chore: fix bugs and make sure connected robots work

ref: N25B-350

.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=15
-
-# Timeout in milliseconds for socket polling. Increase this number if network latency/jitter is high, often the case when using Wi-Fi. Perhaps 500 ms. A symptom of this issue is transcriptions getting cut off.
-BEHAVIOUR_SETTINGS__SOCKET_POLLER_TIMEOUT_MS=100
-
-
-
-# For an exhaustive list of options, see the control_backend.core.config module in the docs.

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:
 

src/control_backend/agents/actuation/robot_gesture_agent.py

@@ -33,7 +33,7 @@ class RobotGestureAgent(BaseAgent):
     def __init__(
         self,
         name: str,
-        address: str,
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
         gesture_data=None,
         single_gesture_data=None,
@@ -83,6 +83,8 @@ class RobotGestureAgent(BaseAgent):
             self.subsocket.close()
         if self.pubsocket:
             self.pubsocket.close()
+        if self.repsocket:
+            self.repsocket.close()
         await super().stop()
 
     async def handle_message(self, msg: InternalMessage):

src/control_backend/agents/bdi/agentspeak_generator.py

@@ -145,10 +145,7 @@ class AgentSpeakGenerator:
                 type=TriggerType.ADDED_BELIEF,
                 trigger_literal=AstLiteral("user_said", [AstVar("Message")]),
                 context=[AstLiteral("phase", [AstString("end")])],
-                body=[
+                body=[AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("reply"))],
-                    AstStatement(StatementType.DO_ACTION, AstLiteral("notify_user_said")),
-                    AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("reply")),
-                ],
             )
         )
 
@@ -160,7 +157,7 @@ class AgentSpeakGenerator:
 
         previous_goal = None
         for goal in phase.goals:
-            self._process_goal(goal, phase, previous_goal, main_goal=True)
+            self._process_goal(goal, phase, previous_goal)
             previous_goal = goal
 
         for trigger in phase.triggers:
@@ -174,40 +171,25 @@ class AgentSpeakGenerator:
             self._astify(to_phase) if to_phase else AstLiteral("phase", [AstString("end")])
         )
 
-        context = [from_phase_ast]
+        context = [from_phase_ast, ~AstLiteral("responded_this_turn")]
-        if from_phase:
+        if from_phase and from_phase.goals:
-            for goal in from_phase.goals:
+            context.append(self._astify(from_phase.goals[-1], achieved=True))
-                context.append(self._astify(goal, achieved=True))
 
         body = [
             AstStatement(StatementType.REMOVE_BELIEF, from_phase_ast),
             AstStatement(StatementType.ADD_BELIEF, to_phase_ast),
         ]
 
-        # if from_phase:
+        if from_phase:
-        #     body.extend(
+            body.extend(
-        #         [
-        #             AstStatement(
-        #                 StatementType.TEST_GOAL, AstLiteral("user_said", [AstVar("Message")])
-        #             ),
-        #             AstStatement(
-        #                 StatementType.REPLACE_BELIEF, AstLiteral("user_said", [AstVar("Message")])
-        #             ),
-        #         ]
-        #     )
-
-        # Notify outside world about transition
-        body.append(
-            AstStatement(
-                StatementType.DO_ACTION,
-                AstLiteral(
-                    "notify_transition_phase",
                 [
-                        AstString(str(from_phase.id)),
+                    AstStatement(
-                        AstString(str(to_phase.id) if to_phase else "end"),
+                        StatementType.TEST_GOAL, AstLiteral("user_said", [AstVar("Message")])
-                    ],
                     ),
-            )
+                    AstStatement(
+                        StatementType.REPLACE_BELIEF, AstLiteral("user_said", [AstVar("Message")])
+                    ),
+                ]
             )
 
         self._asp.plans.append(
@@ -231,11 +213,6 @@ class AgentSpeakGenerator:
     def _add_default_loop(self, phase: Phase) -> None:
         actions = []
 
-        actions.append(
-            AstStatement(
-                StatementType.DO_ACTION, AstLiteral("notify_user_said", [AstVar("Message")])
-            )
-        )
         actions.append(AstStatement(StatementType.REMOVE_BELIEF, AstLiteral("responded_this_turn")))
         actions.append(AstStatement(StatementType.ACHIEVE_GOAL, AstLiteral("check_triggers")))
 
@@ -259,7 +236,6 @@ class AgentSpeakGenerator:
         phase: Phase,
         previous_goal: Goal | None = None,
         continues_response: bool = False,
-        main_goal: bool = False,
     ) -> None:
         context: list[AstExpression] = [self._astify(phase)]
         context.append(~self._astify(goal, achieved=True))
@@ -269,13 +245,6 @@ class AgentSpeakGenerator:
             context.append(~AstLiteral("responded_this_turn"))
 
         body = []
-        if main_goal:  # UI only needs to know about the main goals
-            body.append(
-                AstStatement(
-                    StatementType.DO_ACTION,
-                    AstLiteral("notify_goal_start", [AstString(self.slugify(goal))]),
-                )
-            )
 
         subgoals = []
         for step in goal.plan.steps:
@@ -314,23 +283,11 @@ class AgentSpeakGenerator:
         body = []
         subgoals = []
 
-        body.append(
-            AstStatement(
-                StatementType.DO_ACTION,
-                AstLiteral("notify_trigger_start", [AstString(self.slugify(trigger))]),
-            )
-        )
         for step in trigger.plan.steps:
             body.append(self._step_to_statement(step))
             if isinstance(step, Goal):
                 step.can_fail = False  # triggers are continuous sequence
                 subgoals.append(step)
-        body.append(
-            AstStatement(
-                StatementType.DO_ACTION,
-                AstLiteral("notify_trigger_end", [AstString(self.slugify(trigger))]),
-            )
-        )
 
         self._asp.plans.append(
             AstPlan(
@@ -341,9 +298,6 @@ class AgentSpeakGenerator:
             )
         )
 
-        # Force trigger (from UI)
-        self._asp.plans.append(AstPlan(TriggerType.ADDED_GOAL, self._astify(trigger), [], body))
-
         for subgoal in subgoals:
             self._process_goal(subgoal, phase, continues_response=True)
 
@@ -378,7 +332,13 @@ class AgentSpeakGenerator:
 
     @_astify.register
     def _(self, sb: SemanticBelief) -> AstExpression:
-        return AstLiteral(self.slugify(sb))
+        return AstLiteral(self.get_semantic_belief_slug(sb))
+
+    @staticmethod
+    def get_semantic_belief_slug(sb: SemanticBelief) -> str:
+        # If you need a method like this for other types, make a public slugify singledispatch for
+        #  all types.
+        return f"semantic_{AgentSpeakGenerator._slugify_str(sb.name)}"
 
     @_astify.register
     def _(self, ib: InferredBelief) -> AstExpression:

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -1,6 +1,5 @@
 import asyncio
 import copy
-import json
 import time
 from collections.abc import Iterable
 
@@ -14,7 +13,7 @@ from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.llm_prompt_message import LLMPromptMessage
-from control_backend.schemas.ri_message import GestureCommand, RIEndpoint, SpeechCommand
+from control_backend.schemas.ri_message import SpeechCommand
 
 DELIMITER = ";\n"  # TODO: temporary until we support lists in AgentSpeak
 
@@ -101,6 +100,7 @@ class BDICoreAgent(BaseAgent):
             maybe_more_work = True
             while maybe_more_work:
                 maybe_more_work = False
+                self.logger.debug("Stepping BDI.")
                 if self.bdi_agent.step():
                     maybe_more_work = True
 
@@ -155,17 +155,6 @@ class BDICoreAgent(BaseAgent):
                     body=cmd.model_dump_json(),
                 )
                 await self.send(out_msg)
-            case settings.agent_settings.user_interrupt_name:
-                content = msg.body
-                self.logger.debug("Received user interruption: %s", content)
-
-                match msg.thread:
-                    case "force_phase_transition":
-                        self._set_goal("transition_phase")
-                    case "force_trigger":
-                        self._force_trigger(msg.body)
-                    case _:
-                        self.logger.warning("Received unknow user interruption: %s", msg)
 
     def _apply_belief_changes(self, belief_changes: BeliefMessage):
         """
@@ -212,33 +201,14 @@ class BDICoreAgent(BaseAgent):
             agentspeak.runtime.Intention(),
         )
 
-        # Check for transitions
-        self.bdi_agent.call(
-            agentspeak.Trigger.addition,
-            agentspeak.GoalType.achievement,
-            agentspeak.Literal("transition_phase"),
-            agentspeak.runtime.Intention(),
-        )
-
-        # Check triggers
-        self.bdi_agent.call(
-            agentspeak.Trigger.addition,
-            agentspeak.GoalType.achievement,
-            agentspeak.Literal("check_triggers"),
-            agentspeak.runtime.Intention(),
-        )
-
         self._wake_bdi_loop.set()
 
         self.logger.debug(f"Added belief {self.format_belief_string(name, args)}")
 
-    def _remove_belief(self, name: str, args: Iterable[str] | None):
+    def _remove_belief(self, name: str, args: Iterable[str]):
         """
         Removes a specific belief (with arguments), if it exists.
         """
-        if args is None:
-            term = agentspeak.Literal(name)
-        else:
         new_args = (agentspeak.Literal(arg) for arg in args)
         term = agentspeak.Literal(name, new_args)
 
@@ -280,37 +250,6 @@ class BDICoreAgent(BaseAgent):
 
         self.logger.debug(f"Removed {removed_count} beliefs.")
 
-    def _set_goal(self, name: str, args: Iterable[str] | None = None):
-        args = args or []
-
-        if args:
-            merged_args = DELIMITER.join(arg for arg in args)
-            new_args = (agentspeak.Literal(merged_args),)
-            term = agentspeak.Literal(name, new_args)
-        else:
-            term = agentspeak.Literal(name)
-
-        self.bdi_agent.call(
-            agentspeak.Trigger.addition,
-            agentspeak.GoalType.achievement,
-            term,
-            agentspeak.runtime.Intention(),
-        )
-
-        self._wake_bdi_loop.set()
-
-        self.logger.debug(f"Set goal !{self.format_belief_string(name, args)}.")
-
-    def _force_trigger(self, name: str):
-        self.bdi_agent.call(
-            agentspeak.Trigger.addition,
-            agentspeak.GoalType.achievement,
-            agentspeak.Literal(name),
-            agentspeak.runtime.Intention(),
-        )
-
-        self.logger.info("Manually forced trigger %s.", name)
-
     def _add_custom_actions(self) -> None:
         """
         Add any custom actions here. Inside `@self.actions.add()`, the first argument is
@@ -319,7 +258,7 @@ class BDICoreAgent(BaseAgent):
         """
 
         @self.actions.add(".reply", 2)
-        def _reply(agent, term, intention):
+        def _reply(agent: "BDICoreAgent", term, intention):
             """
             Let the LLM generate a response to a user's utterance with the current norms and goals.
             """
@@ -352,7 +291,7 @@ class BDICoreAgent(BaseAgent):
             yield
 
         @self.actions.add(".say", 1)
-        def _say(agent, term, intention):
+        def _say(agent: "BDICoreAgent", term, intention):
             """
             Make the robot say the given text instantly.
             """
@@ -366,21 +305,12 @@ class BDICoreAgent(BaseAgent):
                 sender=settings.agent_settings.bdi_core_name,
                 body=speech_command.model_dump_json(),
             )
-
+            # TODO: add to conversation history
             self.add_behavior(self.send(speech_message))
-
-            chat_history_message = InternalMessage(
-                to=settings.agent_settings.llm_name,
-                thread="assistant_message",
-                body=str(message_text),
-            )
-
-            self.add_behavior(self.send(chat_history_message))
-
             yield
 
         @self.actions.add(".gesture", 2)
-        def _gesture(agent, term, intention):
+        def _gesture(agent: "BDICoreAgent", term, intention):
             """
             Make the robot perform the given gesture instantly.
             """
@@ -393,113 +323,13 @@ class BDICoreAgent(BaseAgent):
                 gesture_name,
             )
 
-            if str(gesture_type) == "single":
+            # gesture = Gesture(type=gesture_type, name=gesture_name)
-                endpoint = RIEndpoint.GESTURE_SINGLE
+            # gesture_message = InternalMessage(
-            elif str(gesture_type) == "tag":
+            #     to=settings.agent_settings.robot_gesture_name,
-                endpoint = RIEndpoint.GESTURE_TAG
+            #     sender=settings.agent_settings.bdi_core_name,
-            else:
+            #     body=gesture.model_dump_json(),
-                self.logger.warning("Gesture type %s could not be resolved.", gesture_type)
+            # )
-                endpoint = RIEndpoint.GESTURE_SINGLE
+            # asyncio.create_task(agent.send(gesture_message))
-
-            gesture_command = GestureCommand(endpoint=endpoint, data=gesture_name)
-            gesture_message = InternalMessage(
-                to=settings.agent_settings.robot_gesture_name,
-                sender=settings.agent_settings.bdi_core_name,
-                body=gesture_command.model_dump_json(),
-            )
-            self.add_behavior(self.send(gesture_message))
-            yield
-
-        @self.actions.add(".notify_user_said", 1)
-        def _notify_user_said(agent, term, intention):
-            user_said = agentspeak.grounded(term.args[0], intention.scope)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.llm_name, thread="user_message", body=str(user_said)
-            )
-
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_trigger_start", 1)
-        def _notify_trigger_start(agent, term, intention):
-            """
-            Notify the UI about the trigger we just started doing.
-            """
-            trigger_name = agentspeak.grounded(term.args[0], intention.scope)
-
-            self.logger.debug("Started trigger %s", trigger_name)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.user_interrupt_name,
-                sender=self.name,
-                thread="trigger_start",
-                body=str(trigger_name),
-            )
-
-            # TODO: check with Pim
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_trigger_end", 1)
-        def _notify_trigger_end(agent, term, intention):
-            """
-            Notify the UI about the trigger we just started doing.
-            """
-            trigger_name = agentspeak.grounded(term.args[0], intention.scope)
-
-            self.logger.debug("Finished trigger %s", trigger_name)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.user_interrupt_name,
-                sender=self.name,
-                thread="trigger_end",
-                body=str(trigger_name),
-            )
-
-            # TODO: check with Pim
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_goal_start", 1)
-        def _notify_goal_start(agent, term, intention):
-            """
-            Notify the UI about the goal we just started chasing.
-            """
-            goal_name = agentspeak.grounded(term.args[0], intention.scope)
-
-            self.logger.debug("Started chasing goal %s", goal_name)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.user_interrupt_name,
-                sender=self.name,
-                thread="goal_start",
-                body=str(goal_name),
-            )
-
-            self.add_behavior(self.send(msg))
-
-            yield
-
-        @self.actions.add(".notify_transition_phase", 2)
-        def _notify_transition_phase(agent, term, intention):
-            """
-            Notify the BDI program manager about a phase transition.
-            """
-            old = agentspeak.grounded(term.args[0], intention.scope)
-            new = agentspeak.grounded(term.args[1], intention.scope)
-
-            msg = InternalMessage(
-                to=settings.agent_settings.bdi_program_manager_name,
-                thread="transition_phase",
-                body=json.dumps({"old": str(old), "new": str(new)}),
-            )
-
-            self.add_behavior(self.send(msg))
-
             yield
 
     async def _send_to_llm(self, text: str, norms: str, goals: str):
@@ -511,14 +341,13 @@ class BDICoreAgent(BaseAgent):
             to=settings.agent_settings.llm_name,
             sender=self.name,
             body=prompt.model_dump_json(),
-            thread="prompt_message",
         )
         await self.send(msg)
         self.logger.info("Message sent to LLM agent: %s", text)
 
     @staticmethod
-    def format_belief_string(name: str, args: Iterable[str] | None = []):
+    def format_belief_string(name: str, args: Iterable[str] = []):
         """
         Given a belief's name and its args, return a string of the form "name(*args)"
         """
-        return f"{name}{'(' if args else ''}{','.join(args or [])}{')' if args else ''}"
+        return f"{name}{'(' if args else ''}{','.join(args)}{')' if args else ''}"

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,5 +1,4 @@
 import asyncio
-import json
 
 import zmq
 from pydantic import ValidationError
@@ -8,16 +7,9 @@ from zmq.asyncio import Context
 from control_backend.agents import BaseAgent
 from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.config import settings
-from control_backend.schemas.belief_list import BeliefList, GoalList
+from control_backend.schemas.belief_list import BeliefList
 from control_backend.schemas.internal_message import InternalMessage
-from control_backend.schemas.program import (
+from control_backend.schemas.program import Belief, ConditionalNorm, InferredBelief, Program
-    Belief,
-    ConditionalNorm,
-    Goal,
-    InferredBelief,
-    Phase,
-    Program,
-)
 
 
 class BDIProgramManager(BaseAgent):
@@ -32,20 +24,20 @@ class BDIProgramManager(BaseAgent):
     :ivar sub_socket: The ZMQ SUB socket used to receive program updates.
     """
 
-    _program: Program
-    _phase: Phase | None
-
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.sub_socket = None
 
-    def _initialize_internal_state(self, program: Program):
-        self._program = program
-        self._phase = program.phases[0]  # start in first phase
-
     async def _create_agentspeak_and_send_to_bdi(self, program: Program):
         """
-        Convert a received program into an AgentSpeak file and send it to the BDI Core Agent.
+        Convert a received program into BDI beliefs and send them to the BDI Core Agent.
+
+        Currently, it takes the **first phase** of the program and extracts:
+        -   **Norms**: Constraints or rules the agent must follow.
+        -   **Goals**: Objectives the agent must achieve.
+
+        These are sent as a ``BeliefMessage`` with ``replace=True``, meaning they will
+        overwrite any existing norms/goals of the same name in the BDI agent.
 
         :param program: The program object received from the API.
         """
@@ -67,45 +59,17 @@ class BDIProgramManager(BaseAgent):
 
         await self.send(msg)
 
-    async def handle_message(self, msg: InternalMessage):
+    @staticmethod
-        match msg.thread:
+    def _extract_beliefs_from_program(program: Program) -> list[Belief]:
-            case "transition_phase":
-                phases = json.loads(msg.body)
-
-                await self._transition_phase(phases["old"], phases["new"])
-
-    async def _transition_phase(self, old: str, new: str):
-        assert old == str(self._phase.id)
-
-        if new == "end":
-            self._phase = None
-            return
-
-        for phase in self._program.phases:
-            if str(phase.id) == new:
-                self._phase = phase
-
-        await self._send_beliefs_to_semantic_belief_extractor()
-        await self._send_goals_to_semantic_belief_extractor()
-
-        # Notify user interaction agent
-        msg = InternalMessage(
-            to=settings.agent_settings.user_interrupt_name,
-            thread="transition_phase",
-            body=str(self._phase.id),
-        )
-
-        self.add_behavior(self.send(msg))
-
-    def _extract_current_beliefs(self) -> list[Belief]:
         beliefs: list[Belief] = []
 
-        for norm in self._phase.norms:
+        for phase in program.phases:
+            for norm in phase.norms:
                 if isinstance(norm, ConditionalNorm):
-                beliefs += self._extract_beliefs_from_belief(norm.condition)
+                    beliefs += BDIProgramManager._extract_beliefs_from_belief(norm.condition)
 
-        for trigger in self._phase.triggers:
+            for trigger in phase.triggers:
-            beliefs += self._extract_beliefs_from_belief(trigger.condition)
+                beliefs += BDIProgramManager._extract_beliefs_from_belief(trigger.condition)
 
         return beliefs
 
@@ -117,11 +81,13 @@ class BDIProgramManager(BaseAgent):
             ) + BDIProgramManager._extract_beliefs_from_belief(belief.right)
         return [belief]
 
-    async def _send_beliefs_to_semantic_belief_extractor(self):
+    async def _send_beliefs_to_semantic_belief_extractor(self, program: Program):
         """
         Extract beliefs from the program and send them to the Semantic Belief Extractor Agent.
+
+        :param program: The program received from the API.
         """
-        beliefs = BeliefList(beliefs=self._extract_current_beliefs())
+        beliefs = BeliefList(beliefs=self._extract_beliefs_from_program(program))
 
         message = InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -132,69 +98,12 @@ class BDIProgramManager(BaseAgent):
 
         await self.send(message)
 
-    def _extract_current_goals(self) -> list[Goal]:
-        """
-        Extract all goals from the program, including subgoals.
-
-        :return: A list of Goal objects.
-        """
-        goals: list[Goal] = []
-
-        def extract_goals_from_goal(goal_: Goal) -> list[Goal]:
-            goals_: list[Goal] = [goal]
-            for plan in goal_.plan:
-                if isinstance(plan, Goal):
-                    goals_.extend(extract_goals_from_goal(plan))
-            return goals_
-
-        for goal in self._phase.goals:
-            goals.extend(extract_goals_from_goal(goal))
-
-        return goals
-
-    async def _send_goals_to_semantic_belief_extractor(self):
-        """
-        Extract goals for the current phase and send them to the Semantic Belief Extractor Agent.
-        """
-        goals = GoalList(goals=self._extract_current_goals())
-
-        message = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            sender=self.name,
-            body=goals.model_dump_json(),
-            thread="goals",
-        )
-
-        await self.send(message)
-
-    async def _send_clear_llm_history(self):
-        """
-        Clear the LLM Agent's conversation history.
-
-        Sends an empty history to the LLM Agent to reset its state.
-        """
-        message = InternalMessage(
-            to=settings.agent_settings.llm_name,
-            body="clear_history",
-        )
-        await self.send(message)
-        self.logger.debug("Sent message to LLM agent to clear history.")
-
-        extractor_msg = InternalMessage(
-            to=settings.agent_settings.text_belief_extractor_name,
-            thread="conversation_history",
-            body="reset",
-        )
-        await self.send(extractor_msg)
-        self.logger.debug("Sent message to extractor agent to clear history.")
-
     async def _receive_programs(self):
         """
         Continuous loop that receives program updates from the HTTP endpoint.
 
         It listens to the ``program`` topic on the internal ZMQ SUB socket.
         When a program is received, it is validated and forwarded to BDI via :meth:`_send_to_bdi`.
-        Additionally, the LLM history is cleared via :meth:`_send_clear_llm_history`.
         """
         while True:
             topic, body = await self.sub_socket.recv_multipart()
@@ -202,17 +111,12 @@ class BDIProgramManager(BaseAgent):
             try:
                 program = Program.model_validate_json(body)
             except ValidationError:
-                self.logger.warning("Received an invalid program.")
+                self.logger.exception("Received an invalid program.")
                 continue
 
-            self._initialize_internal_state(program)
-
-            await self._send_clear_llm_history()
-
             await asyncio.gather(
                 self._create_agentspeak_and_send_to_bdi(program),
-                self._send_beliefs_to_semantic_belief_extractor(),
+                self._send_beliefs_to_semantic_belief_extractor(program),
-                self._send_goals_to_semantic_belief_extractor(),
             )
 
     async def setup(self):

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

@@ -1,6 +1,5 @@
 norms("").
 
 +user_said(Message) : norms(Norms) <-
-  .notify_user_said(Message);
   -user_said(Message);
   .reply(Message, Norms).

src/control_backend/agents/bdi/text_belief_extractor_agent.py

@@ -2,45 +2,17 @@ import asyncio
 import json
 
 import httpx
-from pydantic import BaseModel, ValidationError
+from pydantic import ValidationError
 
 from control_backend.agents.base import BaseAgent
 from control_backend.agents.bdi.agentspeak_generator import AgentSpeakGenerator
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.belief_list import BeliefList, GoalList
+from control_backend.schemas.belief_list import BeliefList
 from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.chat_history import ChatHistory, ChatMessage
-from control_backend.schemas.program import Goal, SemanticBelief
+from control_backend.schemas.program import SemanticBelief
-
-type JSONLike = None | bool | int | float | str | list["JSONLike"] | dict[str, "JSONLike"]
-
-
-class BeliefState(BaseModel):
-    true: set[InternalBelief] = set()
-    false: set[InternalBelief] = set()
-
-    def difference(self, other: "BeliefState") -> "BeliefState":
-        return BeliefState(
-            true=self.true - other.true,
-            false=self.false - other.false,
-        )
-
-    def union(self, other: "BeliefState") -> "BeliefState":
-        return BeliefState(
-            true=self.true | other.true,
-            false=self.false | other.false,
-        )
-
-    def __sub__(self, other):
-        return self.difference(other)
-
-    def __or__(self, other):
-        return self.union(other)
-
-    def __bool__(self):
-        return bool(self.true) or bool(self.false)
 
 
 class TextBeliefExtractorAgent(BaseAgent):
@@ -55,14 +27,12 @@ class TextBeliefExtractorAgent(BaseAgent):
     the message itself.
     """
 
-    def __init__(self, name: str):
+    def __init__(self, name: str, temperature: float = settings.llm_settings.code_temperature):
         super().__init__(name)
-        self._llm = self.LLM(self, settings.llm_settings.n_parallel)
+        self.beliefs: dict[str, bool] = {}
-        self.belief_inferrer = SemanticBeliefInferrer(self._llm)
+        self.available_beliefs: list[SemanticBelief] = []
-        self.goal_inferrer = GoalAchievementInferrer(self._llm)
-        self._current_beliefs = BeliefState()
-        self._current_goal_completions: dict[str, bool] = {}
         self.conversation = ChatHistory(messages=[])
+        self.temperature = temperature
 
     async def setup(self):
         """
@@ -83,14 +53,13 @@ class TextBeliefExtractorAgent(BaseAgent):
             case settings.agent_settings.transcription_name:
                 self.logger.debug("Received text from transcriber: %s", msg.body)
                 self._apply_conversation_message(ChatMessage(role="user", content=msg.body))
-                await self._user_said(msg.body)
                 await self._infer_new_beliefs()
-                await self._infer_goal_completions()
+                await self._user_said(msg.body)
             case settings.agent_settings.llm_name:
                 self.logger.debug("Received text from LLM: %s", msg.body)
                 self._apply_conversation_message(ChatMessage(role="assistant", content=msg.body))
             case settings.agent_settings.bdi_program_manager_name:
-                await self._handle_program_manager_message(msg)
+                self._handle_program_manager_message(msg)
             case _:
                 self.logger.info("Discarding message from %s", sender)
                 return
@@ -105,33 +74,12 @@ class TextBeliefExtractorAgent(BaseAgent):
         length_limit = settings.behaviour_settings.conversation_history_length_limit
         self.conversation.messages = (self.conversation.messages + [message])[-length_limit:]
 
-    async def _handle_program_manager_message(self, msg: InternalMessage):
+    def _handle_program_manager_message(self, msg: InternalMessage):
         """
-        Handle a message from the program manager: extract available beliefs and goals from it.
+        Handle a message from the program manager: extract available beliefs from it.
 
         :param msg: The received message from the program manager.
         """
-        match msg.thread:
-            case "beliefs":
-                self._handle_beliefs_message(msg)
-                await self._infer_new_beliefs()
-            case "goals":
-                self._handle_goals_message(msg)
-                await self._infer_goal_completions()
-            case "conversation_history":
-                if msg.body == "reset":
-                    self._reset()
-            case _:
-                self.logger.warning("Received unexpected message from %s", msg.sender)
-
-    def _reset(self):
-        self.conversation = ChatHistory(messages=[])
-        self.belief_inferrer.available_beliefs.clear()
-        self._current_beliefs = BeliefState()
-        self.goal_inferrer.goals.clear()
-        self._current_goal_completions = {}
-
-    def _handle_beliefs_message(self, msg: InternalMessage):
         try:
             belief_list = BeliefList.model_validate_json(msg.body)
         except ValidationError:
@@ -140,30 +88,10 @@ class TextBeliefExtractorAgent(BaseAgent):
             )
             return
 
-        available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
+        self.available_beliefs = [b for b in belief_list.beliefs if isinstance(b, SemanticBelief)]
-        self.belief_inferrer.available_beliefs = available_beliefs
         self.logger.debug(
-            "Received %d semantic beliefs from the program manager: %s",
+            "Received %d beliefs from the program manager.",
-            len(available_beliefs),
+            len(self.available_beliefs),
-            ", ".join(b.name for b in available_beliefs),
-        )
-
-    def _handle_goals_message(self, msg: InternalMessage):
-        try:
-            goals_list = GoalList.model_validate_json(msg.body)
-        except ValidationError:
-            self.logger.warning(
-                "Received message from program manager but it is not a valid list of goals."
-            )
-            return
-
-        # Use only goals that can fail, as the others are always assumed to be completed
-        available_goals = [g for g in goals_list.goals if g.can_fail]
-        self.goal_inferrer.goals = available_goals
-        self.logger.debug(
-            "Received %d failable goals from the program manager: %s",
-            len(available_goals),
-            ", ".join(g.name for g in available_goals),
         )
 
     async def _user_said(self, text: str):
@@ -172,212 +100,121 @@ class TextBeliefExtractorAgent(BaseAgent):
 
         :param text: User's transcribed text.
         """
+        belief = {"beliefs": {"user_said": [text]}, "type": "belief_extraction_text"}
+        payload = json.dumps(belief)
+
         belief_msg = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
+            to=settings.agent_settings.bdi_belief_collector_name,
             sender=self.name,
-            body=BeliefMessage(
+            body=payload,
-                replace=[InternalBelief(name="user_said", arguments=[text])],
-            ).model_dump_json(),
             thread="beliefs",
         )
         await self.send(belief_msg)
 
     async def _infer_new_beliefs(self):
-        conversation_beliefs = await self.belief_inferrer.infer_from_conversation(self.conversation)
-
-        new_beliefs = conversation_beliefs - self._current_beliefs
-        if not new_beliefs:
-            self.logger.debug("No new beliefs detected.")
-            return
-
-        self._current_beliefs |= new_beliefs
-
-        belief_changes = BeliefMessage(
-            create=list(new_beliefs.true),
-            delete=list(new_beliefs.false),
-        )
-
-        message = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            sender=self.name,
-            body=belief_changes.model_dump_json(),
-            thread="beliefs",
-        )
-        await self.send(message)
-
-    async def _infer_goal_completions(self):
-        goal_completions = await self.goal_inferrer.infer_from_conversation(self.conversation)
-
-        new_achieved = [
-            InternalBelief(name=goal, arguments=None)
-            for goal, achieved in goal_completions.items()
-            if achieved and self._current_goal_completions.get(goal) != achieved
-        ]
-        new_not_achieved = [
-            InternalBelief(name=goal, arguments=None)
-            for goal, achieved in goal_completions.items()
-            if not achieved and self._current_goal_completions.get(goal) != achieved
-        ]
-        for goal, achieved in goal_completions.items():
-            self._current_goal_completions[goal] = achieved
-
-        if not new_achieved and not new_not_achieved:
-            self.logger.debug("No goal achievement changes detected.")
-            return
-
-        belief_changes = BeliefMessage(
-            create=new_achieved,
-            delete=new_not_achieved,
-        )
-        message = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            sender=self.name,
-            body=belief_changes.model_dump_json(),
-            thread="beliefs",
-        )
-        await self.send(message)
-
-    class LLM:
         """
-        Class that handles sending structured generation requests to an LLM.
+        Process conversation history to extract beliefs, semantically. Any changed beliefs are sent
-        """
+        to the BDI core.
-
-        def __init__(self, agent: "TextBeliefExtractorAgent", n_parallel: int):
-            self._agent = agent
-            self._semaphore = asyncio.Semaphore(n_parallel)
-
-        async def query(self, prompt: str, schema: dict, tries: int = 3) -> JSONLike | None:
-            """
-            Query the LLM with the given prompt and schema, return an instance of a dict conforming
-            to this schema. Try ``tries`` times, or return None.
-
-            :param prompt: Prompt to be queried.
-            :param schema: Schema to be queried.
-            :param tries: Number of times to try to query the LLM.
-            :return: An instance of a dict conforming to this schema, or None if failed.
-            """
-            try_count = 0
-            while try_count < tries:
-                try_count += 1
-
-                try:
-                    return await self._query_llm(prompt, schema)
-                except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
-                    if try_count < tries:
-                        continue
-                    self._agent.logger.exception(
-                        "Failed to get LLM response after %d tries.",
-                        try_count,
-                        exc_info=e,
-                    )
-
-            return None
-
-        async def _query_llm(self, prompt: str, schema: dict) -> JSONLike:
-            """
-            Query an LLM with the given prompt and schema, return an instance of a dict conforming
-            to that schema.
-
-            :param prompt: The prompt to be queried.
-            :param schema: Schema to use during response.
-            :return: A dict conforming to this schema.
-            :raises httpx.HTTPStatusError: If the LLM server responded with an error.
-            :raises json.JSONDecodeError: If the LLM response was not valid JSON. May happen if the
-                response was cut off early due to length limitations.
-            :raises KeyError: If the LLM server responded with no error, but the response was
-                invalid.
-            """
-            async with self._semaphore:
-                async with httpx.AsyncClient() as client:
-                    response = await client.post(
-                        settings.llm_settings.local_llm_url,
-                        json={
-                            "model": settings.llm_settings.local_llm_model,
-                            "messages": [{"role": "user", "content": prompt}],
-                            "response_format": {
-                                "type": "json_schema",
-                                "json_schema": {
-                                    "name": "Beliefs",
-                                    "strict": True,
-                                    "schema": schema,
-                                },
-                            },
-                            "reasoning_effort": "low",
-                            "temperature": settings.llm_settings.code_temperature,
-                            "stream": False,
-                        },
-                        timeout=30.0,
-                    )
-                    response.raise_for_status()
-
-                    response_json = response.json()
-                    json_message = response_json["choices"][0]["message"]["content"]
-                    return json.loads(json_message)
-
-
-class SemanticBeliefInferrer:
-    """
-    Class that handles only prompting an LLM for semantic beliefs.
-    """
-
-    def __init__(
-        self,
-        llm: "TextBeliefExtractorAgent.LLM",
-        available_beliefs: list[SemanticBelief] | None = None,
-    ):
-        self._llm = llm
-        self.available_beliefs: list[SemanticBelief] = available_beliefs or []
-
-    async def infer_from_conversation(self, conversation: ChatHistory) -> BeliefState:
-        """
-        Process conversation history to extract beliefs, semantically. The result is an object that
-        describes all beliefs that hold or don't hold based on the full conversation.
-
-        :param conversation: The conversation history to be processed.
-        :return: An object that describes beliefs.
         """
         # Return instantly if there are no beliefs to infer
         if not self.available_beliefs:
-            return BeliefState()
+            return
 
-        n_parallel = max(1, min(settings.llm_settings.n_parallel - 1, len(self.available_beliefs)))
+        candidate_beliefs = await self._infer_turn()
-        all_beliefs: list[dict[str, bool | None] | None] = await asyncio.gather(
+        belief_changes = BeliefMessage()
-            *[
+        for belief_key, belief_value in candidate_beliefs.items():
-                self._infer_beliefs(conversation, beliefs)
+            if belief_value is None:
-                for beliefs in self._split_into_chunks(self.available_beliefs, n_parallel)
-            ]
-        )
-        retval = BeliefState()
-        for beliefs in all_beliefs:
-            if beliefs is None:
                 continue
-            for belief_name, belief_holds in beliefs.items():
+            old_belief_value = self.beliefs.get(belief_key)
-                if belief_holds is None:
+            if belief_value == old_belief_value:
                 continue
-                belief = InternalBelief(name=belief_name, arguments=None)
+
-                if belief_holds:
+            self.beliefs[belief_key] = belief_value
-                    retval.true.add(belief)
+
+            belief = InternalBelief(name=belief_key, arguments=None)
+            if belief_value:
+                belief_changes.create.append(belief)
             else:
-                    retval.false.add(belief)
+                belief_changes.delete.append(belief)
-        return retval
+
+        # Return if there were no changes in beliefs
+        if not belief_changes.has_values():
+            return
+
+        beliefs_message = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            body=belief_changes.model_dump_json(),
+            thread="beliefs",
+        )
+        await self.send(beliefs_message)
 
     @staticmethod
     def _split_into_chunks[T](items: list[T], n: int) -> list[list[T]]:
-        """
-        Split a list into ``n`` chunks, making each chunk approximately ``len(items) / n`` long.
-
-        :param items: The list of items to split.
-        :param n: The number of desired chunks.
-        :return: A list of chunks each approximately ``len(items) / n`` long.
-        """
         k, m = divmod(len(items), n)
         return [items[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)]
 
+    async def _infer_turn(self) -> dict:
+        """
+        Process the stored conversation history to extract semantic beliefs. Returns a list of
+        beliefs that have been set to ``True``, ``False`` or ``None``.
+
+        :return: A dict mapping belief names to a value ``True``, ``False`` or ``None``.
+        """
+        n_parallel = max(1, min(settings.llm_settings.n_parallel - 1, len(self.available_beliefs)))
+        all_beliefs = await asyncio.gather(
+            *[
+                self._infer_beliefs(self.conversation, beliefs)
+                for beliefs in self._split_into_chunks(self.available_beliefs, n_parallel)
+            ]
+        )
+        retval = {}
+        for beliefs in all_beliefs:
+            if beliefs is None:
+                continue
+            retval.update(beliefs)
+        return retval
+
+    @staticmethod
+    def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
+        return AgentSpeakGenerator.slugify(belief), {
+            "type": ["boolean", "null"],
+            "description": belief.description,
+        }
+
+    @staticmethod
+    def _create_beliefs_schema(beliefs: list[SemanticBelief]) -> dict:
+        belief_schemas = [
+            TextBeliefExtractorAgent._create_belief_schema(belief) for belief in beliefs
+        ]
+
+        return {
+            "type": "object",
+            "properties": dict(belief_schemas),
+            "required": [name for name, _ in belief_schemas],
+        }
+
+    @staticmethod
+    def _format_message(message: ChatMessage):
+        return f"{message.role.upper()}:\n{message.content}"
+
+    @staticmethod
+    def _format_conversation(conversation: ChatHistory):
+        return "\n\n".join(
+            [TextBeliefExtractorAgent._format_message(message) for message in conversation.messages]
+        )
+
+    @staticmethod
+    def _format_beliefs(beliefs: list[SemanticBelief]):
+        return "\n".join(
+            [f"- {AgentSpeakGenerator.slugify(belief)}: {belief.description}" for belief in beliefs]
+        )
+
     async def _infer_beliefs(
         self,
         conversation: ChatHistory,
         beliefs: list[SemanticBelief],
-    ) -> dict[str, bool | None] | None:
+    ) -> dict | None:
         """
         Infer given beliefs based on the given conversation.
         :param conversation: The conversation to infer beliefs from.
@@ -404,79 +241,70 @@ Respond with a JSON similar to the following, but with the property names as giv
 
         schema = self._create_beliefs_schema(beliefs)
 
-        return await self._llm.query(prompt, schema)
+        return await self._retry_query_llm(prompt, schema)
 
-    @staticmethod
+    async def _retry_query_llm(self, prompt: str, schema: dict, tries: int = 3) -> dict | None:
-    def _create_belief_schema(belief: SemanticBelief) -> tuple[str, dict]:
-        return AgentSpeakGenerator.slugify(belief), {
-            "type": ["boolean", "null"],
-            "description": belief.description,
-        }
-
-    @staticmethod
-    def _create_beliefs_schema(beliefs: list[SemanticBelief]) -> dict:
-        belief_schemas = [
-            SemanticBeliefInferrer._create_belief_schema(belief) for belief in beliefs
-        ]
-
-        return {
-            "type": "object",
-            "properties": dict(belief_schemas),
-            "required": [name for name, _ in belief_schemas],
-        }
-
-    @staticmethod
-    def _format_message(message: ChatMessage):
-        return f"{message.role.upper()}:\n{message.content}"
-
-    @staticmethod
-    def _format_conversation(conversation: ChatHistory):
-        return "\n\n".join(
-            [SemanticBeliefInferrer._format_message(message) for message in conversation.messages]
-        )
-
-    @staticmethod
-    def _format_beliefs(beliefs: list[SemanticBelief]):
-        return "\n".join(
-            [f"- {AgentSpeakGenerator.slugify(belief)}: {belief.description}" for belief in beliefs]
-        )
-
-
-class GoalAchievementInferrer(SemanticBeliefInferrer):
-    def __init__(self, llm: TextBeliefExtractorAgent.LLM):
-        super().__init__(llm)
-        self.goals = []
-
-    async def infer_from_conversation(self, conversation: ChatHistory) -> dict[str, bool]:
         """
-        Determine which goals have been achieved based on the given conversation.
+        Query the LLM with the given prompt and schema, return an instance of a dict conforming
+        to this schema. Try ``tries`` times, or return None.
 
-        :param conversation: The conversation to infer goal completion from.
+        :param prompt: Prompt to be queried.
-        :return: A mapping of goals and a boolean whether they have been achieved.
+        :param schema: Schema to be queried.
+        :return: An instance of a dict conforming to this schema, or None if failed.
         """
-        if not self.goals:
+        try_count = 0
-            return {}
+        while try_count < tries:
+            try_count += 1
 
-        goals_achieved = await asyncio.gather(
+            try:
-            *[self._infer_goal(conversation, g) for g in self.goals]
+                return await self._query_llm(prompt, schema)
+            except (httpx.HTTPError, json.JSONDecodeError, KeyError) as e:
+                if try_count < tries:
+                    continue
+                self.logger.exception(
+                    "Failed to get LLM response after %d tries.",
+                    try_count,
+                    exc_info=e,
                 )
-        return {
-            f"achieved_{AgentSpeakGenerator.slugify(goal)}": achieved
-            for goal, achieved in zip(self.goals, goals_achieved, strict=True)
-        }
 
-    async def _infer_goal(self, conversation: ChatHistory, goal: Goal) -> bool:
+        return None
-        prompt = f"""{self._format_conversation(conversation)}
 
-Given the above conversation, what has the following goal been achieved?
+    async def _query_llm(self, prompt: str, schema: dict) -> dict:
+        """
+        Query an LLM with the given prompt and schema, return an instance of a dict conforming to
+        that schema.
 
-The name of the goal: {goal.name}
+        :param prompt: The prompt to be queried.
-Description of the goal: {goal.description}
+        :param schema: Schema to use during response.
+        :return: A dict conforming to this schema.
+        :raises httpx.HTTPStatusError: If the LLM server responded with an error.
+        :raises json.JSONDecodeError: If the LLM response was not valid JSON. May happen if the
+            response was cut off early due to length limitations.
+        :raises KeyError: If the LLM server responded with no error, but the response was invalid.
+        """
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                settings.llm_settings.local_llm_url,
+                json={
+                    "model": settings.llm_settings.local_llm_model,
+                    "messages": [{"role": "user", "content": prompt}],
+                    "response_format": {
+                        "type": "json_schema",
+                        "json_schema": {
+                            "name": "Beliefs",
+                            "strict": True,
+                            "schema": schema,
+                        },
+                    },
+                    "reasoning_effort": "low",
+                    "temperature": self.temperature,
+                    "stream": False,
+                },
+                timeout=None,
+            )
+            response.raise_for_status()
 
-Answer with literally only `true` or `false` (without backticks)."""
+            response_json = response.json()
-
+            json_message = response_json["choices"][0]["message"]["content"]
-        schema = {
+            beliefs = json.loads(json_message)
-            "type": "boolean",
+            return beliefs
-        }
-
-        return await self._llm.query(prompt, schema)

src/control_backend/agents/communication/ri_communication_agent.py

@@ -8,6 +8,7 @@ from zmq.asyncio import Context
 from control_backend.agents import BaseAgent
 from control_backend.agents.actuation.robot_gesture_agent import RobotGestureAgent
 from control_backend.core.config import settings
+from control_backend.schemas.internal_message import InternalMessage
 
 from ..actuation.robot_speech_agent import RobotSpeechAgent
 from ..perception import VADAgent
@@ -38,7 +39,7 @@ 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)
@@ -47,6 +48,8 @@ class RICommunicationAgent(BaseAgent):
         self._req_socket: azmq.Socket | None = None
         self.pub_socket: azmq.Socket | None = None
         self.connected = False
+        self.gesture_agent: RobotGestureAgent | None = None
+        self.speech_agent: RobotSpeechAgent | None = None
 
     async def setup(self):
         """
@@ -140,6 +143,7 @@ class RICommunicationAgent(BaseAgent):
 
             # At this point, we have a valid response
             try:
+                self.logger.debug("Negotiation successful. Handling rn")
                 await self._handle_negotiation_response(received_message)
                 # Let UI know that we're connected
                 topic = b"ping"
@@ -168,7 +172,7 @@ 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}"
 
@@ -188,6 +192,7 @@ class RICommunicationAgent(BaseAgent):
                         address=addr,
                         bind=bind,
                     )
+                    self.speech_agent = robot_speech_agent
                     robot_gesture_agent = RobotGestureAgent(
                         settings.agent_settings.robot_gesture_name,
                         address=addr,
@@ -195,6 +200,7 @@ class RICommunicationAgent(BaseAgent):
                         gesture_data=gesture_data,
                         single_gesture_data=single_gesture_data,
                     )
+                    self.gesture_agent = robot_gesture_agent
                     await robot_speech_agent.start()
                     await asyncio.sleep(0.1)  # Small delay
                     await robot_gesture_agent.start()
@@ -225,6 +231,7 @@ class RICommunicationAgent(BaseAgent):
         while self._running:
             if not self.connected:
                 await asyncio.sleep(settings.behaviour_settings.sleep_s)
+                self.logger.debug("Not connected, skipping ping loop iteration.")
                 continue
 
             # We need to listen and send pings.
@@ -248,7 +255,6 @@ class RICommunicationAgent(BaseAgent):
                     self._req_socket.recv_json(), timeout=seconds_to_wait_total / 2
                 )
 
-                if "endpoint" in message and message["endpoint"] != "ping":
                 self.logger.debug(f'Received message "{message}" from RI.')
                 if "endpoint" not in message:
                     self.logger.warning("No received endpoint in message, expected ping endpoint.")
@@ -289,13 +295,36 @@ class RICommunicationAgent(BaseAgent):
         # Tell UI we're disconnected.
         topic = b"ping"
         data = json.dumps(False).encode()
+        self.logger.debug("1")
         if self.pub_socket:
             try:
+                self.logger.debug("2")
                 await asyncio.wait_for(self.pub_socket.send_multipart([topic, data]), 5)
             except TimeoutError:
+                self.logger.debug("3")
                 self.logger.warning("Connection ping for router timed out.")
 
         # Try to reboot/renegotiate
+        if self.gesture_agent is not None:
+            await self.gesture_agent.stop()
+
+        if self.speech_agent is not None:
+            await self.speech_agent.stop()
+
+        if self.pub_socket is not None:
+            self.pub_socket.close()
+
         self.logger.debug("Restarting communication negotiation.")
-        if await self._negotiate_connection(max_retries=1):
+        if await self._negotiate_connection(max_retries=2):
             self.connected = True
+
+    async def handle_message(self, msg: InternalMessage):
+        """
+        Handle an incoming message.
+
+        Currently not implemented for this agent.
+
+        :param msg: The received message.
+        :raises NotImplementedError: Always, since this method is not implemented.
+        """
+        self.logger.warning("custom warning for handle msg in ri coms %s", self.name)

src/control_backend/agents/llm/llm_agent.py

@@ -46,23 +46,14 @@ class LLMAgent(BaseAgent):
         :param msg: The received internal message.
         """
         if msg.sender == settings.agent_settings.bdi_core_name:
-            match msg.thread:
+            self.logger.debug("Processing message from BDI core.")
-                case "prompt_message":
             try:
                 prompt_message = LLMPromptMessage.model_validate_json(msg.body)
                 await self._process_bdi_message(prompt_message)
             except ValidationError:
                 self.logger.debug("Prompt message from BDI core is invalid.")
-                case "assistant_message":
-                    self.history.append({"role": "assistant", "content": msg.body})
-                case "user_message":
-                    self.history.append({"role": "user", "content": msg.body})
-        elif msg.sender == settings.agent_settings.bdi_program_manager_name:
-            if msg.body == "clear_history":
-                self.logger.debug("Clearing conversation history.")
-                self.history.clear()
         else:
-            self.logger.debug("Message ignored.")
+            self.logger.debug("Message ignored (not from BDI core.")
 
     async def _process_bdi_message(self, message: LLMPromptMessage):
         """
@@ -123,6 +114,13 @@ class LLMAgent(BaseAgent):
         :param goals: Goals the LLM should achieve.
         :yield: Fragments of the LLM-generated content (e.g., sentences/phrases).
         """
+        self.history.append(
+            {
+                "role": "user",
+                "content": prompt,
+            }
+        )
+
         instructions = LLMInstructions(norms if norms else None, goals if goals else None)
         messages = [
             {

src/control_backend/agents/perception/vad_agent.py

@@ -7,6 +7,7 @@ import zmq.asyncio as azmq
 
 from control_backend.agents import BaseAgent
 from control_backend.core.config import settings
+from control_backend.schemas.internal_message import InternalMessage
 
 from ...schemas.program_status import PROGRAM_STATUS, ProgramStatus
 from .transcription_agent.transcription_agent import TranscriptionAgent
@@ -86,6 +87,12 @@ class VADAgent(BaseAgent):
         self.audio_buffer = np.array([], dtype=np.float32)
         self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
         self._ready = asyncio.Event()
+
+        # Pause control
+        self._reset_needed = False
+        self._paused = asyncio.Event()
+        self._paused.set()  # Not paused at start
+
         self.model = None
 
     async def setup(self):
@@ -103,11 +110,12 @@ class VADAgent(BaseAgent):
 
         self._connect_audio_in_socket()
 
-        audio_out_address = self._connect_audio_out_socket()
+        audio_out_port = self._connect_audio_out_socket()
-        if audio_out_address is None:
+        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 +168,13 @@ 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 self.audio_out_socket.bind_to_random_port("tcp://localhost", max_tries=100)
-            return settings.zmq_settings.vad_pub_address
         except zmq.ZMQBindError:
             self.logger.error("Failed to bind an audio output socket after 100 tries.")
             self.audio_out_socket = None
@@ -213,6 +220,16 @@ class VADAgent(BaseAgent):
         """
         await self._ready.wait()
         while self._running:
+            await self._paused.wait()
+
+            # After being unpaused, reset stream and buffers
+            if self._reset_needed:
+                self.logger.debug("Resuming: resetting stream and buffers.")
+                await self._reset_stream()
+                self.audio_buffer = np.array([], dtype=np.float32)
+                self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
+                self._reset_needed = False
+
             assert self.audio_in_poller is not None
             data = await self.audio_in_poller.poll()
             if data is None:
@@ -229,11 +246,10 @@ class VADAgent(BaseAgent):
             assert self.model is not None
             prob = self.model(torch.from_numpy(chunk), settings.vad_settings.sample_rate_hz).item()
             non_speech_patience = settings.behaviour_settings.vad_non_speech_patience_chunks
-            begin_silence_length = settings.behaviour_settings.vad_begin_silence_chunks
             prob_threshold = settings.behaviour_settings.vad_prob_threshold
 
             if prob > prob_threshold:
-                if self.i_since_speech > non_speech_patience + begin_silence_length:
+                if self.i_since_speech > non_speech_patience:
                     self.logger.debug("Speech started.")
                 self.audio_buffer = np.append(self.audio_buffer, chunk)
                 self.i_since_speech = 0
@@ -247,12 +263,35 @@ class VADAgent(BaseAgent):
                 continue
 
             # Speech probably ended. Make sure we have a usable amount of data.
-            if len(self.audio_buffer) > begin_silence_length * len(chunk):
+            if len(self.audio_buffer) >= 3 * len(chunk):
                 self.logger.debug("Speech ended.")
                 assert self.audio_out_socket is not None
                 await self.audio_out_socket.send(self.audio_buffer[: -2 * len(chunk)].tobytes())
 
-            # At this point, we know that there is no speech.
+            # At this point, we know that the speech has ended.
-            # Prepend the last few chunks that had no speech, for a more fluent boundary.
+            # Prepend the last chunk that had no speech, for a more fluent boundary
-            self.audio_buffer = np.append(self.audio_buffer, chunk)
+            self.audio_buffer = chunk
-            self.audio_buffer = self.audio_buffer[-begin_silence_length * len(chunk) :]
+
+    async def handle_message(self, msg: InternalMessage):
+        """
+        Handle incoming messages.
+
+        Expects messages to pause or resume the VAD processing from User Interrupt Agent.
+
+        :param msg: The received internal message.
+        """
+        sender = msg.sender
+
+        if sender == settings.agent_settings.user_interrupt_name:
+            if msg.body == "PAUSE":
+                self.logger.info("Pausing VAD processing.")
+                self._paused.clear()
+                # If the robot needs to pick up speaking where it left off, do not set _reset_needed
+                self._reset_needed = True
+            elif msg.body == "RESUME":
+                self.logger.info("Resuming VAD processing.")
+                self._paused.set()
+            else:
+                self.logger.warning(f"Unknown command from User Interrupt Agent: {msg.body}")
+        else:
+            self.logger.debug(f"Ignoring message from unknown sender: {sender}")

src/control_backend/agents/user_interrupt/user_interrupt_agent.py

@@ -6,7 +6,12 @@ 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
+from control_backend.schemas.ri_message import (
+    GestureCommand,
+    PauseCommand,
+    RIEndpoint,
+    SpeechCommand,
+)
 
 
 class UserInterruptAgent(BaseAgent):
@@ -71,6 +76,19 @@ class UserInterruptAgent(BaseAgent):
                     "Forwarded button press (override) with context '%s' to BDIProgramManager.",
                     event_context,
                 )
+            elif event_type == "pause":
+                self.logger.debug(
+                    "Received pause/resume button press with context '%s'.", event_context
+                )
+                await self._send_pause_command(event_context)
+                if event_context:
+                    self.logger.info("Sent pause command.")
+                else:
+                    self.logger.info("Sent resume command.")
+
+            elif event_type in ["next_phase", "reset_phase", "reset_experiment"]:
+                await self._send_experiment_control_to_bdi_core(event_type)
+
             else:
                 self.logger.warning(
                     "Received button press with unknown type '%s' (context: '%s').",
@@ -78,6 +96,36 @@ class UserInterruptAgent(BaseAgent):
                     event_context,
                 )
 
+    async def _send_experiment_control_to_bdi_core(self, type):
+        """
+        method to send experiment control buttons to bdi core.
+
+        :param type: the type of control button we should send to the bdi core.
+        """
+        # Switch which thread we should send to bdi core
+        thread = ""
+        match type:
+            case "next_phase":
+                thread = "force_next_phase"
+            case "reset_phase":
+                thread = "reset_current_phase"
+            case "reset_experiment":
+                thread = "reset_experiment"
+            case _:
+                self.logger.warning(
+                    "Received unknown experiment control type '%s' to send to BDI Core.",
+                    type,
+                )
+
+        out_msg = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            thread=thread,
+            body="",
+        )
+        self.logger.debug("Sending experiment control '%s' to BDI Core.", thread)
+        await self.send(out_msg)
+
     async def _send_to_speech_agent(self, text_to_say: str):
         """
         method to send prioritized speech command to RobotSpeechAgent.
@@ -130,6 +178,38 @@ class UserInterruptAgent(BaseAgent):
             belief_id,
         )
 
+    async def _send_pause_command(self, pause):
+        """
+        Send a pause command to the Robot Interface via the RI Communication Agent.
+        Send a pause command to the other internal agents; for now just VAD agent.
+        """
+        cmd = PauseCommand(data=pause)
+        message = InternalMessage(
+            to=settings.agent_settings.ri_communication_name,
+            sender=self.name,
+            body=cmd.model_dump_json(),
+        )
+        await self.send(message)
+
+        if pause == "true":
+            # Send pause to VAD agent
+            vad_message = InternalMessage(
+                to=settings.agent_settings.vad_name,
+                sender=self.name,
+                body="PAUSE",
+            )
+            await self.send(vad_message)
+            self.logger.info("Sent pause command to VAD Agent and RI Communication Agent.")
+        else:
+            # Send resume to VAD agent
+            vad_message = InternalMessage(
+                to=settings.agent_settings.vad_name,
+                sender=self.name,
+                body="RESUME",
+            )
+            await self.send(vad_message)
+            self.logger.info("Sent resume command to VAD Agent and RI Communication Agent.")
+
     async def setup(self):
         """
         Initialize the agent.

src/control_backend/core/agent_system.py

@@ -131,7 +131,6 @@ class BaseAgent(ABC):
         :param message: The message to send.
         """
         target = AgentDirectory.get(message.to)
-        message.sender = self.name
         if target:
             await target.inbox.put(message)
             self.logger.debug(f"Sent message {message.body} to {message.to} via regular inbox.")
@@ -193,16 +192,7 @@ class BaseAgent(ABC):
 
         :param coro: The coroutine to execute as a task.
         """
-
+        task = asyncio.create_task(coro)
-        async def try_coro(coro_: Coroutine):
-            try:
-                await coro_
-            except asyncio.CancelledError:
-                self.logger.debug("A behavior was canceled successfully: %s", coro_)
-            except Exception:
-                self.logger.warning("An exception occurred in a behavior.", exc_info=True)
-
-        task = asyncio.create_task(try_coro(coro))
         self._tasks.add(task)
         task.add_done_callback(self._tasks.discard)
         return task

src/control_backend/core/config.py

@@ -1,12 +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 ri_command_address: Address for sending commands to the Robot Interface.
-    :ivar vad_pub_address: Address that the VAD agent binds to and publishes audio segments to.
+    :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"
@@ -73,7 +61,6 @@ class BehaviourSettings(BaseModel):
     :ivar vad_prob_threshold: Probability threshold for Voice Activity Detection.
     :ivar vad_initial_since_speech: Initial value for 'since speech' counter in VAD.
     :ivar vad_non_speech_patience_chunks: Number of non-speech chunks to wait before speech ended.
-    :ivar vad_begin_silence_chunks: The number of chunks of silence to prepend to speech chunks.
     :ivar transcription_max_concurrent_tasks: Maximum number of concurrent transcription tasks.
     :ivar transcription_words_per_minute: Estimated words per minute for transcription timing.
     :ivar transcription_words_per_token: Estimated words per token for transcription timing.
@@ -81,8 +68,6 @@ class BehaviourSettings(BaseModel):
     :ivar conversation_history_length_limit: The maximum amount of messages to extract beliefs from.
     """
 
-    # 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
@@ -90,8 +75,7 @@ class BehaviourSettings(BaseModel):
     # VAD settings
     vad_prob_threshold: float = 0.5
     vad_initial_since_speech: int = 100
-    vad_non_speech_patience_chunks: int = 15
+    vad_non_speech_patience_chunks: int = 3
-    vad_begin_silence_chunks: int = 6
 
     # transcription behaviour
     transcription_max_concurrent_tasks: int = 3
@@ -115,8 +99,6 @@ class LLMSettings(BaseModel):
     :ivar n_parallel: The number of parallel calls allowed to be made to the LLM.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     local_llm_url: str = "http://localhost:1234/v1/chat/completions"
     local_llm_model: str = "gpt-oss"
     chat_temperature: float = 1.0
@@ -133,8 +115,6 @@ 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
@@ -148,8 +128,6 @@ 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"
@@ -161,7 +139,6 @@ 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.
@@ -174,8 +151,6 @@ class Settings(BaseSettings):
 
     ui_url: str = "http://localhost:5173"
 
-    ri_host: str = "localhost"
-
     zmq_settings: ZMQSettings = ZMQSettings()
 
     agent_settings: AgentSettings = AgentSettings()

src/control_backend/schemas/belief_list.py

@@ -1,7 +1,6 @@
 from pydantic import BaseModel
 
 from control_backend.schemas.program import Belief as ProgramBelief
-from control_backend.schemas.program import Goal
 
 
 class BeliefList(BaseModel):
@@ -13,7 +12,3 @@ class BeliefList(BaseModel):
     """
 
     beliefs: list[ProgramBelief]
-
-
-class GoalList(BaseModel):
-    goals: list[Goal]

src/control_backend/schemas/belief_message.py

@@ -13,9 +13,6 @@ class Belief(BaseModel):
     name: str
     arguments: list[str] | None
 
-    # To make it hashable
-    model_config = {"frozen": True}
-
 
 class BeliefMessage(BaseModel):
     """

src/control_backend/schemas/internal_message.py

@@ -12,6 +12,6 @@ class InternalMessage(BaseModel):
     """
 
     to: str
-    sender: str | None = None
+    sender: str
     body: str
     thread: str | None = None

src/control_backend/schemas/program.py

@@ -117,7 +117,7 @@ class Goal(ProgramElement):
     :ivar can_fail: Whether we can fail to achieve the goal after executing the plan.
     """
 
-    description: str = ""
+    description: str
     plan: Plan
     can_fail: bool = True
 
@@ -180,6 +180,7 @@ class Trigger(ProgramElement):
     :ivar plan: The plan to execute.
     """
 
+    name: str = ""
     condition: Belief
     plan: Plan
 

src/control_backend/schemas/ri_message.py

@@ -14,6 +14,7 @@ class RIEndpoint(str, Enum):
     GESTURE_TAG = "actuate/gesture/tag"
     PING = "ping"
     NEGOTIATE_PORTS = "negotiate/ports"
+    PAUSE = ""
 
 
 class RIMessage(BaseModel):
@@ -64,3 +65,15 @@ class GestureCommand(RIMessage):
         if self.endpoint not in allowed:
             raise ValueError("endpoint must be GESTURE_SINGLE or GESTURE_TAG")
         return self
+
+
+class PauseCommand(RIMessage):
+    """
+    A specific command to pause or unpause the robot's actions.
+
+    :ivar endpoint: Fixed to ``RIEndpoint.PAUSE``.
+    :ivar data: A boolean indicating whether to pause (True) or unpause (False).
+    """
+
+    endpoint: RIEndpoint = RIEndpoint(RIEndpoint.PAUSE)
+    data: bool

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(
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
-        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
-    )
     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(
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
-        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
-    )
     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/bdi/test_bdi_program_manager.py

@@ -80,7 +80,6 @@ async def test_receive_programs_valid_and_invalid():
     manager._internal_pub_socket = AsyncMock()
     manager.sub_socket = sub
     manager._create_agentspeak_and_send_to_bdi = AsyncMock()
-    manager._send_clear_llm_history = AsyncMock()
 
     try:
         # Will give StopAsyncIteration when the predefined `sub.recv_multipart` side-effects run out
@@ -93,24 +92,3 @@ async def test_receive_programs_valid_and_invalid():
     forwarded: Program = manager._create_agentspeak_and_send_to_bdi.await_args[0][0]
     assert forwarded.phases[0].norms[0].name == "N1"
     assert forwarded.phases[0].goals[0].name == "G1"
-
-    # Verify history clear was triggered
-    assert manager._send_clear_llm_history.await_count == 1
-
-
-@pytest.mark.asyncio
-async def test_send_clear_llm_history(mock_settings):
-    # Ensure the mock returns a string for the agent name (just like in your LLM tests)
-    mock_settings.agent_settings.llm_agent_name = "llm_agent"
-
-    manager = BDIProgramManager(name="program_manager_test")
-    manager.send = AsyncMock()
-
-    await manager._send_clear_llm_history()
-
-    assert manager.send.await_count == 2
-    msg: InternalMessage = manager.send.await_args_list[0][0][0]
-
-    # Verify the content and recipient
-    assert msg.body == "clear_history"
-    assert msg.to == "llm_agent"

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -6,13 +6,10 @@ import httpx
 import pytest
 
 from control_backend.agents.bdi import TextBeliefExtractorAgent
-from control_backend.agents.bdi.text_belief_extractor_agent import BeliefState
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 from control_backend.schemas.belief_list import BeliefList
-from control_backend.schemas.belief_message import Belief as InternalBelief
 from control_backend.schemas.belief_message import BeliefMessage
-from control_backend.schemas.chat_history import ChatHistory, ChatMessage
 from control_backend.schemas.program import (
     ConditionalNorm,
     KeywordBelief,
@@ -26,20 +23,10 @@ from control_backend.schemas.program import (
 
 
 @pytest.fixture
-def llm():
+def agent():
-    llm = TextBeliefExtractorAgent.LLM(MagicMock(), 4)
-    llm._query_llm = AsyncMock()
-    return llm
-
-
-@pytest.fixture
-def agent(llm):
-    with patch(
-        "control_backend.agents.bdi.text_belief_extractor_agent.TextBeliefExtractorAgent.LLM",
-        return_value=llm,
-    ):
     agent = TextBeliefExtractorAgent("text_belief_agent")
     agent.send = AsyncMock()
+    agent._query_llm = AsyncMock()
     return agent
 
 
@@ -115,12 +102,24 @@ async def test_handle_message_from_transcriber(agent, mock_settings):
 
     agent.send.assert_awaited_once()  # noqa  # `agent.send` has no such property, but we mock it.
     sent: InternalMessage = agent.send.call_args.args[0]  # noqa
-    assert sent.to == mock_settings.agent_settings.bdi_core_name
+    assert sent.to == mock_settings.agent_settings.bdi_belief_collector_name
     assert sent.thread == "beliefs"
-    parsed = BeliefMessage.model_validate_json(sent.body)
+    parsed = json.loads(sent.body)
-    replaced_last = parsed.replace.pop()
+    assert parsed == {"beliefs": {"user_said": [transcription]}, "type": "belief_extraction_text"}
-    assert replaced_last.name == "user_said"
+
-    assert replaced_last.arguments == [transcription]
+
+@pytest.mark.asyncio
+async def test_process_user_said(agent, mock_settings):
+    transcription = "this is a test"
+
+    await agent._user_said(transcription)
+
+    agent.send.assert_awaited_once()  # noqa  # `agent.send` has no such property, but we mock it.
+    sent: InternalMessage = agent.send.call_args.args[0]  # noqa
+    assert sent.to == mock_settings.agent_settings.bdi_belief_collector_name
+    assert sent.thread == "beliefs"
+    parsed = json.loads(sent.body)
+    assert parsed["beliefs"]["user_said"] == [transcription]
 
 
 @pytest.mark.asyncio
@@ -145,46 +144,46 @@ async def test_query_llm():
         "control_backend.agents.bdi.text_belief_extractor_agent.httpx.AsyncClient",
         return_value=mock_async_client,
     ):
-        llm = TextBeliefExtractorAgent.LLM(MagicMock(), 4)
+        agent = TextBeliefExtractorAgent("text_belief_agent")
 
-        res = await llm._query_llm("hello world", {"type": "null"})
+        res = await agent._query_llm("hello world", {"type": "null"})
         # Response content was set as "null", so should be deserialized as None
         assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_success(llm):
+async def test_retry_query_llm_success(agent):
-    llm._query_llm.return_value = None
+    agent._query_llm.return_value = None
-    res = await llm.query("hello world", {"type": "null"})
+    res = await agent._retry_query_llm("hello world", {"type": "null"})
 
-    llm._query_llm.assert_called_once()
+    agent._query_llm.assert_called_once()
     assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_success_after_failure(llm):
+async def test_retry_query_llm_success_after_failure(agent):
-    llm._query_llm.side_effect = [KeyError(), "real value"]
+    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await llm.query("hello world", {"type": "string"})
+    res = await agent._retry_query_llm("hello world", {"type": "string"})
 
-    assert llm._query_llm.call_count == 2
+    assert agent._query_llm.call_count == 2
     assert res == "real value"
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_failures(llm):
+async def test_retry_query_llm_failures(agent):
-    llm._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
+    agent._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
-    res = await llm.query("hello world", {"type": "string"})
+    res = await agent._retry_query_llm("hello world", {"type": "string"})
 
-    assert llm._query_llm.call_count == 3
+    assert agent._query_llm.call_count == 3
     assert res is None
 
 
 @pytest.mark.asyncio
-async def test_retry_query_llm_fail_immediately(llm):
+async def test_retry_query_llm_fail_immediately(agent):
-    llm._query_llm.side_effect = [KeyError(), "real value"]
+    agent._query_llm.side_effect = [KeyError(), "real value"]
-    res = await llm.query("hello world", {"type": "string"}, tries=1)
+    res = await agent._retry_query_llm("hello world", {"type": "string"}, tries=1)
 
-    assert llm._query_llm.call_count == 1
+    assert agent._query_llm.call_count == 1
     assert res is None
 
 
@@ -193,7 +192,7 @@ async def test_extracting_semantic_beliefs(agent):
     """
     The Program Manager sends beliefs to this agent. Test whether the agent handles them correctly.
     """
-    assert len(agent.belief_inferrer.available_beliefs) == 0
+    assert len(agent.available_beliefs) == 0
     beliefs = BeliefList(
         beliefs=[
             KeywordBelief(
@@ -214,28 +213,26 @@ async def test_extracting_semantic_beliefs(agent):
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
             body=beliefs.model_dump_json(),
-            thread="beliefs",
         ),
     )
-    assert len(agent.belief_inferrer.available_beliefs) == 2
+    assert len(agent.available_beliefs) == 2
 
 
 @pytest.mark.asyncio
-async def test_handle_invalid_beliefs(agent, sample_program):
+async def test_handle_invalid_program(agent, sample_program):
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    assert len(agent.belief_inferrer.available_beliefs) == 2
+    assert len(agent.available_beliefs) == 2
 
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
             sender=settings.agent_settings.bdi_program_manager_name,
             body=json.dumps({"phases": "Invalid"}),
-            thread="beliefs",
         ),
     )
 
-    assert len(agent.belief_inferrer.available_beliefs) == 2
+    assert len(agent.available_beliefs) == 2
 
 
 @pytest.mark.asyncio
@@ -257,13 +254,13 @@ async def test_handle_robot_response(agent):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_with_beliefs(agent, llm, sample_program):
+async def test_simulated_real_turn_with_beliefs(agent, sample_program):
     """Test sending user message to extract beliefs from."""
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
     # Send a user message with the belief that there's no more booze
-    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
     assert len(agent.conversation.messages) == 0
     await agent.handle_message(
         InternalMessage(
@@ -278,20 +275,20 @@ async def test_simulated_real_turn_with_beliefs(agent, llm, sample_program):
     assert agent.send.call_count == 2
 
     # First should be the beliefs message
-    message: InternalMessage = agent.send.call_args_list[1].args[0]
+    message: InternalMessage = agent.send.call_args_list[0].args[0]
     beliefs = BeliefMessage.model_validate_json(message.body)
     assert len(beliefs.create) == 1
     assert beliefs.create[0].name == "no_more_booze"
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_no_beliefs(agent, llm, sample_program):
+async def test_simulated_real_turn_no_beliefs(agent, sample_program):
     """Test a user message to extract beliefs from, but no beliefs are formed."""
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
     # Send a user message with no new beliefs
-    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": None}
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": None}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -305,17 +302,17 @@ async def test_simulated_real_turn_no_beliefs(agent, llm, sample_program):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_no_new_beliefs(agent, llm, sample_program):
+async def test_simulated_real_turn_no_new_beliefs(agent, sample_program):
     """
     Test a user message to extract beliefs from, but no new beliefs are formed because they already
     existed.
     """
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent._current_beliefs = BeliefState(true={InternalBelief(name="is_pirate", arguments=None)})
+    agent.beliefs["is_pirate"] = True
 
     # Send a user message with the belief the user is a pirate, still
-    llm._query_llm.return_value = {"is_pirate": True, "no_more_booze": None}
+    agent._query_llm.return_value = {"is_pirate": True, "no_more_booze": None}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -329,19 +326,17 @@ async def test_simulated_real_turn_no_new_beliefs(agent, llm, sample_program):
 
 
 @pytest.mark.asyncio
-async def test_simulated_real_turn_remove_belief(agent, llm, sample_program):
+async def test_simulated_real_turn_remove_belief(agent, sample_program):
     """
     Test a user message to extract beliefs from, but an existing belief is determined no longer to
     hold.
     """
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
-    agent._current_beliefs = BeliefState(
+    agent.beliefs["no_more_booze"] = True
-        true={InternalBelief(name="no_more_booze", arguments=None)},
-    )
 
     # Send a user message with the belief the user is a pirate, still
-    llm._query_llm.return_value = {"is_pirate": None, "no_more_booze": False}
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": False}
     await agent.handle_message(
         InternalMessage(
             to=settings.agent_settings.text_belief_extractor_name,
@@ -354,23 +349,18 @@ async def test_simulated_real_turn_remove_belief(agent, llm, sample_program):
     assert agent.send.call_count == 2
 
     # Agent's current beliefs should've changed
-    assert any(b.name == "no_more_booze" for b in agent._current_beliefs.false)
+    assert not agent.beliefs["no_more_booze"]
 
 
 @pytest.mark.asyncio
-async def test_llm_failure_handling(agent, llm, sample_program):
+async def test_llm_failure_handling(agent, sample_program):
     """
     Check that the agent handles failures gracefully without crashing.
     """
-    llm._query_llm.side_effect = httpx.HTTPError("")
+    agent._query_llm.side_effect = httpx.HTTPError("")
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
-    agent.belief_inferrer.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
 
-    belief_changes = await agent.belief_inferrer.infer_from_conversation(
+    belief_changes = await agent._infer_turn()
-        ChatHistory(
-            messages=[ChatMessage(role="user", content="Good day!")],
-        ),
-    )
 
-    assert len(belief_changes.true) == 0
+    assert len(belief_changes) == 0
-    assert len(belief_changes.false) == 0

test/unit/agents/llm/test_llm_agent.py

@@ -265,23 +265,3 @@ 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