_settings.user_interrupt_name,
+                sender=self.name,
+                thread="goal_start",
+                body=str(goal_name),
+            )
+
+            self.add_behavior(self.send(msg))
+
+            yield
+
+        @self.actions.add(".notify_transition_phase", 2)
+        def _notify_transition_phase(agent, term, intention):
+            """
+            Notify the BDI program manager about a phase transition.
+            """
+            old = agentspeak.grounded(term.args[0], intention.scope)
+            new = agentspeak.grounded(term.args[1], intention.scope)
+
+            msg = InternalMessage(
+                to=settings.agent_settings.bdi_program_manager_name,
+                thread="transition_phase",
+                body=json.dumps({"old": str(old), "new": str(new)}),
+            )
+
+            self.add_behavior(self.send(msg))
+
             yield
 
     async def _send_to_llm(self, text: str, norms: str, goals: str):
@@ -341,13 +521,14 @@ class BDICoreAgent(BaseAgent):
             to=settings.agent_settings.llm_name,
             sender=self.name,
             body=prompt.model_dump_json(),
+            thread="prompt_message",
         )
         await self.send(msg)
         self.logger.info("Message sent to LLM agent: %s", text)
 
     @staticmethod
-    def format_belief_string(name: str, args: Iterable[str] = []):
+    def format_belief_string(name: str, args: Iterable[str] | None = []):
         """
         Given a belief's name and its args, return a string of the form "name(*args)"
         """
-        return f"{name}{'(' if args else ''}{','.join(args)}{')' if args else ''}"
+        return f"{name}{'(' if args else ''}{','.join(args or [])}{')' if args else ''}"

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,4 +1,5 @@
 import asyncio
+import json
 
 import zmq
 from pydantic import ValidationError
@@ -7,9 +8,16 @@ from zmq.asyncio import Context
 from control_backend.agents import BaseAgent
 from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.config import settings
-from control_backend.schemas.belief_list import BeliefList
+from control_backend.schemas.belief_list import BeliefList, GoalList
 from control_backend.schemas.internal_message import InternalMessage
-from control_backend.schemas.program import Belief, ConditionalNorm, InferredBelief, Program
+from control_backend.schemas.program import (
+    Belief,
+    ConditionalNorm,
+    Goal,
+    InferredBelief,
+    Phase,
+    Program,
+)
 
 
 class BDIProgramManager(BaseAgent):
@@ -24,20 +32,30 @@ class BDIProgramManager(BaseAgent):
     :ivar sub_socket: The ZMQ SUB socket used to receive program updates.
     """
 
+    _program: Program
+    _phase: Phase | None
+
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.sub_socket = None
 
+    def _initialize_internal_state(self, program: Program):
+        self._program = program
+        self._phase = program.phases[0]  # start in first phase
+        self._goal_mapping: dict[str, Goal] = {}
+        for phase in program.phases:
+            for goal in phase.goals:
+                self._populate_goal_mapping_with_goal(goal)
+
+    def _populate_goal_mapping_with_goal(self, goal: Goal):
+        self._goal_mapping[str(goal.id)] = goal
+        for step in goal.plan.steps:
+            if isinstance(step, Goal):
+                self._populate_goal_mapping_with_goal(step)
+
     async def _create_agentspeak_and_send_to_bdi(self, program: Program):
         """
-        Convert a received program into BDI beliefs and send them to the BDI Core Agent.
-
-        Currently, it takes the **first phase** of the program and extracts:
-        -   **Norms**: Constraints or rules the agent must follow.
-        -   **Goals**: Objectives the agent must achieve.
-
-        These are sent as a ``BeliefMessage`` with ``replace=True``, meaning they will
-        overwrite any existing norms/goals of the same name in the BDI agent.
+        Convert a received program into an AgentSpeak file and send it to the BDI Core Agent.
 
         :param program: The program object received from the API.
         """
@@ -59,17 +77,63 @@ class BDIProgramManager(BaseAgent):
 
         await self.send(msg)
 
-    @staticmethod
-    def _extract_beliefs_from_program(program: Program) -> list[Belief]:
+    async def handle_message(self, msg: InternalMessage):
+        match msg.thread:
+            case "transition_phase":
+                phases = json.loads(msg.body)
+
+                await self._transition_phase(phases["old"], phases["new"])
+            case "achieve_goal":
+                goal_id = msg.body
+                await self._send_achieved_goal_to_semantic_belief_extractor(goal_id)
+
+    async def _transition_phase(self, old: str, new: str):
+        if old != str(self._phase.id):
+            self.logger.warning(
+                f"Phase transition desync detected! ASL requested move from '{old}', "
+                f"but Python is currently in '{self._phase.id}'. Request ignored."
+            )
+            return
+
+        if new == "end":
+            self._phase = None
+            # Notify user interaction agent
+            msg = InternalMessage(
+                to=settings.agent_settings.user_interrupt_name,
+                thread="transition_phase",
+                body="end",
+            )
+            self.logger.info("Transitioned to end phase, notifying UserInterruptAgent.")
+
+            self.add_behavior(self.send(msg))
+            return
+
+        for phase in self._program.phases:
+            if str(phase.id) == new:
+                self._phase = phase
+
+        await self._send_beliefs_to_semantic_belief_extractor()
+        await self._send_goals_to_semantic_belief_extractor()
+
+        # Notify user interaction agent
+        msg = InternalMessage(
+            to=settings.agent_settings.user_interrupt_name,
+            thread="transition_phase",
+            body=str(self._phase.id),
+        )
+        self.logger.info(f"Transitioned to phase {new}, notifying UserInterruptAgent.")
+
+        self.add_behavior(self.send(msg))
+
+    def _extract_current_beliefs(self) -> list[Belief]:
         beliefs: list[Belief] = []
 
-        for phase in program.phases:
-            for norm in phase.norms:
-                if isinstance(norm, ConditionalNorm):
-                    beliefs += BDIProgramManager._extract_beliefs_from_belief(norm.condition)
+        for norm in self._phase.norms:
+            if isinstance(norm, ConditionalNorm):
+                beliefs += self._extract_beliefs_from_belief(norm.condition)
 
-            for trigger in phase.triggers:
-                beliefs += BDIProgramManager._extract_beliefs_from_belief(trigger.condition)
+        for trigger in self._phase.triggers:
+            beliefs += self._extract_beliefs_from_belief(trigger.condition)
 
         return beliefs
 
@@ -81,13 +145,11 @@ class BDIProgramManager(BaseAgent):
             ) + BDIProgramManager._extract_beliefs_from_belief(belief.right)
         return [belief]
 
-    async def _send_beliefs_to_semantic_belief_extractor(self, program: Program):
+    async def _send_beliefs_to_semantic_belief_extractor(self):
         """
         Extract beliefs from the program and send them to the Semantic Belief Extractor Agent.
-
-        :param program: The program received from the API.
         """
-        beliefs = BeliefList(beliefs=self._extract_beliefs_from_program(program))
+        beliefs = BeliefList(beliefs=self._extract_current_beliefs())
 
         message = InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -98,12 +160,94 @@ class BDIProgramManager(BaseAgent):
 
         await self.send(message)
 
+    @staticmethod
+    def _extract_goals_from_goal(goal: Goal) -> list[Goal]:
+        """
+        Extract all goals from a given goal, that is: the goal itself and any subgoals.
+
+        :return: All goals within and including the given goal.
+        """
+        goals: list[Goal] = [goal]
+        for plan in goal.plan:
+            if isinstance(plan, Goal):
+                goals.extend(BDIProgramManager._extract_goals_from_goal(plan))
+        return goals
+
+    def _extract_current_goals(self) -> list[Goal]:
+        """
+        Extract all goals from the program, including subgoals.
+
+        :return: A list of Goal objects.
+        """
+        goals: list[Goal] = []
+
+        for goal in self._phase.goals:
+            goals.extend(self._extract_goals_from_goal(goal))
+
+        return goals
+
+    async def _send_goals_to_semantic_belief_extractor(self):
+        """
+        Extract goals for the current phase and send them to the Semantic Belief Extractor Agent.
+        """
+        goals = GoalList(goals=self._extract_current_goals())
+
+        message = InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=self.name,
+            body=goals.model_dump_json(),
+            thread="goals",
+        )
+
+        await self.send(message)
+
+    async def _send_achieved_goal_to_semantic_belief_extractor(self, achieved_goal_id: str):
+        """
+        Inform the semantic belief extractor when a goal is marked achieved.
+
+        :param achieved_goal_id: The id of the achieved goal.
+        """
+        goal = self._goal_mapping.get(achieved_goal_id)
+        if goal is None:
+            self.logger.debug(f"Goal with ID {achieved_goal_id} marked achieved but was not found.")
+            return
+
+        goals = self._extract_goals_from_goal(goal)
+        message = InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            body=GoalList(goals=goals).model_dump_json(),
+            thread="achieved_goals",
+        )
+        await self.send(message)
+
+    async def _send_clear_llm_history(self):
+        """
+        Clear the LLM Agent's conversation history.
+
+        Sends an empty history to the LLM Agent to reset its state.
+        """
+        message = InternalMessage(
+            to=settings.agent_settings.llm_name,
+            body="clear_history",
+        )
+        await self.send(message)
+        self.logger.debug("Sent message to LLM agent to clear history.")
+
+        extractor_msg = InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            thread="conversation_history",
+            body="reset",
+        )
+        await self.send(extractor_msg)
+        self.logger.debug("Sent message to extractor agent to clear history.")
+
     async def _receive_programs(self):
         """
         Continuous loop that receives program updates from the HTTP endpoint.
 
         It listens to the ``program`` topic on the internal ZMQ SUB socket.
         When a program is received, it is validated and forwarded to BDI via :meth:`_send_to_bdi`.
+        Additionally, the LLM history is cleared via :meth:`_send_clear_llm_history`.
         """
         while True:
             topic, body = await self.sub_socket.recv_multipart()
@@ -111,21 +255,43 @@ class BDIProgramManager(BaseAgent):
             try:
                 program = Program.model_validate_json(body)
             except ValidationError:
-                self.logger.exception("Received an invalid program.")
+                self.logger.warning("Received an invalid program.")
                 continue
 
+            self._initialize_internal_state(program)
+            await self._send_program_to_user_interrupt(program)
+            await self._send_clear_llm_history()
+
             await asyncio.gather(
                 self._create_agentspeak_and_send_to_bdi(program),
-                self._send_beliefs_to_semantic_belief_extractor(program),
+                self._send_beliefs_to_semantic_belief_extractor(),
+                self._send_goals_to_semantic_belief_extractor(),
             )
 
+    async def _send_program_to_user_interrupt(self, program: Program):
+        """
+        Send the received program to the User Interrupt Agent.
+
+        :param program: The program object received from the API.
+        """
+        msg = InternalMessage(
+            sender=self.name,
+            to=settings.agent_settings.user_interrupt_name,
+            body=program.model_dump_json(),
+            thread="new_program",
+        )
+
+        await self.send(msg)
+
     async def setup(self):
         """
         Initialize the agent.
 
         Connects the internal ZMQ SUB socket and subscribes to the 'program' topic.
-        Starts the background behavior to receive programs.
+        Starts the background behavior to receive programs. Initializes a default program.
         """
+        await self._create_agentspeak_and_send_to_bdi(Program(phases=[]))
+
         context = Context.instance()
 
         self.sub_socket = context.socket(zmq.SUB)

src/control_backend/agents/bdi/belief_collector_agent.py

@@ -1,152 +0,0 @@
-import json
-
-from pydantic import ValidationError
-
-from control_backend.agents.base import BaseAgent
-from control_backend.core.agent_system import InternalMessage
-from control_backend.core.config import settings
-from control_backend.schemas.belief_message import Belief, BeliefMessage
-
-
-class BDIBeliefCollectorAgent(BaseAgent):
-    """
-    BDI Belief Collector Agent.
-
-    This agent acts as a central aggregator for beliefs derived from various sources (e.g., text,
-    emotion, vision). It receives raw extracted data from other agents,
-    normalizes them into valid :class:`Belief` objects, and forwards them as a unified packet to the
-    BDI Core Agent.
-
-    It serves as a funnel to ensure the BDI agent receives a consistent stream of beliefs.
-    """
-
-    async def setup(self):
-        """
-        Initialize the agent.
-        """
-        self.logger.info("Setting up %s", self.name)
-
-    async def handle_message(self, msg: InternalMessage):
-        """
-        Handle incoming messages from other extractor agents.
-
-        Routes the message to specific handlers based on the 'type' field in the JSON body.
-        Supported types:
-        -   ``belief_extraction_text``: Handled by :meth:`_handle_belief_text`
-        -   ``emotion_extraction_text``: Handled by :meth:`_handle_emo_text`
-
-        :param msg: The received internal message.
-        """
-        sender_node = msg.sender
-
-        # Parse JSON payload
-        try:
-            payload = json.loads(msg.body)
-        except Exception as e:
-            self.logger.warning(
-                "BeliefCollector: failed to parse JSON from %s. Body=%r Error=%s",
-                sender_node,
-                msg.body,
-                e,
-            )
-            return
-
-        msg_type = payload.get("type")
-
-        # Prefer explicit 'type' field
-        if msg_type == "belief_extraction_text":
-            self.logger.debug("Message routed to _handle_belief_text (sender=%s)", sender_node)
-            await self._handle_belief_text(payload, sender_node)
-        # This is not implemented yet, but we keep the structure for future use
-        elif msg_type == "emotion_extraction_text":
-            self.logger.debug("Message routed to _handle_emo_text (sender=%s)", sender_node)
-            await self._handle_emo_text(payload, sender_node)
-        else:
-            self.logger.warning(
-                "Unrecognized message (sender=%s, type=%r). Ignoring.", sender_node, msg_type
-            )
-
-    async def _handle_belief_text(self, payload: dict, origin: str):
-        """
-        Process text-based belief extraction payloads.
-
-        Expected payload format::
-
-            {
-                "type": "belief_extraction_text",
-                "beliefs": {
-                    "user_said": ["Can you help me?"],
-                    "intention": ["ask_help"]
-                }
-            }
-
-        Validates and converts the dictionary items into :class:`Belief` objects.
-
-        :param payload: The dictionary payload containing belief data.
-        :param origin: The name of the sender agent.
-        """
-        beliefs = payload.get("beliefs", {})
-
-        if not beliefs:
-            self.logger.debug("Received empty beliefs set.")
-            return
-
-        def try_create_belief(name, arguments) -> Belief | None:
-            """
-            Create a belief object from name and arguments, or return None silently if the input is
-            not correct.
-
-            :param name: The name of the belief.
-            :param arguments: The arguments of the belief.
-            :return: A Belief object if the input is valid or None.
-            """
-            try:
-                return Belief(name=name, arguments=arguments, replace=name == "user_said")
-            except ValidationError:
-                return None
-
-        beliefs = [
-            belief
-            for name, arguments in beliefs.items()
-            if (belief := try_create_belief(name, arguments)) is not None
-        ]
-
-        self.logger.debug("Forwarding %d beliefs.", len(beliefs))
-        for belief in beliefs:
-            for argument in belief.arguments:
-                self.logger.debug("  - %s %s", belief.name, argument)
-
-        await self._send_beliefs_to_bdi(beliefs, origin=origin)
-
-    async def _handle_emo_text(self, payload: dict, origin: str):
-        """
-        Process emotion extraction payloads.
-
-        **TODO**: Implement this method once emotion recognition is integrated.
-
-        :param payload: The dictionary payload containing emotion data.
-        :param origin: The name of the sender agent.
-        """
-        pass
-
-    async def _send_beliefs_to_bdi(self, beliefs: list[Belief], origin: str | None = None):
-        """
-        Send a list of aggregated beliefs to the BDI Core Agent.
-
-        Wraps the beliefs in a :class:`BeliefMessage` and sends it via the 'beliefs' thread.
-
-        :param beliefs: The list of Belief objects to send.
-        :param origin: (Optional) The original source of the beliefs (unused currently).
-        """
-        if not beliefs:
-            return
-
-        msg = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            sender=self.name,
-            body=BeliefMessage(create=beliefs).model_dump_json(),
-            thread="beliefs",
-        )
-
-        await self.send(msg)
-        self.logger.info("Sent %d belief(s) to BDI core.", len(beliefs))

src/control_backend/agents/bdi/default_behavior.asl

@@ -1,5 +1,34 @@
-norms("").
+phase("end").
+keyword_said(Keyword) :- (user_said(Message) & .substring(Keyword, Message, Pos)) & (Pos >= 0).
 
-+user_said(Message) : norms(Norms) <-
-  -user_said(Message);
-  .reply(Message, Norms).
+
++!reply_with_goal(Goal)
+  :   user_said(Message)
+  <-  +responded_this_turn;
+      .findall(Norm, norm(Norm), Norms);
+      .reply_with_goal(Message, Norms, Goal).
+
++!say(Text)
+  <-  +responded_this_turn;
+      .say(Text).
+
++!reply
+  :   user_said(Message)
+  <-  +responded_this_turn;
+      .findall(Norm, norm(Norm), Norms);
+      .reply(Message, Norms).
+
++!notify_cycle
+  <-  .notify_ui;
+      .wait(1).
+
++user_said(Message)
+  :   phase("end")
+  <-  .notify_user_said(Message);
+      !reply.
+
++!check_triggers
+  <-  true.
+
++!transition_phase
+  <-  true.

src/control_backend/agents/bdi/text_belief_extractor_agent.py

@@ -2,17 +2,51 @@ import asyncio
 import json
 
 import httpx
-from pydantic import ValidationError
+from pydantic import BaseModel, ValidationError
 
 from control_backend.agents.base import BaseAgent
 from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.belief_list import BeliefList
+from control_backend.schemas.belief_list import BeliefList, GoalList
 from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.chat_history import ChatHistory, ChatMessage
-from control_backend.schemas.program import SemanticBelief
+from control_backend.schemas.program import BaseGoal, SemanticBelief
+
+type JSONLike = None | bool | int | float | str | list["JSONLike"] | dict[str, "JSONLike"]
+
+
+class BeliefState(BaseModel):
+    """
+    Represents the state of inferred semantic beliefs.
+
+    Maintains sets of beliefs that are currently considered true or false.
+    """
+
+    true: set[InternalBelief] = set()
+    false: set[InternalBelief] = set()
+
+    def difference(self, other: "BeliefState") -> "BeliefState":
+        return BeliefState(
+            true=self.true - other.true,
+            false=self.false - other.false,
+        )
+
+    def union(self, other: "BeliefState") -> "BeliefState":
+        return BeliefState(
+            true=self.true | other.true,
+            false=self.false | other.false,
+        )
+
+    def __sub__(self, other):
+        return self.difference(other)
+
+    def __or__(self, other):
+        return self.union(other)
+
+    def __bool__(self):
+        return bool(self.true) or bool(self.false)
 
 
 class TextBeliefExtractorAgent(BaseAgent):
@@ -27,12 +61,15 @@ class TextBeliefExtractorAgent(BaseAgent):
     the message itself.
     """
 
-    def __init__(self, name: str, temperature: float = settings.llm_settings.code_temperature):
+    def __init__(self, name: str):
         super().__init__(name)
-        self.beliefs: dict[str, bool] = {}
-        self.available_beliefs: list[SemanticBelief] = []
+        self._llm = self.LLM(self, settings.llm_settings.n_parallel)
+        self.belief_inferrer = SemanticBeliefInferrer(self._llm)
+        self.goal_inferrer = GoalAchievementInferrer(self._llm)
+        self._current_beliefs = BeliefState()
+        self._current_goal_completions: dict[str, bool] = {}
+        self._force_completed_goals: set[BaseGoal] = set()
         self.conversation = ChatHistory(messages=[])
-        self.temperature = temperature
 
     async def setup(self):
         """
@@ -53,13 +90,14 @@ class TextBeliefExtractorAgent(BaseAgent):
             case settings.agent_settings.transcription_name:
                 self.logger.debug("Received text from transcriber: %s", msg.body)
                 self._apply_conversation_message(ChatMessage(role="user", content=msg.body))
-                await self._infer_new_beliefs()
                 await self._user_said(msg.body)
+                await self._infer_new_beliefs()
+                await self._infer_goal_completions()
             case settings.agent_settings.llm_name:
                 self.logger.debug("Received text from LLM: %s", msg.body)
                 self._apply_conversation_message(ChatMessage(role="assistant", content=msg.body))
             case settings.agent_settings.bdi_program_manager_name:
-                self._handle_program_manager_message(msg)
+                await self._handle_program_manager_message(msg)
             case _:
                 self.logger.info("Discarding message from %s", sender)
                 return
@@ -74,12 +112,35 @@ class TextBeliefExtractorAgent(BaseAgent):
         length_limit = settings.behaviour_settings.conversation_history_length_limit
         self.conversation.messages = (self.conversation.messages + [message])[-length_limit:]
 
-    def _handle_program_manager_message(self, msg: InternalMessage):
+    async def _handle_program_manager_message(self, msg: InternalMessage):
         """
-        Handle a message from the program manager: extract available beliefs from it.
+        Handle a message from the program manager: extract available beliefs and goals from it.
 
         :param msg: The received message from the program manager.
         """
+        match msg.thread:
+            case "beliefs":
+                self._handle_beliefs_message(msg)
+                await self._infer_new_beliefs()
+            case "goals":
+                self._handle_goals_message(msg)
+                await self._infer_goal_completions()
+            case "achieved_goals":
+                self._handle_goal_achieved_message(msg)
+            case "conversation_history":
+                if msg.body == "reset":
+                    self._reset_phase()
+            case _:
+                self.logger.warning("Received unexpected message from %s", msg.sender)
+
+    def _reset_phase(self):
+        self.conversation = ChatHistory(messages=[])
+        self.belief_inferrer.available_beliefs.clear()
+        self._current_beliefs = BeliefState()
+        self.goal_inferrer.goals.clear()
+        self._current_goal_completions = {}
+
+    def _handle_beliefs_message(self, msg: InternalMessage):
         try:
             belief_list = BeliefList.model_validate_json(msg.body)
         except ValidationError:
@@ -88,133 +149,265 @@ class TextBeliefExtractorAgent(BaseAgent):
             )
             return
 
-        self.available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
+        available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
+        self.belief_inferrer.available_beliefs = available_beliefs
         self.logger.debug(
-            "Received %d beliefs from the program manager.",
-            len(self.available_beliefs),
+            "Received %d semantic beliefs from the program manager: %s",
+            len(available_beliefs),
+            ", ".join(b.name for b in available_beliefs),
         )
 
+    def _handle_goals_message(self, msg: InternalMessage):
+        try:
+            goals_list = GoalList.model_validate_json(msg.body)
+        except ValidationError:
+            self.logger.warning(
+                "Received message from program manager but it is not a valid list of goals."
+            )
+            return
+
+        # Use only goals that can fail, as the others are always assumed to be completed
+        available_goals = {g for g in goals_list.goals if g.can_fail}
+        available_goals -= self._force_completed_goals
+        self.goal_inferrer.goals = available_goals
+        self.logger.debug(
+            "Received %d failable goals from the program manager: %s",
+            len(available_goals),
+            ", ".join(g.name for g in available_goals),
+        )
+
+    def _handle_goal_achieved_message(self, msg: InternalMessage):
+        # NOTE: When goals can be marked unachieved, remember to re-add them to the goal_inferrer
+        try:
+            goals_list = GoalList.model_validate_json(msg.body)
+        except ValidationError:
+            self.logger.warning(
+                "Received goal achieved message from the program manager, "
+                "but it is not a valid list of goals."
+            )
+            return
+
+        for goal in goals_list.goals:
+            self._force_completed_goals.add(goal)
+            self._current_goal_completions[f"achieved_{AgentSpeakGenerator.slugify(goal)}"] = True
+
+        self.goal_inferrer.goals -= self._force_completed_goals
+
     async def _user_said(self, text: str):
         """
         Create a belief for the user's full speech.
 
         :param text: User's transcribed text.
         """
-        belief = {"beliefs": {"user_said": [text]}, "type": "belief_extraction_text"}
-        payload = json.dumps(belief)
-
         belief_msg = InternalMessage(
-            to=settings.agent_settings.bdi_belief_collector_name,
+            to=settings.agent_settings.bdi_core_name,
             sender=self.name,
-            body=payload,
+            body=BeliefMessage(
+                replace=[InternalBelief(name="user_said", arguments=[text])],
+            ).model_dump_json(),
             thread="beliefs",
         )
         await self.send(belief_msg)
 
     async def _infer_new_beliefs(self):
-        """
-        Process conversation history to extract beliefs, semantically. Any changed beliefs are sent
-        to the BDI core.
-        """
-        # Return instantly if there are no beliefs to infer
-        if not self.available_beliefs:
+        conversation_beliefs = await self.belief_inferrer.infer_from_conversation(self.conversation)
+
+        new_beliefs = conversation_beliefs - self._current_beliefs
+        if not new_beliefs:
+            self.logger.debug("No new beliefs detected.")
             return
 
-        candidate_beliefs = await self._infer_turn()
-        belief_changes = BeliefMessage()
-        for belief_key, belief_value in candidate_beliefs.items():
-            if belief_value is None:
-                continue
-            old_belief_value = self.beliefs.get(belief_key)
-            if belief_value == old_belief_value:
-                continue
+        self._current_beliefs |= new_beliefs
 
-            self.beliefs[belief_key] = belief_value
+        belief_changes = BeliefMessage(
+            create=list(new_beliefs.true),
+            delete=list(new_beliefs.false),
+        )
 
-            belief = InternalBelief(name=belief_key, arguments=None)
-            if belief_value:
-                belief_changes.create.append(belief)
-            else:
-                belief_changes.delete.append(belief)
-
-        # Return if there were no changes in beliefs
-        if not belief_changes.has_values():
-            return
-
-        beliefs_message = InternalMessage(
+        message = InternalMessage(
             to=settings.agent_settings.bdi_core_name,
             sender=self.name,
             body=belief_changes.model_dump_json(),
             thread="beliefs",
         )
-        await self.send(beliefs_message)
+        await self.send(message)
 
-    @staticmethod
-    def _split_into_chunks[T](items: list[T], n: int) -> list[list[T]]:
-        k, m = divmod(len(items), n)
-        return [items[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)]
+    async def _infer_goal_completions(self):
+        goal_completions = await self.goal_inferrer.infer_from_conversation(self.conversation)
 
-    async def _infer_turn(self) -> dict:
+        new_achieved = [
+            InternalBelief(name=goal, arguments=None)
+            for goal, achieved in goal_completions.items()
+            if achieved and self._current_goal_completions.get(goal) != achieved
+        ]
+        new_not_achieved = [
+            InternalBelief(name=goal, arguments=None)
+            for goal, achieved in goal_completions.items()
+            if not achieved and self._current_goal_completions.get(goal) != achieved
+        ]
+        for goal, achieved in goal_completions.items():
+            self._current_goal_completions[goal] = achieved
+
+        if not new_achieved and not new_not_achieved:
+            self.logger.debug("No goal achievement changes detected.")
+            return
+
+        belief_changes = BeliefMessage(
+            create=new_achieved,
+            delete=new_not_achieved,
+        )
+        message = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            body=belief_changes.model_dump_json(),
+            thread="beliefs",
+        )
+        await self.send(message)
+
+    class LLM:
         """
-        Process the stored conversation history to extract semantic beliefs. Returns a list of
-        beliefs that have been set to ``True``, ``False`` or ``None``.
-
-        :return: A dict mapping belief names to a value ``True``, ``False`` or ``None``.
+        Class that handles sending structured generation requests to an LLM.
         """
+
+        def __init__(self, agent: "TextBeliefExtractorAgent", n_parallel: int):
+            self._agent = agent
+            self._semaphore = asyncio.Semaphore(n_parallel)
+
+        async def query(self, prompt: str, schema: dict, tries: int = 3) -> JSONLike | None:
+            """
+            Query the LLM with the given prompt and schema, return an instance of a dict conforming
+            to this schema. Try ``tries`` times, or return None.
+
+            :param prompt: Prompt to be queried.
+            :param schema: Schema to be queried.
+            :param tries: Number of times to try to query the LLM.
+            :return: An instance of a dict conforming to this schema, or None if failed.
+            """
+            try_count = 0
+            while try_count < tries:
+                try_count += 1
+
+                try:
+                    return await self._query_llm(prompt, schema)
+                except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
+                    if try_count < tries:
+                        continue
+                    self._agent.logger.exception(
+                        "Failed to get LLM response after %d tries.",
+                        try_count,
+                        exc_info=e,
+                    )
+
+            return None
+
+        async def _query_llm(self, prompt: str, schema: dict) -> JSONLike:
+            """
+            Query an LLM with the given prompt and schema, return an instance of a dict conforming
+            to that schema.
+
+            :param prompt: The prompt to be queried.
+            :param schema: Schema to use during response.
+            :return: A dict conforming to this schema.
+            :raises httpx.HTTPStatusError: If the LLM server responded with an error.
+            :raises json.JSONDecodeError: If the LLM response was not valid JSON. May happen if the
+                response was cut off early due to length limitations.
+            :raises KeyError: If the LLM server responded with no error, but the response was
+                invalid.
+            """
+            async with self._semaphore:
+                async with httpx.AsyncClient() as client:
+                    response = await client.post(
+                        settings.llm_settings.local_llm_url,
+                        headers={"Authorization": f"Bearer {settings.llm_settings.api_key}"}
+                        if settings.llm_settings.api_key
+                        else {},
+                        json={
+                            "model": settings.llm_settings.local_llm_model,
+                            "messages": [{"role": "user", "content": prompt}],
+                            "response_format": {
+                                "type": "json_schema",
+                                "json_schema": {
+                                    "name": "Beliefs",
+                                    "strict": True,
+                                    "schema": schema,
+                                },
+                            },
+                            "reasoning_effort": "low",
+                            "temperature": settings.llm_settings.code_temperature,
+                            "stream": False,
+                        },
+                        timeout=30.0,
+                    )
+                    response.raise_for_status()
+
+                    response_json = response.json()
+                    json_message = response_json["choices"][0]["message"]["content"]
+                    return json.loads(json_message)
+
+
+class SemanticBeliefInferrer:
+    """
+    Infers semantic beliefs from conversation history using an LLM.
+    """
+
+    def __init__(
+        self,
+        llm: "TextBeliefExtractorAgent.LLM",
+        available_beliefs: list[SemanticBelief] | None = None,
+    ):
+        self._llm = llm
+        self.available_beliefs: list[SemanticBelief] = available_beliefs or []
+
+    async def infer_from_conversation(self, conversation: ChatHistory) -> BeliefState:
+        """
+        Process conversation history to extract beliefs, semantically. The result is an object that
+        describes all beliefs that hold or don't hold based on the full conversation.
+
+        :param conversation: The conversation history to be processed.
+        :return: An object that describes beliefs.
+        """
+        # Return instantly if there are no beliefs to infer
+        if not self.available_beliefs:
+            return BeliefState()
+
         n_parallel = max(1, min(settings.llm_settings.n_parallel - 1, len(self.available_beliefs)))
-        all_beliefs = await asyncio.gather(
+        all_beliefs: list[dict[str, bool | None] | None] = await asyncio.gather(
             *[
-                self._infer_beliefs(self.conversation, beliefs)
+                self._infer_beliefs(conversation, beliefs)
                 for beliefs in self._split_into_chunks(self.available_beliefs, n_parallel)
             ]
         )
-        retval = {}
+        retval = BeliefState()
         for beliefs in all_beliefs:
             if beliefs is None:
                 continue
-            retval.update(beliefs)
+            for belief_name, belief_holds in beliefs.items():
+                if belief_holds is None:
+                    continue
+                belief = InternalBelief(name=belief_name, arguments=None)
+                if belief_holds:
+                    retval.true.add(belief)
+                else:
+                    retval.false.add(belief)
         return retval
 
     @staticmethod
-    def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
-        return AgentSpeakGenerator.slugify(belief), {
-            "type": ["boolean", "null"],
-            "description": belief.description,
-        }
+    def _split_into_chunks[T](items: list[T], n: int) -> list[list[T]]:
+        """
+        Split a list into ``n`` chunks, making each chunk approximately ``len(items) / n`` long.
 
-    @staticmethod
-    def _create_beliefs_schema(beliefs: list[SemanticBelief]) -> dict:
-        belief_schemas = [
-            TextBeliefExtractorAgent._create_belief_schema(belief) for belief in beliefs
-        ]
-
-        return {
-            "type": "object",
-            "properties": dict(belief_schemas),
-            "required": [name for name, _ in belief_schemas],
-        }
-
-    @staticmethod
-    def _format_message(message: ChatMessage):
-        return f"{message.role.upper()}:\n{message.content}"
-
-    @staticmethod
-    def _format_conversation(conversation: ChatHistory):
-        return "\n\n".join(
-            [TextBeliefExtractorAgent._format_message(message) for message in conversation.messages]
-        )
-
-    @staticmethod
-    def _format_beliefs(beliefs: list[SemanticBelief]):
-        return "\n".join(
-            [f"- {AgentSpeakGenerator.slugify(belief)}: {belief.description}" for belief in beliefs]
-        )
+        :param items: The list of items to split.
+        :param n: The number of desired chunks.
+        :return: A list of chunks each approximately ``len(items) / n`` long.
+        """
+        k, m = divmod(len(items), n)
+        return [items[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)]
 
     async def _infer_beliefs(
         self,
         conversation: ChatHistory,
         beliefs: list[SemanticBelief],
-    ) -> dict | None:
+    ) -> dict[str, bool | None] | None:
         """
         Infer given beliefs based on the given conversation.
         :param conversation: The conversation to infer beliefs from.
@@ -241,70 +434,83 @@ Respond with a JSON similar to the following, but with the property names as giv
 
         schema = self._create_beliefs_schema(beliefs)
 
-        return await self._retry_query_llm(prompt, schema)
+        return await self._llm.query(prompt, schema)
 
-    async def _retry_query_llm(self, prompt: str, schema: dict, tries: int = 3) -> dict | None:
+    @staticmethod
+    def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
+        return AgentSpeakGenerator.slugify(belief), {
+            "type": ["boolean", "null"],
+            "description": belief.description,
+        }
+
+    @staticmethod
+    def _create_beliefs_schema(beliefs: list[SemanticBelief]) -> dict:
+        belief_schemas = [
+            SemanticBeliefInferrer._create_belief_schema(belief) for belief in beliefs
+        ]
+
+        return {
+            "type": "object",
+            "properties": dict(belief_schemas),
+            "required": [name for name, _ in belief_schemas],
+        }
+
+    @staticmethod
+    def _format_message(message: ChatMessage):
+        return f"{message.role.upper()}:\n{message.content}"
+
+    @staticmethod
+    def _format_conversation(conversation: ChatHistory):
+        return "\n\n".join(
+            [SemanticBeliefInferrer._format_message(message) for message in conversation.messages]
+        )
+
+    @staticmethod
+    def _format_beliefs(beliefs: list[SemanticBelief]):
+        return "\n".join(
+            [f"- {AgentSpeakGenerator.slugify(belief)}: {belief.description}" for belief in beliefs]
+        )
+
+
+class GoalAchievementInferrer(SemanticBeliefInferrer):
+    """
+    Infers whether specific conversational goals have been achieved using an LLM.
+    """
+
+    def __init__(self, llm: TextBeliefExtractorAgent.LLM):
+        super().__init__(llm)
+        self.goals: set[BaseGoal] = set()
+
+    async def infer_from_conversation(self, conversation: ChatHistory) -> dict[str, bool]:
         """
-        Query the LLM with the given prompt and schema, return an instance of a dict conforming
-        to this schema. Try ``tries`` times, or return None.
+        Determine which goals have been achieved based on the given conversation.
 
-        :param prompt: Prompt to be queried.
-        :param schema: Schema to be queried.
-        :return: An instance of a dict conforming to this schema, or None if failed.
+        :param conversation: The conversation to infer goal completion from.
+        :return: A mapping of goals and a boolean whether they have been achieved.
         """
-        try_count = 0
-        while try_count < tries:
-            try_count += 1
+        if not self.goals:
+            return {}
 
-            try:
-                return await self._query_llm(prompt, schema)
-            except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
-                if try_count < tries:
-                    continue
-                self.logger.exception(
-                    "Failed to get LLM response after %d tries.",
-                    try_count,
-                    exc_info=e,
-                )
+        goals_achieved = await asyncio.gather(
+            *[self._infer_goal(conversation, g) for g in self.goals]
+        )
+        return {
+            f"achieved_{AgentSpeakGenerator.slugify(goal)}": achieved
+            for goal, achieved in zip(self.goals, goals_achieved, strict=True)
+        }
 
-        return None
+    async def _infer_goal(self, conversation: ChatHistory, goal: BaseGoal) -> bool:
+        prompt = f"""{self._format_conversation(conversation)}
 
-    async def _query_llm(self, prompt: str, schema: dict) -> dict:
-        """
-        Query an LLM with the given prompt and schema, return an instance of a dict conforming to
-        that schema.
+Given the above conversation, what has the following goal been achieved?
 
-        :param prompt: The prompt to be queried.
-        :param schema: Schema to use during response.
-        :return: A dict conforming to this schema.
-        :raises httpx.HTTPStatusError: If the LLM server responded with an error.
-        :raises json.JSONDecodeError: If the LLM response was not valid JSON. May happen if the
-            response was cut off early due to length limitations.
-        :raises KeyError: If the LLM server responded with no error, but the response was invalid.
-        """
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                settings.llm_settings.local_llm_url,
-                json={
-                    "model": settings.llm_settings.local_llm_model,
-                    "messages": [{"role": "user", "content": prompt}],
-                    "response_format": {
-                        "type": "json_schema",
-                        "json_schema": {
-                            "name": "Beliefs",
-                            "strict": True,
-                            "schema": schema,
-                        },
-                    },
-                    "reasoning_effort": "low",
-                    "temperature": self.temperature,
-                    "stream": False,
-                },
-                timeout=None,
-            )
-            response.raise_for_status()
+The name of the goal: {goal.name}
+Description of the goal: {goal.description}
 
-            response_json = response.json()
-            json_message = response_json["choices"][0]["message"]["content"]
-            beliefs = json.loads(json_message)
-            return beliefs
+Answer with literally only `true` or `false` (without backticks)."""
+
+        schema = {
+            "type": "boolean",
+        }
+
+        return await self._llm.query(prompt, schema)

src/control_backend/agents/communication/__init__.py

@@ -1 +1,5 @@
+"""
+Agents responsible for external communication and service discovery.
+"""
+
 from .ri_communication_agent import RICommunicationAgent as RICommunicationAgent

src/control_backend/agents/communication/ri_communication_agent.py

@@ -3,12 +3,17 @@ import json
 
 import zmq
 import zmq.asyncio as azmq
+from pydantic import ValidationError
 from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
 from control_backend.agents.actuation.robot_gesture_agent import RobotGestureAgent
+from control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognition_agent import (  # noqa
+    VisualEmotionRecognitionAgent,
+)
 from control_backend.core.config import settings
 from control_backend.schemas.internal_message import InternalMessage
+from control_backend.schemas.ri_message import PauseCommand
 
 from ..actuation.robot_speech_agent import RobotSpeechAgent
 from ..perception import VADAgent
@@ -39,7 +44,7 @@ class RICommunicationAgent(BaseAgent):
     def __init__(
         self,
         name: str,
-        address=settings.zmq_settings.ri_command_address,
+        address=settings.zmq_settings.ri_communication_address,
         bind=False,
     ):
         super().__init__(name)
@@ -172,7 +177,7 @@ class RICommunicationAgent(BaseAgent):
             bind = port_data["bind"]
 
             if not bind:
-                addr = f"tcp://localhost:{port}"
+                addr = f"tcp://{settings.ri_host}:{port}"
             else:
                 addr = f"tcp://*:{port}"
 
@@ -207,6 +212,13 @@ class RICommunicationAgent(BaseAgent):
                 case "audio":
                     vad_agent = VADAgent(audio_in_address=addr, audio_in_bind=bind)
                     await vad_agent.start()
+                case "video":
+                    visual_emotion_agent = VisualEmotionRecognitionAgent(
+                        settings.agent_settings.visual_emotion_recognition_name,
+                        socket_address=addr,
+                        bind=bind,
+                    )
+                    await visual_emotion_agent.start()
                 case _:
                     self.logger.warning("Unhandled negotiation id: %s", id)
 
@@ -255,7 +267,8 @@ class RICommunicationAgent(BaseAgent):
                     self._req_socket.recv_json(), timeout=seconds_to_wait_total / 2
                 )
 
-                self.logger.debug(f'Received message "{message}" from RI.')
+                if "endpoint" in message and message["endpoint"] != "ping":
+                    self.logger.debug(f'Received message "{message}" from RI.')
                 if "endpoint" not in message:
                     self.logger.warning("No received endpoint in message, expected ping endpoint.")
                     continue
@@ -319,12 +332,9 @@ class RICommunicationAgent(BaseAgent):
             self.connected = True
 
     async def handle_message(self, msg: InternalMessage):
-        """
-        Handle an incoming message.
-
-        Currently not implemented for this agent.
-
-        :param msg: The received message.
-        :raises NotImplementedError: Always, since this method is not implemented.
-        """
-        self.logger.warning("custom warning for handle msg in ri coms %s", self.name)
+        try:
+            pause_command = PauseCommand.model_validate_json(msg.body)
+            await self._req_socket.send_json(pause_command.model_dump())
+            self.logger.debug(await self._req_socket.recv_json())
+        except ValidationError:
+            self.logger.warning("Incorrect message format for PauseCommand.")

src/control_backend/agents/llm/__init__.py

@@ -1 +1,5 @@
+"""
+Agents that interface with Large Language Models for natural language processing and generation.
+"""
+
 from .llm_agent import LLMAgent as LLMAgent

src/control_backend/agents/llm/llm_agent.py

@@ -1,3 +1,4 @@
+import asyncio
 import json
 import re
 import uuid
@@ -32,6 +33,10 @@ class LLMAgent(BaseAgent):
     def __init__(self, name: str):
         super().__init__(name)
         self.history = []
+        self._querying = False
+        self._interrupted = False
+        self._interrupted_message = ""
+        self._go_ahead = asyncio.Event()
 
     async def setup(self):
         self.logger.info("Setting up %s.", self.name)
@@ -46,14 +51,23 @@ class LLMAgent(BaseAgent):
         :param msg: The received internal message.
         """
         if msg.sender == settings.agent_settings.bdi_core_name:
-            self.logger.debug("Processing message from BDI core.")
-            try:
-                prompt_message = LLMPromptMessage.model_validate_json(msg.body)
-                await self._process_bdi_message(prompt_message)
-            except ValidationError:
-                self.logger.debug("Prompt message from BDI core is invalid.")
+            match msg.thread:
+                case "prompt_message":
+                    try:
+                        prompt_message = LLMPromptMessage.model_validate_json(msg.body)
+                        self.add_behavior(self._process_bdi_message(prompt_message))  # no block
+                    except ValidationError:
+                        self.logger.debug("Prompt message from BDI core is invalid.")
+                case "assistant_message":
+                    self._apply_conversation_message({"role": "assistant", "content": msg.body})
+                case "user_message":
+                    self._apply_conversation_message({"role": "user", "content": msg.body})
+        elif msg.sender == settings.agent_settings.bdi_program_manager_name:
+            if msg.body == "clear_history":
+                self.logger.debug("Clearing conversation history.")
+                self.history.clear()
         else:
-            self.logger.debug("Message ignored (not from BDI core.")
+            self.logger.debug("Message ignored.")
 
     async def _process_bdi_message(self, message: LLMPromptMessage):
         """
@@ -64,12 +78,45 @@ class LLMAgent(BaseAgent):
 
         :param message: The parsed prompt message containing text, norms, and goals.
         """
+        if self._querying:
+            self.logger.debug("Received another BDI prompt while processing previous message.")
+            self._interrupted = True  # interrupt the previous processing
+            await self._go_ahead.wait()  # wait until we get the go-ahead
+
+        message.text = f"{self._interrupted_message} {message.text}"
+
+        self._go_ahead.clear()
+        self._querying = True
         full_message = ""
         async for chunk in self._query_llm(message.text, message.norms, message.goals):
+            if self._interrupted:
+                self._interrupted_message = message.text
+                self.logger.debug("Interrupted processing of previous message.")
+                break
             await self._send_reply(chunk)
             full_message += chunk
-        self.logger.debug("Finished processing BDI message. Response sent in chunks to BDI core.")
-        await self._send_full_reply(full_message)
+        else:
+            self._querying = False
+
+            self._apply_conversation_message(
+                {
+                    "role": "assistant",
+                    "content": full_message,
+                }
+            )
+            self.logger.debug(
+                "Finished processing BDI message. Response sent in chunks to BDI core."
+            )
+            await self._send_full_reply(full_message)
+
+        self._go_ahead.set()
+        self._interrupted = False
+
+    def _apply_conversation_message(self, message: dict[str, str]):
+        if len(self.history) > 0 and message["role"] == self.history[-1]["role"]:
+            self.history[-1]["content"] += " " + message["content"]
+            return
+        self.history.append(message)
 
     async def _send_reply(self, msg: str):
         """
@@ -114,13 +161,6 @@ class LLMAgent(BaseAgent):
         :param goals: Goals the LLM should achieve.
         :yield: Fragments of the LLM-generated content (e.g., sentences/phrases).
         """
-        self.history.append(
-            {
-                "role": "user",
-                "content": prompt,
-            }
-        )
-
         instructions = LLMInstructions(norms if norms else None, goals if goals else None)
         messages = [
             {
@@ -157,13 +197,6 @@ class LLMAgent(BaseAgent):
             # Yield any remaining tail
             if current_chunk:
                 yield current_chunk
-
-            self.history.append(
-                {
-                    "role": "assistant",
-                    "content": full_message,
-                }
-            )
         except httpx.HTTPError as err:
             self.logger.error("HTTP error.", exc_info=err)
             yield "LLM service unavailable."
@@ -183,6 +216,9 @@ class LLMAgent(BaseAgent):
             async with client.stream(
                 "POST",
                 settings.llm_settings.local_llm_url,
+                headers={"Authorization": f"Bearer {settings.llm_settings.api_key}"}
+                if settings.llm_settings.api_key
+                else {},
                 json={
                     "model": settings.llm_settings.local_llm_model,
                     "messages": messages,

src/control_backend/agents/perception/__init__.py

@@ -1,3 +1,8 @@
+"""
+Agents responsible for processing sensory input, such as audio transcription and voice activity
+detection.
+"""
+
 from .transcription_agent.transcription_agent import (
     TranscriptionAgent as TranscriptionAgent,
 )

src/control_backend/agents/perception/transcription_agent/speech_recognizer.py

@@ -145,4 +145,6 @@ class OpenAIWhisperSpeechRecognizer(SpeechRecognizer):
 
     def recognize_speech(self, audio: np.ndarray) -> str:
         self.load_model()
-        return whisper.transcribe(self.model, audio, **self._get_decode_options(audio))["text"]
+        return whisper.transcribe(self.model, audio, **self._get_decode_options(audio))[
+            "text"
+        ].strip()

src/control_backend/agents/perception/transcription_agent/transcription_agent.py

@@ -74,7 +74,7 @@ class TranscriptionAgent(BaseAgent):
 
     def _connect_audio_in_socket(self):
         """
-        Helper to connect the ZMQ SUB socket for audio input.
+        Connects the ZMQ SUB socket for receiving audio data.
         """
         self.audio_in_socket = azmq.Context.instance().socket(zmq.SUB)
         self.audio_in_socket.setsockopt_string(zmq.SUBSCRIBE, "")

src/control_backend/agents/perception/vad_agent.py

@@ -110,12 +110,11 @@ class VADAgent(BaseAgent):
 
         self._connect_audio_in_socket()
 
-        audio_out_port = self._connect_audio_out_socket()
-        if audio_out_port is None:
+        audio_out_address = self._connect_audio_out_socket()
+        if audio_out_address is None:
             self.logger.error("Could not bind output socket, stopping.")
             await self.stop()
             return
-        audio_out_address = f"tcp://localhost:{audio_out_port}"
 
         # Connect to internal communication socket
         self.program_sub_socket = azmq.Context.instance().socket(zmq.SUB)
@@ -168,13 +167,14 @@ class VADAgent(BaseAgent):
             self.audio_in_socket.connect(self.audio_in_address)
         self.audio_in_poller = SocketPoller[bytes](self.audio_in_socket)
 
-    def _connect_audio_out_socket(self) -> int | None:
+    def _connect_audio_out_socket(self) -> str | None:
         """
-        Returns the port bound, or None if binding failed.
+        Returns the address that was bound to, or None if binding failed.
         """
         try:
             self.audio_out_socket = azmq.Context.instance().socket(zmq.PUB)
-            return self.audio_out_socket.bind_to_random_port("tcp://localhost", max_tries=100)
+            self.audio_out_socket.bind(settings.zmq_settings.vad_pub_address)
+            return settings.zmq_settings.vad_pub_address
         except zmq.ZMQBindError:
             self.logger.error("Failed to bind an audio output socket after 100 tries.")
             self.audio_out_socket = None
@@ -246,10 +246,11 @@ class VADAgent(BaseAgent):
             assert self.model is not None
             prob = self.model(torch.from_numpy(chunk), settings.vad_settings.sample_rate_hz).item()
             non_speech_patience = settings.behaviour_settings.vad_non_speech_patience_chunks
+            begin_silence_length = settings.behaviour_settings.vad_begin_silence_chunks
             prob_threshold = settings.behaviour_settings.vad_prob_threshold
 
             if prob > prob_threshold:
-                if self.i_since_speech > non_speech_patience:
+                if self.i_since_speech > non_speech_patience + begin_silence_length:
                     self.logger.debug("Speech started.")
                 self.audio_buffer = np.append(self.audio_buffer, chunk)
                 self.i_since_speech = 0
@@ -263,7 +264,7 @@ class VADAgent(BaseAgent):
                 continue
 
             # Speech probably ended. Make sure we have a usable amount of data.
-            if len(self.audio_buffer) >= 3 * len(chunk):
+            if len(self.audio_buffer) > begin_silence_length * len(chunk):
                 self.logger.debug("Speech ended.")
                 assert self.audio_out_socket is not None
                 await self.audio_out_socket.send(self.audio_buffer[: -2 * len(chunk)].tobytes())

src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognition_agent.py

@@ -0,0 +1,166 @@
+import json
+import time
+from collections import Counter, defaultdict
+
+import cv2
+import numpy as np
+import zmq
+import zmq.asyncio as azmq
+from pydantic_core import ValidationError
+
+from control_backend.agents import BaseAgent
+from control_backend.agents.perception.visual_emotion_recognition_agent.visual_emotion_recognizer import (  # noqa
+    DeepFaceEmotionRecognizer,
+)
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
+from control_backend.schemas.belief_message import Belief
+
+
+class VisualEmotionRecognitionAgent(BaseAgent):
+    def __init__(
+        self,
+        name: str,
+        socket_address: str,
+        bind: bool = False,
+        timeout_ms: int = 1000,
+        window_duration: int = settings.behaviour_settings.visual_emotion_recognition_window_duration_s,  # noqa
+        min_frames_required: int = settings.behaviour_settings.visual_emotion_recognition_min_frames_per_face,  # noqa
+    ):
+        """
+        Initialize the Visual Emotion Recognition Agent.
+
+        :param name: Name of the agent
+        :param socket_address: Address of the socket to connect or bind to
+        :param bind: Whether to bind to the socket address (True) or connect (False)
+        :param timeout_ms: Timeout for socket receive operations in milliseconds
+        :param window_duration: Duration in seconds over which to aggregate emotions
+        :param min_frames_required: Minimum number of frames per face required to consider a face
+        valid
+        """
+        super().__init__(name)
+        self.socket_address = socket_address
+        self.socket_bind = bind
+        self.timeout_ms = timeout_ms
+        self.window_duration = window_duration
+        self.min_frames_required = min_frames_required
+
+    async def setup(self):
+        """
+        Initialize the agent resources.
+        1. Initializes the :class:`VisualEmotionRecognizer`.
+        2. Connects to the video input ZMQ socket.
+        3. Starts the background emotion recognition loop.
+        """
+        self.logger.info("Setting up %s.", self.name)
+
+        self.emotion_recognizer = DeepFaceEmotionRecognizer()
+
+        self.video_in_socket = azmq.Context.instance().socket(zmq.SUB)
+
+        if self.socket_bind:
+            self.video_in_socket.bind(self.socket_address)
+        else:
+            self.video_in_socket.connect(self.socket_address)
+
+        self.video_in_socket.setsockopt_string(zmq.SUBSCRIBE, "")
+        self.video_in_socket.setsockopt(zmq.RCVTIMEO, self.timeout_ms)
+        self.video_in_socket.setsockopt(zmq.CONFLATE, 1)
+
+        self.add_behavior(self.emotion_update_loop())
+
+    async def emotion_update_loop(self):
+        """
+        Background loop to receive video frames, recognize emotions, and update beliefs.
+        1. Receives video frames from the ZMQ socket.
+        2. Uses the :class:`VisualEmotionRecognizer` to detect emotions.
+        3. Aggregates emotions over a time window.
+        4. Sends updates to the BDI Core Agent about detected emotions.
+        """
+        # Next time to process the window and update emotions
+        next_window_time = time.time() + self.window_duration
+
+        # Tracks counts of detected emotions per face index
+        face_stats = defaultdict(Counter)
+
+        prev_dominant_emotions = set()
+
+        while self._running:
+            try:
+                frame_bytes = await self.video_in_socket.recv()
+
+                # Convert bytes to a numpy buffer
+                nparr = np.frombuffer(frame_bytes, np.uint8)
+
+                # Decode image into the generic Numpy Array DeepFace expects
+                frame_image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+                if frame_image is None:
+                    # Could not decode image, skip this frame
+                    continue
+
+                # Get the dominant emotion from each face
+                current_emotions = self.emotion_recognizer.sorted_dominant_emotions(frame_image)
+                # Update emotion counts for each detected face
+                for i, emotion in enumerate(current_emotions):
+                    face_stats[i][emotion] += 1
+
+                # If window duration has passed, process the collected stats
+                if time.time() >= next_window_time:
+                    window_dominant_emotions = set()
+                    # Determine dominant emotion for each face in the window
+                    for _, counter in face_stats.items():
+                        total_detections = sum(counter.values())
+
+                        if total_detections >= self.min_frames_required:
+                            dominant_emotion = counter.most_common(1)[0][0]
+                            window_dominant_emotions.add(dominant_emotion)
+
+                    await self.update_emotions(prev_dominant_emotions, window_dominant_emotions)
+                    prev_dominant_emotions = window_dominant_emotions
+                    face_stats.clear()
+                    next_window_time = time.time() + self.window_duration
+
+            except zmq.Again:
+                self.logger.warning("No video frame received within timeout.")
+
+    async def update_emotions(self, prev_emotions: set[str], emotions: set[str]):
+        """
+        Compare emotions from previous window and current emotions,
+        send updates to BDI Core Agent.
+        """
+        emotions_to_remove = prev_emotions - emotions
+        emotions_to_add = emotions - prev_emotions
+
+        if not emotions_to_add and not emotions_to_remove:
+            return
+
+        emotion_beliefs_remove = []
+        for emotion in emotions_to_remove:
+            self.logger.info(f"Emotion '{emotion}' has disappeared.")
+            try:
+                emotion_beliefs_remove.append(
+                    Belief(name="emotion_detected", arguments=[emotion], remove=True)
+                )
+            except ValidationError:
+                self.logger.warning("Invalid belief for emotion removal: %s", emotion)
+
+        emotion_beliefs_add = []
+        for emotion in emotions_to_add:
+            self.logger.info(f"New emotion detected: '{emotion}'")
+            try:
+                emotion_beliefs_add.append(Belief(name="emotion_detected", arguments=[emotion]))
+            except ValidationError:
+                self.logger.warning("Invalid belief for new emotion: %s", emotion)
+
+        beliefs_list_add = [b.model_dump() for b in emotion_beliefs_add]
+        beliefs_list_remove = [b.model_dump() for b in emotion_beliefs_remove]
+        payload = {"create": beliefs_list_add, "delete": beliefs_list_remove}
+
+        message = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            body=json.dumps(payload),
+            thread="beliefs",
+        )
+        await self.send(message)

src/control_backend/agents/perception/visual_emotion_recognition_agent/visual_emotion_recognizer.py

@@ -0,0 +1,55 @@
+import abc
+
+import numpy as np
+from deepface import DeepFace
+
+
+class VisualEmotionRecognizer(abc.ABC):
+    @abc.abstractmethod
+    def load_model(self):
+        """Load the visual emotion recognition model into memory."""
+        pass
+
+    @abc.abstractmethod
+    def sorted_dominant_emotions(self, image) -> list[str]:
+        """
+        Recognize dominant emotions from faces in the given image.
+        Emotions can be one of ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral'].
+        To minimize false positives, consider filtering faces with low confidence.
+
+        :param image: The input image for emotion recognition.
+        :return: List of dominant emotion detected for each face in the image, 
+        sorted per face.
+        """
+        pass
+
+class DeepFaceEmotionRecognizer(VisualEmotionRecognizer):
+    """
+    DeepFace-based implementation of VisualEmotionRecognizer.
+    DeepFape has proven to be quite a pessimistic model, so expect sad, fear and neutral 
+    emotions to be over-represented.
+    """
+    def __init__(self):
+        self.load_model()
+    
+    def load_model(self):
+        print("Loading Deepface Emotion Model...")
+        dummy_img = np.zeros((224, 224, 3), dtype=np.uint8)
+        # analyze does not take a model as an argument, calling it once on a dummy image to load 
+        # the model
+        DeepFace.analyze(dummy_img, actions=['emotion'], enforce_detection=False)
+        print("Deepface Emotion Model loaded.")
+    
+    def sorted_dominant_emotions(self, image) -> list[str]:
+        analysis = DeepFace.analyze(image, 
+                                    actions=['emotion'], 
+                                    enforce_detection=False
+                                    )
+        
+        # Sort faces by x coordinate to maintain left-to-right order
+        analysis.sort(key=lambda face: face['region']['x'])
+
+        analysis = [face for face in analysis if face['face_confidence'] >= 0.90]
+        
+        dominant_emotions = [face['dominant_emotion'] for face in analysis]
+        return dominant_emotions

src/control_backend/agents/user_interrupt/user_interrupt_agent.py

@@ -4,8 +4,11 @@ import zmq
 from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
+from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
+from control_backend.schemas.belief_message import Belief, BeliefMessage
+from control_backend.schemas.program import ConditionalNorm, Program
 from control_backend.schemas.ri_message import (
     GestureCommand,
     PauseCommand,
@@ -23,29 +26,55 @@ class UserInterruptAgent(BaseAgent):
 
     - Send a prioritized message to the `RobotSpeechAgent`
     - Send a prioritized gesture to the `RobotGestureAgent`
-    - Send a belief override to the `BDIProgramManager`in order to activate a
+    - Send a belief override to the `BDI Core` in order to activate a
         trigger/conditional norm or complete a goal.
 
     Prioritized actions clear the current RI queue before inserting the new item,
     ensuring they are executed immediately after Pepper's current action has been fulfilled.
 
-    :ivar sub_socket: The ZMQ SUB socket used to receive user intterupts.
+    :ivar sub_socket: The ZMQ SUB socket used to receive user interrupts.
     """
 
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.sub_socket = None
+        self.pub_socket = None
+        self._trigger_map = {}
+        self._trigger_reverse_map = {}
+
+        self._goal_map = {}  # id -> sluggified goal
+        self._goal_reverse_map = {}  # sluggified goal -> id
+
+        self._cond_norm_map = {}  # id -> sluggified cond norm
+        self._cond_norm_reverse_map = {}  # sluggified cond norm -> id
+
+    async def setup(self):
+        """
+        Initialize the agent by setting up ZMQ sockets for receiving button events and
+        publishing updates.
+        """
+        context = Context.instance()
+
+        self.sub_socket = context.socket(zmq.SUB)
+        self.sub_socket.connect(settings.zmq_settings.internal_sub_address)
+        self.sub_socket.subscribe("button_pressed")
+
+        self.pub_socket = context.socket(zmq.PUB)
+        self.pub_socket.connect(settings.zmq_settings.internal_pub_address)
+
+        self.add_behavior(self._receive_button_event())
 
     async def _receive_button_event(self):
         """
-        The behaviour of the UserInterruptAgent.
-        Continuous loop that receives button_pressed events from the button_pressed HTTP endpoint.
-        These events contain a type and a context.
+        Main loop to receive and process button press events from the UI.
 
-        These are the different types and contexts:
-        - type: "speech", context: string that the robot has to say.
-        - type: "gesture", context: single gesture name that the robot has to perform.
-        - type: "override", context: belief_id that overrides the goal/trigger/conditional norm.
+        Handles different event types:
+        - `speech`: Triggers immediate robot speech.
+        - `gesture`: Triggers an immediate robot gesture.
+        - `override`: Forces a belief, trigger, or goal completion in the BDI core.
+        - `override_unachieve`: Removes a belief from the BDI core.
+        - `pause`: Toggles the system's pause state.
+        - `next_phase` / `reset_phase`: Controls experiment flow.
         """
         while True:
             topic, body = await self.sub_socket.recv_multipart()
@@ -58,73 +87,208 @@ class UserInterruptAgent(BaseAgent):
                 self.logger.error("Received invalid JSON payload on topic %s", topic)
                 continue
 
-            if event_type == "speech":
-                await self._send_to_speech_agent(event_context)
-                self.logger.info(
-                    "Forwarded button press (speech) with context '%s' to RobotSpeechAgent.",
-                    event_context,
-                )
-            elif event_type == "gesture":
-                await self._send_to_gesture_agent(event_context)
-                self.logger.info(
-                    "Forwarded button press (gesture) with context '%s' to RobotGestureAgent.",
-                    event_context,
-                )
-            elif event_type == "override":
-                await self._send_to_program_manager(event_context)
-                self.logger.info(
-                    "Forwarded button press (override) with context '%s' to BDIProgramManager.",
-                    event_context,
-                )
-            elif event_type == "pause":
-                self.logger.debug(
-                    "Received pause/resume button press with context '%s'.", event_context
-                )
-                await self._send_pause_command(event_context)
-                if event_context:
-                    self.logger.info("Sent pause command.")
-                else:
-                    self.logger.info("Sent resume command.")
+            self.logger.debug("Received event type %s", event_type)
 
-            elif event_type in ["next_phase", "reset_phase", "reset_experiment"]:
-                await self._send_experiment_control_to_bdi_core(event_type)
+            match event_type:
+                case "speech":
+                    await self._send_to_speech_agent(event_context)
+                    self.logger.info(
+                        "Forwarded button press (speech) with context '%s' to RobotSpeechAgent.",
+                        event_context,
+                    )
+                case "gesture":
+                    await self._send_to_gesture_agent(event_context)
+                    self.logger.info(
+                        "Forwarded button press (gesture) with context '%s' to RobotGestureAgent.",
+                        event_context,
+                    )
+                case "override":
+                    ui_id = str(event_context)
+                    if asl_trigger := self._trigger_map.get(ui_id):
+                        await self._send_to_bdi("force_trigger", asl_trigger)
+                        self.logger.info(
+                            "Forwarded button press (override) with context '%s' to BDI Core.",
+                            event_context,
+                        )
+                    elif asl_cond_norm := self._cond_norm_map.get(ui_id):
+                        await self._send_to_bdi_belief(asl_cond_norm, "cond_norm")
+                        self.logger.info(
+                            "Forwarded button press (override) with context '%s' to BDI Core.",
+                            event_context,
+                        )
+                    elif asl_goal := self._goal_map.get(ui_id):
+                        await self._send_to_bdi_belief(asl_goal, "goal")
+                        self.logger.info(
+                            "Forwarded button press (override) with context '%s' to BDI Core.",
+                            event_context,
+                        )
+                        # Send achieve_goal to program manager to update semantic belief extractor
+                        goal_achieve_msg = InternalMessage(
+                            to=settings.agent_settings.bdi_program_manager_name,
+                            thread="achieve_goal",
+                            body=ui_id,
+                        )
 
-            else:
-                self.logger.warning(
-                    "Received button press with unknown type '%s' (context: '%s').",
-                    event_type,
-                    event_context,
-                )
+                        await self.send(goal_achieve_msg)
+                    else:
+                        self.logger.warning("Could not determine which element to override.")
+                case "override_unachieve":
+                    ui_id = str(event_context)
+                    if asl_cond_norm := self._cond_norm_map.get(ui_id):
+                        await self._send_to_bdi_belief(asl_cond_norm, "cond_norm", True)
+                        self.logger.info(
+                            "Forwarded button press (override_unachieve)"
+                            "with context '%s' to BDI Core.",
+                            event_context,
+                        )
+                    else:
+                        self.logger.warning(
+                            "Could not determine which conditional norm to unachieve."
+                        )
 
-    async def _send_experiment_control_to_bdi_core(self, type):
+                case "pause":
+                    self.logger.debug(
+                        "Received pause/resume button press with context '%s'.", event_context
+                    )
+                    await self._send_pause_command(event_context)
+                    if event_context:
+                        self.logger.info("Sent pause command.")
+                    else:
+                        self.logger.info("Sent resume command.")
+
+                case "next_phase" | "reset_phase":
+                    await self._send_experiment_control_to_bdi_core(event_type)
+                case _:
+                    self.logger.warning(
+                        "Received button press with unknown type '%s' (context: '%s').",
+                        event_type,
+                        event_context,
+                    )
+
+    async def handle_message(self, msg: InternalMessage):
         """
-        method to send experiment control buttons to bdi core.
+        Handles internal messages from other agents, such as program updates or trigger
+        notifications.
 
-        :param type: the type of control button we should send to the bdi core.
+        :param msg: The incoming :class:`~control_backend.core.agent_system.InternalMessage`.
         """
-        # Switch which thread we should send to bdi core
-        thread = ""
-        match type:
-            case "next_phase":
-                thread = "force_next_phase"
-            case "reset_phase":
-                thread = "reset_current_phase"
-            case "reset_experiment":
-                thread = "reset_experiment"
+        match msg.thread:
+            case "new_program":
+                self._create_mapping(msg.body)
+            case "trigger_start":
+                # msg.body is the sluggified trigger
+                asl_slug = msg.body
+                ui_id = self._trigger_reverse_map.get(asl_slug)
+
+                if ui_id:
+                    payload = {"type": "trigger_update", "id": ui_id, "achieved": True}
+                    await self._send_experiment_update(payload)
+                    self.logger.info(f"UI Update: Trigger {asl_slug} started (ID: {ui_id})")
+            case "trigger_end":
+                asl_slug = msg.body
+                ui_id = self._trigger_reverse_map.get(asl_slug)
+                if ui_id:
+                    payload = {"type": "trigger_update", "id": ui_id, "achieved": False}
+                    await self._send_experiment_update(payload)
+                    self.logger.info(f"UI Update: Trigger {asl_slug} ended (ID: {ui_id})")
+            case "transition_phase":
+                new_phase_id = msg.body
+                self.logger.info(f"Phase transition detected: {new_phase_id}")
+
+                payload = {"type": "phase_update", "id": new_phase_id}
+
+                await self._send_experiment_update(payload)
+            case "goal_start":
+                goal_name = msg.body
+                ui_id = self._goal_reverse_map.get(goal_name)
+                if ui_id:
+                    payload = {"type": "goal_update", "id": ui_id, "active": True}
+                    await self._send_experiment_update(payload)
+                    self.logger.info(f"UI Update: Goal {goal_name} started (ID: {ui_id})")
+            case "active_norms_update":
+                active_norms_asl = [
+                    s.strip("() '\",") for s in msg.body.split(",") if s.strip("() '\",")
+                ]
+                await self._broadcast_cond_norms(active_norms_asl)
             case _:
-                self.logger.warning(
-                    "Received unknown experiment control type '%s' to send to BDI Core.",
-                    type,
-                )
+                self.logger.debug(f"Received internal message on unhandled thread: {msg.thread}")
 
-        out_msg = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            sender=self.name,
-            thread=thread,
-            body="",
-        )
-        self.logger.debug("Sending experiment control '%s' to BDI Core.", thread)
-        await self.send(out_msg)
+    async def _broadcast_cond_norms(self, active_slugs: list[str]):
+        """
+        Broadcasts the current activation state of all conditional norms to the UI.
+
+        :param active_slugs: A list of sluggified norm names currently active in the BDI core.
+        """
+        updates = []
+        for asl_slug, ui_id in self._cond_norm_reverse_map.items():
+            is_active = asl_slug in active_slugs
+            updates.append({"id": ui_id, "active": is_active})
+
+        payload = {"type": "cond_norms_state_update", "norms": updates}
+
+        if self.pub_socket:
+            topic = b"status"
+            body = json.dumps(payload).encode("utf-8")
+            await self.pub_socket.send_multipart([topic, body])
+            # self.logger.info(f"UI Update: Active norms {updates}")
+
+    def _create_mapping(self, program_json: str):
+        """
+        Creates a bidirectional mapping between UI identifiers and AgentSpeak slugs.
+
+        :param program_json: The JSON representation of the behavioral program.
+        """
+        try:
+            program = Program.model_validate_json(program_json)
+            self._trigger_map = {}
+            self._trigger_reverse_map = {}
+            self._goal_map = {}
+            self._cond_norm_map = {}
+            self._cond_norm_reverse_map = {}
+
+            for phase in program.phases:
+                for trigger in phase.triggers:
+                    slug = AgentSpeakGenerator.slugify(trigger)
+                    self._trigger_map[str(trigger.id)] = slug
+                    self._trigger_reverse_map[slug] = str(trigger.id)
+
+                for goal in phase.goals:
+                    self._goal_map[str(goal.id)] = AgentSpeakGenerator.slugify(goal)
+                    self._goal_reverse_map[AgentSpeakGenerator.slugify(goal)] = str(goal.id)
+
+                for goal, id in self._goal_reverse_map.items():
+                    self.logger.debug(f"Goal mapping: UI ID {goal} -> {id}")
+
+                for norm in phase.norms:
+                    if isinstance(norm, ConditionalNorm):
+                        asl_slug = AgentSpeakGenerator.slugify(norm)
+
+                        norm_id = str(norm.id)
+
+                        self._cond_norm_map[norm_id] = asl_slug
+                        self._cond_norm_reverse_map[norm.norm] = norm_id
+                        self.logger.debug("Added conditional norm %s", asl_slug)
+
+            self.logger.info(
+                f"Mapped {len(self._trigger_map)} triggers and {len(self._goal_map)} goals "
+                f"and {len(self._cond_norm_map)} conditional norms for UserInterruptAgent."
+            )
+        except Exception as e:
+            self.logger.error(f"Mapping failed: {e}")
+
+    async def _send_experiment_update(self, data, should_log: bool = True):
+        """
+        Publishes an experiment state update to the internal ZMQ bus for the UI.
+
+        :param data: The update payload.
+        :param should_log: Whether to log the update.
+        """
+        if self.pub_socket:
+            topic = b"experiment"
+            body = json.dumps(data).encode("utf-8")
+            await self.pub_socket.send_multipart([topic, body])
+            if should_log:
+                self.logger.debug(f"Sent experiment update: {data}")
 
     async def _send_to_speech_agent(self, text_to_say: str):
         """
@@ -157,26 +321,62 @@ class UserInterruptAgent(BaseAgent):
         )
         await self.send(out_msg)
 
-    async def _send_to_program_manager(self, belief_id: str):
-        """
-        Send a button_override belief to the BDIProgramManager.
+    async def _send_to_bdi(self, thread: str, body: str):
+        """Send slug of trigger to BDI"""
+        msg = InternalMessage(to=settings.agent_settings.bdi_core_name, thread=thread, body=body)
+        await self.send(msg)
+        self.logger.info(f"Directly forced {thread} in BDI: {body}")
 
-        :param belief_id: The belief_id that overrides the goal/trigger/conditional norm.
-        this id can belong to a basic belief or an inferred belief.
-        See also: https://utrechtuniversity.youtrack.cloud/articles/N25B-A-27/UI-components
+    async def _send_to_bdi_belief(self, asl: str, asl_type: str, unachieve: bool = False):
+        """Send belief to BDI Core"""
+        if asl_type == "goal":
+            belief_name = f"achieved_{asl}"
+        elif asl_type == "cond_norm":
+            belief_name = f"force_{asl}"
+        else:
+            self.logger.warning("Tried to send belief with unknown type")
+        belief = Belief(name=belief_name, arguments=None)
+        self.logger.debug(f"Sending belief to BDI Core: {belief_name}")
+        # Conditional norms are unachieved by removing the belief
+        belief_message = (
+            BeliefMessage(delete=[belief]) if unachieve else BeliefMessage(create=[belief])
+        )
+        msg = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            thread="beliefs",
+            body=belief_message.model_dump_json(),
+        )
+        await self.send(msg)
+
+    async def _send_experiment_control_to_bdi_core(self, type):
         """
-        data = {"belief": belief_id}
-        message = InternalMessage(
-            to=settings.agent_settings.bdi_program_manager_name,
+        method to send experiment control buttons to bdi core.
+
+        :param type: the type of control button we should send to the bdi core.
+        """
+        # Switch which thread we should send to bdi core
+        thread = ""
+        match type:
+            case "next_phase":
+                thread = "force_next_phase"
+            case "reset_phase":
+                thread = "reset_current_phase"
+            case "reset_experiment":
+                thread = "reset_experiment"
+            case _:
+                self.logger.warning(
+                    "Received unknown experiment control type '%s' to send to BDI Core.",
+                    type,
+                )
+
+        out_msg = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
             sender=self.name,
-            body=json.dumps(data),
-            thread="belief_override_id",
-        )
-        await self.send(message)
-        self.logger.info(
-            "Sent button_override belief with id '%s' to Program manager.",
-            belief_id,
+            thread=thread,
+            body="",
         )
+        self.logger.debug("Sending experiment control '%s' to BDI Core.", thread)
+        await self.send(out_msg)
 
     async def _send_pause_command(self, pause):
         """
@@ -209,18 +409,3 @@ class UserInterruptAgent(BaseAgent):
             )
             await self.send(vad_message)
             self.logger.info("Sent resume command to VAD Agent and RI Communication Agent.")
-
-    async def setup(self):
-        """
-        Initialize the agent.
-
-        Connects the internal ZMQ SUB socket and subscribes to the 'button_pressed' topic.
-        Starts the background behavior to receive the user interrupts.
-        """
-        context = Context.instance()
-
-        self.sub_socket = context.socket(zmq.SUB)
-        self.sub_socket.connect(settings.zmq_settings.internal_sub_address)
-        self.sub_socket.subscribe("button_pressed")
-
-        self.add_behavior(self._receive_button_event())

src/control_backend/api/v1/endpoints/button_pressed.py

@@ -1,31 +0,0 @@
-import logging
-
-from fastapi import APIRouter, Request
-
-from control_backend.schemas.events import ButtonPressedEvent
-
-logger = logging.getLogger(__name__)
-router = APIRouter()
-
-
-@router.post("/button_pressed", status_code=202)
-async def receive_button_event(event: ButtonPressedEvent, request: Request):
-    """
-    Endpoint to handle external button press events.
-
-    Validates the event payload and publishes it to the internal 'button_pressed' topic.
-    Subscribers (in this case user_interrupt_agent) will pick this up to trigger
-    specific behaviors or state changes.
-
-    :param event: The parsed ButtonPressedEvent object.
-    :param request: The FastAPI request object.
-    """
-    logger.debug("Received button event: %s | %s", event.type, event.context)
-
-    topic = b"button_pressed"
-    body = event.model_dump_json().encode()
-
-    pub_socket = request.app.state.endpoints_pub_socket
-    await pub_socket.send_multipart([topic, body])
-
-    return {"status": "Event received"}

src/control_backend/api/v1/endpoints/logs.py

@@ -1,8 +1,9 @@
 import logging
+from pathlib import Path
 
 import zmq
-from fastapi import APIRouter
-from fastapi.responses import StreamingResponse
+from fastapi import APIRouter, HTTPException
+from fastapi.responses import FileResponse, StreamingResponse
 from zmq.asyncio import Context
 
 from control_backend.core.config import settings
@@ -38,3 +39,29 @@ async def log_stream():
             yield f"data: {message}\n\n"
 
     return StreamingResponse(gen(), media_type="text/event-stream")
+
+
+LOGGING_DIR = Path(settings.logging_settings.experiment_log_directory).resolve()
+
+
+@router.get("/logs/files")
+@router.get("/api/logs/files")
+async def log_directory():
+    """
+    Get a list of all log files stored in the experiment log file directory.
+    """
+    return [f.name for f in LOGGING_DIR.glob("*.log")]
+
+
+@router.get("/logs/files/{filename}")
+@router.get("/api/logs/files/{filename}")
+async def log_file(filename: str):
+    # Prevent path-traversal
+    file_path = (LOGGING_DIR / filename).resolve()  # This .resolve() is important
+    if not file_path.is_relative_to(LOGGING_DIR):
+        raise HTTPException(status_code=400, detail="Invalid filename.")
+
+    if not file_path.is_file():
+        raise HTTPException(status_code=404, detail="File not found.")
+
+    return FileResponse(file_path, filename=file_path.name)

src/control_backend/api/v1/endpoints/robot.py

@@ -137,7 +137,6 @@ async def ping_stream(request: Request):
                 logger.info("Client disconnected from SSE")
                 break
 
-            logger.debug(f"Yielded new connection event in robot ping router: {str(connected)}")
             connectedJson = json.dumps(connected)
             yield (f"data: {connectedJson}\n\n")
 

src/control_backend/api/v1/endpoints/sse.py

@@ -1,12 +0,0 @@
-from fastapi import APIRouter, Request
-
-router = APIRouter()
-
-
-# TODO: implement
-@router.get("/sse")
-async def sse(request: Request):
-    """
-    Placeholder for future Server-Sent Events endpoint.
-    """
-    pass

src/control_backend/api/v1/endpoints/user_interact.py

@@ -0,0 +1,94 @@
+import asyncio
+import logging
+
+import zmq
+import zmq.asyncio
+from fastapi import APIRouter, Request
+from fastapi.responses import StreamingResponse
+from zmq.asyncio import Context
+
+from control_backend.core.config import settings
+from control_backend.schemas.events import ButtonPressedEvent
+
+logger = logging.getLogger(__name__)
+router = APIRouter()
+
+
+@router.post("/button_pressed", status_code=202)
+async def receive_button_event(event: ButtonPressedEvent, request: Request):
+    """
+    Endpoint to handle external button press events.
+
+    Validates the event payload and publishes it to the internal 'button_pressed' topic.
+    Subscribers (in this case user_interrupt_agent) will pick this up to trigger
+    specific behaviors or state changes.
+
+    :param event: The parsed ButtonPressedEvent object.
+    :param request: The FastAPI request object.
+    """
+    logger.debug("Received button event: %s | %s", event.type, event.context)
+
+    topic = b"button_pressed"
+    body = event.model_dump_json().encode()
+
+    pub_socket = request.app.state.endpoints_pub_socket
+    await pub_socket.send_multipart([topic, body])
+
+    return {"status": "Event received"}
+
+
+@router.get("/experiment_stream")
+async def experiment_stream(request: Request):
+    # Use the asyncio-compatible context
+    context = Context.instance()
+    socket = context.socket(zmq.SUB)
+
+    # Connect and subscribe
+    socket.connect(settings.zmq_settings.internal_sub_address)
+    socket.subscribe(b"experiment")
+
+    async def gen():
+        try:
+            while True:
+                # Check if client closed the tab
+                if await request.is_disconnected():
+                    logger.error("Client disconnected from experiment stream.")
+                    break
+
+                try:
+                    parts = await asyncio.wait_for(socket.recv_multipart(), timeout=10.0)
+                    _, message = parts
+                    yield f"data: {message.decode().strip()}\n\n"
+                except TimeoutError:
+                    continue
+        finally:
+            socket.close()
+
+    return StreamingResponse(gen(), media_type="text/event-stream")
+
+
+@router.get("/status_stream")
+async def status_stream(request: Request):
+    context = Context.instance()
+    socket = context.socket(zmq.SUB)
+    socket.connect(settings.zmq_settings.internal_sub_address)
+
+    socket.subscribe(b"status")
+
+    async def gen():
+        try:
+            while True:
+                if await request.is_disconnected():
+                    break
+                try:
+                    # Shorter timeout since this is frequent
+                    parts = await asyncio.wait_for(socket.recv_multipart(), timeout=0.5)
+                    _, message = parts
+                    yield f"data: {message.decode().strip()}\n\n"
+                except TimeoutError:
+                    yield ": ping\n\n"  # Keep the connection alive
+                    continue
+        finally:
+            socket.close()
+
+    return StreamingResponse(gen(), media_type="text/event-stream")

src/control_backend/api/v1/router.py

@@ -1,17 +1,15 @@
 from fastapi.routing import APIRouter
 
-from control_backend.api.v1.endpoints import button_pressed, logs, message, program, robot, sse
+from control_backend.api.v1.endpoints import logs, message, program, robot, user_interact
 
 api_router = APIRouter()
 
 api_router.include_router(message.router, tags=["Messages"])
 
-api_router.include_router(sse.router, tags=["SSE"])
-
 api_router.include_router(robot.router, prefix="/robot", tags=["Pings", "Commands"])
 
 api_router.include_router(logs.router, tags=["Logs"])
 
 api_router.include_router(program.router, tags=["Program"])
 
-api_router.include_router(button_pressed.router, tags=["Button Pressed Events"])
+api_router.include_router(user_interact.router, tags=["Button Pressed Events"])

src/control_backend/core/agent_system.py

@@ -22,10 +22,22 @@ class AgentDirectory:
 
     @staticmethod
     def register(name: str, agent: "BaseAgent"):
+        """
+        Registers an agent instance with a unique name.
+
+        :param name: The name of the agent.
+        :param agent: The :class:`BaseAgent` instance.
+        """
         _agent_directory[name] = agent
 
     @staticmethod
     def get(name: str) -> "BaseAgent | None":
+        """
+        Retrieves a registered agent instance by name.
+
+        :param name: The name of the agent to retrieve.
+        :return: The :class:`BaseAgent` instance, or None if not found.
+        """
         return _agent_directory.get(name)
 
 
@@ -60,6 +72,9 @@ class BaseAgent(ABC):
         self._tasks: set[asyncio.Task] = set()
         self._running = False
 
+        self._internal_pub_socket: None | azmq.Socket = None
+        self._internal_sub_socket: None | azmq.Socket = None
+
         # Register immediately
         AgentDirectory.register(name, self)
 
@@ -117,7 +132,7 @@ class BaseAgent(ABC):
             task.cancel()
         self.logger.info(f"Agent {self.name} stopped")
 
-    async def send(self, message: InternalMessage):
+    async def send(self, message: InternalMessage, should_log: bool = True):
         """
         Send a message to another agent.
 
@@ -130,16 +145,26 @@ class BaseAgent(ABC):
 
         :param message: The message to send.
         """
-        target = AgentDirectory.get(message.to)
-        if target:
-            await target.inbox.put(message)
-            self.logger.debug(f"Sent message {message.body} to {message.to} via regular inbox.")
-        else:
-            # Apparently target agent is on a different process, send via ZMQ
-            topic = f"internal/{message.to}".encode()
-            body = message.model_dump_json().encode()
-            await self._internal_pub_socket.send_multipart([topic, body])
-            self.logger.debug(f"Sent message {message.body} to {message.to} via ZMQ.")
+        message.sender = self.name
+        to = message.to
+        receivers = [to] if isinstance(to, str) else to
+
+        for receiver in receivers:
+            target = AgentDirectory.get(receiver)
+
+            if target:
+                await target.inbox.put(message)
+                if should_log:
+                    self.logger.debug(
+                        f"Sent message {message.body} to {message.to} via regular inbox."
+                    )
+            else:
+                # Apparently target agent is on a different process, send via ZMQ
+                topic = f"internal/{receiver}".encode()
+                body = message.model_dump_json().encode()
+                await self._internal_pub_socket.send_multipart([topic, body])
+                if should_log:
+                    self.logger.debug(f"Sent message {message.body} to {message.to} via ZMQ.")
 
     async def _process_inbox(self):
         """
@@ -149,7 +174,6 @@ class BaseAgent(ABC):
         """
         while self._running:
             msg = await self.inbox.get()
-            self.logger.debug(f"Received message from {msg.sender}.")
             await self.handle_message(msg)
 
     async def _receive_internal_zmq_loop(self):
@@ -192,7 +216,16 @@ class BaseAgent(ABC):
 
         :param coro: The coroutine to execute as a task.
         """
-        task = asyncio.create_task(coro)
+
+        async def try_coro(coro_: Coroutine):
+            try:
+                await coro_
+            except asyncio.CancelledError:
+                self.logger.debug("A behavior was canceled successfully: %s", coro_)
+            except Exception:
+                self.logger.warning("An exception occurred in a behavior.", exc_info=True)
+
+        task = asyncio.create_task(try_coro(coro))
         self._tasks.add(task)
         task.add_done_callback(self._tasks.discard)
         return task

src/control_backend/core/config.py

@@ -1,3 +1,12 @@
+"""
+An exhaustive overview of configurable options. All of these can be set using environment variables
+by nesting with double underscores (__). Start from the ``Settings`` class.
+
+For example, ``settings.ri_host`` becomes ``RI_HOST``, and
+``settings.zmq_settings.ri_communication_address`` becomes
+``ZMQ_SETTINGS__RI_COMMUNICATION_ADDRESS``.
+"""
+
 from pydantic import BaseModel
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -8,16 +17,17 @@ class ZMQSettings(BaseModel):
 
     :ivar internal_pub_address: Address for the internal PUB socket.
     :ivar internal_sub_address: Address for the internal SUB socket.
-    :ivar ri_command_address: Address for sending commands to the Robot Interface.
-    :ivar ri_communication_address: Address for receiving communication from the Robot Interface.
-    :ivar vad_agent_address: Address for the Voice Activity Detection (VAD) agent.
+    :ivar ri_communication_address: Address for the endpoint that the Robot Interface connects to.
+    :ivar vad_pub_address: Address that the VAD agent binds to and publishes audio segments to.
     """
 
+    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
+
     internal_pub_address: str = "tcp://localhost:5560"
     internal_sub_address: str = "tcp://localhost:5561"
-    ri_command_address: str = "tcp://localhost:0000"
     ri_communication_address: str = "tcp://*:5555"
     internal_gesture_rep_adress: str = "tcp://localhost:7788"
+    vad_pub_address: str = "inproc://vad_stream"
 
 
 class AgentSettings(BaseModel):
@@ -25,7 +35,6 @@ class AgentSettings(BaseModel):
     Names of the various agents in the system. These names are used for routing messages.
 
     :ivar bdi_core_name: Name of the BDI Core Agent.
-    :ivar bdi_belief_collector_name: Name of the Belief Collector Agent.
     :ivar bdi_program_manager_name: Name of the BDI Program Manager Agent.
     :ivar text_belief_extractor_name: Name of the Text Belief Extractor Agent.
     :ivar vad_name: Name of the Voice Activity Detection (VAD) Agent.
@@ -36,10 +45,12 @@ class AgentSettings(BaseModel):
     :ivar robot_speech_name: Name of the Robot Speech Agent.
     """
 
+    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
+
     # agent names
     bdi_core_name: str = "bdi_core_agent"
-    bdi_belief_collector_name: str = "belief_collector_agent"
     bdi_program_manager_name: str = "bdi_program_manager_agent"
+    visual_emotion_recognition_name: str = "visual_emotion_recognition_agent"
     text_belief_extractor_name: str = "text_belief_extractor_agent"
     vad_name: str = "vad_agent"
     llm_name: str = "llm_agent"
@@ -61,13 +72,20 @@ class BehaviourSettings(BaseModel):
     :ivar vad_prob_threshold: Probability threshold for Voice Activity Detection.
     :ivar vad_initial_since_speech: Initial value for 'since speech' counter in VAD.
     :ivar vad_non_speech_patience_chunks: Number of non-speech chunks to wait before speech ended.
+    :ivar vad_begin_silence_chunks: The number of chunks of silence to prepend to speech chunks.
     :ivar transcription_max_concurrent_tasks: Maximum number of concurrent transcription tasks.
     :ivar transcription_words_per_minute: Estimated words per minute for transcription timing.
     :ivar transcription_words_per_token: Estimated words per token for transcription timing.
     :ivar transcription_token_buffer: Buffer for transcription tokens.
     :ivar conversation_history_length_limit: The maximum amount of messages to extract beliefs from.
+    :ivar visual_emotion_recognition_window_duration_s: Duration in seconds over which to aggregate 
+    emotions and update emotion beliefs.
+    :ivar visual_emotion_recognition_min_frames_per_face: Minimum number of frames per face required
+    to consider a face valid.
     """
 
+    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
+
     sleep_s: float = 1.0
     comm_setup_max_retries: int = 5
     socket_poller_timeout_ms: int = 100
@@ -75,7 +93,8 @@ class BehaviourSettings(BaseModel):
     # VAD settings
     vad_prob_threshold: float = 0.5
     vad_initial_since_speech: int = 100
-    vad_non_speech_patience_chunks: int = 3
+    vad_non_speech_patience_chunks: int = 15
+    vad_begin_silence_chunks: int = 6
 
     # transcription behaviour
     transcription_max_concurrent_tasks: int = 3
@@ -86,6 +105,9 @@ class BehaviourSettings(BaseModel):
     # Text belief extractor settings
     conversation_history_length_limit: int = 10
 
+    # Visual Emotion Recognition settings
+    visual_emotion_recognition_window_duration_s: int = 5
+    visual_emotion_recognition_min_frames_per_face: int = 3
 
 class LLMSettings(BaseModel):
     """
@@ -99,8 +121,11 @@ class LLMSettings(BaseModel):
     :ivar n_parallel: The number of parallel calls allowed to be made to the LLM.
     """
 
+    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
+
     local_llm_url: str = "http://localhost:1234/v1/chat/completions"
     local_llm_model: str = "gpt-oss"
+    api_key: str = ""
     chat_temperature: float = 1.0
     code_temperature: float = 0.3
     n_parallel: int = 4
@@ -115,6 +140,8 @@ class VADSettings(BaseModel):
     :ivar sample_rate_hz: Sample rate in Hz for the VAD model.
     """
 
+    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
+
     repo_or_dir: str = "snakers4/silero-vad"
     model_name: str = "silero_vad"
     sample_rate_hz: int = 16000
@@ -128,17 +155,34 @@ class SpeechModelSettings(BaseModel):
     :ivar openai_model_name: Model name for OpenAI-based speech recognition.
     """
 
+    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
+
     # model identifiers for speech recognition
     mlx_model_name: str = "mlx-community/whisper-small.en-mlx"
     openai_model_name: str = "small.en"
 
 
+class LoggingSettings(BaseModel):
+    """
+    Configuration for logging.
+
+    :ivar logging_config_file: Path to the logging configuration file.
+    :ivar experiment_log_directory: Location of the experiment logs. Must match the logging config.
+    :ivar experiment_logger_name: Name of the experiment logger. Must match the logging config.
+    """
+
+    logging_config_file: str = ".logging_config.yaml"
+    experiment_log_directory: str = "experiment_logs"
+    experiment_logger_name: str = "experiment"
+
+
 class Settings(BaseSettings):
     """
     Global application settings.
 
     :ivar app_title: Title of the application.
     :ivar ui_url: URL of the frontend UI.
+    :ivar ri_host: The hostname of the Robot Interface.
     :ivar zmq_settings: ZMQ configuration.
     :ivar agent_settings: Agent name configuration.
     :ivar behaviour_settings: Behavior configuration.
@@ -151,6 +195,10 @@ class Settings(BaseSettings):
 
     ui_url: str = "http://localhost:5173"
 
+    ri_host: str = "localhost"
+
+    logging_settings: LoggingSettings = LoggingSettings()
+
     zmq_settings: ZMQSettings = ZMQSettings()
 
     agent_settings: AgentSettings = AgentSettings()

src/control_backend/logging/__init__.py

@@ -1 +1,4 @@
+from .dated_file_handler import DatedFileHandler as DatedFileHandler
+from .optional_field_formatter import OptionalFieldFormatter as OptionalFieldFormatter
+from .partial_filter import PartialFilter as PartialFilter
 from .setup_logging import setup_logging as setup_logging

src/control_backend/logging/dated_file_handler.py

@@ -0,0 +1,38 @@
+from datetime import datetime
+from logging import FileHandler
+from pathlib import Path
+
+
+class DatedFileHandler(FileHandler):
+    def __init__(self, file_prefix: str, **kwargs):
+        if not file_prefix:
+            raise ValueError("`file_prefix` argument cannot be empty.")
+        self._file_prefix = file_prefix
+        kwargs["filename"] = self._make_filename()
+        super().__init__(**kwargs)
+
+    def _make_filename(self) -> str:
+        """
+        Create the filename for the current logfile, using the configured file prefix and the
+        current date and time. If the directory does not exist, it gets created.
+
+        :return: A filepath.
+        """
+        filepath = Path(f"{self._file_prefix}-{datetime.now():%Y%m%d-%H%M%S}.log")
+        if not filepath.parent.is_dir():
+            filepath.parent.mkdir(parents=True, exist_ok=True)
+        return str(filepath)
+
+    def do_rollover(self):
+        """
+        Close the current logfile and create a new one with the current date and time.
+        """
+        self.acquire()
+        try:
+            if self.stream:
+                self.stream.close()
+
+            self.baseFilename = self._make_filename()
+            self.stream = self._open()
+        finally:
+            self.release()

src/control_backend/logging/optional_field_formatter.py

@@ -0,0 +1,67 @@
+import logging
+import re
+
+
+class OptionalFieldFormatter(logging.Formatter):
+    """
+    Logging formatter that supports optional fields marked by `?`.
+
+    Optional fields are denoted by placing a `?` after the field name inside
+    the parentheses, e.g., `%(role?)s`. If the field is not provided in the
+    log call's `extra` dict, it will use the default value from `defaults`
+    or `None` if no default is specified.
+
+    :param fmt: Format string with optional `%(name?)s` style fields.
+    :type fmt: str or None
+    :param datefmt: Date format string, passed to parent :class:`logging.Formatter`.
+    :type datefmt: str or None
+    :param style: Formatting style, must be '%'. Passed to parent.
+    :type style: str
+    :param defaults: Default values for optional fields when not provided.
+    :type defaults: dict or None
+
+    :example:
+
+        >>> formatter = OptionalFieldFormatter(
+        ...     fmt="%(asctime)s %(levelname)s %(role?)s %(message)s",
+        ...     defaults={"role": ""-""}
+        ... )
+        >>> handler = logging.StreamHandler()
+        >>> handler.setFormatter(formatter)
+        >>> logger = logging.getLogger(__name__)
+        >>> logger.addHandler(handler)
+        >>>
+        >>> logger.chat("Hello there!", extra={"role": "USER"})
+        2025-01-15 10:30:00 CHAT USER Hello there!
+        >>>
+        >>> logger.info("A logging message")
+        2025-01-15 10:30:01 INFO - A logging message
+
+    .. note::
+        Only `%`-style formatting is supported. The `{` and `$` styles are not
+        compatible with this formatter.
+
+    .. seealso::
+        :class:`logging.Formatter` for base formatter documentation.
+    """
+
+    # Match %(name?)s or %(name?)d etc.
+    OPTIONAL_PATTERN = re.compile(r"%\((\w+)\?\)([sdifFeEgGxXocrba%])")
+
+    def __init__(self, fmt=None, datefmt=None, style="%", defaults=None):
+        self.defaults = defaults or {}
+
+        self.optional_fields = set(self.OPTIONAL_PATTERN.findall(fmt or ""))
+
+        # Convert %(name?)s to %(name)s for standard formatting
+        normalized_fmt = self.OPTIONAL_PATTERN.sub(r"%(\1)\2", fmt or "")
+
+        super().__init__(normalized_fmt, datefmt, style)
+
+    def format(self, record):
+        for field, _ in self.optional_fields:
+            if not hasattr(record, field):
+                default = self.defaults.get(field, None)
+                setattr(record, field, default)
+
+        return super().format(record)

src/control_backend/logging/partial_filter.py

@@ -0,0 +1,10 @@
+import logging
+
+
+class PartialFilter(logging.Filter):
+    """
+    Class to filter any log records that have the "partial" attribute set to ``True``.
+    """
+
+    def filter(self, record):
+        return getattr(record, "partial", False) is not True

src/control_backend/logging/setup_logging.py

@@ -37,7 +37,7 @@ def add_logging_level(level_name: str, level_num: int, method_name: str | None =
     setattr(logging, method_name, log_to_root)
 
 
-def setup_logging(path: str = ".logging_config.yaml") -> None:
+def setup_logging(path: str = settings.logging_settings.logging_config_file) -> None:
     """
     Setup logging configuration of the CB. Tries to load the logging configuration from a file,
     in which we specify custom loggers, formatters, handlers, etc.
@@ -65,7 +65,7 @@ def setup_logging(path: str = ".logging_config.yaml") -> None:
 
             # Patch ZMQ PUBHandler to know about custom levels
             if custom_levels:
-                for logger_name in ("control_backend",):
+                for logger_name in config.get("loggers", {}):
                     logger = logging.getLogger(logger_name)
                     for handler in logger.handlers:
                         if isinstance(handler, PUBHandler):

src/control_backend/main.py

@@ -26,7 +26,6 @@ from zmq.asyncio import Context
 
 # BDI agents
 from control_backend.agents.bdi import (
-    BDIBeliefCollectorAgent,
     BDICoreAgent,
     TextBeliefExtractorAgent,
 )
@@ -122,12 +121,6 @@ async def lifespan(app: FastAPI):
                 "name": settings.agent_settings.bdi_core_name,
             },
         ),
-        "BeliefCollectorAgent": (
-            BDIBeliefCollectorAgent,
-            {
-                "name": settings.agent_settings.bdi_belief_collector_name,
-            },
-        ),
         "TextBeliefExtractorAgent": (
             TextBeliefExtractorAgent,
             {
@@ -172,6 +165,8 @@ async def lifespan(app: FastAPI):
 
     await endpoints_pub_socket.send_multipart([PROGRAM_STATUS, ProgramStatus.STOPPING.value])
     # Additional shutdown logic goes here
+    for agent in agents:
+        await agent.stop()
 
     logger.info("Application shutdown complete.")
 

src/control_backend/schemas/belief_list.py

@@ -1,5 +1,6 @@
 from pydantic import BaseModel
 
+from control_backend.schemas.program import BaseGoal
 from control_backend.schemas.program import Belief as ProgramBelief
 
 
@@ -12,3 +13,13 @@ class BeliefList(BaseModel):
     """
 
     beliefs: list[ProgramBelief]
+
+
+class GoalList(BaseModel):
+    """
+    Represents a list of goals, used for communicating multiple goals between agents.
+
+    :ivar goals: The list of goals.
+    """
+
+    goals: list[BaseGoal]

src/control_backend/schemas/belief_message.py

@@ -11,7 +11,10 @@ class Belief(BaseModel):
     """
 
     name: str
-    arguments: list[str] | None
+    arguments: list[str] | None = None
+
+    # To make it hashable
+    model_config = {"frozen": True}
 
 
 class BeliefMessage(BaseModel):

src/control_backend/schemas/chat_history.py

@@ -2,9 +2,22 @@ from pydantic import BaseModel
 
 
 class ChatMessage(BaseModel):
+    """
+    Represents a single message in a conversation.
+
+    :ivar role: The role of the speaker (e.g., 'user', 'assistant').
+    :ivar content: The text content of the message.
+    """
+
     role: str
     content: str
 
 
 class ChatHistory(BaseModel):
+    """
+    Represents a sequence of chat messages, forming a conversation history.
+
+    :ivar messages: An ordered list of :class:`ChatMessage` objects.
+    """
+
     messages: list[ChatMessage]

src/control_backend/schemas/events.py

@@ -2,5 +2,13 @@ from pydantic import BaseModel
 
 
 class ButtonPressedEvent(BaseModel):
+    """
+    Represents a button press event from the UI.
+
+    :ivar type: The type of event (e.g., 'speech', 'gesture', 'override').
+    :ivar context: Additional data associated with the event (e.g., speech text, gesture name,
+    or ID).
+    """
+
     type: str
     context: str

src/control_backend/schemas/internal_message.py

@@ -1,3 +1,5 @@
+from collections.abc import Iterable
+
 from pydantic import BaseModel
 
 
@@ -5,13 +7,13 @@ class InternalMessage(BaseModel):
     """
     Standard message envelope for communication between agents within the Control Backend.
 
-    :ivar to: The name of the destination agent.
+    :ivar to: The name(s) of the destination agent(s).
     :ivar sender: The name of the sending agent.
     :ivar body: The string payload (often a JSON-serialized model).
     :ivar thread: An optional thread identifier/topic to categorize the message (e.g., 'beliefs').
     """
 
-    to: str
-    sender: str
+    to: str | Iterable[str]
+    sender: str | None = None
     body: str
     thread: str | None = None

src/control_backend/schemas/program.py

@@ -15,21 +15,28 @@ class ProgramElement(BaseModel):
     name: str
     id: UUID4
 
+    # To make program elements hashable
+    model_config = {"frozen": True}
+
 
 class LogicalOperator(Enum):
+    """
+    Logical operators for combining beliefs.
+    """
+
     AND = "AND"
     OR = "OR"
 
 
-type Belief = KeywordBelief | SemanticBelief | InferredBelief
-type BasicBelief = KeywordBelief | SemanticBelief
+type Belief = KeywordBelief | SemanticBelief | InferredBelief | EmotionBelief
+type BasicBelief = KeywordBelief | SemanticBelief | EmotionBelief
 
 
 class KeywordBelief(ProgramElement):
     """
-    Represents a belief that is set when the user spoken text contains a certain keyword.
+    Represents a belief that is activated when a specific keyword is detected in the user's speech.
 
-    :ivar keyword: The keyword on which this belief gets set.
+    :ivar keyword: The string to look for in the transcription.
     """
 
     name: str = ""
@@ -38,9 +45,11 @@ class KeywordBelief(ProgramElement):
 
 class SemanticBelief(ProgramElement):
     """
-    Represents a belief that is set by semantic LLM validation.
+    Represents a belief whose truth value is determined by an LLM analyzing the conversation
+    context.
 
-    :ivar description: Description of how to form the belief, used by the LLM.
+    :ivar description: A natural language description of what this belief represents,
+    used as a prompt for the LLM.
     """
 
     description: str
@@ -48,13 +57,11 @@ class SemanticBelief(ProgramElement):
 
 class InferredBelief(ProgramElement):
     """
-    Represents a belief that gets formed by combining two beliefs with a logical AND or OR.
+    Represents a belief derived from other beliefs using logical operators.
 
-    These beliefs can also be :class:`InferredBelief`, leading to arbitrarily deep nesting.
-
-    :ivar operator: The logical operator to apply.
-    :ivar left: The left part of the logical expression.
-    :ivar right: The right part of the logical expression.
+    :ivar operator: The :class:`LogicalOperator` (AND/OR) to apply.
+    :ivar left: The left operand (another belief).
+    :ivar right: The right operand (another belief).
     """
 
     name: str = ""
@@ -62,8 +69,24 @@ class InferredBelief(ProgramElement):
     left: Belief
     right: Belief
 
+class EmotionBelief(ProgramElement):
+    """
+    Represents a belief that is set when a certain emotion is detected.
+
+    :ivar emotion: The emotion on which this belief gets set.
+    """
+
+    name: str = ""
+    emotion: str
 
 class Norm(ProgramElement):
+    """
+    Base class for behavioral norms that guide the robot's interactions.
+
+    :ivar norm: The textual description of the norm.
+    :ivar critical: Whether this norm is considered critical and should be strictly enforced.
+    """
+
     name: str = ""
     norm: str
     critical: bool = False
@@ -71,10 +94,7 @@ class Norm(ProgramElement):
 
 class BasicNorm(Norm):
     """
-    Represents a behavioral norm.
-
-    :ivar norm: The actual norm text describing the behavior.
-    :ivar critical: When true, this norm should absolutely not be violated (checked separately).
+    A simple behavioral norm that is always considered for activation when its phase is active.
     """
 
     pass
@@ -82,9 +102,9 @@ class BasicNorm(Norm):
 
 class ConditionalNorm(Norm):
     """
-    Represents a norm that is only active when a condition is met (i.e., a certain belief holds).
+    A behavioral norm that is only active when a specific condition (belief) is met.
 
-    :ivar condition: When to activate this norm.
+    :ivar condition: The :class:`Belief` that must hold for this norm to be active.
     """
 
     condition: Belief
@@ -105,31 +125,41 @@ class Plan(ProgramElement):
     steps: list[PlanElement]
 
 
-class Goal(ProgramElement):
+class BaseGoal(ProgramElement):
     """
-    Represents an objective to be achieved. To reach the goal, we should execute
-    the corresponding plan. If we can fail to achieve a goal after executing the plan,
-    for example when the achieving of the goal is dependent on the user's reply, this means
-    that the achieved status will be set from somewhere else in the program.
+    Represents an objective to be achieved. This base version does not include a plan to achieve
+    this goal, and is used in semantic belief extraction.
 
     :ivar description: A description of the goal, used to determine if it has been achieved.
-    :ivar plan: The plan to execute.
     :ivar can_fail: Whether we can fail to achieve the goal after executing the plan.
     """
 
-    description: str
-    plan: Plan
+    description: str = ""
     can_fail: bool = True
 
 
+class Goal(BaseGoal):
+    """
+    Represents an objective to be achieved. To reach the goal, we should execute the corresponding
+    plan. It inherits from the BaseGoal a variable `can_fail`, which if true will cause the
+    completion to be determined based on the conversation.
+
+    Instances of this goal are not hashable because a plan is not hashable.
+
+    :ivar plan: The plan to execute.
+    """
+
+    plan: Plan
+
+
 type Action = SpeechAction | GestureAction | LLMAction
 
 
 class SpeechAction(ProgramElement):
     """
-    Represents the action of the robot speaking a literal text.
+    An action where the robot speaks a predefined literal text.
 
-    :ivar text: The text to speak.
+    :ivar text: The text content to be spoken.
     """
 
     name: str = ""
@@ -138,11 +168,10 @@ class SpeechAction(ProgramElement):
 
 class Gesture(BaseModel):
     """
-    Represents a gesture to be performed. Can be either a single gesture,
-    or a random gesture from a category (tag).
+    Defines a physical gesture for the robot to perform.
 
-    :ivar type: The type of the gesture, "tag" or "single".
-    :ivar name: The name of the single gesture or tag.
+    :ivar type: Whether to use a specific "single" gesture or a random one from a "tag" category.
+    :ivar name: The identifier for the gesture or tag.
     """
 
     type: Literal["tag", "single"]
@@ -151,9 +180,9 @@ class Gesture(BaseModel):
 
 class GestureAction(ProgramElement):
     """
-    Represents the action of the robot performing a gesture.
+    An action where the robot performs a physical gesture.
 
-    :ivar gesture: The gesture to perform.
+    :ivar gesture: The :class:`Gesture` definition.
     """
 
     name: str = ""
@@ -162,10 +191,9 @@ class GestureAction(ProgramElement):
 
 class LLMAction(ProgramElement):
     """
-    Represents the action of letting an LLM generate a reply based on its chat history
-    and an additional goal added in the prompt.
+    An action that triggers an LLM-generated conversational response.
 
-    :ivar goal: The extra (temporary) goal to add to the LLM.
+    :ivar goal: A temporary conversational goal to guide the LLM's response generation.
     """
 
     name: str = ""
@@ -174,24 +202,23 @@ class LLMAction(ProgramElement):
 
 class Trigger(ProgramElement):
     """
-    Represents a belief-based trigger. When a belief is set, the corresponding plan is executed.
+    Defines a reactive behavior: when the condition (belief) is met, the plan is executed.
 
-    :ivar condition: When to activate the trigger.
-    :ivar plan: The plan to execute.
+    :ivar condition: The :class:`Belief` that triggers this behavior.
+    :ivar plan: The :class:`Plan` to execute upon activation.
     """
 
-    name: str = ""
     condition: Belief
     plan: Plan
 
 
 class Phase(ProgramElement):
     """
-    A distinct phase within a program, containing norms, goals, and triggers.
+    A logical stage in the interaction program, grouping norms, goals, and triggers.
 
-    :ivar norms: List of norms active in this phase.
-    :ivar goals: List of goals to pursue in this phase.
-    :ivar triggers: List of triggers that define transitions out of this phase.
+    :ivar norms: List of norms active during this phase.
+    :ivar goals: List of goals the robot pursues in this phase.
+    :ivar triggers: List of reactive behaviors defined for this phase.
     """
 
     name: str = ""
@@ -202,9 +229,15 @@ class Phase(ProgramElement):
 
 class Program(BaseModel):
     """
-    Represents a complete interaction program, consisting of a sequence or set of phases.
+    The top-level container for a complete robot behavior definition.
 
-    :ivar phases: The list of phases that make up the program.
+    :ivar phases: An ordered list of :class:`Phase` objects defining the interaction flow.
     """
 
     phases: list[Phase]
+
+
+if __name__ == "__main__":
+    input = input("Enter program JSON: ")
+    program = Program.model_validate_json(input)
+    print(program)

test/integration/agents/perception/vad_agent/test_vad_agent.py

@@ -40,7 +40,7 @@ async def test_normal_setup(per_transcription_agent):
     per_vad_agent = VADAgent("tcp://localhost:12345", False)
     per_vad_agent._streaming_loop = AsyncMock()
 
-    async def swallow_background_task(coro):
+    def swallow_background_task(coro):
         coro.close()
 
     per_vad_agent.add_behavior = swallow_background_task
@@ -91,7 +91,7 @@ def test_out_socket_creation(zmq_context):
     assert per_vad_agent.audio_out_socket is not None
 
     zmq_context.return_value.socket.assert_called_once_with(zmq.PUB)
-    zmq_context.return_value.socket.return_value.bind_to_random_port.assert_called_once()
+    zmq_context.return_value.socket.return_value.bind.assert_called_once_with("inproc://vad_stream")
 
 
 @pytest.mark.asyncio
@@ -106,7 +106,7 @@ async def test_out_socket_creation_failure(zmq_context):
     per_vad_agent._streaming_loop = AsyncMock()
     per_vad_agent._connect_audio_out_socket = MagicMock(return_value=None)
 
-    async def swallow_background_task(coro):
+    def swallow_background_task(coro):
         coro.close()
 
     per_vad_agent.add_behavior = swallow_background_task
@@ -126,7 +126,7 @@ async def test_stop(zmq_context, per_transcription_agent):
     per_vad_agent._reset_stream = AsyncMock()
     per_vad_agent._streaming_loop = AsyncMock()
 
-    async def swallow_background_task(coro):
+    def swallow_background_task(coro):
         coro.close()
 
     per_vad_agent.add_behavior = swallow_background_task
@@ -150,6 +150,7 @@ async def test_application_startup_complete(zmq_context):
     vad_agent._running = True
     vad_agent._reset_stream = AsyncMock()
     vad_agent.program_sub_socket = AsyncMock()
+    vad_agent.program_sub_socket.close = MagicMock()
     vad_agent.program_sub_socket.recv_multipart.side_effect = [
         (PROGRAM_STATUS, ProgramStatus.RUNNING.value),
     ]

test/unit/agents/actuation/test_robot_gesture_agent.py

@@ -28,7 +28,11 @@ async def test_setup_bind(zmq_context, mocker):
     settings = mocker.patch("control_backend.agents.actuation.robot_gesture_agent.settings")
     settings.zmq_settings.internal_sub_address = "tcp://internal:1234"
 
-    agent.add_behavior = MagicMock()
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    agent.add_behavior = MagicMock(side_effect=close_coro)
 
     await agent.setup()
 
@@ -55,7 +59,11 @@ async def test_setup_connect(zmq_context, mocker):
     settings = mocker.patch("control_backend.agents.actuation.robot_gesture_agent.settings")
     settings.zmq_settings.internal_sub_address = "tcp://internal:1234"
 
-    agent.add_behavior = MagicMock()
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    agent.add_behavior = MagicMock(side_effect=close_coro)
 
     await agent.setup()
 
@@ -73,7 +81,7 @@ async def test_setup_connect(zmq_context, mocker):
 async def test_handle_message_sends_valid_gesture_command():
     """Internal message with valid gesture tag is forwarded to robot pub socket."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.pubsocket = pubsocket
 
     payload = {
@@ -91,7 +99,7 @@ async def test_handle_message_sends_valid_gesture_command():
 async def test_handle_message_sends_non_gesture_command():
     """Internal message with non-gesture endpoint is not forwarded by this agent."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.pubsocket = pubsocket
 
     payload = {"endpoint": "some_other_endpoint", "data": "invalid_tag_not_in_list"}
@@ -107,7 +115,7 @@ async def test_handle_message_sends_non_gesture_command():
 async def test_handle_message_rejects_invalid_gesture_tag():
     """Internal message with invalid gesture tag is not forwarded."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.pubsocket = pubsocket
 
     # Use a tag that's not in gesture_data
@@ -119,11 +127,70 @@ async def test_handle_message_rejects_invalid_gesture_tag():
     pubsocket.send_json.assert_not_awaited()
 
 
+@pytest.mark.asyncio
+async def test_handle_message_sends_valid_single_gesture_command():
+    """Internal message with valid single gesture is forwarded."""
+    pubsocket = AsyncMock()
+    agent = RobotGestureAgent("robot_gesture", single_gesture_data=["wave", "point"], address="")
+    agent.pubsocket = pubsocket
+
+    payload = {
+        "endpoint": RIEndpoint.GESTURE_SINGLE,
+        "data": "wave",
+    }
+    msg = InternalMessage(to="robot", sender="tester", body=json.dumps(payload))
+
+    await agent.handle_message(msg)
+
+    pubsocket.send_json.assert_awaited_once()
+
+
+@pytest.mark.asyncio
+async def test_handle_message_rejects_invalid_single_gesture():
+    """Internal message with invalid single gesture is not forwarded."""
+    pubsocket = AsyncMock()
+    agent = RobotGestureAgent("robot_gesture", single_gesture_data=["wave", "point"], address="")
+    agent.pubsocket = pubsocket
+
+    payload = {
+        "endpoint": RIEndpoint.GESTURE_SINGLE,
+        "data": "dance",
+    }
+    msg = InternalMessage(to="robot", sender="tester", body=json.dumps(payload))
+
+    await agent.handle_message(msg)
+
+    pubsocket.send_json.assert_not_awaited()
+
+
+@pytest.mark.asyncio
+async def test_zmq_command_loop_valid_single_gesture_payload():
+    """UI command with valid single gesture is read from SUB and published."""
+    command = {"endpoint": RIEndpoint.GESTURE_SINGLE, "data": "wave"}
+    fake_socket = AsyncMock()
+
+    async def recv_once():
+        agent._running = False
+        return b"command", json.dumps(command).encode("utf-8")
+
+    fake_socket.recv_multipart = recv_once
+    fake_socket.send_json = AsyncMock()
+
+    agent = RobotGestureAgent("robot_gesture", single_gesture_data=["wave", "point"], address="")
+    agent.subsocket = fake_socket
+    agent.pubsocket = fake_socket
+    agent._running = True
+
+    await agent._zmq_command_loop()
+
+    fake_socket.send_json.assert_awaited_once()
+
+
 @pytest.mark.asyncio
 async def test_handle_message_invalid_payload():
     """Invalid payload is caught and does not send."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.pubsocket = pubsocket
 
     msg = InternalMessage(to="robot", sender="tester", body=json.dumps({"bad": "data"}))
@@ -142,12 +209,12 @@ async def test_zmq_command_loop_valid_gesture_payload():
     async def recv_once():
         # stop after first iteration
         agent._running = False
-        return (b"command", json.dumps(command).encode("utf-8"))
+        return b"command", json.dumps(command).encode("utf-8")
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -165,12 +232,12 @@ async def test_zmq_command_loop_valid_non_gesture_payload():
 
     async def recv_once():
         agent._running = False
-        return (b"command", json.dumps(command).encode("utf-8"))
+        return b"command", json.dumps(command).encode("utf-8")
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -188,12 +255,12 @@ async def test_zmq_command_loop_invalid_gesture_tag():
 
     async def recv_once():
         agent._running = False
-        return (b"command", json.dumps(command).encode("utf-8"))
+        return b"command", json.dumps(command).encode("utf-8")
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -210,12 +277,12 @@ async def test_zmq_command_loop_invalid_json():
 
     async def recv_once():
         agent._running = False
-        return (b"command", b"{not_json}")
+        return b"command", b"{not_json}"
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -232,12 +299,12 @@ async def test_zmq_command_loop_ignores_send_gestures_topic():
 
     async def recv_once():
         agent._running = False
-        return (b"send_gestures", b"{}")
+        return b"send_gestures", b"{}"
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -259,7 +326,9 @@ async def test_fetch_gestures_loop_without_amount():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
+    agent = RobotGestureAgent(
+        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
+    )
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -287,7 +356,9 @@ async def test_fetch_gestures_loop_with_amount():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
+    agent = RobotGestureAgent(
+        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
+    )
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -315,7 +386,7 @@ async def test_fetch_gestures_loop_with_integer_request():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -340,7 +411,7 @@ async def test_fetch_gestures_loop_with_invalid_json():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -365,7 +436,7 @@ async def test_fetch_gestures_loop_with_non_integer_json():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -381,7 +452,7 @@ async def test_fetch_gestures_loop_with_non_integer_json():
 def test_gesture_data_attribute():
     """Test that gesture_data returns the expected list."""
     gesture_data = ["hello", "yes", "no", "wave"]
-    agent = RobotGestureAgent("robot_gesture", gesture_data=gesture_data)
+    agent = RobotGestureAgent("robot_gesture", gesture_data=gesture_data, address="")
 
     assert agent.gesture_data == gesture_data
     assert isinstance(agent.gesture_data, list)
@@ -398,7 +469,7 @@ async def test_stop_closes_sockets():
     pubsocket = MagicMock()
     subsocket = MagicMock()
     repsocket = MagicMock()
-    agent = RobotGestureAgent("robot_gesture")
+    agent = RobotGestureAgent("robot_gesture", address="")
     agent.pubsocket = pubsocket
     agent.subsocket = subsocket
     agent.repsocket = repsocket
@@ -407,15 +478,14 @@ async def test_stop_closes_sockets():
 
     pubsocket.close.assert_called_once()
     subsocket.close.assert_called_once()
-    # Note: repsocket is not closed in stop() method, but you might want to add it
-    # repsocket.close.assert_called_once()
+    repsocket.close.assert_called_once()
 
 
 @pytest.mark.asyncio
 async def test_initialization_with_custom_gesture_data():
     """Agent can be initialized with custom gesture data."""
     custom_gestures = ["custom1", "custom2", "custom3"]
-    agent = RobotGestureAgent("robot_gesture", gesture_data=custom_gestures)
+    agent = RobotGestureAgent("robot_gesture", gesture_data=custom_gestures, address="")
 
     assert agent.gesture_data == custom_gestures
 
@@ -432,7 +502,7 @@ async def test_fetch_gestures_loop_handles_exception():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
     agent.repsocket = fake_repsocket
     agent.logger = MagicMock()
     agent._running = True

test/unit/agents/actuation/test_robot_speech_agent.py

@@ -30,7 +30,11 @@ async def test_setup_bind(zmq_context, mocker):
     settings = mocker.patch("control_backend.agents.actuation.robot_speech_agent.settings")
     settings.zmq_settings.internal_sub_address = "tcp://internal:1234"
 
-    agent.add_behavior = MagicMock()
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    agent.add_behavior = MagicMock(side_effect=close_coro)
 
     await agent.setup()
 
@@ -48,7 +52,11 @@ async def test_setup_connect(zmq_context, mocker):
     settings = mocker.patch("control_backend.agents.actuation.robot_speech_agent.settings")
     settings.zmq_settings.internal_sub_address = "tcp://internal:1234"
 
-    agent.add_behavior = MagicMock()
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    agent.add_behavior = MagicMock(side_effect=close_coro)
 
     await agent.setup()
 

test/unit/agents/bdi/test_agentspeak_ast.py

@@ -0,0 +1,186 @@
+import pytest
+
+from control_backend.agents.bdi.agentspeak_ast import (
+    AstAtom,
+    AstBinaryOp,
+    AstLiteral,
+    AstLogicalExpression,
+    AstNumber,
+    AstPlan,
+    AstProgram,
+    AstRule,
+    AstStatement,
+    AstString,
+    AstVar,
+    BinaryOperatorType,
+    StatementType,
+    TriggerType,
+    _coalesce_expr,
+)
+
+
+def test_ast_atom():
+    atom = AstAtom("test")
+    assert str(atom) == "test"
+    assert atom._to_agentspeak() == "test"
+
+
+def test_ast_var():
+    var = AstVar("Variable")
+    assert str(var) == "Variable"
+    assert var._to_agentspeak() == "Variable"
+
+
+def test_ast_number():
+    num = AstNumber(42)
+    assert str(num) == "42"
+    num_float = AstNumber(3.14)
+    assert str(num_float) == "3.14"
+
+
+def test_ast_string():
+    s = AstString("hello")
+    assert str(s) == '"hello"'
+
+
+def test_ast_literal():
+    lit = AstLiteral("functor", [AstAtom("atom"), AstNumber(1)])
+    assert str(lit) == "functor(atom, 1)"
+    lit_empty = AstLiteral("functor")
+    assert str(lit_empty) == "functor"
+
+
+def test_ast_binary_op():
+    left = AstNumber(1)
+    right = AstNumber(2)
+    op = AstBinaryOp(left, BinaryOperatorType.GREATER_THAN, right)
+    assert str(op) == "1 > 2"
+
+    # Test logical wrapper
+    assert isinstance(op.left, AstLogicalExpression)
+    assert isinstance(op.right, AstLogicalExpression)
+
+
+def test_ast_binary_op_parens():
+    # 1 > 2
+    inner = AstBinaryOp(AstNumber(1), BinaryOperatorType.GREATER_THAN, AstNumber(2))
+    # (1 > 2) & 3
+    outer = AstBinaryOp(inner, BinaryOperatorType.AND, AstNumber(3))
+    assert str(outer) == "(1 > 2) & 3"
+
+    # 3 & (1 > 2)
+    outer_right = AstBinaryOp(AstNumber(3), BinaryOperatorType.AND, inner)
+    assert str(outer_right) == "3 & (1 > 2)"
+
+
+def test_ast_binary_op_parens_negated():
+    inner = AstLogicalExpression(AstAtom("foo"), negated=True)
+    outer = AstBinaryOp(inner, BinaryOperatorType.AND, AstAtom("bar"))
+    # The current implementation checks `if self.left.negated: l_str = f"({l_str})"`
+    # str(inner) is "not foo"
+    # so we expect "(not foo) & bar"
+    assert str(outer) == "(not foo) & bar"
+
+    outer_right = AstBinaryOp(AstAtom("bar"), BinaryOperatorType.AND, inner)
+    assert str(outer_right) == "bar & (not foo)"
+
+
+def test_ast_logical_expression_negation():
+    expr = AstLogicalExpression(AstAtom("true"), negated=True)
+    assert str(expr) == "not true"
+
+    expr_neg_neg = ~expr
+    assert str(expr_neg_neg) == "true"
+    assert not expr_neg_neg.negated
+
+    # Invert a non-logical expression (wraps it)
+    term = AstAtom("true")
+    inverted = ~term
+    assert isinstance(inverted, AstLogicalExpression)
+    assert inverted.negated
+    assert str(inverted) == "not true"
+
+
+def test_ast_logical_expression_no_negation():
+    # _as_logical on already logical expression
+    expr = AstLogicalExpression(AstAtom("x"))
+    # Doing binary op will call _as_logical
+    op = AstBinaryOp(expr, BinaryOperatorType.AND, AstAtom("y"))
+    assert isinstance(op.left, AstLogicalExpression)
+    assert op.left is expr  # Should reuse instance
+
+
+def test_ast_operators():
+    t1 = AstAtom("a")
+    t2 = AstAtom("b")
+
+    assert str(t1 & t2) == "a & b"
+    assert str(t1 | t2) == "a | b"
+    assert str(t1 >= t2) == "a >= b"
+    assert str(t1 > t2) == "a > b"
+    assert str(t1 <= t2) == "a <= b"
+    assert str(t1 < t2) == "a < b"
+    assert str(t1 == t2) == "a == b"
+    assert str(t1 != t2) == r"a \== b"
+
+
+def test_coalesce_expr():
+    t = AstAtom("a")
+    assert str(t & "b") == 'a & "b"'
+    assert str(t & 1) == "a & 1"
+    assert str(t & 1.5) == "a & 1.5"
+
+    with pytest.raises(TypeError):
+        _coalesce_expr(None)
+
+
+def test_ast_statement():
+    stmt = AstStatement(StatementType.DO_ACTION, AstLiteral("action"))
+    assert str(stmt) == ".action"
+
+
+def test_ast_rule():
+    # Rule with condition
+    rule = AstRule(AstLiteral("head"), AstLiteral("body"))
+    assert str(rule) == "head :- body."
+
+    # Rule without condition
+    rule_simple = AstRule(AstLiteral("fact"))
+    assert str(rule_simple) == "fact."
+
+
+def test_ast_plan():
+    plan = AstPlan(
+        TriggerType.ADDED_GOAL,
+        AstLiteral("goal"),
+        [AstLiteral("context")],
+        [AstStatement(StatementType.DO_ACTION, AstLiteral("action"))],
+    )
+    output = str(plan)
+    # verify parts exist
+    assert "+!goal" in output
+    assert ":   context" in output
+    assert "<-  .action." in output
+
+
+def test_ast_plan_no_context():
+    plan = AstPlan(
+        TriggerType.ADDED_GOAL,
+        AstLiteral("goal"),
+        [],
+        [AstStatement(StatementType.DO_ACTION, AstLiteral("action"))],
+    )
+    output = str(plan)
+    assert "+!goal" in output
+    assert ":   " not in output
+    assert "<-  .action." in output
+
+
+def test_ast_program():
+    prog = AstProgram(
+        rules=[AstRule(AstLiteral("fact"))],
+        plans=[AstPlan(TriggerType.ADDED_BELIEF, AstLiteral("b"), [], [])],
+    )
+    output = str(prog)
+    assert "fact." in output
+    assert "+b" in output

test/unit/agents/bdi/test_agentspeak_generator.py

@@ -0,0 +1,187 @@
+import uuid
+
+import pytest
+
+from control_backend.agents.bdi.agentspeak_ast import AstProgram
+from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
+from control_backend.schemas.program import (
+    BasicNorm,
+    ConditionalNorm,
+    Gesture,
+    GestureAction,
+    Goal,
+    InferredBelief,
+    KeywordBelief,
+    LLMAction,
+    LogicalOperator,
+    Phase,
+    Plan,
+    Program,
+    SemanticBelief,
+    SpeechAction,
+    Trigger,
+)
+
+
+@pytest.fixture
+def generator():
+    return AgentSpeakGenerator()
+
+
+def test_generate_empty_program(generator):
+    prog = Program(phases=[])
+    code = generator.generate(prog)
+    assert 'phase("end").' in code
+    assert "!notify_cycle" in code
+
+
+def test_generate_basic_norm(generator):
+    norm = BasicNorm(id=uuid.uuid4(), name="n1", norm="be nice")
+    phase = Phase(id=uuid.uuid4(), norms=[norm], goals=[], triggers=[])
+    prog = Program(phases=[phase])
+
+    code = generator.generate(prog)
+    assert f'norm("be nice") :- phase("{phase.id}").' in code
+
+
+def test_generate_critical_norm(generator):
+    norm = BasicNorm(id=uuid.uuid4(), name="n1", norm="safety", critical=True)
+    phase = Phase(id=uuid.uuid4(), norms=[norm], goals=[], triggers=[])
+    prog = Program(phases=[phase])
+
+    code = generator.generate(prog)
+    assert f'critical_norm("safety") :- phase("{phase.id}").' in code
+
+
+def test_generate_conditional_norm(generator):
+    cond = KeywordBelief(id=uuid.uuid4(), name="k1", keyword="please")
+    norm = ConditionalNorm(id=uuid.uuid4(), name="n1", norm="help", condition=cond)
+    phase = Phase(id=uuid.uuid4(), norms=[norm], goals=[], triggers=[])
+    prog = Program(phases=[phase])
+
+    code = generator.generate(prog)
+    assert 'norm("help")' in code
+    assert 'keyword_said("please")' in code
+    assert f"force_norm_{generator._slugify_str(norm.norm)}" in code
+
+
+def test_generate_goal_and_plan(generator):
+    action = SpeechAction(id=uuid.uuid4(), name="s1", text="hello")
+    plan = Plan(id=uuid.uuid4(), name="p1", steps=[action])
+    # IMPORTANT: can_fail must be False for +achieved_ belief to be added
+    goal = Goal(id=uuid.uuid4(), name="g1", description="desc", plan=plan, can_fail=False)
+    phase = Phase(id=uuid.uuid4(), norms=[], goals=[goal], triggers=[])
+    prog = Program(phases=[phase])
+
+    code = generator.generate(prog)
+    # Check trigger for goal
+    goal_slug = generator._slugify_str(goal.name)
+    assert f"+!{goal_slug}" in code
+    assert f'phase("{phase.id}")' in code
+    assert '!say("hello")' in code
+
+    # Check success belief addition
+    assert f"+achieved_{goal_slug}" in code
+
+
+def test_generate_subgoal(generator):
+    subplan = Plan(id=uuid.uuid4(), name="p2", steps=[])
+    subgoal = Goal(id=uuid.uuid4(), name="sub1", description="sub", plan=subplan)
+
+    plan = Plan(id=uuid.uuid4(), name="p1", steps=[subgoal])
+    goal = Goal(id=uuid.uuid4(), name="g1", description="main", plan=plan)
+    phase = Phase(id=uuid.uuid4(), norms=[], goals=[goal], triggers=[])
+    prog = Program(phases=[phase])
+
+    code = generator.generate(prog)
+    subgoal_slug = generator._slugify_str(subgoal.name)
+    # Main goal calls subgoal
+    assert f"!{subgoal_slug}" in code
+    # Subgoal plan exists
+    assert f"+!{subgoal_slug}" in code
+
+
+def test_generate_trigger(generator):
+    cond = SemanticBelief(id=uuid.uuid4(), name="s1", description="desc")
+    plan = Plan(id=uuid.uuid4(), name="p1", steps=[])
+    trigger = Trigger(id=uuid.uuid4(), name="t1", condition=cond, plan=plan)
+    phase = Phase(id=uuid.uuid4(), norms=[], goals=[], triggers=[trigger])
+    prog = Program(phases=[phase])
+
+    code = generator.generate(prog)
+    # Trigger logic is added to check_triggers
+    assert f"{generator.slugify(cond)}" in code
+    assert f'notify_trigger_start("{generator.slugify(trigger)}")' in code
+    assert f'notify_trigger_end("{generator.slugify(trigger)}")' in code
+
+
+def test_phase_transition(generator):
+    phase1 = Phase(id=uuid.uuid4(), name="p1", norms=[], goals=[], triggers=[])
+    phase2 = Phase(id=uuid.uuid4(), name="p2", norms=[], goals=[], triggers=[])
+    prog = Program(phases=[phase1, phase2])
+
+    code = generator.generate(prog)
+    assert "transition_phase" in code
+    assert f'phase("{phase1.id}")' in code
+    assert f'phase("{phase2.id}")' in code
+    assert "force_transition_phase" in code
+
+
+def test_astify_gesture(generator):
+    gesture = Gesture(type="single", name="wave")
+    action = GestureAction(id=uuid.uuid4(), name="g1", gesture=gesture)
+    ast = generator._astify(action)
+    assert str(ast) == 'gesture("single", "wave")'
+
+
+def test_astify_llm_action(generator):
+    action = LLMAction(id=uuid.uuid4(), name="l1", goal="be funny")
+    ast = generator._astify(action)
+    assert str(ast) == 'reply_with_goal("be funny")'
+
+
+def test_astify_inferred_belief_and(generator):
+    left = KeywordBelief(id=uuid.uuid4(), name="k1", keyword="a")
+    right = KeywordBelief(id=uuid.uuid4(), name="k2", keyword="b")
+    inf = InferredBelief(
+        id=uuid.uuid4(), name="i1", operator=LogicalOperator.AND, left=left, right=right
+    )
+
+    ast = generator._astify(inf)
+    assert 'keyword_said("a") & keyword_said("b")' == str(ast)
+
+
+def test_astify_inferred_belief_or(generator):
+    left = KeywordBelief(id=uuid.uuid4(), name="k1", keyword="a")
+    right = KeywordBelief(id=uuid.uuid4(), name="k2", keyword="b")
+    inf = InferredBelief(
+        id=uuid.uuid4(), name="i1", operator=LogicalOperator.OR, left=left, right=right
+    )
+
+    ast = generator._astify(inf)
+    assert 'keyword_said("a") | keyword_said("b")' == str(ast)
+
+
+def test_astify_semantic_belief(generator):
+    sb = SemanticBelief(id=uuid.uuid4(), name="s1", description="desc")
+    ast = generator._astify(sb)
+    assert str(ast) == f"semantic_{generator._slugify_str(sb.name)}"
+
+
+def test_slugify_not_implemented(generator):
+    with pytest.raises(NotImplementedError):
+        generator.slugify("not a program element")
+
+
+def test_astify_not_implemented(generator):
+    with pytest.raises(NotImplementedError):
+        generator._astify("not a program element")
+
+
+def test_process_phase_transition_from_none(generator):
+    # Initialize AstProgram manually as we are bypassing generate()
+    generator._asp = AstProgram()
+    # Should safely return doing nothing
+    generator._add_phase_transition(None, None)
+
+    assert len(generator._asp.plans) == 0

test/unit/agents/bdi/test_bdi_core_agent.py

@@ -45,23 +45,34 @@ async def test_setup_no_asl(mock_agentspeak_env, agent):
 
 
 @pytest.mark.asyncio
-async def test_handle_belief_collector_message(agent, mock_settings):
+async def test_handle_belief_message(agent, mock_settings):
     """Test that incoming beliefs are added to the BDI agent"""
     beliefs = [Belief(name="user_said", arguments=["Hello"])]
     msg = InternalMessage(
         to="bdi_agent",
-        sender=mock_settings.agent_settings.bdi_belief_collector_name,
+        sender=mock_settings.agent_settings.text_belief_extractor_name,
         body=BeliefMessage(create=beliefs).model_dump_json(),
         thread="beliefs",
     )
 
     await agent.handle_message(msg)
 
-    # Expect bdi_agent.call to be triggered to add belief
-    args = agent.bdi_agent.call.call_args.args
-    assert args[0] == agentspeak.Trigger.addition
-    assert args[1] == agentspeak.GoalType.belief
-    assert args[2] == agentspeak.Literal("user_said", (agentspeak.Literal("Hello"),))
+    # Check for the specific call we expect among all calls
+    # bdi_agent.call is called multiple times (for transition_phase, check_triggers)
+    # We want to confirm the belief addition call exists
+    found_call = False
+    for call in agent.bdi_agent.call.call_args_list:
+        args = call.args
+        if (
+            args[0] == agentspeak.Trigger.addition
+            and args[1] == agentspeak.GoalType.belief
+            and args[2].functor == "user_said"
+            and args[2].args[0].functor == "Hello"
+        ):
+            found_call = True
+            break
+
+    assert found_call, "Expected belief addition call not found in bdi_agent.call history"
 
 
 @pytest.mark.asyncio
@@ -71,25 +82,33 @@ async def test_handle_delete_belief_message(agent, mock_settings):
 
     msg = InternalMessage(
         to="bdi_agent",
-        sender=mock_settings.agent_settings.bdi_belief_collector_name,
+        sender=mock_settings.agent_settings.text_belief_extractor_name,
         body=BeliefMessage(delete=beliefs).model_dump_json(),
         thread="beliefs",
     )
     await agent.handle_message(msg)
 
-    # Expect bdi_agent.call to be triggered to remove belief
-    args = agent.bdi_agent.call.call_args.args
-    assert args[0] == agentspeak.Trigger.removal
-    assert args[1] == agentspeak.GoalType.belief
-    assert args[2] == agentspeak.Literal("user_said", (agentspeak.Literal("Hello"),))
+    found_call = False
+    for call in agent.bdi_agent.call.call_args_list:
+        args = call.args
+        if (
+            args[0] == agentspeak.Trigger.removal
+            and args[1] == agentspeak.GoalType.belief
+            and args[2].functor == "user_said"
+            and args[2].args[0].functor == "Hello"
+        ):
+            found_call = True
+            break
+
+    assert found_call
 
 
 @pytest.mark.asyncio
-async def test_incorrect_belief_collector_message(agent, mock_settings):
+async def test_incorrect_belief_message(agent, mock_settings):
     """Test that incorrect message format triggers an exception."""
     msg = InternalMessage(
         to="bdi_agent",
-        sender=mock_settings.agent_settings.bdi_belief_collector_name,
+        sender=mock_settings.agent_settings.text_belief_extractor_name,
         body=json.dumps({"bad_format": "bad_format"}),
         thread="beliefs",
     )
@@ -171,7 +190,11 @@ def test_remove_belief_success_wakes_loop(agent):
     agent._remove_belief("remove_me", ["x"])
 
     assert agent.bdi_agent.call.called
-    trigger, goaltype, literal, *_ = agent.bdi_agent.call.call_args.args
+
+    call_args = agent.bdi_agent.call.call_args.args
+    trigger = call_args[0]
+    goaltype = call_args[1]
+    literal = call_args[2]
 
     assert trigger == agentspeak.Trigger.removal
     assert goaltype == agentspeak.GoalType.belief
@@ -288,3 +311,216 @@ async def test_deadline_sleep_branch(agent):
 
     duration = time.time() - start_time
     assert duration >= 0.004  # loop slept until deadline
+
+
+@pytest.mark.asyncio
+async def test_handle_new_program(agent):
+    agent._load_asl = AsyncMock()
+    agent.add_behavior = MagicMock()
+    # Mock existing loop task so it can be cancelled
+    mock_task = MagicMock()
+    mock_task.cancel = MagicMock()
+    agent._bdi_loop_task = mock_task
+
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    agent.add_behavior = MagicMock(side_effect=close_coro)
+
+    msg = InternalMessage(to="bdi_agent", thread="new_program", body="path/to/asl.asl")
+
+    await agent.handle_message(msg)
+
+    mock_task.cancel.assert_called_once()
+    agent._load_asl.assert_awaited_once_with("path/to/asl.asl")
+    agent.add_behavior.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_handle_user_interrupts(agent, mock_settings):
+    mock_settings.agent_settings.user_interrupt_name = "user_interrupt_agent"
+    # force_phase_transition
+    agent._set_goal = MagicMock()
+    msg = InternalMessage(
+        to="bdi_agent",
+        sender=mock_settings.agent_settings.user_interrupt_name,
+        thread="force_phase_transition",
+        body="",
+    )
+    await agent.handle_message(msg)
+    agent._set_goal.assert_called_with("transition_phase")
+
+    # force_trigger
+    agent._force_trigger = MagicMock()
+    msg.thread = "force_trigger"
+    msg.body = "trigger_x"
+    await agent.handle_message(msg)
+    agent._force_trigger.assert_called_with("trigger_x")
+
+    # force_norm
+    agent._force_norm = MagicMock()
+    msg.thread = "force_norm"
+    msg.body = "norm_y"
+    await agent.handle_message(msg)
+    agent._force_norm.assert_called_with("norm_y")
+
+    # force_next_phase
+    agent._force_next_phase = MagicMock()
+    msg.thread = "force_next_phase"
+    msg.body = ""
+    await agent.handle_message(msg)
+    agent._force_next_phase.assert_called_once()
+
+    # unknown interrupt
+    agent.logger = MagicMock()
+    msg.thread = "unknown_thing"
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_custom_action_reply_with_goal(agent):
+    agent._send_to_llm = MagicMock(side_effect=agent.send)
+    agent._add_custom_actions()
+    action_fn = agent.actions.actions[(".reply_with_goal", 3)]
+
+    mock_term = MagicMock(args=["msg", "norms", "goal"])
+    gen = action_fn(agent, mock_term, MagicMock())
+    next(gen)
+    agent._send_to_llm.assert_called_with("msg", "norms", "goal")
+
+
+@pytest.mark.asyncio
+async def test_custom_action_notify_norms(agent):
+    agent._add_custom_actions()
+    action_fn = agent.actions.actions[(".notify_norms", 1)]
+
+    mock_term = MagicMock(args=["norms_list"])
+    gen = action_fn(agent, mock_term, MagicMock())
+    next(gen)
+
+    agent.send.assert_called()
+    msg = agent.send.call_args[0][0]
+    assert msg.thread == "active_norms_update"
+    assert msg.body == "norms_list"
+
+
+@pytest.mark.asyncio
+async def test_custom_action_say(agent):
+    agent._add_custom_actions()
+    action_fn = agent.actions.actions[(".say", 1)]
+
+    mock_term = MagicMock(args=["hello"])
+    gen = action_fn(agent, mock_term, MagicMock())
+    next(gen)
+
+    assert agent.send.call_count == 2
+    msgs = [c[0][0] for c in agent.send.call_args_list]
+    assert any(m.to == settings.agent_settings.robot_speech_name for m in msgs)
+    assert any(
+        m.to == settings.agent_settings.llm_name and m.thread == "assistant_message" for m in msgs
+    )
+
+
+@pytest.mark.asyncio
+async def test_custom_action_gesture(agent):
+    agent._add_custom_actions()
+    # Test single
+    action_fn = agent.actions.actions[(".gesture", 2)]
+    mock_term = MagicMock(args=["single", "wave"])
+    gen = action_fn(agent, mock_term, MagicMock())
+    next(gen)
+    msg = agent.send.call_args[0][0]
+    assert "actuate/gesture/single" in msg.body
+
+    # Test tag
+    mock_term.args = ["tag", "happy"]
+    gen = action_fn(agent, mock_term, MagicMock())
+    next(gen)
+    msg = agent.send.call_args[0][0]
+    assert "actuate/gesture/tag" in msg.body
+
+
+@pytest.mark.asyncio
+async def test_custom_action_notify_user_said(agent):
+    agent._add_custom_actions()
+    action_fn = agent.actions.actions[(".notify_user_said", 1)]
+    mock_term = MagicMock(args=["hello"])
+    gen = action_fn(agent, mock_term, MagicMock())
+    next(gen)
+    msg = agent.send.call_args[0][0]
+    assert msg.to == settings.agent_settings.llm_name
+    assert msg.thread == "user_message"
+
+
+@pytest.mark.asyncio
+async def test_custom_action_notify_trigger_start_end(agent):
+    agent._add_custom_actions()
+    # Start
+    action_fn = agent.actions.actions[(".notify_trigger_start", 1)]
+    gen = action_fn(agent, MagicMock(args=["t1"]), MagicMock())
+    next(gen)
+    assert agent.send.call_args[0][0].thread == "trigger_start"
+
+    # End
+    action_fn = agent.actions.actions[(".notify_trigger_end", 1)]
+    gen = action_fn(agent, MagicMock(args=["t1"]), MagicMock())
+    next(gen)
+    assert agent.send.call_args[0][0].thread == "trigger_end"
+
+
+@pytest.mark.asyncio
+async def test_custom_action_notify_goal_start(agent):
+    agent._add_custom_actions()
+    action_fn = agent.actions.actions[(".notify_goal_start", 1)]
+    gen = action_fn(agent, MagicMock(args=["g1"]), MagicMock())
+    next(gen)
+    assert agent.send.call_args[0][0].thread == "goal_start"
+
+
+@pytest.mark.asyncio
+async def test_custom_action_notify_transition_phase(agent):
+    agent._add_custom_actions()
+    action_fn = agent.actions.actions[(".notify_transition_phase", 2)]
+    gen = action_fn(agent, MagicMock(args=["old", "new"]), MagicMock())
+    next(gen)
+    msg = agent.send.call_args[0][0]
+    assert msg.thread == "transition_phase"
+    assert "old" in msg.body and "new" in msg.body
+
+
+def test_remove_belief_no_args(agent):
+    agent._wake_bdi_loop = MagicMock()
+    agent.bdi_agent.call.return_value = True
+    agent._remove_belief("fact", None)
+    assert agent.bdi_agent.call.called
+
+
+def test_set_goal_with_args(agent):
+    agent._wake_bdi_loop = MagicMock()
+    agent._set_goal("goal", ["arg1", "arg2"])
+    assert agent.bdi_agent.call.called
+
+
+def test_format_belief_string():
+    assert BDICoreAgent.format_belief_string("b") == "b"
+    assert BDICoreAgent.format_belief_string("b", ["a1", "a2"]) == "b(a1,a2)"
+
+
+def test_force_norm(agent):
+    agent._add_belief = MagicMock()
+    agent._force_norm("be_polite")
+    agent._add_belief.assert_called_with("force_be_polite")
+
+
+def test_force_trigger(agent):
+    agent._set_goal = MagicMock()
+    agent._force_trigger("trig")
+    agent._set_goal.assert_called_with("trig")
+
+
+def test_force_next_phase(agent):
+    agent._set_goal = MagicMock()
+    agent._force_next_phase()
+    agent._set_goal.assert_called_with("force_transition_phase")

test/unit/agents/bdi/test_bdi_program_manager.py

@@ -1,13 +1,13 @@
 import asyncio
+import json
 import sys
 import uuid
-from unittest.mock import AsyncMock
+from unittest.mock import AsyncMock, MagicMock, mock_open, patch
 
 import pytest
 
 from control_backend.agents.bdi.bdi_program_manager import BDIProgramManager
 from control_backend.core.agent_system import InternalMessage
-from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.program import BasicNorm, Goal, Phase, Plan, Program
 
 # Fix Windows Proactor loop for zmq
@@ -48,24 +48,26 @@ def make_valid_program_json(norm="N1", goal="G1") -> str:
     ).model_dump_json()
 
 
-@pytest.mark.skip(reason="Functionality being rebuilt.")
 @pytest.mark.asyncio
-async def test_send_to_bdi():
+async def test_create_agentspeak_and_send_to_bdi(mock_settings):
     manager = BDIProgramManager(name="program_manager_test")
     manager.send = AsyncMock()
 
     program = Program.model_validate_json(make_valid_program_json())
-    await manager._create_agentspeak_and_send_to_bdi(program)
+
+    with patch("builtins.open", mock_open()) as mock_file:
+        await manager._create_agentspeak_and_send_to_bdi(program)
+
+        # Check file writing
+        mock_file.assert_called_with("src/control_backend/agents/bdi/agentspeak.asl", "w")
+        handle = mock_file()
+        handle.write.assert_called()
 
     assert manager.send.await_count == 1
     msg: InternalMessage = manager.send.await_args[0][0]
-    assert msg.thread == "beliefs"
-
-    beliefs = BeliefMessage.model_validate_json(msg.body)
-    names = {b.name: b.arguments for b in beliefs.beliefs}
-
-    assert "norms" in names and names["norms"] == ["N1"]
-    assert "goals" in names and names["goals"] == ["G1"]
+    assert msg.thread == "new_program"
+    assert msg.to == mock_settings.agent_settings.bdi_core_name
+    assert msg.body == "src/control_backend/agents/bdi/agentspeak.asl"
 
 
 @pytest.mark.asyncio
@@ -80,6 +82,10 @@ async def test_receive_programs_valid_and_invalid():
     manager._internal_pub_socket = AsyncMock()
     manager.sub_socket = sub
     manager._create_agentspeak_and_send_to_bdi = AsyncMock()
+    manager._send_clear_llm_history = AsyncMock()
+    manager._send_program_to_user_interrupt = AsyncMock()
+    manager._send_beliefs_to_semantic_belief_extractor = AsyncMock()
+    manager._send_goals_to_semantic_belief_extractor = AsyncMock()
 
     try:
         # Will give StopAsyncIteration when the predefined `sub.recv_multipart` side-effects run out
@@ -92,3 +98,200 @@ async def test_receive_programs_valid_and_invalid():
     forwarded: Program = manager._create_agentspeak_and_send_to_bdi.await_args[0][0]
     assert forwarded.phases[0].norms[0].name == "N1"
     assert forwarded.phases[0].goals[0].name == "G1"
+
+    # Verify history clear was triggered exactly once (for the valid program)
+    # The invalid program loop `continue`s before calling _send_clear_llm_history
+    assert manager._send_clear_llm_history.await_count == 1
+
+
+@pytest.mark.asyncio
+async def test_send_clear_llm_history(mock_settings):
+    # Ensure the mock returns a string for the agent name (just like in your LLM tests)
+    mock_settings.agent_settings.llm_agent_name = "llm_agent"
+
+    manager = BDIProgramManager(name="program_manager_test")
+    manager.send = AsyncMock()
+
+    await manager._send_clear_llm_history()
+
+    assert manager.send.await_count == 2
+    msg: InternalMessage = manager.send.await_args_list[0][0][0]
+
+    # Verify the content and recipient
+    assert msg.body == "clear_history"
+
+
+@pytest.mark.asyncio
+async def test_handle_message_transition_phase(mock_settings):
+    mock_settings.agent_settings.user_interrupt_name = "user_interrupt_agent"
+    manager = BDIProgramManager(name="program_manager_test")
+    manager.send = AsyncMock()
+
+    # Setup state
+    prog = Program.model_validate_json(make_valid_program_json(norm="N1", goal="G1"))
+    manager._initialize_internal_state(prog)
+
+    # Test valid transition (to same phase for simplicity, or we need 2 phases)
+    # Let's create a program with 2 phases
+    phase2_id = uuid.uuid4()
+    phase2 = Phase(id=phase2_id, name="Phase 2", norms=[], goals=[], triggers=[])
+    prog.phases.append(phase2)
+    manager._initialize_internal_state(prog)
+
+    current_phase_id = str(prog.phases[0].id)
+    next_phase_id = str(phase2_id)
+
+    payload = json.dumps({"old": current_phase_id, "new": next_phase_id})
+    msg = InternalMessage(to="me", sender="bdi", body=payload, thread="transition_phase")
+
+    await manager.handle_message(msg)
+
+    assert str(manager._phase.id) == next_phase_id
+
+    # Allow background tasks to run (add_behavior)
+    await asyncio.sleep(0)
+
+    # Check notifications sent
+    # 1. beliefs to extractor
+    # 2. goals to extractor
+    # 3. notification to user interrupt
+
+    assert manager.send.await_count >= 3
+
+    # Verify user interrupt notification
+    calls = manager.send.await_args_list
+    ui_msgs = [
+        c[0][0] for c in calls if c[0][0].to == mock_settings.agent_settings.user_interrupt_name
+    ]
+    assert len(ui_msgs) > 0
+    assert ui_msgs[-1].body == next_phase_id
+
+
+@pytest.mark.asyncio
+async def test_handle_message_transition_phase_desync():
+    manager = BDIProgramManager(name="program_manager_test")
+    manager.logger = MagicMock()
+
+    prog = Program.model_validate_json(make_valid_program_json())
+    manager._initialize_internal_state(prog)
+
+    current_phase_id = str(prog.phases[0].id)
+
+    # Request transition from WRONG old phase
+    payload = json.dumps({"old": "wrong_id", "new": "some_new_id"})
+    msg = InternalMessage(to="me", sender="bdi", body=payload, thread="transition_phase")
+
+    await manager.handle_message(msg)
+
+    # Should warn and do nothing
+    manager.logger.warning.assert_called_once()
+    assert "Phase transition desync detected" in manager.logger.warning.call_args[0][0]
+    assert str(manager._phase.id) == current_phase_id
+
+
+@pytest.mark.asyncio
+async def test_handle_message_transition_phase_end(mock_settings):
+    mock_settings.agent_settings.user_interrupt_name = "user_interrupt_agent"
+    manager = BDIProgramManager(name="program_manager_test")
+    manager.send = AsyncMock()
+
+    prog = Program.model_validate_json(make_valid_program_json())
+    manager._initialize_internal_state(prog)
+    current_phase_id = str(prog.phases[0].id)
+
+    payload = json.dumps({"old": current_phase_id, "new": "end"})
+    msg = InternalMessage(to="me", sender="bdi", body=payload, thread="transition_phase")
+
+    await manager.handle_message(msg)
+
+    assert manager._phase is None
+
+    # Allow background tasks to run (add_behavior)
+    await asyncio.sleep(0)
+
+    # Verify notification to user interrupt
+    assert manager.send.await_count == 1
+    msg_sent = manager.send.await_args[0][0]
+    assert msg_sent.to == mock_settings.agent_settings.user_interrupt_name
+    assert msg_sent.body == "end"
+
+
+@pytest.mark.asyncio
+async def test_handle_message_achieve_goal(mock_settings):
+    mock_settings.agent_settings.text_belief_extractor_name = "text_belief_extractor_agent"
+    manager = BDIProgramManager(name="program_manager_test")
+    manager.send = AsyncMock()
+
+    prog = Program.model_validate_json(make_valid_program_json(goal="TargetGoal"))
+    manager._initialize_internal_state(prog)
+
+    goal_id = str(prog.phases[0].goals[0].id)
+
+    msg = InternalMessage(to="me", sender="ui", body=goal_id, thread="achieve_goal")
+
+    await manager.handle_message(msg)
+
+    # Should send achieved goals to text extractor
+    assert manager.send.await_count == 1
+    msg_sent = manager.send.await_args[0][0]
+    assert msg_sent.to == mock_settings.agent_settings.text_belief_extractor_name
+    assert msg_sent.thread == "achieved_goals"
+
+    # Verify body
+    from control_backend.schemas.belief_list import GoalList
+
+    gl = GoalList.model_validate_json(msg_sent.body)
+    assert len(gl.goals) == 1
+    assert gl.goals[0].name == "TargetGoal"
+
+
+@pytest.mark.asyncio
+async def test_handle_message_achieve_goal_not_found():
+    manager = BDIProgramManager(name="program_manager_test")
+    manager.send = AsyncMock()
+    manager.logger = MagicMock()
+
+    prog = Program.model_validate_json(make_valid_program_json())
+    manager._initialize_internal_state(prog)
+
+    msg = InternalMessage(to="me", sender="ui", body="non_existent_id", thread="achieve_goal")
+
+    await manager.handle_message(msg)
+
+    manager.send.assert_not_called()
+    manager.logger.debug.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_setup(mock_settings):
+    manager = BDIProgramManager(name="program_manager_test")
+    manager.send = AsyncMock()
+
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    manager.add_behavior = MagicMock(side_effect=close_coro)
+
+    mock_context = MagicMock()
+    mock_sub = MagicMock()
+    mock_context.socket.return_value = mock_sub
+
+    with patch(
+        "control_backend.agents.bdi.bdi_program_manager.Context.instance", return_value=mock_context
+    ):
+        # We also need to mock file writing in _create_agentspeak_and_send_to_bdi
+        with patch("builtins.open", new_callable=MagicMock):
+            await manager.setup()
+
+    # Check logic
+    # 1. Sends default empty program to BDI
+    assert manager.send.await_count == 1
+    assert manager.send.await_args[0][0].to == mock_settings.agent_settings.bdi_core_name
+
+    # 2. Connects SUB socket
+    mock_sub.connect.assert_called_with(mock_settings.zmq_settings.internal_sub_address)
+    mock_sub.subscribe.assert_called_with("program")
+
+    # 3. Adds behavior
+    manager.add_behavior.assert_called()

test/unit/agents/bdi/test_belief_collector.py

@@ -1,135 +0,0 @@
-import json
-from unittest.mock import AsyncMock
-
-import pytest
-
-from control_backend.agents.bdi import (
-    BDIBeliefCollectorAgent,
-)
-from control_backend.core.agent_system import InternalMessage
-from control_backend.core.config import settings
-from control_backend.schemas.belief_message import Belief
-
-
-@pytest.fixture
-def agent():
-    agent = BDIBeliefCollectorAgent("belief_collector_agent")
-    return agent
-
-
-def make_msg(body: dict, sender: str = "sender"):
-    return InternalMessage(to="collector", sender=sender, body=json.dumps(body))
-
-
-@pytest.mark.asyncio
-async def test_handle_message_routes_belief_text(agent, mocker):
-    """
-    Test that when a message is received, _handle_belief_text is called with that message.
-    """
-    payload = {"type": "belief_extraction_text", "beliefs": {"user_said": [["hi"]]}}
-    spy = mocker.patch.object(agent, "_handle_belief_text", new_callable=AsyncMock)
-
-    await agent.handle_message(make_msg(payload))
-
-    spy.assert_awaited_once_with(payload, "sender")
-
-
-@pytest.mark.asyncio
-async def test_handle_message_routes_emotion(agent, mocker):
-    payload = {"type": "emotion_extraction_text"}
-    spy = mocker.patch.object(agent, "_handle_emo_text", new_callable=AsyncMock)
-
-    await agent.handle_message(make_msg(payload))
-
-    spy.assert_awaited_once_with(payload, "sender")
-
-
-@pytest.mark.asyncio
-async def test_handle_message_bad_json(agent, mocker):
-    agent._handle_belief_text = AsyncMock()
-    bad_msg = InternalMessage(to="collector", sender="sender", body="not json")
-
-    await agent.handle_message(bad_msg)
-
-    agent._handle_belief_text.assert_not_awaited()
-
-
-@pytest.mark.asyncio
-async def test_handle_belief_text_sends_when_beliefs_exist(agent, mocker):
-    payload = {"type": "belief_extraction_text", "beliefs": {"user_said": ["hello"]}}
-    spy = mocker.patch.object(agent, "_send_beliefs_to_bdi", new_callable=AsyncMock)
-    expected = [Belief(name="user_said", arguments=["hello"])]
-
-    await agent._handle_belief_text(payload, "origin")
-
-    spy.assert_awaited_once_with(expected, origin="origin")
-
-
-@pytest.mark.asyncio
-async def test_handle_belief_text_no_send_when_empty(agent, mocker):
-    payload = {"type": "belief_extraction_text", "beliefs": {}}
-    spy = mocker.patch.object(agent, "_send_beliefs_to_bdi", new_callable=AsyncMock)
-
-    await agent._handle_belief_text(payload, "origin")
-
-    spy.assert_not_awaited()
-
-
-@pytest.mark.asyncio
-async def test_send_beliefs_to_bdi(agent):
-    agent.send = AsyncMock()
-    beliefs = [Belief(name="user_said", arguments=["hello", "world"])]
-
-    await agent._send_beliefs_to_bdi(beliefs, origin="origin")
-
-    agent.send.assert_awaited_once()
-    sent: InternalMessage = agent.send.call_args.args[0]
-    assert sent.to == settings.agent_settings.bdi_core_name
-    assert sent.thread == "beliefs"
-    assert json.loads(sent.body)["create"] == [belief.model_dump() for belief in beliefs]
-
-
-@pytest.mark.asyncio
-async def test_setup_executes(agent):
-    """Covers setup and asserts the agent has a name."""
-    await agent.setup()
-    assert agent.name == "belief_collector_agent"  # simple property assertion
-
-
-@pytest.mark.asyncio
-async def test_handle_message_unrecognized_type_executes(agent):
-    """Covers the else branch for unrecognized message type."""
-    payload = {"type": "unknown_type"}
-    msg = make_msg(payload, sender="tester")
-    # Wrap send to ensure nothing is sent
-    agent.send = AsyncMock()
-    await agent.handle_message(msg)
-    # Assert no messages were sent
-    agent.send.assert_not_awaited()
-
-
-@pytest.mark.asyncio
-async def test_handle_emo_text_executes(agent):
-    """Covers the _handle_emo_text method."""
-    # The method does nothing, but we can assert it returns None
-    result = await agent._handle_emo_text({}, "origin")
-    assert result is None
-
-
-@pytest.mark.asyncio
-async def test_send_beliefs_to_bdi_empty_executes(agent):
-    """Covers early return when beliefs are empty."""
-    agent.send = AsyncMock()
-    await agent._send_beliefs_to_bdi({})
-    # Assert that nothing was sent
-    agent.send.assert_not_awaited()
-
-
-@pytest.mark.asyncio
-async def test_handle_belief_text_invalid_returns_none(agent, mocker):
-    payload = {"type": "belief_extraction_text", "beliefs": {"user_said": "invalid-argument"}}
-
-    result = await agent._handle_belief_text(payload, "origin")
-
-    # The method itself returns None
-    assert result is None

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -6,11 +6,15 @@ import httpx
 import pytest
 
 from control_backend.agents.bdi import TextBeliefExtractorAgent
+from control_backend.agents.bdi.text_belief_extractor_agent import BeliefState
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 from control_backend.schemas.belief_list import BeliefList
+from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
+from control_backend.schemas.chat_history import ChatHistory, ChatMessage
 from control_backend.schemas.program import (
+    BaseGoal,  # Changed from Goal
     ConditionalNorm,
     KeywordBelief,
     LLMAction,
@@ -23,11 +27,22 @@ from control_backend.schemas.program import (
 
 
 @pytest.fixture
-def agent():
-    agent = TextBeliefExtractorAgent("text_belief_agent")
-    agent.send = AsyncMock()
-    agent._query_llm = AsyncMock()
-    return agent
+def llm():
+    llm = TextBeliefExtractorAgent.LLM(MagicMock(), 4)
+    # We must ensure _query_llm returns a dictionary so iterating it doesn't fail
+    llm._query_llm = AsyncMock(return_value={})
+    return llm
+
+
+@pytest.fixture
+def agent(llm):
+    with patch(
+        "control_backend.agents.bdi.text_belief_extractor_agent.TextBeliefExtractorAgent.LLM",
+        return_value=llm,
+    ):
+        agent = TextBeliefExtractorAgent("text_belief_agent")
+        agent.send = AsyncMock()
+        return agent
 
 
 @pytest.fixture
@@ -102,24 +117,12 @@ async def test_handle_message_from_transcriber(agent, mock_settings):
 
     agent.send.assert_awaited_once()  # noqa  # `agent.send` has no such property, but we mock it.
     sent: InternalMessage = agent.send.call_args.args[0]  # noqa
-    assert sent.to == mock_settings.agent_settings.bdi_belief_collector_name
+    assert sent.to == mock_settings.agent_settings.bdi_core_name
     assert sent.thread == "beliefs"
-    parsed = json.loads(sent.body)
-    assert parsed == {"beliefs": {"user_said": [transcription]}, "type": "belief_extraction_text"}
-
-
-@pytest.mark.asyncio
-async def test_process_user_said(agent, mock_settings):
-    transcription = "this is a test"
-
-    await agent._user_said(transcription)
-
-    agent.send.assert_awaited_once()  # noqa  # `agent.send` has no such property, but we mock it.
-    sent: InternalMessage = agent.send.call_args.args[0]  # noqa
-    assert sent.to == mock_settings.agent_settings.bdi_belief_collector_name
-    assert sent.thread == "beliefs"
-    parsed = json.loads(sent.body)
-    assert parsed["beliefs"]["user_said"] == [transcription]
+    parsed = BeliefMessage.model_validate_json(sent.body)
+    replaced_last = parsed.replace.pop()
+    assert replaced_last.name == "user_said"
+    assert replaced_last.arguments == [transcription]
 
 
 @pytest.mark.asyncio
@@ -144,46 +147,46 @@ async def test_query_llm():
         "control_backend.agents.bdi.text_belief_extractor_agent.httpx.AsyncClient",
         return_value=mock_async_client,
     ):
-        agent = TextBeliefExtractorAgent("text_belief_agent")
+        llm = TextBeliefExtractorAgent.LLM(MagicMock(), 4)
 
-        res = await agent._query_llm("hello world", {"type": "null"})
+        res = await llm._query_llm("hello world", {"type": "null"})
         # Response content was set as "null", so should be deserialized as None
         assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_success(agent):
-    agent._query_llm.return_value = None
-    res = await agent._retry_query_llm("hello world", {"type": "null"})
+async def test_retry_query_llm_success(llm):
+    llm._query_llm.return_value = None
+    res = await llm.query("hello world", {"type": "null"})
 
-    agent._query_llm.assert_called_once()
+    llm._query_llm.assert_called_once()
     assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_success_after_failure(agent):
-    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await agent._retry_query_llm("hello world", {"type": "string"})
+async def test_retry_query_llm_success_after_failure(llm):
+    llm._query_llm.side_effect = [KeyError(), "real value"]
+    res = await llm.query("hello world", {"type": "string"})
 
-    assert agent._query_llm.call_count == 2
+    assert llm._query_llm.call_count == 2
     assert res == "real value"
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_failures(agent):
-    agent._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
-    res = await agent._retry_query_llm("hello world", {"type": "string"})
+async def test_retry_query_llm_failures(llm):
+    llm._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
+    res = await llm.query("hello world", {"type": "string"})
 
-    assert agent._query_llm.call_count == 3
+    assert llm._query_llm.call_count == 3
     assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_fail_immediately(agent):
-    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await agent._retry_query_llm("hello world", {"type": "string"}, tries=1)
+async def test_retry_query_llm_fail_immediately(llm):
+    llm._query_llm.side_effect = [KeyError(), "real value"]
+    res = await llm.query("hello world", {"type": "string"}, tries=1)
 
-    assert agent._query_llm.call_count == 1
+    assert llm._query_llm.call_count == 1
     assert res is None
 
 
@@ -192,7 +195,7 @@ async def test_extracting_semantic_beliefs(agent):
     """
     The Program Manager sends beliefs to this agent. Test whether the agent handles them correctly.
     """
-    assert len(agent.available_beliefs) == 0
+    assert len(agent.belief_inferrer.available_beliefs) == 0
     beliefs = BeliefList(
         beliefs=[
             KeywordBelief(
@@ -213,26 +216,28 @@ async def test_extracting_semantic_beliefs(agent):
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
             body=beliefs.model_dump_json(),
+            thread="beliefs",
         ),
     )
-    assert len(agent.available_beliefs) == 2
+    assert len(agent.belief_inferrer.available_beliefs) == 2
 
 
 @pytest.mark.asyncio
-async def test_handle_invalid_program(agent, sample_program):
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    assert len(agent.available_beliefs) == 2
+async def test_handle_invalid_beliefs(agent, sample_program):
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    assert len(agent.belief_inferrer.available_beliefs) == 2
 
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
             body=json.dumps({"phases": "Invalid"}),
+            thread="beliefs",
         ),
     )
 
-    assert len(agent.available_beliefs) == 2
+    assert len(agent.belief_inferrer.available_beliefs) == 2
 
 
 @pytest.mark.asyncio
@@ -254,13 +259,13 @@ async def test_handle_robot_response(agent):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_with_beliefs(agent, sample_program):
+async def test_simulated_real_turn_with_beliefs(agent, llm, sample_program):
     """Test sending user message to extract beliefs from."""
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
     # Send a user message with the belief that there's no more booze
-    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
+    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
     assert len(agent.conversation.messages) == 0
     await agent.handle_message(
         InternalMessage(
@@ -275,20 +280,20 @@ async def test_simulated_real_turn_with_beliefs(agent, sample_program):
     assert agent.send.call_count == 2
 
     # First should be the beliefs message
-    message: InternalMessage = agent.send.call_args_list[0].args[0]
+    message: InternalMessage = agent.send.call_args_list[1].args[0]
     beliefs = BeliefMessage.model_validate_json(message.body)
     assert len(beliefs.create) == 1
     assert beliefs.create[0].name == "no_more_booze"
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_no_beliefs(agent, sample_program):
+async def test_simulated_real_turn_no_beliefs(agent, llm, sample_program):
     """Test a user message to extract beliefs from, but no beliefs are formed."""
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
     # Send a user message with no new beliefs
-    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": None}
+    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": None}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -302,17 +307,17 @@ async def test_simulated_real_turn_no_beliefs(agent, sample_program):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_no_new_beliefs(agent, sample_program):
+async def test_simulated_real_turn_no_new_beliefs(agent, llm, sample_program):
     """
     Test a user message to extract beliefs from, but no new beliefs are formed because they already
     existed.
     """
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent.beliefs["is_pirate"] = True
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent._current_beliefs = BeliefState(true={InternalBelief(name="is_pirate", arguments=None)})
 
     # Send a user message with the belief the user is a pirate, still
-    agent._query_llm.return_value = {"is_pirate": True, "no_more_booze": None}
+    llm._query_llm.return_value = {"is_pirate": True, "no_more_booze": None}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -326,17 +331,19 @@ async def test_simulated_real_turn_no_new_beliefs(agent, sample_program):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_remove_belief(agent, sample_program):
+async def test_simulated_real_turn_remove_belief(agent, llm, sample_program):
     """
     Test a user message to extract beliefs from, but an existing belief is determined no longer to
     hold.
     """
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent.beliefs["no_more_booze"] = True
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent._current_beliefs = BeliefState(
+        true={InternalBelief(name="no_more_booze", arguments=None)},
+    )
 
     # Send a user message with the belief the user is a pirate, still
-    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": False}
+    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": False}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -349,18 +356,199 @@ async def test_simulated_real_turn_remove_belief(agent, sample_program):
     assert agent.send.call_count == 2
 
     # Agent's current beliefs should've changed
-    assert not agent.beliefs["no_more_booze"]
+    assert any(b.name == "no_more_booze" for b in agent._current_beliefs.false)
 
 
 @pytest.mark.asyncio
-async def test_llm_failure_handling(agent, sample_program):
+async def test_infer_goal_completions_sends_beliefs(agent, llm):
+    """Test that inferred goal completions are sent to the BDI core."""
+    goal = BaseGoal(
+        id=uuid.uuid4(), name="Say Hello", description="The user said hello", can_fail=True
+    )
+    agent.goal_inferrer.goals = {goal}
+
+    # Mock goal inference: goal is achieved
+    llm.query = AsyncMock(return_value=True)
+
+    await agent._infer_goal_completions()
+
+    # Should send belief change to BDI core
+    agent.send.assert_awaited_once()
+    sent: InternalMessage = agent.send.call_args.args[0]
+    assert sent.to == settings.agent_settings.bdi_core_name
+    assert sent.thread == "beliefs"
+
+    parsed = BeliefMessage.model_validate_json(sent.body)
+    assert len(parsed.create) == 1
+    assert parsed.create[0].name == "achieved_say_hello"
+
+
+@pytest.mark.asyncio
+async def test_llm_failure_handling(agent, llm, sample_program):
     """
     Check that the agent handles failures gracefully without crashing.
     """
-    agent._query_llm.side_effect = httpx.HTTPError("")
-    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    llm._query_llm.side_effect = httpx.HTTPError("")
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
-    belief_changes = await agent._infer_turn()
+    belief_changes = await agent.belief_inferrer.infer_from_conversation(
+        ChatHistory(
+            messages=[ChatMessage(role="user", content="Good day!")],
+        ),
+    )
 
-    assert len(belief_changes) == 0
+    assert len(belief_changes.true) == 0
+    assert len(belief_changes.false) == 0
+
+
+def test_belief_state_bool():
+    # Empty
+    bs = BeliefState()
+    assert not bs
+
+    # True set
+    bs_true = BeliefState(true={InternalBelief(name="a", arguments=None)})
+    assert bs_true
+
+    # False set
+    bs_false = BeliefState(false={InternalBelief(name="a", arguments=None)})
+    assert bs_false
+
+
+@pytest.mark.asyncio
+async def test_handle_beliefs_message_validation_error(agent, mock_settings):
+    # Invalid JSON
+    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
+    msg = InternalMessage(
+        to="me",
+        sender=mock_settings.agent_settings.bdi_program_manager_name,
+        thread="beliefs",
+        body="invalid json",
+    )
+    # Should log warning and return
+    agent.logger = MagicMock()
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+    # Invalid Model
+    msg.body = json.dumps({"beliefs": [{"invalid": "obj"}]})
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_handle_goals_message_validation_error(agent, mock_settings):
+    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
+    msg = InternalMessage(
+        to="me",
+        sender=mock_settings.agent_settings.bdi_program_manager_name,
+        thread="goals",
+        body="invalid json",
+    )
+    agent.logger = MagicMock()
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_handle_goal_achieved_message_validation_error(agent, mock_settings):
+    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
+    msg = InternalMessage(
+        to="me",
+        sender=mock_settings.agent_settings.bdi_program_manager_name,
+        thread="achieved_goals",
+        body="invalid json",
+    )
+    agent.logger = MagicMock()
+    await agent.handle_message(msg)
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_goal_inferrer_infer_from_conversation(agent, llm):
+    # Setup goals
+    # Use BaseGoal object as typically received by the extractor
+    g1 = BaseGoal(id=uuid.uuid4(), name="g1", description="desc", can_fail=True)
+
+    # Use real GoalAchievementInferrer
+    from control_backend.agents.bdi.text_belief_extractor_agent import GoalAchievementInferrer
+
+    inferrer = GoalAchievementInferrer(llm)
+    inferrer.goals = {g1}
+
+    # Mock LLM response
+    llm._query_llm.return_value = True
+
+    completions = await inferrer.infer_from_conversation(ChatHistory(messages=[]))
+    assert completions
+    # slugify uses slugify library, hard to predict exact string without it,
+    # but we can check values
+    assert list(completions.values())[0] is True
+
+
+def test_apply_conversation_message_limit(agent):
+    with patch("control_backend.agents.bdi.text_belief_extractor_agent.settings") as mock_s:
+        mock_s.behaviour_settings.conversation_history_length_limit = 2
+        agent.conversation.messages = []
+
+        agent._apply_conversation_message(ChatMessage(role="user", content="1"))
+        agent._apply_conversation_message(ChatMessage(role="assistant", content="2"))
+        agent._apply_conversation_message(ChatMessage(role="user", content="3"))
+
+        assert len(agent.conversation.messages) == 2
+        assert agent.conversation.messages[0].content == "2"
+        assert agent.conversation.messages[1].content == "3"
+
+
+@pytest.mark.asyncio
+async def test_handle_program_manager_reset(agent):
+    with patch("control_backend.agents.bdi.text_belief_extractor_agent.settings") as mock_s:
+        mock_s.agent_settings.bdi_program_manager_name = "pm"
+        agent.conversation.messages = [ChatMessage(role="user", content="hi")]
+        agent.belief_inferrer.available_beliefs = [
+            SemanticBelief(id=uuid.uuid4(), name="b", description="d")
+        ]
+
+        msg = InternalMessage(to="me", sender="pm", thread="conversation_history", body="reset")
+        await agent.handle_message(msg)
+
+        assert len(agent.conversation.messages) == 0
+        assert len(agent.belief_inferrer.available_beliefs) == 0
+
+
+def test_split_into_chunks():
+    from control_backend.agents.bdi.text_belief_extractor_agent import SemanticBeliefInferrer
+
+    items = [1, 2, 3, 4, 5]
+    chunks = SemanticBeliefInferrer._split_into_chunks(items, 2)
+    assert len(chunks) == 2
+    assert len(chunks[0]) + len(chunks[1]) == 5
+
+
+@pytest.mark.asyncio
+async def test_infer_beliefs_call(agent, llm):
+    from control_backend.agents.bdi.text_belief_extractor_agent import SemanticBeliefInferrer
+
+    inferrer = SemanticBeliefInferrer(llm)
+    sb = SemanticBelief(id=uuid.uuid4(), name="is_happy", description="User is happy")
+
+    llm.query = AsyncMock(return_value={"is_happy": True})
+
+    res = await inferrer._infer_beliefs(ChatHistory(messages=[]), [sb])
+    assert res == {"is_happy": True}
+    llm.query.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_infer_goal_call(agent, llm):
+    from control_backend.agents.bdi.text_belief_extractor_agent import GoalAchievementInferrer
+
+    inferrer = GoalAchievementInferrer(llm)
+    goal = BaseGoal(id=uuid.uuid4(), name="g1", description="d")
+
+    llm.query = AsyncMock(return_value=True)
+
+    res = await inferrer._infer_goal(ChatHistory(messages=[]), goal)
+    assert res is True
+    llm.query.assert_called_once()

test/unit/agents/communication/test_ri_communication_agent.py

@@ -4,6 +4,8 @@ from unittest.mock import ANY, AsyncMock, MagicMock, patch
 import pytest
 
 from control_backend.agents.communication.ri_communication_agent import RICommunicationAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.schemas.ri_message import PauseCommand, RIEndpoint
 
 
 def speech_agent_path():
@@ -53,7 +55,11 @@ async def test_setup_success_connects_and_starts_robot(zmq_context):
         MockGesture.return_value.start = AsyncMock()
         agent = RICommunicationAgent("ri_comm", address="tcp://localhost:5555", bind=False)
 
-        agent.add_behavior = MagicMock()
+        def close_coro(coro):
+            coro.close()
+            return MagicMock()
+
+        agent.add_behavior = MagicMock(side_effect=close_coro)
 
         await agent.setup()
 
@@ -83,7 +89,11 @@ async def test_setup_binds_when_requested(zmq_context):
 
     agent = RICommunicationAgent("ri_comm", address="tcp://localhost:5555", bind=True)
 
-    agent.add_behavior = MagicMock()
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    agent.add_behavior = MagicMock(side_effect=close_coro)
 
     with (
         patch(speech_agent_path(), autospec=True) as MockSpeech,
@@ -151,6 +161,7 @@ async def test_handle_negotiation_response_updates_req_socket(zmq_context):
 @pytest.mark.asyncio
 async def test_handle_disconnection_publishes_and_reconnects():
     pub_socket = AsyncMock()
+    pub_socket.close = MagicMock()
     agent = RICommunicationAgent("ri_comm")
     agent.pub_socket = pub_socket
     agent.connected = True
@@ -233,6 +244,25 @@ async def test_handle_negotiation_response_unhandled_id():
     )
 
 
+@pytest.mark.asyncio
+async def test_handle_negotiation_response_audio(zmq_context):
+    agent = RICommunicationAgent("ri_comm")
+
+    with patch(
+        "control_backend.agents.communication.ri_communication_agent.VADAgent", autospec=True
+    ) as MockVAD:
+        MockVAD.return_value.start = AsyncMock()
+
+        await agent._handle_negotiation_response(
+            {"data": [{"id": "audio", "port": 7000, "bind": False}]}
+        )
+
+        MockVAD.assert_called_once_with(
+            audio_in_address="tcp://localhost:7000", audio_in_bind=False
+        )
+        MockVAD.return_value.start.assert_awaited_once()
+
+
 @pytest.mark.asyncio
 async def test_stop_closes_sockets():
     req = MagicMock()
@@ -323,6 +353,7 @@ async def test_listen_loop_generic_exception():
 @pytest.mark.asyncio
 async def test_handle_disconnection_timeout(monkeypatch):
     pub = AsyncMock()
+    pub.close = MagicMock()
     pub.send_multipart = AsyncMock(side_effect=TimeoutError)
 
     agent = RICommunicationAgent("ri_comm")
@@ -365,3 +396,38 @@ async def test_negotiate_req_socket_none_causes_retry(zmq_context):
     result = await agent._negotiate_connection(max_retries=1)
 
     assert result is False
+
+
+@pytest.mark.asyncio
+async def test_handle_message_pause_command(zmq_context):
+    """Test handle_message with a valid PauseCommand."""
+    agent = RICommunicationAgent("ri_comm")
+    agent._req_socket = AsyncMock()
+    agent.logger = MagicMock()
+
+    agent._req_socket.recv_json.return_value = {"status": "ok"}
+
+    pause_cmd = PauseCommand(data=True)
+    msg = InternalMessage(to="ri_comm", sender="user_int", body=pause_cmd.model_dump_json())
+
+    await agent.handle_message(msg)
+
+    agent._req_socket.send_json.assert_awaited_once()
+    args = agent._req_socket.send_json.await_args[0][0]
+    assert args["endpoint"] == RIEndpoint.PAUSE.value
+    assert args["data"] is True
+
+
+@pytest.mark.asyncio
+async def test_handle_message_invalid_pause_command(zmq_context):
+    """Test handle_message with invalid JSON."""
+    agent = RICommunicationAgent("ri_comm")
+    agent._req_socket = AsyncMock()
+    agent.logger = MagicMock()
+
+    msg = InternalMessage(to="ri_comm", sender="user_int", body="invalid json")
+
+    await agent.handle_message(msg)
+
+    agent.logger.warning.assert_called_with("Incorrect message format for PauseCommand.")
+    agent._req_socket.send_json.assert_not_called()

test/unit/agents/llm/test_llm_agent.py

@@ -58,17 +58,64 @@ async def test_llm_processing_success(mock_httpx_client, mock_settings):
         to="llm_agent",
         sender=mock_settings.agent_settings.bdi_core_name,
         body=prompt.model_dump_json(),
+        thread="prompt_message",  # REQUIRED: thread must match handle_message logic
     )
 
+    agent._process_bdi_message = AsyncMock()
+
     await agent.handle_message(msg)
 
+    agent._process_bdi_message.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_process_bdi_message_success(mock_httpx_client, mock_settings):
+    # Setup the mock response for the stream
+    mock_response = MagicMock()
+    mock_response.raise_for_status = MagicMock()
+
+    # Simulate stream lines
+    lines = [
+        b'data: {"choices": [{"delta": {"content": "Hello"}}]}',
+        b'data: {"choices": [{"delta": {"content": " world"}}]}',
+        b'data: {"choices": [{"delta": {"content": "."}}]}',
+        b"data: [DONE]",
+    ]
+
+    async def aiter_lines_gen():
+        for line in lines:
+            yield line.decode()
+
+    mock_response.aiter_lines.side_effect = aiter_lines_gen
+
+    mock_stream_context = MagicMock()
+    mock_stream_context.__aenter__ = AsyncMock(return_value=mock_response)
+    mock_stream_context.__aexit__ = AsyncMock(return_value=None)
+
+    # Configure the client
+    mock_httpx_client.stream = MagicMock(return_value=mock_stream_context)
+
+    # Setup Agent
+    agent = LLMAgent("llm_agent")
+    agent.send = AsyncMock()  # Mock the send method to verify replies
+
+    mock_logger = MagicMock()
+    agent.logger = mock_logger
+
+    # Simulate receiving a message from BDI
+    prompt = LLMPromptMessage(text="Hi", norms=[], goals=[])
+
+    await agent._process_bdi_message(prompt)
+
     # Verification
     # "Hello world." constitutes one sentence/chunk based on punctuation split
-    # The agent should call send once with the full sentence
+    # The agent should call send once with the full sentence, PLUS once more for full reply
     assert agent.send.called
-    args = agent.send.call_args_list[0][0][0]
-    assert args.to == mock_settings.agent_settings.bdi_core_name
-    assert "Hello world." in args.body
+
+    # Check args. We expect at least one call sending "Hello world."
+    calls = agent.send.call_args_list
+    bodies = [c[0][0].body for c in calls]
+    assert any("Hello world." in b for b in bodies)
 
 
 @pytest.mark.asyncio
@@ -76,22 +123,15 @@ async def test_llm_processing_errors(mock_httpx_client, mock_settings):
     agent = LLMAgent("llm_agent")
     agent.send = AsyncMock()
     prompt = LLMPromptMessage(text="Hi", norms=[], goals=[])
-    msg = InternalMessage(
-        to="llm",
-        sender=mock_settings.agent_settings.bdi_core_name,
-        body=prompt.model_dump_json(),
-    )
 
-    # HTTP Error
+    # HTTP Error: stream method RAISES exception immediately
     mock_httpx_client.stream = MagicMock(side_effect=httpx.HTTPError("Fail"))
-    await agent.handle_message(msg)
-    assert "LLM service unavailable." in agent.send.call_args[0][0].body
 
-    # General Exception
-    agent.send.reset_mock()
-    mock_httpx_client.stream = MagicMock(side_effect=Exception("Boom"))
-    await agent.handle_message(msg)
-    assert "Error processing the request." in agent.send.call_args[0][0].body
+    await agent._process_bdi_message(prompt)
+
+    # Check that error message was sent
+    assert agent.send.called
+    assert "LLM service unavailable." in agent.send.call_args_list[0][0][0].body
 
 
 @pytest.mark.asyncio
@@ -113,16 +153,13 @@ async def test_llm_json_error(mock_httpx_client, mock_settings):
 
     agent = LLMAgent("llm_agent")
     agent.send = AsyncMock()
+    # Ensure logger is mocked
+    agent.logger = MagicMock()
 
-    with patch.object(agent.logger, "error") as log:
-        prompt = LLMPromptMessage(text="Hi", norms=[], goals=[])
-        msg = InternalMessage(
-            to="llm",
-            sender=mock_settings.agent_settings.bdi_core_name,
-            body=prompt.model_dump_json(),
-        )
-        await agent.handle_message(msg)
-        log.assert_called()  # Should log JSONDecodeError
+    prompt = LLMPromptMessage(text="Hi", norms=[], goals=[])
+    await agent._process_bdi_message(prompt)
+
+    agent.logger.error.assert_called()  # Should log JSONDecodeError
 
 
 def test_llm_instructions():
@@ -157,6 +194,7 @@ async def test_handle_message_validation_error_branch_no_send(mock_httpx_client,
         to="llm_agent",
         sender=mock_settings.agent_settings.bdi_core_name,
         body=invalid_json,
+        thread="prompt_message",
     )
 
     await agent.handle_message(msg)
@@ -265,3 +303,48 @@ async def test_stream_query_llm_skips_non_data_lines(mock_httpx_client, mock_set
 
     # Only the valid 'data:' line should yield content
     assert tokens == ["Hi"]
+
+
+@pytest.mark.asyncio
+async def test_clear_history_command(mock_settings):
+    """Test that the 'clear_history' message clears the agent's memory."""
+    # setup LLM to have some history
+    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
+    agent = LLMAgent("llm_agent")
+    agent.history = [
+        {"role": "user", "content": "Old conversation context"},
+        {"role": "assistant", "content": "Old response"},
+    ]
+    assert len(agent.history) == 2
+    msg = InternalMessage(
+        to="llm_agent",
+        sender=mock_settings.agent_settings.bdi_program_manager_name,
+        body="clear_history",
+    )
+    await agent.handle_message(msg)
+    assert len(agent.history) == 0
+
+
+@pytest.mark.asyncio
+async def test_handle_assistant_and_user_messages(mock_settings):
+    agent = LLMAgent("llm_agent")
+
+    # Assistant message
+    msg_ast = InternalMessage(
+        to="llm_agent",
+        sender=mock_settings.agent_settings.bdi_core_name,
+        thread="assistant_message",
+        body="I said this",
+    )
+    await agent.handle_message(msg_ast)
+    assert agent.history[-1] == {"role": "assistant", "content": "I said this"}
+
+    # User message
+    msg_usr = InternalMessage(
+        to="llm_agent",
+        sender=mock_settings.agent_settings.bdi_core_name,
+        thread="user_message",
+        body="User said this",
+    )
+    await agent.handle_message(msg_usr)
+    assert agent.history[-1] == {"role": "user", "content": "User said this"}

test/unit/agents/perception/transcription_agent/test_speech_recognizer.py

@@ -55,4 +55,6 @@ def test_get_decode_options():
     assert isinstance(options["sample_len"], int)
 
     # When disabled, it should not limit output length based on input size
-    assert "sample_rate" not in options
+    recognizer = OpenAIWhisperSpeechRecognizer(limit_output_length=False)
+    options = recognizer._get_decode_options(audio)
+    assert "sample_len" not in options

test/unit/agents/perception/transcription_agent/test_transcription_agent.py

@@ -36,7 +36,12 @@ async def test_transcription_agent_flow(mock_zmq_context):
         agent.send = AsyncMock()
 
         agent._running = True
-        agent.add_behavior = AsyncMock()
+
+        def close_coro(coro):
+            coro.close()
+            return MagicMock()
+
+        agent.add_behavior = MagicMock(side_effect=close_coro)
 
         await agent.setup()
 
@@ -143,7 +148,12 @@ async def test_transcription_loop_continues_after_error(mock_zmq_context):
         agent = TranscriptionAgent("tcp://in")
         agent._running = True  # ← REQUIRED to enter the loop
         agent.send = AsyncMock()  # should never be called
-        agent.add_behavior = AsyncMock()  # match other tests
+
+        def close_coro(coro):
+            coro.close()
+            return MagicMock()
+
+        agent.add_behavior = MagicMock(side_effect=close_coro)  # match other tests
 
         await agent.setup()
 
@@ -180,7 +190,12 @@ async def test_transcription_continue_branch_when_empty(mock_zmq_context):
         # Make loop runnable
         agent._running = True
         agent.send = AsyncMock()
-        agent.add_behavior = AsyncMock()
+
+        def close_coro(coro):
+            coro.close()
+            return MagicMock()
+
+        agent.add_behavior = MagicMock(side_effect=close_coro)
 
         await agent.setup()
 

test/unit/agents/perception/vad_agent/test_vad_agent_unit.py

@@ -0,0 +1,152 @@
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from control_backend.agents.perception.vad_agent import VADAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.schemas.program_status import PROGRAM_STATUS, ProgramStatus
+
+
+@pytest.fixture(autouse=True)
+def mock_zmq():
+    with patch("zmq.asyncio.Context") as mock:
+        mock.instance.return_value = MagicMock()
+        yield mock
+
+
+@pytest.fixture
+def agent():
+    return VADAgent("tcp://localhost:5555", False)
+
+
+@pytest.mark.asyncio
+async def test_handle_message_pause(agent):
+    agent._paused = MagicMock()
+    # It starts set (not paused)
+
+    msg = InternalMessage(to="vad", sender="user_interrupt_agent", body="PAUSE")
+
+    # We need to mock settings to match sender name
+    with patch("control_backend.agents.perception.vad_agent.settings") as mock_settings:
+        mock_settings.agent_settings.user_interrupt_name = "user_interrupt_agent"
+
+        await agent.handle_message(msg)
+
+        agent._paused.clear.assert_called_once()
+        assert agent._reset_needed is True
+
+
+@pytest.mark.asyncio
+async def test_handle_message_resume(agent):
+    agent._paused = MagicMock()
+    msg = InternalMessage(to="vad", sender="user_interrupt_agent", body="RESUME")
+
+    with patch("control_backend.agents.perception.vad_agent.settings") as mock_settings:
+        mock_settings.agent_settings.user_interrupt_name = "user_interrupt_agent"
+
+        await agent.handle_message(msg)
+
+        agent._paused.set.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_handle_message_unknown_command(agent):
+    agent._paused = MagicMock()
+    msg = InternalMessage(to="vad", sender="user_interrupt_agent", body="UNKNOWN")
+
+    with patch("control_backend.agents.perception.vad_agent.settings") as mock_settings:
+        mock_settings.agent_settings.user_interrupt_name = "user_interrupt_agent"
+        agent.logger = MagicMock()
+
+        await agent.handle_message(msg)
+
+        agent._paused.clear.assert_not_called()
+        agent._paused.set.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_handle_message_unknown_sender(agent):
+    agent._paused = MagicMock()
+    msg = InternalMessage(to="vad", sender="other_agent", body="PAUSE")
+
+    with patch("control_backend.agents.perception.vad_agent.settings") as mock_settings:
+        mock_settings.agent_settings.user_interrupt_name = "user_interrupt_agent"
+
+        await agent.handle_message(msg)
+
+        agent._paused.clear.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_status_loop_waits_for_running(agent):
+    agent._running = True
+    agent.program_sub_socket = AsyncMock()
+    agent.program_sub_socket.close = MagicMock()
+    agent._reset_stream = AsyncMock()
+
+    # Sequence of messages:
+    # 1. Wrong topic
+    # 2. Right topic, wrong status (STARTING)
+    # 3. Right topic, RUNNING -> Should break loop
+
+    agent.program_sub_socket.recv_multipart.side_effect = [
+        (b"wrong_topic", b"whatever"),
+        (PROGRAM_STATUS, ProgramStatus.STARTING.value),
+        (PROGRAM_STATUS, ProgramStatus.RUNNING.value),
+    ]
+
+    await agent._status_loop()
+
+    assert agent._reset_stream.await_count == 1
+    agent.program_sub_socket.close.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_setup_success(agent, mock_zmq):
+    def close_coro(coro):
+        coro.close()
+        return MagicMock()
+
+    agent.add_behavior = MagicMock(side_effect=close_coro)
+
+    mock_context = mock_zmq.instance.return_value
+    mock_sub = MagicMock()
+    mock_pub = MagicMock()
+
+    # We expect multiple socket calls:
+    # 1. audio_in (SUB)
+    # 2. audio_out (PUB)
+    # 3. program_sub (SUB)
+    mock_context.socket.side_effect = [mock_sub, mock_pub, mock_sub]
+
+    with patch("control_backend.agents.perception.vad_agent.torch.hub.load") as mock_load:
+        mock_load.return_value = (MagicMock(), None)
+
+        with patch("control_backend.agents.perception.vad_agent.TranscriptionAgent") as MockTrans:
+            mock_trans_instance = MockTrans.return_value
+            mock_trans_instance.start = AsyncMock()
+
+            await agent.setup()
+
+            mock_trans_instance.start.assert_awaited_once()
+
+    assert agent.add_behavior.call_count == 2  # streaming_loop + status_loop
+    assert agent.audio_in_socket is not None
+    assert agent.audio_out_socket is not None
+    assert agent.program_sub_socket is not None
+
+
+@pytest.mark.asyncio
+async def test_reset_stream(agent):
+    mock_poller = MagicMock()
+    agent.audio_in_poller = mock_poller
+
+    # poll(1) returns not None twice, then None
+    mock_poller.poll = AsyncMock(side_effect=[b"data", b"data", None])
+
+    agent._ready = MagicMock()
+
+    await agent._reset_stream()
+
+    assert mock_poller.poll.await_count == 3
+    agent._ready.set.assert_called_once()

test/unit/agents/perception/vad_agent/test_vad_streaming.py

@@ -5,6 +5,16 @@ import pytest
 import zmq
 
 from control_backend.agents.perception.vad_agent import VADAgent
+from control_backend.core.config import settings
+
+
+# We don't want to use real ZMQ in unit tests, for example because it can give errors when sockets
+# aren't closed properly.
+@pytest.fixture(autouse=True)
+def mock_zmq():
+    with patch("zmq.asyncio.Context") as mock:
+        mock.instance.return_value = MagicMock()
+        yield mock
 
 
 @pytest.fixture
@@ -126,6 +136,54 @@ async def test_no_data(audio_out_socket, vad_agent):
     assert len(vad_agent.audio_buffer) == 0
 
 
+@pytest.mark.asyncio
+async def test_streaming_loop_reset_needed(audio_out_socket, vad_agent):
+    """Test that _reset_needed branch works as expected."""
+    vad_agent._reset_needed = True
+    vad_agent._ready.set()
+    vad_agent._paused.set()
+    vad_agent._running = True
+    vad_agent.audio_buffer = np.array([1.0], dtype=np.float32)
+    vad_agent.i_since_speech = 0
+
+    # Mock _reset_stream to stop the loop by setting _running=False
+    async def mock_reset():
+        vad_agent._running = False
+
+    vad_agent._reset_stream = mock_reset
+
+    # Needs a poller to avoid AssertionError
+    vad_agent.audio_in_poller = AsyncMock()
+    vad_agent.audio_in_poller.poll.return_value = None
+
+    await vad_agent._streaming_loop()
+
+    assert vad_agent._reset_needed is False
+    assert len(vad_agent.audio_buffer) == 0
+    assert vad_agent.i_since_speech == settings.behaviour_settings.vad_initial_since_speech
+
+
+@pytest.mark.asyncio
+async def test_streaming_loop_no_data_clears_buffer(audio_out_socket, vad_agent):
+    """Test that if poll returns None, buffer is cleared if not empty."""
+    vad_agent.audio_buffer = np.array([1.0], dtype=np.float32)
+    vad_agent._ready.set()
+    vad_agent._paused.set()
+    vad_agent._running = True
+
+    class MockPoller:
+        async def poll(self, timeout_ms=None):
+            vad_agent._running = False  # stop after one poll
+            return None
+
+    vad_agent.audio_in_poller = MockPoller()
+
+    await vad_agent._streaming_loop()
+
+    assert len(vad_agent.audio_buffer) == 0
+    assert vad_agent.i_since_speech == settings.behaviour_settings.vad_initial_since_speech
+
+
 @pytest.mark.asyncio
 async def test_vad_model_load_failure_stops_agent(vad_agent):
     """
@@ -140,12 +198,10 @@ async def test_vad_model_load_failure_stops_agent(vad_agent):
         # Patch stop to an AsyncMock so we can check it was awaited
         vad_agent.stop = AsyncMock()
 
-        result = await vad_agent.setup()
+        await vad_agent.setup()
 
         # Assert stop was called
         vad_agent.stop.assert_awaited_once()
-        # Assert setup returned None
-        assert result is None
 
 
 @pytest.mark.asyncio
@@ -155,7 +211,7 @@ async def test_audio_out_bind_failure_sets_none_and_logs(vad_agent, caplog):
     audio_out_socket is set to None, None is returned, and an error is logged.
     """
     mock_socket = MagicMock()
-    mock_socket.bind_to_random_port.side_effect = zmq.ZMQBindError()
+    mock_socket.bind.side_effect = zmq.ZMQBindError()
     with patch("control_backend.agents.perception.vad_agent.azmq.Context.instance") as mock_ctx:
         mock_ctx.return_value.socket.return_value = mock_socket
 

test/unit/agents/test_base.py

@@ -0,0 +1,24 @@
+import logging
+
+from control_backend.agents.base import BaseAgent
+
+
+class MyAgent(BaseAgent):
+    async def setup(self):
+        pass
+
+    async def handle_message(self, msg):
+        pass
+
+
+def test_base_agent_logger_init():
+    # When defining a subclass, __init_subclass__ runs
+    # The BaseAgent in agents/base.py sets the logger
+    assert hasattr(MyAgent, "logger")
+    assert isinstance(MyAgent.logger, logging.Logger)
+    # The logger name depends on the package.
+    # Since this test file is running as a module, __package__ might be None or the test package.
+    # In 'src/control_backend/agents/base.py', it uses __package__ of base.py which is
+    #  'control_backend.agents'.
+    # So logger name should be control_backend.agents.MyAgent
+    assert MyAgent.logger.name == "control_backend.agents.MyAgent"

test/unit/agents/user_interrupt/test_user_interrupt.py

@@ -7,6 +7,15 @@ import pytest
 from control_backend.agents.user_interrupt.user_interrupt_agent import UserInterruptAgent
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
+from control_backend.schemas.program import (
+    ConditionalNorm,
+    Goal,
+    KeywordBelief,
+    Phase,
+    Plan,
+    Program,
+    Trigger,
+)
 from control_backend.schemas.ri_message import RIEndpoint
 
 
@@ -16,6 +25,7 @@ def agent():
     agent.send = AsyncMock()
     agent.logger = MagicMock()
     agent.sub_socket = AsyncMock()
+    agent.pub_socket = AsyncMock()
     return agent
 
 
@@ -49,21 +59,18 @@ async def test_send_to_gesture_agent(agent):
 
 
 @pytest.mark.asyncio
-async def test_send_to_program_manager(agent):
+async def test_send_to_bdi_belief(agent):
     """Verify belief update format."""
-    context_str = "2"
+    context_str = "some_goal"
 
-    await agent._send_to_program_manager(context_str)
+    await agent._send_to_bdi_belief(context_str, "goal")
 
-    agent.send.assert_awaited_once()
-    sent_msg: InternalMessage = agent.send.call_args.args[0]
+    assert agent.send.await_count == 1
+    sent_msg = agent.send.call_args.args[0]
 
-    assert sent_msg.to == settings.agent_settings.bdi_program_manager_name
-    assert sent_msg.thread == "belief_override_id"
-
-    body = json.loads(sent_msg.body)
-
-    assert body["belief"] == context_str
+    assert sent_msg.to == settings.agent_settings.bdi_core_name
+    assert sent_msg.thread == "beliefs"
+    assert "achieved_some_goal" in sent_msg.body
 
 
 @pytest.mark.asyncio
@@ -77,6 +84,10 @@ async def test_receive_loop_routing_success(agent):
     # Prepare JSON payloads as bytes
     payload_speech = json.dumps({"type": "speech", "context": "Hello Speech"}).encode()
     payload_gesture = json.dumps({"type": "gesture", "context": "Hello Gesture"}).encode()
+    # override calls _send_to_bdi (for trigger/norm) OR _send_to_bdi_belief (for goal).
+
+    # To test routing, we need to populate the maps
+    agent._goal_map["Hello Override"] = "some_goal_slug"
     payload_override = json.dumps({"type": "override", "context": "Hello Override"}).encode()
 
     agent.sub_socket.recv_multipart.side_effect = [
@@ -88,7 +99,7 @@ async def test_receive_loop_routing_success(agent):
 
     agent._send_to_speech_agent = AsyncMock()
     agent._send_to_gesture_agent = AsyncMock()
-    agent._send_to_program_manager = AsyncMock()
+    agent._send_to_bdi_belief = AsyncMock()
 
     try:
         await agent._receive_button_event()
@@ -103,12 +114,12 @@ async def test_receive_loop_routing_success(agent):
     # Gesture
     agent._send_to_gesture_agent.assert_awaited_once_with("Hello Gesture")
 
-    # Override
-    agent._send_to_program_manager.assert_awaited_once_with("Hello Override")
+    # Override (since we mapped it to a goal)
+    agent._send_to_bdi_belief.assert_awaited_once_with("some_goal_slug", "goal")
 
     assert agent._send_to_speech_agent.await_count == 1
     assert agent._send_to_gesture_agent.await_count == 1
-    assert agent._send_to_program_manager.await_count == 1
+    assert agent._send_to_bdi_belief.await_count == 1
 
 
 @pytest.mark.asyncio
@@ -125,7 +136,6 @@ async def test_receive_loop_unknown_type(agent):
 
     agent._send_to_speech_agent = AsyncMock()
     agent._send_to_gesture_agent = AsyncMock()
-    agent._send_to_belief_collector = AsyncMock()
 
     try:
         await agent._receive_button_event()
@@ -137,10 +147,165 @@ async def test_receive_loop_unknown_type(agent):
     # Ensure no handlers were called
     agent._send_to_speech_agent.assert_not_called()
     agent._send_to_gesture_agent.assert_not_called()
-    agent._send_to_belief_collector.assert_not_called()
 
-    agent.logger.warning.assert_called_with(
-        "Received button press with unknown type '%s' (context: '%s').",
-        "unknown_thing",
-        "some_data",
-    )
+    agent.logger.warning.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_create_mapping(agent):
+    # Create a program with a trigger, goal, and conditional norm
+    import uuid
+
+    trigger_id = uuid.uuid4()
+    goal_id = uuid.uuid4()
+    norm_id = uuid.uuid4()
+
+    cond = KeywordBelief(id=uuid.uuid4(), name="k1", keyword="key")
+    plan = Plan(id=uuid.uuid4(), name="p1", steps=[])
+
+    trigger = Trigger(id=trigger_id, name="my_trigger", condition=cond, plan=plan)
+    goal = Goal(id=goal_id, name="my_goal", description="desc", plan=plan)
+
+    cn = ConditionalNorm(id=norm_id, name="my_norm", norm="be polite", condition=cond)
+
+    phase = Phase(id=uuid.uuid4(), name="phase1", norms=[cn], goals=[goal], triggers=[trigger])
+    prog = Program(phases=[phase])
+
+    # Call create_mapping via handle_message
+    msg = InternalMessage(to="me", thread="new_program", body=prog.model_dump_json())
+    await agent.handle_message(msg)
+
+    # Check maps
+    assert str(trigger_id) in agent._trigger_map
+    assert agent._trigger_map[str(trigger_id)] == "trigger_my_trigger"
+
+    assert str(goal_id) in agent._goal_map
+    assert agent._goal_map[str(goal_id)] == "my_goal"
+
+    assert str(norm_id) in agent._cond_norm_map
+    assert agent._cond_norm_map[str(norm_id)] == "norm_be_polite"
+
+
+@pytest.mark.asyncio
+async def test_create_mapping_invalid_json(agent):
+    # Pass invalid json to handle_message thread "new_program"
+    msg = InternalMessage(to="me", thread="new_program", body="invalid json")
+    await agent.handle_message(msg)
+
+    # Should log error and maps should remain empty or cleared
+    agent.logger.error.assert_called()
+
+
+@pytest.mark.asyncio
+async def test_handle_message_trigger_start(agent):
+    # Setup reverse map manually
+    agent._trigger_reverse_map["trigger_slug"] = "ui_id_123"
+
+    msg = InternalMessage(to="me", thread="trigger_start", body="trigger_slug")
+    await agent.handle_message(msg)
+
+    agent.pub_socket.send_multipart.assert_awaited_once()
+    args = agent.pub_socket.send_multipart.call_args[0][0]
+    assert args[0] == b"experiment"
+    payload = json.loads(args[1])
+    assert payload["type"] == "trigger_update"
+    assert payload["id"] == "ui_id_123"
+    assert payload["achieved"] is True
+
+
+@pytest.mark.asyncio
+async def test_handle_message_trigger_end(agent):
+    agent._trigger_reverse_map["trigger_slug"] = "ui_id_123"
+
+    msg = InternalMessage(to="me", thread="trigger_end", body="trigger_slug")
+    await agent.handle_message(msg)
+
+    agent.pub_socket.send_multipart.assert_awaited_once()
+    payload = json.loads(agent.pub_socket.send_multipart.call_args[0][0][1])
+    assert payload["type"] == "trigger_update"
+    assert payload["achieved"] is False
+
+
+@pytest.mark.asyncio
+async def test_handle_message_transition_phase(agent):
+    msg = InternalMessage(to="me", thread="transition_phase", body="phase_id_123")
+    await agent.handle_message(msg)
+
+    agent.pub_socket.send_multipart.assert_awaited_once()
+    payload = json.loads(agent.pub_socket.send_multipart.call_args[0][0][1])
+    assert payload["type"] == "phase_update"
+    assert payload["id"] == "phase_id_123"
+
+
+@pytest.mark.asyncio
+async def test_handle_message_goal_start(agent):
+    agent._goal_reverse_map["goal_slug"] = "goal_id_123"
+
+    msg = InternalMessage(to="me", thread="goal_start", body="goal_slug")
+    await agent.handle_message(msg)
+
+    agent.pub_socket.send_multipart.assert_awaited_once()
+    payload = json.loads(agent.pub_socket.send_multipart.call_args[0][0][1])
+    assert payload["type"] == "goal_update"
+    assert payload["id"] == "goal_id_123"
+    assert payload["active"] is True
+
+
+@pytest.mark.asyncio
+async def test_handle_message_active_norms_update(agent):
+    agent._cond_norm_reverse_map["norm_active"] = "id_1"
+    agent._cond_norm_reverse_map["norm_inactive"] = "id_2"
+
+    # Body is like: "('norm_active', 'other')"
+    # The split logic handles quotes etc.
+    msg = InternalMessage(to="me", thread="active_norms_update", body="'norm_active', 'other'")
+    await agent.handle_message(msg)
+
+    agent.pub_socket.send_multipart.assert_awaited_once()
+    payload = json.loads(agent.pub_socket.send_multipart.call_args[0][0][1])
+    assert payload["type"] == "cond_norms_state_update"
+    norms = {n["id"]: n["active"] for n in payload["norms"]}
+    assert norms["id_1"] is True
+    assert norms["id_2"] is False
+
+
+@pytest.mark.asyncio
+async def test_send_experiment_control(agent):
+    # Test next_phase
+    await agent._send_experiment_control_to_bdi_core("next_phase")
+    agent.send.assert_awaited()
+    msg = agent.send.call_args[0][0]
+    assert msg.thread == "force_next_phase"
+
+    # Test reset_phase
+    await agent._send_experiment_control_to_bdi_core("reset_phase")
+    msg = agent.send.call_args[0][0]
+    assert msg.thread == "reset_current_phase"
+
+    # Test reset_experiment
+    await agent._send_experiment_control_to_bdi_core("reset_experiment")
+    msg = agent.send.call_args[0][0]
+    assert msg.thread == "reset_experiment"
+
+
+@pytest.mark.asyncio
+async def test_send_pause_command(agent):
+    await agent._send_pause_command("true")
+    # Sends to RI and VAD
+    assert agent.send.await_count == 2
+    msgs = [call.args[0] for call in agent.send.call_args_list]
+
+    ri_msg = next(m for m in msgs if m.to == settings.agent_settings.ri_communication_name)
+    assert json.loads(ri_msg.body)["endpoint"] == ""  # PAUSE endpoint
+    assert json.loads(ri_msg.body)["data"] is True
+
+    vad_msg = next(m for m in msgs if m.to == settings.agent_settings.vad_name)
+    assert vad_msg.body == "PAUSE"
+
+    agent.send.reset_mock()
+    await agent._send_pause_command("false")
+    assert agent.send.await_count == 2
+    vad_msg = next(
+        m for m in agent.send.call_args_list if m.args[0].to == settings.agent_settings.vad_name
+    ).args[0]
+    assert vad_msg.body == "RESUME"

test/unit/api/v1/endpoints/test_logs_endpoint.py

@@ -1,7 +1,7 @@
-from unittest.mock import patch
+from unittest.mock import MagicMock, patch
 
 import pytest
-from fastapi import FastAPI
+from fastapi import FastAPI, HTTPException
 from fastapi.testclient import TestClient
 from starlette.responses import StreamingResponse
 
@@ -61,3 +61,67 @@ async def test_log_stream_endpoint_lines(client):
         # Optional: assert subscribe/connect were called
         assert dummy_socket.subscribed  # at least some log levels subscribed
         assert dummy_socket.connected  # connect was called
+
+
+@patch("control_backend.api.v1.endpoints.logs.LOGGING_DIR")
+def test_files_endpoint(LOGGING_DIR, client):
+    file_1, file_2 = MagicMock(), MagicMock()
+    file_1.name = "file_1"
+    file_2.name = "file_2"
+    LOGGING_DIR.glob.return_value = [file_1, file_2]
+    result = client.get("/api/logs/files")
+
+    assert result.status_code == 200
+    assert result.json() == ["file_1", "file_2"]
+
+
+@patch("control_backend.api.v1.endpoints.logs.FileResponse")
+@patch("control_backend.api.v1.endpoints.logs.LOGGING_DIR")
+def test_log_file_endpoint_success(LOGGING_DIR, MockFileResponse, client):
+    mock_file_path = MagicMock()
+    mock_file_path.is_relative_to.return_value = True
+    mock_file_path.is_file.return_value = True
+    mock_file_path.name = "test.log"
+
+    LOGGING_DIR.__truediv__ = MagicMock(return_value=mock_file_path)
+    mock_file_path.resolve.return_value = mock_file_path
+
+    MockFileResponse.return_value = MagicMock()
+
+    result = client.get("/api/logs/files/test.log")
+
+    assert result.status_code == 200
+    MockFileResponse.assert_called_once_with(mock_file_path, filename="test.log")
+
+
+@pytest.mark.asyncio
+@patch("control_backend.api.v1.endpoints.logs.LOGGING_DIR")
+async def test_log_file_endpoint_path_traversal(LOGGING_DIR):
+    from control_backend.api.v1.endpoints.logs import log_file
+
+    mock_file_path = MagicMock()
+    mock_file_path.is_relative_to.return_value = False
+
+    LOGGING_DIR.__truediv__ = MagicMock(return_value=mock_file_path)
+    mock_file_path.resolve.return_value = mock_file_path
+
+    with pytest.raises(HTTPException) as exc_info:
+        await log_file("../secret.txt")
+
+    assert exc_info.value.status_code == 400
+    assert exc_info.value.detail == "Invalid filename."
+
+
+@patch("control_backend.api.v1.endpoints.logs.LOGGING_DIR")
+def test_log_file_endpoint_file_not_found(LOGGING_DIR, client):
+    mock_file_path = MagicMock()
+    mock_file_path.is_relative_to.return_value = True
+    mock_file_path.is_file.return_value = False
+
+    LOGGING_DIR.__truediv__ = MagicMock(return_value=mock_file_path)
+    mock_file_path.resolve.return_value = mock_file_path
+
+    result = client.get("/api/logs/files/nonexistent.log")
+
+    assert result.status_code == 404
+    assert result.json()["detail"] == "File not found."

test/unit/api/v1/endpoints/test_router.py

@@ -11,6 +11,5 @@ def test_router_includes_expected_paths():
     # Ensure at least one route under each prefix exists
     assert any(p.startswith("/robot") for p in paths)
     assert any(p.startswith("/message") for p in paths)
-    assert any(p.startswith("/sse") for p in paths)
     assert any(p.startswith("/logs") for p in paths)
     assert any(p.startswith("/program") for p in paths)

test/unit/api/v1/endpoints/test_sse_endpoint.py

@@ -1,24 +0,0 @@
-import pytest
-from fastapi import FastAPI
-from fastapi.testclient import TestClient
-
-from control_backend.api.v1.endpoints import sse
-
-
-@pytest.fixture
-def app():
-    app = FastAPI()
-    app.include_router(sse.router)
-    return app
-
-
-@pytest.fixture
-def client(app):
-    return TestClient(app)
-
-
-def test_sse_route_exists(client):
-    """Minimal smoke test to ensure /sse route exists and responds."""
-    response = client.get("/sse")
-    # Since implementation is not done, we only assert it doesn't crash
-    assert response.status_code == 200

test/unit/api/v1/endpoints/test_user_interact.py

@@ -0,0 +1,96 @@
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from fastapi import FastAPI
+from fastapi.testclient import TestClient
+
+from control_backend.api.v1.endpoints import user_interact
+
+
+@pytest.fixture
+def app():
+    app = FastAPI()
+    app.include_router(user_interact.router)
+    return app
+
+
+@pytest.fixture
+def client(app):
+    return TestClient(app)
+
+
+@pytest.mark.asyncio
+async def test_receive_button_event(client):
+    mock_pub_socket = AsyncMock()
+    client.app.state.endpoints_pub_socket = mock_pub_socket
+
+    payload = {"type": "speech", "context": "hello"}
+    response = client.post("/button_pressed", json=payload)
+
+    assert response.status_code == 202
+    assert response.json() == {"status": "Event received"}
+
+    mock_pub_socket.send_multipart.assert_awaited_once()
+    args = mock_pub_socket.send_multipart.call_args[0][0]
+    assert args[0] == b"button_pressed"
+    assert "speech" in args[1].decode()
+
+
+@pytest.mark.asyncio
+async def test_receive_button_event_invalid_payload(client):
+    mock_pub_socket = AsyncMock()
+    client.app.state.endpoints_pub_socket = mock_pub_socket
+
+    # Missing context
+    payload = {"type": "speech"}
+    response = client.post("/button_pressed", json=payload)
+
+    assert response.status_code == 422
+    mock_pub_socket.send_multipart.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_experiment_stream_direct_call():
+    """
+    Directly calling the endpoint function to test the streaming logic
+    without dealing with TestClient streaming limitations.
+    """
+    mock_socket = AsyncMock()
+    # 1. recv data
+    # 2. recv timeout
+    # 3. disconnect (request.is_disconnected returns True)
+    mock_socket.recv_multipart.side_effect = [
+        (b"topic", b"message1"),
+        TimeoutError(),
+        (b"topic", b"message2"),  # Should not be reached if disconnect checks work
+    ]
+    mock_socket.close = MagicMock()
+    mock_socket.connect = MagicMock()
+    mock_socket.subscribe = MagicMock()
+
+    mock_context = MagicMock()
+    mock_context.socket.return_value = mock_socket
+
+    with patch(
+        "control_backend.api.v1.endpoints.user_interact.Context.instance", return_value=mock_context
+    ):
+        mock_request = AsyncMock()
+        # is_disconnected sequence:
+        # 1. False (before first recv) -> reads message1
+        # 2. False (before second recv) -> triggers TimeoutError, continues
+        # 3. True (before third recv) -> break loop
+        mock_request.is_disconnected.side_effect = [False, False, True]
+
+        response = await user_interact.experiment_stream(mock_request)
+
+        lines = []
+        # Consume the generator
+        async for line in response.body_iterator:
+            lines.append(line)
+
+        assert "data: message1\n\n" in lines
+        assert len(lines) == 1
+
+        mock_socket.connect.assert_called()
+        mock_socket.subscribe.assert_called_with(b"experiment")
+        mock_socket.close.assert_called()

test/unit/conftest.py

@@ -25,7 +25,6 @@ def mock_settings():
         mock.zmq_settings.internal_sub_address = "tcp://localhost:5561"
         mock.zmq_settings.ri_command_address = "tcp://localhost:0000"
         mock.agent_settings.bdi_core_name = "bdi_core_agent"
-        mock.agent_settings.bdi_belief_collector_name = "belief_collector_agent"
         mock.agent_settings.llm_name = "llm_agent"
         mock.agent_settings.robot_speech_name = "robot_speech_agent"
         mock.agent_settings.transcription_name = "transcription_agent"

test/unit/core/test_agent_system.py

@@ -99,12 +99,75 @@ async def test_send_to_local_agent(monkeypatch):
     # Patch inbox.put
     target.inbox.put = AsyncMock()
 
-    message = InternalMessage(to="receiver", sender="sender", body="hello")
+    message = InternalMessage(to=target.name, sender=sender.name, body="hello")
 
     await sender.send(message)
 
     target.inbox.put.assert_awaited_once_with(message)
-    sender.logger.debug.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_send_to_zmq_agent(monkeypatch):
+    sender = DummyAgent("sender")
+    target = "remote_receiver"
+
+    # Fake logger
+    sender.logger = MagicMock()
+
+    # Fake zmq
+    sender._internal_pub_socket = AsyncMock()
+
+    message = InternalMessage(to=target, sender=sender.name, body="hello")
+
+    await sender.send(message)
+
+    zmq_calls = sender._internal_pub_socket.send_multipart.call_args[0][0]
+    assert zmq_calls[0] == f"internal/{target}".encode()
+
+
+@pytest.mark.asyncio
+async def test_send_to_multiple_local_agents(monkeypatch):
+    sender = DummyAgent("sender")
+    target1 = DummyAgent("receiver1")
+    target2 = DummyAgent("receiver2")
+
+    # Fake logger
+    sender.logger = MagicMock()
+
+    # Patch inbox.put
+    target1.inbox.put = AsyncMock()
+    target2.inbox.put = AsyncMock()
+
+    message = InternalMessage(to=[target1.name, target2.name], sender=sender.name, body="hello")
+
+    await sender.send(message)
+
+    target1.inbox.put.assert_awaited_once_with(message)
+    target2.inbox.put.assert_awaited_once_with(message)
+
+
+@pytest.mark.asyncio
+async def test_send_to_multiple_agents(monkeypatch):
+    sender = DummyAgent("sender")
+    target1 = DummyAgent("receiver1")
+    target2 = "remote_receiver"
+
+    # Fake logger
+    sender.logger = MagicMock()
+
+    # Fake zmq
+    sender._internal_pub_socket = AsyncMock()
+
+    # Patch inbox.put
+    target1.inbox.put = AsyncMock()
+
+    message = InternalMessage(to=[target1.name, target2], sender=sender.name, body="hello")
+
+    await sender.send(message)
+
+    target1.inbox.put.assert_awaited_once_with(message)
+    zmq_calls = sender._internal_pub_socket.send_multipart.call_args[0][0]
+    assert zmq_calls[0] == f"internal/{target2}".encode()
 
 
 @pytest.mark.asyncio

test/unit/logging/test_dated_file_handler.py

@@ -0,0 +1,45 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from control_backend.logging.dated_file_handler import DatedFileHandler
+
+
+@patch("control_backend.logging.dated_file_handler.DatedFileHandler._open")
+def test_reset(open_):
+    stream = MagicMock()
+    open_.return_value = stream
+
+    # A file should be opened when the logger is created
+    handler = DatedFileHandler(file_prefix="anything")
+    assert open_.call_count == 1
+
+    # Upon reset, the current file should be closed, and a new one should be opened
+    handler.do_rollover()
+    assert stream.close.call_count == 1
+    assert open_.call_count == 2
+
+
+@patch("control_backend.logging.dated_file_handler.Path")
+@patch("control_backend.logging.dated_file_handler.DatedFileHandler._open")
+def test_creates_dir(open_, Path_):
+    stream = MagicMock()
+    open_.return_value = stream
+
+    test_path = MagicMock()
+    test_path.parent.is_dir.return_value = False
+    Path_.return_value = test_path
+
+    DatedFileHandler(file_prefix="anything")
+
+    # The directory should've been created
+    test_path.parent.mkdir.assert_called_once()
+
+
+@patch("control_backend.logging.dated_file_handler.DatedFileHandler._open")
+def test_invalid_constructor(_):
+    with pytest.raises(ValueError):
+        DatedFileHandler(file_prefix=None)
+
+    with pytest.raises(ValueError):
+        DatedFileHandler(file_prefix="")

test/unit/logging/test_optional_field_formatter.py

@@ -0,0 +1,218 @@
+import logging
+
+import pytest
+
+from control_backend.logging.optional_field_formatter import OptionalFieldFormatter
+
+
+@pytest.fixture
+def logger():
+    """Create a fresh logger for each test."""
+    logger = logging.getLogger(f"test_{id(object())}")
+    logger.setLevel(logging.DEBUG)
+    logger.handlers = []
+    return logger
+
+
+@pytest.fixture
+def log_output(logger):
+    """Capture log output and return a function to get it."""
+
+    class ListHandler(logging.Handler):
+        def __init__(self):
+            super().__init__()
+            self.records = []
+
+        def emit(self, record):
+            self.records.append(self.format(record))
+
+    handler = ListHandler()
+    logger.addHandler(handler)
+
+    def get_output():
+        return handler.records
+
+    return get_output
+
+
+def test_optional_field_present(logger, log_output):
+    """Optional field should appear when provided in extra."""
+    formatter = OptionalFieldFormatter("%(levelname)s - %(role?)s - %(message)s")
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test message", extra={"role": "user"})
+
+    assert log_output() == ["INFO - user - test message"]
+
+
+def test_optional_field_missing_no_default(logger, log_output):
+    """Missing optional field with no default should be None."""
+    formatter = OptionalFieldFormatter("%(levelname)s - %(role?)s - %(message)s")
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test message")
+
+    assert log_output() == ["INFO - None - test message"]
+
+
+def test_optional_field_missing_with_default(logger, log_output):
+    """Missing optional field should use provided default."""
+    formatter = OptionalFieldFormatter(
+        "%(levelname)s - %(role?)s - %(message)s", defaults={"role": "assistant"}
+    )
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test message")
+
+    assert log_output() == ["INFO - assistant - test message"]
+
+
+def test_optional_field_overrides_default(logger, log_output):
+    """Provided extra value should override default."""
+    formatter = OptionalFieldFormatter(
+        "%(levelname)s - %(role?)s - %(message)s", defaults={"role": "assistant"}
+    )
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test message", extra={"role": "user"})
+
+    assert log_output() == ["INFO - user - test message"]
+
+
+def test_multiple_optional_fields(logger, log_output):
+    """Multiple optional fields should work independently."""
+    formatter = OptionalFieldFormatter(
+        "%(levelname)s - %(role?)s - %(request_id?)s - %(message)s", defaults={"role": "assistant"}
+    )
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test", extra={"request_id": "123"})
+
+    assert log_output() == ["INFO - assistant - 123 - test"]
+
+
+def test_mixed_optional_and_required_fields(logger, log_output):
+    """Standard fields should work alongside optional fields."""
+    formatter = OptionalFieldFormatter("%(levelname)s %(name)s %(role?)s %(message)s")
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test", extra={"role": "user"})
+
+    output = log_output()[0]
+    assert "INFO" in output
+    assert "user" in output
+    assert "test" in output
+
+
+def test_no_optional_fields(logger, log_output):
+    """Formatter should work normally with no optional fields."""
+    formatter = OptionalFieldFormatter("%(levelname)s %(message)s")
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test message")
+
+    assert log_output() == ["INFO test message"]
+
+
+def test_integer_format_specifier(logger, log_output):
+    """Optional fields with %d specifier should work."""
+    formatter = OptionalFieldFormatter(
+        "%(levelname)s %(count?)d %(message)s", defaults={"count": 0}
+    )
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test", extra={"count": 42})
+
+    assert log_output() == ["INFO 42 test"]
+
+
+def test_float_format_specifier(logger, log_output):
+    """Optional fields with %f specifier should work."""
+    formatter = OptionalFieldFormatter(
+        "%(levelname)s %(duration?)f %(message)s", defaults={"duration": 0.0}
+    )
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test", extra={"duration": 1.5})
+
+    assert "1.5" in log_output()[0]
+
+
+def test_empty_string_default(logger, log_output):
+    """Empty string default should work."""
+    formatter = OptionalFieldFormatter("%(levelname)s %(role?)s %(message)s", defaults={"role": ""})
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test")
+
+    assert log_output() == ["INFO  test"]
+
+
+def test_none_format_string():
+    """None format string should not raise."""
+    formatter = OptionalFieldFormatter(fmt=None)
+    assert formatter.optional_fields == set()
+
+
+def test_optional_fields_parsed_correctly():
+    """Check that optional fields are correctly identified."""
+    formatter = OptionalFieldFormatter("%(asctime)s %(role?)s %(level?)d %(name)s")
+
+    assert formatter.optional_fields == {("role", "s"), ("level", "d")}
+
+
+def test_format_string_normalized():
+    """Check that ? is removed from format string."""
+    formatter = OptionalFieldFormatter("%(role?)s %(message)s")
+
+    assert "?" not in formatter._style._fmt
+    assert "%(role)s" in formatter._style._fmt
+
+
+def test_field_with_underscore(logger, log_output):
+    """Field names with underscores should work."""
+    formatter = OptionalFieldFormatter("%(levelname)s %(user_id?)s %(message)s")
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test", extra={"user_id": "abc123"})
+
+    assert log_output() == ["INFO abc123 test"]
+
+
+def test_field_with_numbers(logger, log_output):
+    """Field names with numbers should work."""
+    formatter = OptionalFieldFormatter("%(levelname)s %(field2?)s %(message)s")
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test", extra={"field2": "value"})
+
+    assert log_output() == ["INFO value test"]
+
+
+def test_multiple_log_calls(logger, log_output):
+    """Formatter should work correctly across multiple log calls."""
+    formatter = OptionalFieldFormatter(
+        "%(levelname)s %(role?)s %(message)s", defaults={"role": "other"}
+    )
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("first", extra={"role": "assistant"})
+    logger.info("second")
+    logger.info("third", extra={"role": "user"})
+
+    assert log_output() == [
+        "INFO assistant first",
+        "INFO other second",
+        "INFO user third",
+    ]
+
+
+def test_default_not_mutated(logger, log_output):
+    """Original defaults dict should not be mutated."""
+    defaults = {"role": "other"}
+    formatter = OptionalFieldFormatter("%(levelname)s %(role?)s %(message)s", defaults=defaults)
+    logger.handlers[0].setFormatter(formatter)
+
+    logger.info("test")
+
+    assert defaults == {"role": "other"}

test/unit/logging/test_partial_filter.py

@@ -0,0 +1,83 @@
+import logging
+
+import pytest
+
+from control_backend.logging import PartialFilter
+
+
+@pytest.fixture
+def logger():
+    """Create a fresh logger for each test."""
+    logger = logging.getLogger(f"test_{id(object())}")
+    logger.setLevel(logging.DEBUG)
+    logger.handlers = []
+    return logger
+
+
+@pytest.fixture
+def log_output(logger):
+    """Capture log output and return a function to get it."""
+
+    class ListHandler(logging.Handler):
+        def __init__(self):
+            super().__init__()
+            self.records = []
+
+        def emit(self, record):
+            self.records.append(self.format(record))
+
+    handler = ListHandler()
+    handler.addFilter(PartialFilter())
+    handler.setFormatter(logging.Formatter("%(message)s"))
+    logger.addHandler(handler)
+
+    return lambda: handler.records
+
+
+def test_no_partial_attribute(logger, log_output):
+    """Records without partial attribute should pass through."""
+    logger.info("normal message")
+
+    assert log_output() == ["normal message"]
+
+
+def test_partial_true_filtered(logger, log_output):
+    """Records with partial=True should be filtered out."""
+    logger.info("partial message", extra={"partial": True})
+
+    assert log_output() == []
+
+
+def test_partial_false_passes(logger, log_output):
+    """Records with partial=False should pass through."""
+    logger.info("complete message", extra={"partial": False})
+
+    assert log_output() == ["complete message"]
+
+
+def test_partial_none_passes(logger, log_output):
+    """Records with partial=None should pass through."""
+    logger.info("message", extra={"partial": None})
+
+    assert log_output() == ["message"]
+
+
+def test_partial_truthy_value_passes(logger, log_output):
+    """
+    Records with truthy but non-True partial should pass through, that is, only when it's exactly
+    ``True`` should it pass.
+    """
+    logger.info("message", extra={"partial": "yes"})
+
+    assert log_output() == ["message"]
+
+
+def test_multiple_records_mixed(logger, log_output):
+    """Filter should handle mixed records correctly."""
+    logger.info("first")
+    logger.info("second", extra={"partial": True})
+    logger.info("third", extra={"partial": False})
+    logger.info("fourth", extra={"partial": True})
+    logger.info("fifth")
+
+    assert log_output() == ["first", "third", "fifth"]

test/unit/test_main_sockets.py

@@ -0,0 +1,40 @@
+from unittest.mock import MagicMock, patch
+
+import zmq
+
+from control_backend.main import setup_sockets
+
+
+def test_setup_sockets_proxy():
+    mock_context = MagicMock()
+    mock_pub = MagicMock()
+    mock_sub = MagicMock()
+
+    mock_context.socket.side_effect = [mock_pub, mock_sub]
+
+    with patch("zmq.asyncio.Context.instance", return_value=mock_context):
+        with patch("zmq.proxy") as mock_proxy:
+            setup_sockets()
+
+            mock_pub.bind.assert_called()
+            mock_sub.bind.assert_called()
+            mock_proxy.assert_called_with(mock_sub, mock_pub)
+
+            # Check cleanup
+            mock_pub.close.assert_called()
+            mock_sub.close.assert_called()
+
+
+def test_setup_sockets_proxy_error():
+    mock_context = MagicMock()
+    mock_pub = MagicMock()
+    mock_sub = MagicMock()
+    mock_context.socket.side_effect = [mock_pub, mock_sub]
+
+    with patch("zmq.asyncio.Context.instance", return_value=mock_context):
+        with patch("zmq.proxy", side_effect=zmq.ZMQError):
+            with patch("control_backend.main.logger") as mock_logger:
+                setup_sockets()
+                mock_logger.warning.assert_called()
+                mock_pub.close.assert_called()
+                mock_sub.close.assert_called()