feat: make the pipeline work with Program and AgentSpeak

ref: N25B-429

.env.example

@@ -1,20 +0,0 @@
-# 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=3
-
-# 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.

.gitlab/merge_request_templates/default.md

@@ -1,9 +0,0 @@
-%{first_multiline_commit_description}
-
-To verify:
-
-- [ ] Style checks pass
-- [ ] Pipeline (tests) pass
-- [ ] Documentation is up to date
-- [ ] Tests are up to date (new code is covered)
-- [ ] ...

.logging_config.yaml

@@ -1,7 +1,5 @@
 version: 1
 
-# Maak nieuwe (obvervation action)
-# tussen 20-30
 custom_levels:
   OBSERVATION: 25
   ACTION: 26
@@ -21,8 +19,6 @@ formatters:
     format: "{name} {levelname} {levelno} {message} {created} {relativeCreated}"
     style: "{"
 
-# Maak class = logging.fileHandler
-# 
 handlers:
   console:
     class: logging.StreamHandler
@@ -39,8 +35,6 @@ root:
   level: WARN
   handlers: [console]
 
-# Maak research logger, laagste level (21)
-# Handler: UI Handler
 loggers:
   control_backend:
     level: LLM

README.md

@@ -27,7 +27,6 @@ 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):
 
@@ -35,14 +34,6 @@ 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

@@ -15,6 +15,7 @@ dependencies = [
   "pydantic>=2.12.0",
   "pydantic-settings>=2.11.0",
   "python-json-logger>=4.0.0",
+  "python-slugify>=8.0.4",
   "pyyaml>=6.0.3",
   "pyzmq>=27.1.0",
   "silero-vad>=6.0.0",

src/control_backend/agents/actuation/robot_gesture_agent.py

@@ -28,18 +28,15 @@ class RobotGestureAgent(BaseAgent):
     address = ""
     bind = False
     gesture_data = []
-    single_gesture_data = []
 
     def __init__(
         self,
         name: str,
-        address: str,
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
         gesture_data=None,
-        single_gesture_data=None,
     ):
         self.gesture_data = gesture_data or []
-        self.single_gesture_data = single_gesture_data or []
         super().__init__(name)
         self.address = address
         self.bind = bind
@@ -102,13 +99,7 @@ class RobotGestureAgent(BaseAgent):
                         gesture_command.data,
                     )
                     return
-            elif gesture_command.endpoint == RIEndpoint.GESTURE_SINGLE:
-                if gesture_command.data not in self.single_gesture_data:
-                    self.logger.warning(
-                        "Received gesture '%s' which is not in available gestures. Early returning",
-                        gesture_command.data,
-                    )
-                    return
+
             await self.pubsocket.send_json(gesture_command.model_dump())
         except Exception:
             self.logger.exception("Error processing internal message.")

src/control_backend/agents/bdi/agentspeak_ast.py

@@ -0,0 +1,273 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from enum import StrEnum
+
+
+class AstNode(ABC):
+    """
+    Abstract base class for all elements of an AgentSpeak program.
+    """
+
+    @abstractmethod
+    def _to_agentspeak(self) -> str:
+        """
+        Generates the AgentSpeak code string.
+        """
+        pass
+
+    def __str__(self) -> str:
+        return self._to_agentspeak()
+
+
+class AstExpression(AstNode, ABC):
+    """
+    Intermediate class for anything that can be used in a logical expression.
+    """
+
+    def __and__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.AND, _coalesce_expr(other))
+
+    def __or__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.OR, _coalesce_expr(other))
+
+    def __invert__(self) -> AstLogicalExpression:
+        if isinstance(self, AstLogicalExpression):
+            self.negated = not self.negated
+            return self
+        return AstLogicalExpression(self, negated=True)
+
+
+type ExprCoalescible = AstExpression | str | int | float
+
+
+def _coalesce_expr(value: ExprCoalescible) -> AstExpression:
+    if isinstance(value, AstExpression):
+        return value
+    if isinstance(value, str):
+        return AstString(value)
+    if isinstance(value, (int, float)):
+        return AstNumber(value)
+    raise TypeError(f"Cannot coalesce type {type(value)} into an AstTerm.")
+
+
+@dataclass
+class AstTerm(AstExpression, ABC):
+    """
+    Base class for terms appearing inside literals.
+    """
+
+    def __ge__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.GREATER_EQUALS, _coalesce_expr(other))
+
+    def __gt__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.GREATER_THAN, _coalesce_expr(other))
+
+    def __le__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.LESS_EQUALS, _coalesce_expr(other))
+
+    def __lt__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.LESS_THAN, _coalesce_expr(other))
+
+    def __eq__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.EQUALS, _coalesce_expr(other))
+
+    def __ne__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.NOT_EQUALS, _coalesce_expr(other))
+
+
+@dataclass
+class AstAtom(AstTerm):
+    """
+    Grounded expression in all lowercase.
+    """
+
+    value: str
+
+    def _to_agentspeak(self) -> str:
+        return self.value.lower()
+
+
+@dataclass
+class AstVar(AstTerm):
+    """
+    Ungrounded variable expression. First letter capitalized.
+    """
+
+    name: str
+
+    def _to_agentspeak(self) -> str:
+        return self.name.capitalize()
+
+
+@dataclass
+class AstNumber(AstTerm):
+    value: int | float
+
+    def _to_agentspeak(self) -> str:
+        return str(self.value)
+
+
+@dataclass
+class AstString(AstTerm):
+    value: str
+
+    def _to_agentspeak(self) -> str:
+        return f'"{self.value}"'
+
+
+@dataclass
+class AstLiteral(AstTerm):
+    functor: str
+    terms: list[AstTerm] = field(default_factory=list)
+
+    def _to_agentspeak(self) -> str:
+        if not self.terms:
+            return self.functor
+        args = ", ".join(map(str, self.terms))
+        return f"{self.functor}({args})"
+
+
+class BinaryOperatorType(StrEnum):
+    AND = "&"
+    OR = "|"
+    GREATER_THAN = ">"
+    LESS_THAN = "<"
+    EQUALS = "=="
+    NOT_EQUALS = "\\=="
+    GREATER_EQUALS = ">="
+    LESS_EQUALS = "<="
+
+
+@dataclass
+class AstBinaryOp(AstExpression):
+    left: AstExpression
+    operator: BinaryOperatorType
+    right: AstExpression
+
+    def __post_init__(self):
+        self.left = _as_logical(self.left)
+        self.right = _as_logical(self.right)
+
+    def _to_agentspeak(self) -> str:
+        l_str = str(self.left)
+        r_str = str(self.right)
+
+        assert isinstance(self.left, AstLogicalExpression)
+        assert isinstance(self.right, AstLogicalExpression)
+
+        if isinstance(self.left.expression, AstBinaryOp) or self.left.negated:
+            l_str = f"({l_str})"
+        if isinstance(self.right.expression, AstBinaryOp) or self.right.negated:
+            r_str = f"({r_str})"
+
+        return f"{l_str} {self.operator.value} {r_str}"
+
+
+@dataclass
+class AstLogicalExpression(AstExpression):
+    expression: AstExpression
+    negated: bool = False
+
+    def _to_agentspeak(self) -> str:
+        expr_str = str(self.expression)
+        if isinstance(self.expression, AstBinaryOp) and self.negated:
+            expr_str = f"({expr_str})"
+        return f"{'not ' if self.negated else ''}{expr_str}"
+
+
+def _as_logical(expr: AstExpression) -> AstLogicalExpression:
+    if isinstance(expr, AstLogicalExpression):
+        return expr
+    return AstLogicalExpression(expr)
+
+
+class StatementType(StrEnum):
+    EMPTY = ""
+    DO_ACTION = "."
+    ACHIEVE_GOAL = "!"
+    TEST_GOAL = "?"
+    ADD_BELIEF = "+"
+    REMOVE_BELIEF = "-"
+    REPLACE_BELIEF = "-+"
+
+
+@dataclass
+class AstStatement(AstNode):
+    """
+    A statement that can appear inside a plan.
+    """
+
+    type: StatementType
+    expression: AstExpression
+
+    def _to_agentspeak(self) -> str:
+        return f"{self.type.value}{self.expression}"
+
+
+@dataclass
+class AstRule(AstNode):
+    result: AstExpression
+    condition: AstExpression | None = None
+
+    def __post_init__(self):
+        if self.condition is not None:
+            self.condition = _as_logical(self.condition)
+
+    def _to_agentspeak(self) -> str:
+        if not self.condition:
+            return f"{self.result}."
+        return f"{self.result} :- {self.condition}."
+
+
+class TriggerType(StrEnum):
+    ADDED_BELIEF = "+"
+    # REMOVED_BELIEF = "-"  # TODO
+    # MODIFIED_BELIEF = "^"  # TODO
+    ADDED_GOAL = "+!"
+    # REMOVED_GOAL = "-!"  # TODO
+
+
+@dataclass
+class AstPlan(AstNode):
+    type: TriggerType
+    trigger_literal: AstExpression
+    context: list[AstExpression]
+    body: list[AstStatement]
+
+    def _to_agentspeak(self) -> str:
+        assert isinstance(self.trigger_literal, AstLiteral)
+
+        indent = " " * 6
+        colon = "  :   "
+        arrow = "  <-  "
+
+        lines = []
+
+        lines.append(f"{self.type.value}{self.trigger_literal}")
+
+        if self.context:
+            lines.append(colon + f" &\n{indent}".join(str(c) for c in self.context))
+
+        if self.body:
+            lines.append(arrow + f";\n{indent}".join(str(s) for s in self.body) + ".")
+
+        lines.append("")
+
+        return "\n".join(lines)
+
+
+@dataclass
+class AstProgram(AstNode):
+    rules: list[AstRule] = field(default_factory=list)
+    plans: list[AstPlan] = field(default_factory=list)
+
+    def _to_agentspeak(self) -> str:
+        lines = []
+        lines.extend(map(str, self.rules))
+
+        lines.extend(["", ""])
+        lines.extend(map(str, self.plans))
+
+        return "\n".join(lines)

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -0,0 +1,373 @@
+from functools import singledispatchmethod
+
+from slugify import slugify
+
+from control_backend.agents.bdi.agentspeak_ast import (
+    AstBinaryOp,
+    AstExpression,
+    AstLiteral,
+    AstPlan,
+    AstProgram,
+    AstRule,
+    AstStatement,
+    AstString,
+    AstVar,
+    BinaryOperatorType,
+    StatementType,
+    TriggerType,
+)
+from control_backend.schemas.program import (
+    BasicNorm,
+    ConditionalNorm,
+    GestureAction,
+    Goal,
+    InferredBelief,
+    KeywordBelief,
+    LLMAction,
+    LogicalOperator,
+    Norm,
+    Phase,
+    PlanElement,
+    Program,
+    ProgramElement,
+    SemanticBelief,
+    SpeechAction,
+    Trigger,
+)
+
+
+class AgentSpeakGenerator:
+    _asp: AstProgram
+
+    def generate(self, program: Program) -> str:
+        self._asp = AstProgram()
+
+        self._asp.rules.append(AstRule(self._astify(program.phases[0])))
+        self._add_keyword_inference()
+        self._add_default_plans()
+
+        self._process_phases(program.phases)
+
+        self._add_fallbacks()
+
+        return str(self._asp)
+
+    def _add_keyword_inference(self) -> None:
+        keyword = AstVar("Keyword")
+        message = AstVar("Message")
+        position = AstVar("Pos")
+
+        self._asp.rules.append(
+            AstRule(
+                AstLiteral("keyword_said", [keyword]),
+                AstLiteral("user_said", [message])
+                & AstLiteral(".substring", [keyword, message, position])
+                & (position >= 0),
+            )
+        )
+
+    def _add_default_plans(self):
+        self._add_reply_with_goal_plan()
+        self._add_say_plan()
+        self._add_reply_plan()
+
+    def _add_reply_with_goal_plan(self):
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("reply_with_goal", [AstVar("Goal")]),
+                [AstLiteral("user_said", [AstVar("Message")])],
+                [
+                    AstStatement(StatementType.ADD_BELIEF, AstLiteral("responded_this_turn")),
+                    AstStatement(
+                        StatementType.DO_ACTION,
+                        AstLiteral(
+                            "findall",
+                            [AstVar("Norm"), AstLiteral("norm", [AstVar("Norm")]), AstVar("Norms")],
+                        ),
+                    ),
+                    AstStatement(
+                        StatementType.DO_ACTION,
+                        AstLiteral(
+                            "reply_with_goal", [AstVar("Message"), AstVar("Norms"), AstVar("Goal")]
+                        ),
+                    ),
+                ],
+            )
+        )
+
+    def _add_say_plan(self):
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("say", [AstVar("Text")]),
+                [],
+                [
+                    AstStatement(StatementType.ADD_BELIEF, AstLiteral("responded_this_turn")),
+                    AstStatement(StatementType.DO_ACTION, AstLiteral("say", [AstVar("Text")])),
+                ],
+            )
+        )
+
+    def _add_reply_plan(self):
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("reply"),
+                [AstLiteral("user_said", [AstVar("Message")])],
+                [
+                    AstStatement(StatementType.ADD_BELIEF, AstLiteral("responded_this_turn")),
+                    AstStatement(
+                        StatementType.DO_ACTION,
+                        AstLiteral(
+                            "findall",
+                            [AstVar("Norm"), AstLiteral("norm", [AstVar("Norm")]), AstVar("Norms")],
+                        ),
+                    ),
+                    AstStatement(
+                        StatementType.DO_ACTION,
+                        AstLiteral("reply", [AstVar("Message"), AstVar("Norms")]),
+                    ),
+                ],
+            )
+        )
+
+    def _process_phases(self, phases: list[Phase]) -> None:
+        for curr_phase, next_phase in zip([None] + phases, phases + [None], strict=True):
+            if curr_phase:
+                self._process_phase(curr_phase)
+            self._add_phase_transition(curr_phase, next_phase)
+
+        # End phase behavior
+        # When deleting this, the entire `reply` plan and action can be deleted
+        self._asp.plans.append(
+            AstPlan(
+                type=TriggerType.ADDED_BELIEF,
+                trigger_literal=AstLiteral("user_said", [AstVar("Message")]),
+                context=[AstLiteral("phase", [AstString("end")])],
+                body=[AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("reply"))],
+            )
+        )
+
+    def _process_phase(self, phase: Phase) -> None:
+        for norm in phase.norms:
+            self._process_norm(norm, phase)
+
+        self._add_default_loop(phase)
+
+        previous_goal = None
+        for goal in phase.goals:
+            self._process_goal(goal, phase, previous_goal)
+            previous_goal = goal
+
+        for trigger in phase.triggers:
+            self._process_trigger(trigger, phase)
+
+    def _add_phase_transition(self, from_phase: Phase | None, to_phase: Phase | None) -> None:
+        if from_phase is None:
+            return
+        from_phase_ast = self._astify(from_phase)
+        to_phase_ast = (
+            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))
+
+        body = [
+            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")])
+                    ),
+                ]
+            )
+
+        self._asp.plans.append(
+            AstPlan(TriggerType.ADDED_GOAL, AstLiteral("transition_phase"), context, body)
+        )
+
+    def _process_norm(self, norm: Norm, phase: Phase) -> None:
+        rule: AstRule | None = None
+
+        match norm:
+            case ConditionalNorm(condition=cond):
+                rule = AstRule(self._astify(norm), self._astify(phase) & self._astify(cond))
+            case BasicNorm():
+                rule = AstRule(self._astify(norm), self._astify(phase))
+
+        if not rule:
+            return
+
+        self._asp.rules.append(rule)
+
+    def _add_default_loop(self, phase: Phase) -> None:
+        actions = []
+
+        actions.append(AstStatement(StatementType.REMOVE_BELIEF, AstLiteral("responded_this_turn")))
+        actions.append(AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("check_triggers")))
+
+        for goal in phase.goals:
+            actions.append(AstStatement(StatementType.ACHIEVE_GOAL, self._astify(goal)))
+
+        actions.append(AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("transition_phase")))
+
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_BELIEF,
+                AstLiteral("user_said", [AstVar("Message")]),
+                [self._astify(phase)],
+                actions,
+            )
+        )
+
+    def _process_goal(
+        self,
+        goal: Goal,
+        phase: Phase,
+        previous_goal: Goal | None = None,
+        continues_response: bool = False,
+    ) -> None:
+        context: list[AstExpression] = [self._astify(phase)]
+        context.append(~self._astify(goal, achieved=True))
+        if previous_goal and previous_goal.can_fail:
+            context.append(self._astify(previous_goal, achieved=True))
+        if not continues_response:
+            context.append(~AstLiteral("responded_this_turn"))
+
+        body = []
+
+        subgoals = []
+        for step in goal.plan.steps:
+            body.append(self._step_to_statement(step))
+            if isinstance(step, Goal):
+                subgoals.append(step)
+
+        if not goal.can_fail and not continues_response:
+            body.append(AstStatement(StatementType.ADD_BELIEF, self._astify(goal, achieved=True)))
+
+        self._asp.plans.append(AstPlan(TriggerType.ADDED_GOAL, self._astify(goal), context, body))
+
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                self._astify(goal),
+                context=[],
+                body=[AstStatement(StatementType.EMPTY, AstLiteral("true"))],
+            )
+        )
+
+        prev_goal = None
+        for subgoal in subgoals:
+            self._process_goal(subgoal, phase, prev_goal)
+            prev_goal = subgoal
+
+    def _step_to_statement(self, step: PlanElement) -> AstStatement:
+        match step:
+            case Goal() | SpeechAction() | LLMAction() as a:
+                return AstStatement(StatementType.ACHIEVE_GOAL, self._astify(a))
+            case GestureAction() as a:
+                return AstStatement(StatementType.DO_ACTION, self._astify(a))
+
+    # TODO: separate handling of keyword and others
+    def _process_trigger(self, trigger: Trigger, phase: Phase) -> None:
+        body = []
+        subgoals = []
+
+        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)
+
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("check_triggers"),
+                [self._astify(phase), self._astify(trigger.condition)],
+                body,
+            )
+        )
+
+        for subgoal in subgoals:
+            self._process_goal(subgoal, phase, continues_response=True)
+
+    def _add_fallbacks(self):
+        # Trigger fallback
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("check_triggers"),
+                [],
+                [AstStatement(StatementType.EMPTY, AstLiteral("true"))],
+            )
+        )
+
+        # Phase transition fallback
+        self._asp.plans.append(
+            AstPlan(
+                TriggerType.ADDED_GOAL,
+                AstLiteral("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.")
+
+    @_astify.register
+    def _(self, kwb: KeywordBelief) -> AstExpression:
+        return AstLiteral("keyword_said", [AstString(kwb.keyword)])
+
+    @_astify.register
+    def _(self, sb: SemanticBelief) -> AstExpression:
+        return AstLiteral(f"semantic_{self._slugify_str(sb.description)}")
+
+    @_astify.register
+    def _(self, ib: InferredBelief) -> AstExpression:
+        return AstBinaryOp(
+            self._astify(ib.left),
+            BinaryOperatorType.AND if ib.operator == LogicalOperator.AND else BinaryOperatorType.OR,
+            self._astify(ib.right),
+        )
+
+    @_astify.register
+    def _(self, norm: Norm) -> AstExpression:
+        functor = "critical_norm" if norm.critical else "norm"
+        return AstLiteral(functor, [AstString(norm.norm)])
+
+    @_astify.register
+    def _(self, phase: Phase) -> AstExpression:
+        return AstLiteral("phase", [AstString(str(phase.id))])
+
+    @_astify.register
+    def _(self, goal: Goal, achieved: bool = False) -> AstExpression:
+        return AstLiteral(f"{'achieved_' if achieved else ''}{self._slugify_str(goal.name)}")
+
+    @_astify.register
+    def _(self, sa: SpeechAction) -> AstExpression:
+        return AstLiteral("say", [AstString(sa.text)])
+
+    @_astify.register
+    def _(self, ga: GestureAction) -> AstExpression:
+        gesture = ga.gesture
+        return AstLiteral("gesture", [AstString(gesture.type), AstString(gesture.name)])
+
+    @_astify.register
+    def _(self, la: LLMAction) -> AstExpression:
+        return AstLiteral("reply_with_goal", [AstString(la.goal)])
+
+    @staticmethod
+    def _slugify_str(text: str) -> str:
+        return slugify(text, separator="_", stopwords=["a", "an", "the", "we", "you", "I"])

src/control_backend/agents/bdi/asl_ast.py

@@ -0,0 +1,203 @@
+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

@@ -0,0 +1,425 @@
+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

@@ -42,13 +42,13 @@ class BDICoreAgent(BaseAgent):
 
     bdi_agent: agentspeak.runtime.Agent
 
-    def __init__(self, name: str, asl: str):
+    def __init__(self, name: str):
         super().__init__(name)
-        self.asl_file = asl
         self.env = agentspeak.runtime.Environment()
         # Deep copy because we don't actually want to modify the standard actions globally
         self.actions = copy.deepcopy(agentspeak.stdlib.actions)
         self._wake_bdi_loop = asyncio.Event()
+        self._bdi_loop_task = None
 
     async def setup(self) -> None:
         """
@@ -65,19 +65,22 @@ class BDICoreAgent(BaseAgent):
         await self._load_asl()
 
         # Start the BDI cycle loop
-        self.add_behavior(self._bdi_loop())
+        self._bdi_loop_task = self.add_behavior(self._bdi_loop())
         self._wake_bdi_loop.set()
         self.logger.debug("Setup complete.")
 
-    async def _load_asl(self):
+    async def _load_asl(self, file_name: str | None = None) -> None:
         """
         Load and parse the AgentSpeak source file.
         """
+        file_name = file_name or "src/control_backend/agents/bdi/default_behavior.asl"
+
         try:
-            with open(self.asl_file) as source:
+            with open(file_name) as source:
                 self.bdi_agent = self.env.build_agent(source, self.actions)
+                self.logger.info(f"Loaded new ASL from {file_name}.")
         except FileNotFoundError:
-            self.logger.warning(f"Could not find the specified ASL file at {self.asl_file}.")
+            self.logger.warning(f"Could not find the specified ASL file at {file_name}.")
             self.bdi_agent = agentspeak.runtime.Agent(self.env, self.name)
 
     async def _bdi_loop(self):
@@ -116,6 +119,7 @@ class BDICoreAgent(BaseAgent):
         Handle incoming messages.
 
         - **Beliefs**: Updates the internal belief base.
+        - **Program**: Updates the internal agentspeak file to match the current program.
         - **LLM Responses**: Forwards the generated text to the Robot Speech Agent (actuation).
 
         :param msg: The received internal message.
@@ -130,6 +134,13 @@ class BDICoreAgent(BaseAgent):
                 self.logger.exception("Error processing belief.")
             return
 
+        # New agentspeak file
+        if msg.thread == "new_program":
+            if self._bdi_loop_task:
+                self._bdi_loop_task.cancel()
+            await self._load_asl(msg.body)
+            self.add_behavior(self._bdi_loop())
+
         # The message was not a belief, handle special cases based on sender
         match msg.sender:
             case settings.agent_settings.llm_name:
@@ -160,7 +171,7 @@ class BDICoreAgent(BaseAgent):
                 self._remove_all_with_name(belief.name)
             self._add_belief(belief.name, belief.arguments)
 
-    def _add_belief(self, name: str, args: Iterable[str] = []):
+    def _add_belief(self, name: str, args: list[str] = None):
         """
         Add a single belief to the BDI agent.
 
@@ -168,9 +179,13 @@ class BDICoreAgent(BaseAgent):
         :param args: Arguments for the belief.
         """
         # new_args = (agentspeak.Literal(arg) for arg in args)  # TODO: Eventually support multiple
-        merged_args = DELIMITER.join(arg for arg in args)
-        new_args = (agentspeak.Literal(merged_args),)
-        term = agentspeak.Literal(name, new_args)
+        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,
@@ -235,32 +250,86 @@ class BDICoreAgent(BaseAgent):
         the function expects (which will be located in `term.args`).
         """
 
-        @self.actions.add(".reply", 3)
+        @self.actions.add(".reply", 2)
         def _reply(agent: "BDICoreAgent", term, intention):
             """
-            Sends text to the LLM (AgentSpeak action).
-            Example: .reply("Hello LLM!", "Some norm", "Some goal")
+            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)
-            goals = agentspeak.grounded(term.args[2], intention.scope)
 
             self.logger.debug("Norms: %s", norms)
-            self.logger.debug("Goals: %s", goals)
             self.logger.debug("User text: %s", message_text)
 
-            asyncio.create_task(self._send_to_llm(str(message_text), str(norms), str(goals)))
+            self.add_behavior(self._send_to_llm(str(message_text), str(norms), ""))
             yield
 
-    async def _send_to_llm(self, text: str, norms: str = None, goals: str = None):
+        @self.actions.add(".reply_with_goal", 3)
+        def _reply_with_goal(agent: "BDICoreAgent", term, intention):
+            """
+            Let the LLM generate a response to a user's utterance with the current norms and a
+            specific goal.
+            """
+            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(".say", 1)
+        def _say(agent: "BDICoreAgent", term, intention):
+            """
+            Make the robot say the given text instantly.
+            """
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+
+            self.logger.debug('"say" action called with text=%s', message_text)
+
+            speech_command = SpeechCommand(data=message_text)
+            speech_message = InternalMessage(
+                to=settings.agent_settings.robot_speech_name,
+                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))
+            yield
+
+        @self.actions.add(".gesture", 2)
+        def _gesture(agent: "BDICoreAgent", term, intention):
+            """
+            Make the robot perform the given gesture instantly.
+            """
+            gesture_type = agentspeak.grounded(term.args[0], intention.scope)
+            gesture_name = agentspeak.grounded(term.args[1], intention.scope)
+
+            self.logger.debug(
+                '"gesture" action called with type=%s, name=%s',
+                gesture_type,
+                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))
+            yield
+
+    async def _send_to_llm(self, text: str, norms: str, goals: str):
         """
         Sends a text query to the LLM agent asynchronously.
         """
-        prompt = LLMPromptMessage(
-            text=text,
-            norms=norms.split("\n") if norms else [],
-            goals=goals.split("\n") if norms else [],
-        )
+        prompt = LLMPromptMessage(text=text, norms=norms.split("\n"), goals=goals.split("\n"))
         msg = InternalMessage(
             to=settings.agent_settings.llm_name,
             sender=self.name,

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -3,9 +3,9 @@ from pydantic import ValidationError
 from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
-from control_backend.core.agent_system import InternalMessage
+from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.config import settings
-from control_backend.schemas.belief_message import Belief, BeliefMessage
+from control_backend.schemas.internal_message import InternalMessage
 from control_backend.schemas.program import Program
 
 
@@ -25,7 +25,7 @@ class BDIProgramManager(BaseAgent):
         super().__init__(**kwargs)
         self.sub_socket = None
 
-    async def _send_to_bdi(self, program: Program):
+    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.
 
@@ -38,42 +38,23 @@ class BDIProgramManager(BaseAgent):
 
         :param program: The program object received from the API.
         """
-        first_phase = program.phases[0]
-        norms_belief = Belief(
-            name="norms",
-            arguments=[norm.norm for norm in first_phase.norms],
-            replace=True,
-        )
-        goals_belief = Belief(
-            name="goals",
-            arguments=[goal.description for goal in first_phase.goals],
-            replace=True,
-        )
-        program_beliefs = BeliefMessage(beliefs=[norms_belief, goals_belief])
+        asg = AgentSpeakGenerator()
 
-        message = InternalMessage(
+        asl_str = asg.generate(program)
+
+        file_name = "src/control_backend/agents/bdi/agentspeak.asl"
+
+        with open(file_name, "w") as f:
+            f.write(asl_str)
+
+        msg = InternalMessage(
+            sender=self.name,
             to=settings.agent_settings.bdi_core_name,
-            sender=self.name,
-            body=program_beliefs.model_dump_json(),
-            thread="beliefs",
+            body=file_name,
+            thread="new_program",
         )
-        await self.send(message)
-        self.logger.debug("Sent new norms and goals to the BDI agent.")
 
-    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,
-            sender=self.name,
-            body="clear_history",
-            threads="clear history message",
-        )
-        await self.send(message)
-        self.logger.debug("Sent message to LLM agent to clear history.")
+        await self.send(msg)
 
     async def _receive_programs(self):
         """
@@ -81,20 +62,18 @@ class BDIProgramManager(BaseAgent):
 
         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()
 
             try:
                 program = Program.model_validate_json(body)
-                await self._send_to_bdi(program)
-                await self._send_clear_llm_history()
-
             except ValidationError:
                 self.logger.exception("Received an invalid program.")
                 continue
 
+            await self._create_agentspeak_and_send_to_bdi(program)
+
     async def setup(self):
         """
         Initialize the agent.

src/control_backend/agents/bdi/belief_collector_agent.py

@@ -101,7 +101,7 @@ class BDIBeliefCollectorAgent(BaseAgent):
             :return: A Belief object if the input is valid or None.
             """
             try:
-                return Belief(name=name, arguments=arguments)
+                return Belief(name=name, arguments=arguments, replace=name == "user_said")
             except ValidationError:
                 return None
 

src/control_backend/agents/bdi/default_behavior.asl

@@ -0,0 +1,5 @@
+norms("").
+
++user_said(Message) : norms(Norms) <-
+  -user_said(Message);
+  .reply(Message, Norms).

src/control_backend/agents/bdi/rules.asl

@@ -1,6 +0,0 @@
-norms("").
-goals("").
-
-+user_said(Message) : norms(Norms) & goals(Goals) <-
-  -user_said(Message);
-  .reply(Message, Norms, Goals).

src/control_backend/agents/communication/ri_communication_agent.py

@@ -38,7 +38,7 @@ class RICommunicationAgent(BaseAgent):
     def __init__(
         self,
         name: str,
-        address=settings.zmq_settings.ri_communication_address,
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
     ):
         super().__init__(name)
@@ -168,7 +168,7 @@ class RICommunicationAgent(BaseAgent):
             bind = port_data["bind"]
 
             if not bind:
-                addr = f"tcp://{settings.ri_host}:{port}"
+                addr = f"tcp://localhost:{port}"
             else:
                 addr = f"tcp://*:{port}"
 
@@ -182,7 +182,6 @@ class RICommunicationAgent(BaseAgent):
                             self._req_socket.bind(addr)
                 case "actuation":
                     gesture_data = port_data.get("gestures", [])
-                    single_gesture_data = port_data.get("single_gestures", [])
                     robot_speech_agent = RobotSpeechAgent(
                         settings.agent_settings.robot_speech_name,
                         address=addr,
@@ -193,7 +192,6 @@ class RICommunicationAgent(BaseAgent):
                         address=addr,
                         bind=bind,
                         gesture_data=gesture_data,
-                        single_gesture_data=single_gesture_data,
                     )
                     await robot_speech_agent.start()
                     await asyncio.sleep(0.1)  # Small delay

src/control_backend/agents/llm/llm_agent.py

@@ -52,10 +52,6 @@ class LLMAgent(BaseAgent):
                 await self._process_bdi_message(prompt_message)
             except ValidationError:
                 self.logger.debug("Prompt message from BDI core is invalid.")
-        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.")
 

src/control_backend/agents/perception/vad_agent.py

@@ -103,11 +103,12 @@ class VADAgent(BaseAgent):
 
         self._connect_audio_in_socket()
 
-        audio_out_address = self._connect_audio_out_socket()
-        if audio_out_address is None:
+        audio_out_port = self._connect_audio_out_socket()
+        if audio_out_port 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)
@@ -160,14 +161,13 @@ 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) -> str | None:
+    def _connect_audio_out_socket(self) -> int | None:
         """
-        Returns the address that was bound to, or None if binding failed.
+        Returns the port bound, or None if binding failed.
         """
         try:
             self.audio_out_socket = azmq.Context.instance().socket(zmq.PUB)
-            self.audio_out_socket.bind(settings.zmq_settings.vad_pub_address)
-            return settings.zmq_settings.vad_pub_address
+            return self.audio_out_socket.bind_to_random_port("tcp://localhost", max_tries=100)
         except zmq.ZMQBindError:
             self.logger.error("Failed to bind an audio output socket after 100 tries.")
             self.audio_out_socket = None

src/control_backend/agents/user_interrupt/user_interrupt_agent.py

@@ -1,146 +0,0 @@
-import json
-
-import zmq
-from zmq.asyncio import Context
-
-from control_backend.agents import BaseAgent
-from control_backend.core.agent_system import InternalMessage
-from control_backend.core.config import settings
-from control_backend.schemas.ri_message import GestureCommand, RIEndpoint, SpeechCommand
-
-
-class UserInterruptAgent(BaseAgent):
-    """
-    User Interrupt Agent.
-
-    This agent receives button_pressed events from the external HTTP API
-    (via ZMQ) and uses the associated context to trigger one of the following actions:
-
-    - 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
-        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.
-    """
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.sub_socket = None
-
-    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.
-
-        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.
-        """
-        while True:
-            topic, body = await self.sub_socket.recv_multipart()
-
-            try:
-                event_data = json.loads(body)
-                event_type = event_data.get("type")  # e.g., "speech", "gesture"
-                event_context = event_data.get("context")  # e.g., "Hello, I am Pepper!"
-            except json.JSONDecodeError:
-                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,
-                )
-            else:
-                self.logger.warning(
-                    "Received button press with unknown type '%s' (context: '%s').",
-                    event_type,
-                    event_context,
-                )
-
-    async def _send_to_speech_agent(self, text_to_say: str):
-        """
-        method to send prioritized speech command to RobotSpeechAgent.
-
-        :param text_to_say: The string that the robot has to say.
-        """
-        cmd = SpeechCommand(data=text_to_say, is_priority=True)
-        out_msg = InternalMessage(
-            to=settings.agent_settings.robot_speech_name,
-            sender=self.name,
-            body=cmd.model_dump_json(),
-        )
-        await self.send(out_msg)
-
-    async def _send_to_gesture_agent(self, single_gesture_name: str):
-        """
-        method to send prioritized gesture command to RobotGestureAgent.
-
-        :param single_gesture_name: The gesture tag that the robot has to perform.
-        """
-        # the endpoint is set to always be GESTURE_SINGLE for user interrupts
-        cmd = GestureCommand(
-            endpoint=RIEndpoint.GESTURE_SINGLE, data=single_gesture_name, is_priority=True
-        )
-        out_msg = InternalMessage(
-            to=settings.agent_settings.robot_gesture_name,
-            sender=self.name,
-            body=cmd.model_dump_json(),
-        )
-        await self.send(out_msg)
-
-    async def _send_to_program_manager(self, belief_id: str):
-        """
-        Send a button_override belief to the BDIProgramManager.
-
-        :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
-        """
-        data = {"belief": belief_id}
-        message = InternalMessage(
-            to=settings.agent_settings.bdi_program_manager_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,
-        )
-
-    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/router.py

@@ -1,6 +1,6 @@
 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, sse
 
 api_router = APIRouter()
 
@@ -13,5 +13,3 @@ api_router.include_router(robot.router, prefix="/robot", tags=["Pings", "Command
 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"])

src/control_backend/core/config.py

@@ -1,12 +1,3 @@
-"""
-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
 
@@ -17,17 +8,16 @@ 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_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.
+    :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.
     """
 
-    # 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):
@@ -46,8 +36,6 @@ 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"
@@ -60,7 +48,6 @@ class AgentSettings(BaseModel):
     ri_communication_name: str = "ri_communication_agent"
     robot_speech_name: str = "robot_speech_agent"
     robot_gesture_name: str = "robot_gesture_agent"
-    user_interrupt_name: str = "user_interrupt_agent"
 
 
 class BehaviourSettings(BaseModel):
@@ -79,8 +66,6 @@ class BehaviourSettings(BaseModel):
     :ivar transcription_token_buffer: Buffer for transcription tokens.
     """
 
-    # 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
@@ -105,8 +90,6 @@ class LLMSettings(BaseModel):
     :ivar local_llm_model: Name of the local LLM model to use.
     """
 
-    # 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"
 
@@ -120,8 +103,6 @@ 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
@@ -135,8 +116,6 @@ 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"
@@ -148,7 +127,6 @@ class Settings(BaseSettings):
 
     :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.
@@ -161,8 +139,6 @@ class Settings(BaseSettings):
 
     ui_url: str = "http://localhost:5173"
 
-    ri_host: str = "localhost"
-
     zmq_settings: ZMQSettings = ZMQSettings()
 
     agent_settings: AgentSettings = AgentSettings()

src/control_backend/main.py

@@ -39,9 +39,6 @@ from control_backend.agents.communication import RICommunicationAgent
 # LLM Agents
 from control_backend.agents.llm import LLMAgent
 
-# User Interrupt Agent
-from control_backend.agents.user_interrupt.user_interrupt_agent import UserInterruptAgent
-
 # Other backend imports
 from control_backend.api.v1.router import api_router
 from control_backend.core.config import settings
@@ -120,7 +117,6 @@ async def lifespan(app: FastAPI):
             BDICoreAgent,
             {
                 "name": settings.agent_settings.bdi_core_name,
-                "asl": "src/control_backend/agents/bdi/rules.asl",
             },
         ),
         "BeliefCollectorAgent": (
@@ -141,12 +137,6 @@ async def lifespan(app: FastAPI):
                 "name": settings.agent_settings.bdi_program_manager_name,
             },
         ),
-        "UserInterruptAgent": (
-            UserInterruptAgent,
-            {
-                "name": settings.agent_settings.user_interrupt_name,
-            },
-        ),
     }
 
     agents = []

src/control_backend/schemas/events.py

@@ -1,6 +0,0 @@
-from pydantic import BaseModel
-
-
-class ButtonPressedEvent(BaseModel):
-    type: str
-    context: str

src/control_backend/schemas/program.py

@@ -1,64 +1,202 @@
-from pydantic import BaseModel
+from enum import Enum
+from typing import Literal
+
+from pydantic import UUID4, BaseModel
 
 
-class Norm(BaseModel):
+class ProgramElement(BaseModel):
     """
-    Represents a behavioral norm.
+    Represents a basic element of our behavior program.
 
+    :ivar name: The researcher-assigned name of the element.
     :ivar id: Unique identifier.
-    :ivar label: Human-readable label.
-    :ivar norm: The actual norm text describing the behavior.
     """
 
-    id: str
-    label: str
-    norm: str
+    name: str
+    id: UUID4
 
 
-class Goal(BaseModel):
+class LogicalOperator(Enum):
+    AND = "AND"
+    OR = "OR"
+
+
+type Belief = KeywordBelief | SemanticBelief | InferredBelief
+type BasicBelief = KeywordBelief | SemanticBelief
+
+
+class KeywordBelief(ProgramElement):
     """
-    Represents an objective to be achieved.
+    Represents a belief that is set when the user spoken text contains a certain keyword.
 
-    :ivar id: Unique identifier.
-    :ivar label: Human-readable label.
-    :ivar description: Detailed description of the goal.
-    :ivar achieved: Status flag indicating if the goal has been met.
+    :ivar keyword: The keyword on which this belief gets set.
     """
 
-    id: str
-    label: str
-    description: str
-    achieved: bool
-
-
-class TriggerKeyword(BaseModel):
-    id: str
+    name: str = ""
     keyword: str
 
 
-class KeywordTrigger(BaseModel):
-    id: str
-    label: str
-    type: str
-    keywords: list[TriggerKeyword]
+class SemanticBelief(ProgramElement):
+    """
+    Represents a belief that is set by semantic LLM validation.
+
+    :ivar description: Description of how to form the belief, used by the LLM.
+    """
+
+    name: str = ""
+    description: str
 
 
-class Phase(BaseModel):
+class InferredBelief(ProgramElement):
+    """
+    Represents a belief that gets formed by combining two beliefs with a logical AND or OR.
+
+    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.
+    """
+
+    name: str = ""
+    operator: LogicalOperator
+    left: Belief
+    right: Belief
+
+
+class Norm(ProgramElement):
+    name: str = ""
+    norm: str
+    critical: bool = False
+
+
+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).
+    """
+
+    pass
+
+
+class ConditionalNorm(Norm):
+    """
+    Represents a norm that is only active when a condition is met (i.e., a certain belief holds).
+
+    :ivar condition: When to activate this norm.
+    """
+
+    condition: Belief
+
+
+type PlanElement = Goal | Action
+
+
+class Plan(ProgramElement):
+    """
+    Represents a list of steps to execute. Each of these steps can be a goal (with its own plan)
+    or a simple action.
+
+    :ivar steps: The actions or subgoals to execute, in order.
+    """
+
+    name: str = ""
+    steps: list[PlanElement]
+
+
+class Goal(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.
+
+    :ivar plan: The plan to execute.
+    :ivar can_fail: Whether we can fail to achieve the goal after executing the plan.
+    """
+
+    plan: Plan
+    can_fail: bool = True
+
+
+type Action = SpeechAction | GestureAction | LLMAction
+
+
+class SpeechAction(ProgramElement):
+    """
+    Represents the action of the robot speaking a literal text.
+
+    :ivar text: The text to speak.
+    """
+
+    name: str = ""
+    text: str
+
+
+class Gesture(BaseModel):
+    """
+    Represents a gesture to be performed. Can be either a single gesture,
+    or a random gesture from a category (tag).
+
+    :ivar type: The type of the gesture, "tag" or "single".
+    :ivar name: The name of the single gesture or tag.
+    """
+
+    type: Literal["tag", "single"]
+    name: str
+
+
+class GestureAction(ProgramElement):
+    """
+    Represents the action of the robot performing a gesture.
+
+    :ivar gesture: The gesture to perform.
+    """
+
+    name: str = ""
+    gesture: Gesture
+
+
+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.
+
+    :ivar goal: The extra (temporary) goal to add to the LLM.
+    """
+
+    name: str = ""
+    goal: str
+
+
+class Trigger(ProgramElement):
+    """
+    Represents a belief-based trigger. When a belief is set, the corresponding plan is executed.
+
+    :ivar condition: When to activate the trigger.
+    :ivar plan: The plan to execute.
+    """
+
+    name: str = ""
+    condition: Belief
+    plan: Plan
+
+
+class Phase(ProgramElement):
     """
     A distinct phase within a program, containing norms, goals, and triggers.
 
-    :ivar id: Unique identifier.
-    :ivar label: Human-readable label.
     :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.
     """
 
-    id: str
-    label: str
-    norms: list[Norm]
+    name: str = ""
+    norms: list[BasicNorm | ConditionalNorm]
     goals: list[Goal]
-    triggers: list[KeywordTrigger]
+    triggers: list[Trigger]
 
 
 class Program(BaseModel):

src/control_backend/schemas/ri_message.py

@@ -38,7 +38,6 @@ class SpeechCommand(RIMessage):
 
     endpoint: RIEndpoint = RIEndpoint(RIEndpoint.SPEECH)
     data: str
-    is_priority: bool = False
 
 
 class GestureCommand(RIMessage):
@@ -53,7 +52,6 @@ class GestureCommand(RIMessage):
         RIEndpoint.GESTURE_SINGLE, RIEndpoint.GESTURE_TAG
     ]
     data: str
-    is_priority: bool = False
 
     @model_validator(mode="after")
     def check_endpoint(self):

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

@@ -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.assert_called_once_with("inproc://vad_stream")
+    zmq_context.return_value.socket.return_value.bind_to_random_port.assert_called_once()
 
 
 @pytest.mark.asyncio

test/unit/agents/actuation/test_robot_gesture_agent.py

@@ -73,7 +73,7 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     payload = {
@@ -91,7 +91,7 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     payload = {"endpoint": "some_other_endpoint", "data": "invalid_tag_not_in_list"}
@@ -107,7 +107,7 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     # Use a tag that's not in gesture_data
@@ -123,7 +123,7 @@ async def test_handle_message_rejects_invalid_gesture_tag():
 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     msg = InternalMessage(to="robot", sender="tester", body=json.dumps({"bad": "data"}))
@@ -142,12 +142,12 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -165,12 +165,12 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -188,12 +188,12 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -210,12 +210,12 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -232,12 +232,12 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -259,9 +259,7 @@ 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"], address=""
-    )
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -289,9 +287,7 @@ 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"], address=""
-    )
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -319,7 +315,7 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -344,7 +340,7 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -369,7 +365,7 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -385,7 +381,7 @@ 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, address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=gesture_data)
 
     assert agent.gesture_data == gesture_data
     assert isinstance(agent.gesture_data, list)
@@ -402,7 +398,7 @@ async def test_stop_closes_sockets():
     pubsocket = MagicMock()
     subsocket = MagicMock()
     repsocket = MagicMock()
-    agent = RobotGestureAgent("robot_gesture", address="")
+    agent = RobotGestureAgent("robot_gesture")
     agent.pubsocket = pubsocket
     agent.subsocket = subsocket
     agent.repsocket = repsocket
@@ -419,7 +415,7 @@ async def test_stop_closes_sockets():
 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, address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=custom_gestures)
 
     assert agent.gesture_data == custom_gestures
 
@@ -436,7 +432,7 @@ 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"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent.logger = MagicMock()
     agent._running = True

test/unit/agents/actuation/test_robot_speech_agent.py

@@ -64,7 +64,7 @@ async def test_handle_message_sends_command():
     agent = mock_speech_agent()
     agent.pubsocket = pubsocket
 
-    payload = {"endpoint": "actuate/speech", "data": "hello", "is_priority": False}
+    payload = {"endpoint": "actuate/speech", "data": "hello"}
     msg = InternalMessage(to="robot", sender="tester", body=json.dumps(payload))
 
     await agent.handle_message(msg)
@@ -75,7 +75,7 @@ async def test_handle_message_sends_command():
 @pytest.mark.asyncio
 async def test_zmq_command_loop_valid_payload(zmq_context):
     """UI command is read from SUB and published."""
-    command = {"endpoint": "actuate/speech", "data": "hello", "is_priority": False}
+    command = {"endpoint": "actuate/speech", "data": "hello"}
     fake_socket = AsyncMock()
 
     async def recv_once():

test/unit/agents/bdi/test_bdi_program_manager.py

@@ -39,7 +39,7 @@ async def test_send_to_bdi():
     manager.send = AsyncMock()
 
     program = Program.model_validate_json(make_valid_program_json())
-    await manager._send_to_bdi(program)
+    await manager._create_agentspeak_and_send_to_bdi(program)
 
     assert manager.send.await_count == 1
     msg: InternalMessage = manager.send.await_args[0][0]
@@ -62,8 +62,7 @@ async def test_receive_programs_valid_and_invalid():
 
     manager = BDIProgramManager(name="program_manager_test")
     manager.sub_socket = sub
-    manager._send_to_bdi = AsyncMock()
-    manager._send_clear_llm_history = AsyncMock()
+    manager._create_agentspeak_and_send_to_bdi = AsyncMock()
 
     try:
         # Will give StopAsyncIteration when the predefined `sub.recv_multipart` side-effects run out
@@ -72,28 +71,7 @@ async def test_receive_programs_valid_and_invalid():
         pass
 
     # Only valid Program should have triggered _send_to_bdi
-    assert manager._send_to_bdi.await_count == 1
-    forwarded: Program = manager._send_to_bdi.await_args[0][0]
+    assert manager._create_agentspeak_and_send_to_bdi.await_count == 1
+    forwarded: Program = manager._create_agentspeak_and_send_to_bdi.await_args[0][0]
     assert forwarded.phases[0].norms[0].norm == "N1"
     assert forwarded.phases[0].goals[0].description == "G1"
-
-    # Verify history clear was triggered
-    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 == 1
-    msg: InternalMessage = manager.send.await_args[0][0]
-
-    # Verify the content and recipient
-    assert msg.body == "clear_history"
-    assert msg.to == "llm_agent"

test/unit/agents/communication/test_ri_communication_agent.py

@@ -67,7 +67,6 @@ async def test_setup_success_connects_and_starts_robot(zmq_context):
         address="tcp://localhost:5556",
         bind=False,
         gesture_data=[],
-        single_gesture_data=[],
     )
     agent.add_behavior.assert_called_once()
 

test/unit/agents/llm/test_llm_agent.py

@@ -197,9 +197,6 @@ async def test_query_llm_yields_final_tail_chunk(mock_settings):
     agent = LLMAgent("llm_agent")
     agent.send = AsyncMock()
 
-    agent.logger = MagicMock()
-    agent.logger.llm = MagicMock()
-
     # Patch _stream_query_llm to yield tokens that do NOT end with punctuation
     async def fake_stream(messages):
         yield "Hello"
@@ -265,23 +262,3 @@ 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

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

@@ -7,15 +7,6 @@ import zmq
 from control_backend.agents.perception.vad_agent import VADAgent
 
 
-# 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
 def audio_out_socket():
     return AsyncMock()
@@ -149,10 +140,12 @@ 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()
 
-        await vad_agent.setup()
+        result = 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
@@ -162,7 +155,7 @@ 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.side_effect = zmq.ZMQBindError()
+    mock_socket.bind_to_random_port.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/user_interrupt/test_user_interrupt.py

@@ -1,146 +0,0 @@
-import asyncio
-import json
-from unittest.mock import AsyncMock, MagicMock
-
-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.ri_message import RIEndpoint
-
-
-@pytest.fixture
-def agent():
-    agent = UserInterruptAgent(name="user_interrupt_agent")
-    agent.send = AsyncMock()
-    agent.logger = MagicMock()
-    agent.sub_socket = AsyncMock()
-    return agent
-
-
-@pytest.mark.asyncio
-async def test_send_to_speech_agent(agent):
-    """Verify speech command format."""
-    await agent._send_to_speech_agent("Hello World")
-
-    agent.send.assert_awaited_once()
-    sent_msg: InternalMessage = agent.send.call_args.args[0]
-
-    assert sent_msg.to == settings.agent_settings.robot_speech_name
-    body = json.loads(sent_msg.body)
-    assert body["data"] == "Hello World"
-    assert body["is_priority"] is True
-
-
-@pytest.mark.asyncio
-async def test_send_to_gesture_agent(agent):
-    """Verify gesture command format."""
-    await agent._send_to_gesture_agent("wave_hand")
-
-    agent.send.assert_awaited_once()
-    sent_msg: InternalMessage = agent.send.call_args.args[0]
-
-    assert sent_msg.to == settings.agent_settings.robot_gesture_name
-    body = json.loads(sent_msg.body)
-    assert body["data"] == "wave_hand"
-    assert body["is_priority"] is True
-    assert body["endpoint"] == RIEndpoint.GESTURE_SINGLE.value
-
-
-@pytest.mark.asyncio
-async def test_send_to_program_manager(agent):
-    """Verify belief update format."""
-    context_str = "2"
-
-    await agent._send_to_program_manager(context_str)
-
-    agent.send.assert_awaited_once()
-    sent_msg: InternalMessage = 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
-
-
-@pytest.mark.asyncio
-async def test_receive_loop_routing_success(agent):
-    """
-    Test that the loop correctly:
-    1. Receives 'button_pressed' topic from ZMQ
-    2. Parses the JSON payload to find 'type' and 'context'
-    3. Calls the correct handler method based on 'type'
-    """
-    # 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()
-    payload_override = json.dumps({"type": "override", "context": "Hello Override"}).encode()
-
-    agent.sub_socket.recv_multipart.side_effect = [
-        (b"button_pressed", payload_speech),
-        (b"button_pressed", payload_gesture),
-        (b"button_pressed", payload_override),
-        asyncio.CancelledError,  # Stop the infinite loop
-    ]
-
-    agent._send_to_speech_agent = AsyncMock()
-    agent._send_to_gesture_agent = AsyncMock()
-    agent._send_to_program_manager = AsyncMock()
-
-    try:
-        await agent._receive_button_event()
-    except asyncio.CancelledError:
-        pass
-
-    await asyncio.sleep(0)
-
-    # Speech
-    agent._send_to_speech_agent.assert_awaited_once_with("Hello Speech")
-
-    # Gesture
-    agent._send_to_gesture_agent.assert_awaited_once_with("Hello Gesture")
-
-    # Override
-    agent._send_to_program_manager.assert_awaited_once_with("Hello Override")
-
-    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
-
-
-@pytest.mark.asyncio
-async def test_receive_loop_unknown_type(agent):
-    """Test that unknown 'type' values in the JSON log a warning and do not crash."""
-
-    # Prepare a payload with an unknown type
-    payload_unknown = json.dumps({"type": "unknown_thing", "context": "some_data"}).encode()
-
-    agent.sub_socket.recv_multipart.side_effect = [
-        (b"button_pressed", payload_unknown),
-        asyncio.CancelledError,
-    ]
-
-    agent._send_to_speech_agent = AsyncMock()
-    agent._send_to_gesture_agent = AsyncMock()
-    agent._send_to_belief_collector = AsyncMock()
-
-    try:
-        await agent._receive_button_event()
-    except asyncio.CancelledError:
-        pass
-
-    await asyncio.sleep(0)
-
-    # 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",
-    )

uv.lock

@@ -997,6 +997,7 @@ dependencies = [
     { name = "pydantic" },
     { name = "pydantic-settings" },
     { name = "python-json-logger" },
+    { name = "python-slugify" },
     { name = "pyyaml" },
     { name = "pyzmq" },
     { name = "silero-vad" },
@@ -1046,6 +1047,7 @@ requires-dist = [
     { name = "pydantic", specifier = ">=2.12.0" },
     { name = "pydantic-settings", specifier = ">=2.11.0" },
     { name = "python-json-logger", specifier = ">=4.0.0" },
+    { name = "python-slugify", specifier = ">=8.0.4" },
     { name = "pyyaml", specifier = ">=6.0.3" },
     { name = "pyzmq", specifier = ">=27.1.0" },
     { name = "silero-vad", specifier = ">=6.0.0" },
@@ -1341,6 +1343,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/45/58/38b5afbc1a800eeea951b9285d3912613f2603bdf897a4ab0f4bd7f405fc/python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104", size = 24546, upload-time = "2024-12-16T19:45:44.423Z" },
 ]
 
+[[package]]
+name = "python-slugify"
+version = "8.0.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "text-unidecode" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/87/c7/5e1547c44e31da50a460df93af11a535ace568ef89d7a811069ead340c4a/python-slugify-8.0.4.tar.gz", hash = "sha256:59202371d1d05b54a9e7720c5e038f928f45daaffe41dd10822f3907b937c856", size = 10921, upload-time = "2024-02-08T18:32:45.488Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a4/62/02da182e544a51a5c3ccf4b03ab79df279f9c60c5e82d5e8bec7ca26ac11/python_slugify-8.0.4-py2.py3-none-any.whl", hash = "sha256:276540b79961052b66b7d116620b36518847f52d5fd9e3a70164fc8c50faa6b8", size = 10051, upload-time = "2024-02-08T18:32:43.911Z" },
+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.3"
@@ -1864,6 +1878,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
 ]
 
+[[package]]
+name = "text-unidecode"
+version = "1.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ab/e2/e9a00f0ccb71718418230718b3d900e71a5d16e701a3dae079a21e9cd8f8/text-unidecode-1.3.tar.gz", hash = "sha256:bad6603bb14d279193107714b288be206cac565dfa49aa5b105294dd5c4aab93", size = 76885, upload-time = "2019-08-30T21:36:45.405Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a6/a5/c0b6468d3824fe3fde30dbb5e1f687b291608f9473681bbf7dabbf5a87d7/text_unidecode-1.3-py2.py3-none-any.whl", hash = "sha256:1311f10e8b895935241623731c2ba64f4c455287888b18189350b67134a822e8", size = 78154, upload-time = "2019-08-30T21:37:03.543Z" },
+]
+
 [[package]]
 name = "tiktoken"
 version = "0.12.0"