Merge branch 'dev' into feat/pause-functionality

ref: N25B-350

.gitlab/merge_request_templates/default.md

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

src/control_backend/agents/actuation/robot_gesture_agent.py

@@ -28,6 +28,7 @@ class RobotGestureAgent(BaseAgent):
     address = ""
     bind = False
     gesture_data = []
+    single_gesture_data = []
 
     def __init__(
         self,
@@ -35,8 +36,10 @@ class RobotGestureAgent(BaseAgent):
         address=settings.zmq_settings.ri_command_address,
         bind=False,
         gesture_data=None,
+        single_gesture_data=None,
     ):
         self.gesture_data = gesture_data or []
+        self.single_gesture_data = single_gesture_data or []
         super().__init__(name)
         self.address = address
         self.bind = bind
@@ -99,7 +102,13 @@ class RobotGestureAgent(BaseAgent):
                         gesture_command.data,
                     )
                     return
-
+            elif gesture_command.endpoint == RIEndpoint.GESTURE_SINGLE:
+                if gesture_command.data not in self.single_gesture_data:
+                    self.logger.warning(
+                        "Received gesture '%s' which is not in available gestures. Early returning",
+                        gesture_command.data,
+                    )
+                    return
             await self.pubsocket.send_json(gesture_command.model_dump())
         except Exception:
             self.logger.exception("Error processing internal message.")

src/control_backend/agents/bdi/bdi_core_agent.py

@@ -11,7 +11,7 @@ from pydantic import ValidationError
 from control_backend.agents.base import BaseAgent
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.belief_message import Belief, BeliefMessage
+from control_backend.schemas.belief_message import BeliefMessage
 from control_backend.schemas.llm_prompt_message import LLMPromptMessage
 from control_backend.schemas.ri_message import SpeechCommand
 
@@ -89,9 +89,9 @@ class BDICoreAgent(BaseAgent):
         the agent has deferred intentions (deadlines).
         """
         while self._running:
-            # await (
-            #     self._wake_bdi_loop.wait()
-            # )  # gets set whenever there's an update to the belief base
+            await (
+                self._wake_bdi_loop.wait()
+            )  # gets set whenever there's an update to the belief base
 
             # Agent knows when it's expected to have to do its next thing
             maybe_more_work = True
@@ -124,8 +124,8 @@ class BDICoreAgent(BaseAgent):
 
         if msg.thread == "beliefs":
             try:
-                beliefs = BeliefMessage.model_validate_json(msg.body).beliefs
-                self._apply_beliefs(beliefs)
+                belief_changes = BeliefMessage.model_validate_json(msg.body)
+                self._apply_belief_changes(belief_changes)
             except ValidationError:
                 self.logger.exception("Error processing belief.")
             return
@@ -145,21 +145,28 @@ class BDICoreAgent(BaseAgent):
                 )
                 await self.send(out_msg)
 
-    def _apply_beliefs(self, beliefs: list[Belief]):
+    def _apply_belief_changes(self, belief_changes: BeliefMessage):
         """
         Update the belief base with a list of new beliefs.
 
-        If ``replace=True`` is set on a belief, it removes all existing beliefs with that name
-        before adding the new one.
+        For beliefs in ``belief_changes.replace``, it removes all existing beliefs with that name
+        before adding one new one.
+
+        :param belief_changes: The changes in beliefs to apply.
         """
-        if not beliefs:
+        if not belief_changes.create and not belief_changes.replace and not belief_changes.delete:
             return
 
-        for belief in beliefs:
-            if belief.replace:
-                self._remove_all_with_name(belief.name)
+        for belief in belief_changes.create:
             self._add_belief(belief.name, belief.arguments)
 
+        for belief in belief_changes.replace:
+            self._remove_all_with_name(belief.name)
+            self._add_belief(belief.name, belief.arguments)
+
+        for belief in belief_changes.delete:
+            self._remove_belief(belief.name, belief.arguments)
+
     def _add_belief(self, name: str, args: list[str] = None):
         """
         Add a single belief to the BDI agent.

src/control_backend/agents/bdi/belief_collector_agent.py

@@ -144,7 +144,7 @@ class BDIBeliefCollectorAgent(BaseAgent):
         msg = InternalMessage(
             to=settings.agent_settings.bdi_core_name,
             sender=self.name,
-            body=BeliefMessage(beliefs=beliefs).model_dump_json(),
+            body=BeliefMessage(create=beliefs).model_dump_json(),
             thread="beliefs",
         )
 

src/control_backend/agents/bdi/text_belief_extractor_agent.py

@@ -1,8 +1,23 @@
+import asyncio
 import json
 
+import httpx
+from pydantic import ValidationError
+from slugify import slugify
+
 from control_backend.agents.base import BaseAgent
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
+from control_backend.schemas.belief_message import Belief 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 (
+    Belief,
+    ConditionalNorm,
+    InferredBelief,
+    Program,
+    SemanticBelief,
+)
 
 
 class TextBeliefExtractorAgent(BaseAgent):
@@ -12,46 +27,110 @@ class TextBeliefExtractorAgent(BaseAgent):
     This agent is responsible for processing raw text (e.g., from speech transcription) and
     extracting semantic beliefs from it.
 
-    In the current demonstration version, it performs a simple wrapping of the user's input
-    into a ``user_said`` belief. In a full implementation, this agent would likely interact
-    with an LLM or NLU engine to extract intent, entities, and other structured information.
+    It uses the available beliefs received from the program manager to try to extract beliefs from a
+    user's message, sends and updated beliefs to the BDI core, and forms a ``user_said`` belief from
+    the message itself.
     """
 
+    def __init__(self, name: str):
+        super().__init__(name)
+        self.beliefs: dict[str, bool] = {}
+        self.available_beliefs: list[SemanticBelief] = []
+        self.conversation = ChatHistory(messages=[])
+
     async def setup(self):
         """
         Initialize the agent and its resources.
         """
-        self.logger.info("Settting up %s.", self.name)
-        # Setup LLM belief context if needed (currently demo is just passthrough)
-        self.beliefs = {"mood": ["X"], "car": ["Y"]}
+        self.logger.info("Setting up %s.", self.name)
 
     async def handle_message(self, msg: InternalMessage):
         """
-        Handle incoming messages, primarily from the Transcription Agent.
+        Handle incoming messages. Expect messages from the Transcriber agent, LLM agent, and the
+        Program manager agent.
 
-        :param msg: The received message containing transcribed text.
+        :param msg: The received message.
         """
         sender = msg.sender
-        if sender == settings.agent_settings.transcription_name:
-            self.logger.debug("Received text from transcriber: %s", msg.body)
-            await self._process_transcription_demo(msg.body)
-        else:
-            self.logger.info("Discarding message from %s", sender)
 
-    async def _process_transcription_demo(self, txt: str):
+        match sender:
+            case settings.agent_settings.transcription_name:
+                self.logger.debug("Received text from transcriber: %s", msg.body)
+                self._apply_conversation_message(ChatMessage(role="user", content=msg.body))
+                await self._infer_new_beliefs()
+                await self._user_said(msg.body)
+            case settings.agent_settings.llm_name:
+                self.logger.debug("Received text from LLM: %s", msg.body)
+                self._apply_conversation_message(ChatMessage(role="assistant", content=msg.body))
+            case settings.agent_settings.bdi_program_manager_name:
+                self._handle_program_manager_message(msg)
+            case _:
+                self.logger.info("Discarding message from %s", sender)
+                return
+
+    def _apply_conversation_message(self, message: ChatMessage):
         """
-        Process the transcribed text and generate beliefs.
+        Save the chat message to our conversation history, taking into account the conversation
+        length limit.
 
-        **Demo Implementation:**
-        Currently, this method takes the raw text ``txt`` and wraps it into a belief structure:
-        ``user_said("txt")``.
-
-        This belief is then sent to the :class:`BDIBeliefCollectorAgent`.
-
-        :param txt: The raw transcribed text string.
+        :param message: The chat message to add to the conversation history.
         """
-        # For demo, just wrapping user text as user_said belief
-        belief = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
+        length_limit = settings.behaviour_settings.conversation_history_length_limit
+        self.conversation.messages = (self.conversation.messages + [message])[-length_limit:]
+
+    def _handle_program_manager_message(self, msg: InternalMessage):
+        """
+        Handle a message from the program manager: extract available beliefs from it.
+
+        :param msg: The received message from the program manager.
+        """
+        try:
+            program = Program.model_validate_json(msg.body)
+        except ValidationError:
+            self.logger.warning(
+                "Received message from program manager but it is not a valid program."
+            )
+            return
+
+        self.logger.debug("Received a program from the program manager.")
+
+        self.available_beliefs = self._extract_basic_beliefs_from_program(program)
+
+    # TODO Copied from an incomplete version of the program manager. Use that one instead.
+    @staticmethod
+    def _extract_basic_beliefs_from_program(program: Program) -> list[SemanticBelief]:
+        beliefs = []
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if isinstance(norm, ConditionalNorm):
+                    beliefs += TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
+                        norm.condition
+                    )
+
+            for trigger in phase.triggers:
+                beliefs += TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
+                    trigger.condition
+                )
+
+        return beliefs
+
+    # TODO Copied from an incomplete version of the program manager. Use that one instead.
+    @staticmethod
+    def _extract_basic_beliefs_from_belief(belief: Belief) -> list[SemanticBelief]:
+        if isinstance(belief, InferredBelief):
+            return TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(
+                belief.left
+            ) + TextBeliefExtractorAgent._extract_basic_beliefs_from_belief(belief.right)
+        return [belief]
+
+    async def _user_said(self, text: str):
+        """
+        Create a belief for the user's full speech.
+
+        :param text: User's transcribed text.
+        """
+        belief = {"beliefs": {"user_said": [text]}, "type": "belief_extraction_text"}
         payload = json.dumps(belief)
 
         belief_msg = InternalMessage(
@@ -60,6 +139,207 @@ class TextBeliefExtractorAgent(BaseAgent):
             body=payload,
             thread="beliefs",
         )
-
         await self.send(belief_msg)
-        self.logger.info("Sent %d beliefs to the belief collector.", len(belief["beliefs"]))
+
+    async def _infer_new_beliefs(self):
+        """
+        Process conversation history to extract beliefs, semantically. Any changed beliefs are sent
+        to the BDI core.
+        """
+        # Return instantly if there are no beliefs to infer
+        if not self.available_beliefs:
+            return
+
+        candidate_beliefs = await self._infer_turn()
+        belief_changes = BeliefMessage()
+        for belief_key, belief_value in candidate_beliefs.items():
+            if belief_value is None:
+                continue
+            old_belief_value = self.beliefs.get(belief_key)
+            if belief_value == old_belief_value:
+                continue
+
+            self.beliefs[belief_key] = belief_value
+
+            belief = InternalBelief(name=belief_key, arguments=None)
+            if belief_value:
+                belief_changes.create.append(belief)
+            else:
+                belief_changes.delete.append(belief)
+
+        # Return if there were no changes in beliefs
+        if not belief_changes.has_values():
+            return
+
+        beliefs_message = InternalMessage(
+            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]]:
+        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]:
+        # TODO: use real belief names
+        return belief.name or slugify(belief.description), {
+            "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]):
+        # TODO: use real belief names
+        return "\n".join(
+            [
+                f"- {belief.name or slugify(belief.description)}: {belief.description}"
+                for belief in beliefs
+            ]
+        )
+
+    async def _infer_beliefs(
+        self,
+        conversation: ChatHistory,
+        beliefs: list[SemanticBelief],
+    ) -> dict | None:
+        """
+        Infer given beliefs based on the given conversation.
+        :param conversation: The conversation to infer beliefs from.
+        :param beliefs: The beliefs to infer.
+        :return: A dict containing belief names and a boolean whether they hold, or None if the
+            belief cannot be inferred based on the given conversation.
+        """
+        example = {
+            "example_belief": True,
+        }
+
+        prompt = f"""{self._format_conversation(conversation)}
+
+Given the above conversation, what beliefs can be inferred?
+If there is no relevant information about a belief belief, give null.
+In case messages conflict, prefer using the most recent messages for inference.
+
+Choose from the following list of beliefs, formatted as (belief_name, description):
+{self._format_beliefs(beliefs)}
+
+Respond with a JSON similar to the following, but with the property names as given above:
+{json.dumps(example, indent=2)}
+"""
+
+        schema = self._create_beliefs_schema(beliefs)
+
+        return await self._retry_query_llm(prompt, schema)
+
+    async def _retry_query_llm(self, prompt: str, schema: dict, tries: int = 3) -> dict | 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.
+        :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.logger.exception(
+                    "Failed to get LLM response after %d tries.",
+                    try_count,
+                    exc_info=e,
+                )
+
+        return None
+
+    @staticmethod
+    async def _query_llm(prompt: str, schema: dict) -> dict:
+        """
+        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 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=None,
+            )
+            response.raise_for_status()
+
+            response_json = response.json()
+            json_message = response_json["choices"][0]["message"]["content"]
+            return json.loads(json_message)

src/control_backend/agents/communication/ri_communication_agent.py

@@ -3,11 +3,14 @@ import json
 
 import zmq
 import zmq.asyncio as azmq
+from pydantic import ValidationError
 from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
 from control_backend.agents.actuation.robot_gesture_agent import RobotGestureAgent
+from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
+from control_backend.schemas.ri_message import PauseCommand
 
 from ..actuation.robot_speech_agent import RobotSpeechAgent
 from ..perception import VADAgent
@@ -182,6 +185,7 @@ class RICommunicationAgent(BaseAgent):
                             self._req_socket.bind(addr)
                 case "actuation":
                     gesture_data = port_data.get("gestures", [])
+                    single_gesture_data = port_data.get("single_gestures", [])
                     robot_speech_agent = RobotSpeechAgent(
                         settings.agent_settings.robot_speech_name,
                         address=addr,
@@ -192,6 +196,7 @@ class RICommunicationAgent(BaseAgent):
                         address=addr,
                         bind=bind,
                         gesture_data=gesture_data,
+                        single_gesture_data=single_gesture_data,
                     )
                     await robot_speech_agent.start()
                     await asyncio.sleep(0.1)  # Small delay
@@ -296,3 +301,11 @@ class RICommunicationAgent(BaseAgent):
         self.logger.debug("Restarting communication negotiation.")
         if await self._negotiate_connection(max_retries=1):
             self.connected = True
+
+    async def handle_message(self, msg : InternalMessage):
+        try:
+            pause_command = PauseCommand.model_validate_json(msg.body)
+            self._req_socket.send_json(pause_command.model_dump())
+            self.logger.debug(self._req_socket.recv_json())
+        except ValidationError:
+            self.logger.warning("Incorrect message format for PauseCommand.")

src/control_backend/agents/llm/llm_agent.py

@@ -64,11 +64,12 @@ class LLMAgent(BaseAgent):
 
         :param message: The parsed prompt message containing text, norms, and goals.
         """
+        full_message = ""
         async for chunk in self._query_llm(message.text, message.norms, message.goals):
             await self._send_reply(chunk)
-            self.logger.debug(
-                "Finished processing BDI message. Response sent in chunks to BDI core."
-            )
+            full_message += chunk
+        self.logger.debug("Finished processing BDI message. Response sent in chunks to BDI core.")
+        await self._send_full_reply(full_message)
 
     async def _send_reply(self, msg: str):
         """
@@ -83,6 +84,19 @@ class LLMAgent(BaseAgent):
         )
         await self.send(reply)
 
+    async def _send_full_reply(self, msg: str):
+        """
+        Sends a response message (full) to agents that need it.
+
+        :param msg: The text content of the message.
+        """
+        message = InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=self.name,
+            body=msg,
+        )
+        await self.send(message)
+
     async def _query_llm(
         self, prompt: str, norms: list[str], goals: list[str]
     ) -> AsyncGenerator[str]:
@@ -172,7 +186,7 @@ class LLMAgent(BaseAgent):
                 json={
                     "model": settings.llm_settings.local_llm_model,
                     "messages": messages,
-                    "temperature": 0.3,
+                    "temperature": settings.llm_settings.chat_temperature,
                     "stream": True,
                 },
             ) as response:

src/control_backend/agents/mock_agents/test_pause_ri.py

@@ -0,0 +1,68 @@
+import asyncio
+import json
+
+import zmq
+from zmq.asyncio import Context
+
+from control_backend.agents.base import BaseAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
+
+
+class TestPauseAgent(BaseAgent):
+    def __init__(self, name: str):
+        super().__init__(name)
+        
+    async def setup(self):
+        context = Context.instance()
+        self.pub_socket = context.socket(zmq.PUB)
+        self.pub_socket.connect(settings.zmq_settings.internal_pub_address)
+
+        self.add_behavior(self._pause_command_loop())
+        self.logger.debug("TestPauseAgent setup complete.")
+    
+    async def _pause_command_loop(self):
+        print("Starting Pause command test loop.")
+        while True:
+            pause_command = {
+                "endpoint": "pause",
+                "data": True,
+            }
+
+            message = InternalMessage(
+                to="ri_communication_agent",
+                sender=self.name,
+                body=json.dumps(pause_command),
+            )
+            await self.send(message)
+
+            # User interrupt message 
+            data = {
+                "type": "pause",
+                "context": True,
+            }
+            await self.pub_socket.send_multipart([b"button_pressed", json.dumps(data).encode()])
+
+            self.logger.info("Pausing robot actions.")
+            await asyncio.sleep(15)  # Simulate delay between messages
+
+            pause_command = {
+                "endpoint": "pause",
+                "data": False,
+            }
+            message = InternalMessage(
+                to="ri_communication_agent",
+                sender=self.name,
+                body=json.dumps(pause_command),
+            )
+            await self.send(message)
+
+            # User interrupt message
+            data = {
+                "type": "pause",
+                "context": False,
+            }
+            await self.pub_socket.send_multipart([b"button_pressed", json.dumps(data).encode()])
+
+            self.logger.info("Resuming robot actions.")
+            await asyncio.sleep(15)  # Simulate delay between 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):
@@ -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:
@@ -254,3 +271,27 @@ class VADAgent(BaseAgent):
             # At this point, we know that the speech has ended.
             # Prepend the last chunk that had no speech, for a more fluent boundary
             self.audio_buffer = 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

@@ -0,0 +1,189 @@
+import json
+
+import zmq
+from zmq.asyncio import Context
+
+from control_backend.agents import BaseAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
+from control_backend.schemas.ri_message import (
+    GestureCommand,
+    PauseCommand,
+    RIEndpoint,
+    SpeechCommand,
+)
+
+
+class UserInterruptAgent(BaseAgent):
+    """
+    User Interrupt Agent.
+
+    This agent receives button_pressed events from the external HTTP API
+    (via ZMQ) and uses the associated context to trigger one of the following actions:
+
+    - Send a prioritized message to the `RobotSpeechAgent`
+    - Send a prioritized gesture to the `RobotGestureAgent`
+    - Send a belief override to the `BDIProgramManager`in order to activate a
+        trigger/conditional norm or complete a goal.
+
+    Prioritized actions clear the current RI queue before inserting the new item,
+    ensuring they are executed immediately after Pepper's current action has been fulfilled.
+
+    :ivar sub_socket: The ZMQ SUB socket used to receive user intterupts.
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.sub_socket = None
+
+    async def _receive_button_event(self):
+        """
+        The behaviour of the UserInterruptAgent.
+        Continuous loop that receives button_pressed events from the button_pressed HTTP endpoint.
+        These events contain a type and a context.
+
+        These are the different types and contexts:
+        - type: "speech", context: string that the robot has to say.
+        - type: "gesture", context: single gesture name that the robot has to perform.
+        - type: "override", context: belief_id that overrides the goal/trigger/conditional norm.
+        """
+        while True:
+            topic, body = await self.sub_socket.recv_multipart()
+
+            try:
+                event_data = json.loads(body)
+                event_type = event_data.get("type")  # e.g., "speech", "gesture"
+                event_context = event_data.get("context")  # e.g., "Hello, I am Pepper!"
+            except json.JSONDecodeError:
+                self.logger.error("Received invalid JSON payload on topic %s", topic)
+                continue
+
+            if event_type == "speech":
+                await self._send_to_speech_agent(event_context)
+                self.logger.info(
+                    "Forwarded button press (speech) with context '%s' to RobotSpeechAgent.",
+                    event_context,
+                )
+            elif event_type == "gesture":
+                await self._send_to_gesture_agent(event_context)
+                self.logger.info(
+                    "Forwarded button press (gesture) with context '%s' to RobotGestureAgent.",
+                    event_context,
+                )
+            elif event_type == "override":
+                await self._send_to_program_manager(event_context)
+                self.logger.info(
+                    "Forwarded button press (override) with context '%s' to BDIProgramManager.",
+                    event_context,
+                )
+            elif event_type == "pause":
+                await self._send_pause_command(event_context)
+                if event_context:
+                    self.logger.info("Sent pause command.")
+                else:
+                    self.logger.info("Sent resume command.")
+            else:
+                self.logger.warning(
+                    "Received button press with unknown type '%s' (context: '%s').",
+                    event_type,
+                    event_context,
+                )
+
+    async def _send_to_speech_agent(self, text_to_say: str):
+        """
+        method to send prioritized speech command to RobotSpeechAgent.
+
+        :param text_to_say: The string that the robot has to say.
+        """
+        cmd = SpeechCommand(data=text_to_say, is_priority=True)
+        out_msg = InternalMessage(
+            to=settings.agent_settings.robot_speech_name,
+            sender=self.name,
+            body=cmd.model_dump_json(),
+        )
+        await self.send(out_msg)
+
+    async def _send_to_gesture_agent(self, single_gesture_name: str):
+        """
+        method to send prioritized gesture command to RobotGestureAgent.
+
+        :param single_gesture_name: The gesture tag that the robot has to perform.
+        """
+        # the endpoint is set to always be GESTURE_SINGLE for user interrupts
+        cmd = GestureCommand(
+            endpoint=RIEndpoint.GESTURE_SINGLE, data=single_gesture_name, is_priority=True
+        )
+        out_msg = InternalMessage(
+            to=settings.agent_settings.robot_gesture_name,
+            sender=self.name,
+            body=cmd.model_dump_json(),
+        )
+        await self.send(out_msg)
+
+    async def _send_to_program_manager(self, belief_id: str):
+        """
+        Send a button_override belief to the BDIProgramManager.
+
+        :param belief_id: The belief_id that overrides the goal/trigger/conditional norm.
+        this id can belong to a basic belief or an inferred belief.
+        See also: https://utrechtuniversity.youtrack.cloud/articles/N25B-A-27/UI-components
+        """
+        data = {"belief": belief_id}
+        message = InternalMessage(
+            to=settings.agent_settings.bdi_program_manager_name,
+            sender=self.name,
+            body=json.dumps(data),
+            thread="belief_override_id",
+        )
+        await self.send(message)
+        self.logger.info(
+            "Sent button_override belief with id '%s' to Program manager.",
+            belief_id,
+        )
+
+    async def _send_pause_command(self, pause : bool):
+        """
+        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:
+            # 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.
+
+        Connects the internal ZMQ SUB socket and subscribes to the 'button_pressed' topic.
+        Starts the background behavior to receive the user interrupts.
+        """
+        context = Context.instance()
+
+        self.sub_socket = context.socket(zmq.SUB)
+        self.sub_socket.connect(settings.zmq_settings.internal_sub_address)
+        self.sub_socket.subscribe("button_pressed")
+
+        self.add_behavior(self._receive_button_event())

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

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

src/control_backend/api/v1/router.py

@@ -1,6 +1,6 @@
 from fastapi.routing import APIRouter
 
-from control_backend.api.v1.endpoints import logs, message, program, robot, sse
+from control_backend.api.v1.endpoints import button_pressed, logs, message, program, robot, sse
 
 api_router = APIRouter()
 
@@ -13,3 +13,5 @@ api_router.include_router(robot.router, prefix="/robot", tags=["Pings", "Command
 api_router.include_router(logs.router, tags=["Logs"])
 
 api_router.include_router(program.router, tags=["Program"])
+
+api_router.include_router(button_pressed.router, tags=["Button Pressed Events"])

src/control_backend/core/config.py

@@ -48,6 +48,7 @@ class AgentSettings(BaseModel):
     ri_communication_name: str = "ri_communication_agent"
     robot_speech_name: str = "robot_speech_agent"
     robot_gesture_name: str = "robot_gesture_agent"
+    user_interrupt_name: str = "user_interrupt_agent"
 
 
 class BehaviourSettings(BaseModel):
@@ -64,6 +65,7 @@ class BehaviourSettings(BaseModel):
     :ivar transcription_words_per_minute: Estimated words per minute for transcription timing.
     :ivar transcription_words_per_token: Estimated words per token for transcription timing.
     :ivar transcription_token_buffer: Buffer for transcription tokens.
+    :ivar conversation_history_length_limit: The maximum amount of messages to extract beliefs from.
     """
 
     sleep_s: float = 1.0
@@ -81,6 +83,9 @@ class BehaviourSettings(BaseModel):
     transcription_words_per_token: float = 0.75  # (3 words = 4 tokens)
     transcription_token_buffer: int = 10
 
+    # Text belief extractor settings
+    conversation_history_length_limit: int = 10
+
 
 class LLMSettings(BaseModel):
     """
@@ -88,10 +93,17 @@ class LLMSettings(BaseModel):
 
     :ivar local_llm_url: URL for the local LLM API.
     :ivar local_llm_model: Name of the local LLM model to use.
+    :ivar chat_temperature: The temperature to use while generating chat responses.
+    :ivar code_temperature: The temperature to use while generating code-like responses like during
+        belief inference.
+    :ivar n_parallel: The number of parallel calls allowed to be made to the LLM.
     """
 
     local_llm_url: str = "http://localhost:1234/v1/chat/completions"
     local_llm_model: str = "gpt-oss"
+    chat_temperature: float = 1.0
+    code_temperature: float = 0.3
+    n_parallel: int = 4
 
 
 class VADSettings(BaseModel):

src/control_backend/main.py

@@ -40,6 +40,10 @@ from control_backend.agents.communication import RICommunicationAgent
 from control_backend.agents.llm import LLMAgent
 
 # Other backend imports
+from control_backend.agents.mock_agents.test_pause_ri import TestPauseAgent
+
+# User Interrupt Agent
+from control_backend.agents.user_interrupt.user_interrupt_agent import UserInterruptAgent
 from control_backend.api.v1.router import api_router
 from control_backend.core.config import settings
 from control_backend.logging import setup_logging
@@ -138,6 +142,18 @@ async def lifespan(app: FastAPI):
                 "name": settings.agent_settings.bdi_program_manager_name,
             },
         ),
+        "TestPauseAgent": (
+            TestPauseAgent,
+            {
+                "name": "pause_test_agent",
+            },
+        ),
+        "UserInterruptAgent": (
+            UserInterruptAgent,
+            {
+                "name": settings.agent_settings.user_interrupt_name,
+            },
+        ),
     }
 
     agents = []

src/control_backend/schemas/belief_message.py

@@ -6,18 +6,27 @@ class Belief(BaseModel):
     Represents a single belief in the BDI system.
 
     :ivar name: The functor or name of the belief (e.g., 'user_said').
-    :ivar arguments: A list of string arguments for the belief.
-    :ivar replace: If True, existing beliefs with this name should be replaced by this one.
+    :ivar arguments: A list of string arguments for the belief, or None if the belief has no
+        arguments.
     """
 
     name: str
-    arguments: list[str]
-    replace: bool = False
+    arguments: list[str] | None
 
 
 class BeliefMessage(BaseModel):
     """
-    A container for transporting a list of beliefs between agents.
+    A container for communicating beliefs between agents.
+
+    :ivar create: Beliefs to create.
+    :ivar delete: Beliefs to delete.
+    :ivar replace: Beliefs to replace. Deletes all beliefs with the same name, replacing them with
+        one new belief.
     """
 
-    beliefs: list[Belief]
+    create: list[Belief] = []
+    delete: list[Belief] = []
+    replace: list[Belief] = []
+
+    def has_values(self) -> bool:
+        return len(self.create) > 0 or len(self.delete) > 0 or len(self.replace) > 0

src/control_backend/schemas/chat_history.py

@@ -0,0 +1,10 @@
+from pydantic import BaseModel
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+
+class ChatHistory(BaseModel):
+    messages: list[ChatMessage]

src/control_backend/schemas/events.py

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

src/control_backend/schemas/program.py

@@ -194,7 +194,7 @@ class Phase(ProgramElement):
     """
 
     name: str = ""
-    norms: list[Norm]
+    norms: list[BasicNorm | ConditionalNorm]
     goals: list[Goal]
     triggers: list[Trigger]
 

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 = "pause"
 
 
 class RIMessage(BaseModel):
@@ -38,6 +39,7 @@ class SpeechCommand(RIMessage):
 
     endpoint: RIEndpoint = RIEndpoint(RIEndpoint.SPEECH)
     data: str
+    is_priority: bool = False
 
 
 class GestureCommand(RIMessage):
@@ -52,6 +54,7 @@ class GestureCommand(RIMessage):
         RIEndpoint.GESTURE_SINGLE, RIEndpoint.GESTURE_TAG
     ]
     data: str
+    is_priority: bool = False
 
     @model_validator(mode="after")
     def check_endpoint(self):
@@ -62,3 +65,14 @@ 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/unit/agents/actuation/test_robot_speech_agent.py

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

test/unit/agents/bdi/test_bdi_core_agent.py

@@ -51,7 +51,7 @@ async def test_handle_belief_collector_message(agent, mock_settings):
     msg = InternalMessage(
         to="bdi_agent",
         sender=mock_settings.agent_settings.bdi_belief_collector_name,
-        body=BeliefMessage(beliefs=beliefs).model_dump_json(),
+        body=BeliefMessage(create=beliefs).model_dump_json(),
         thread="beliefs",
     )
 
@@ -64,6 +64,26 @@ async def test_handle_belief_collector_message(agent, mock_settings):
     assert args[2] == agentspeak.Literal("user_said", (agentspeak.Literal("Hello"),))
 
 
+@pytest.mark.asyncio
+async def test_handle_delete_belief_message(agent, mock_settings):
+    """Test that incoming beliefs to be deleted are removed from the BDI agent"""
+    beliefs = [Belief(name="user_said", arguments=["Hello"])]
+
+    msg = InternalMessage(
+        to="bdi_agent",
+        sender=mock_settings.agent_settings.bdi_belief_collector_name,
+        body=BeliefMessage(delete=beliefs).model_dump_json(),
+        thread="beliefs",
+    )
+    await agent.handle_message(msg)
+
+    # Expect bdi_agent.call to be triggered to remove belief
+    args = agent.bdi_agent.call.call_args.args
+    assert args[0] == agentspeak.Trigger.removal
+    assert args[1] == agentspeak.GoalType.belief
+    assert args[2] == agentspeak.Literal("user_said", (agentspeak.Literal("Hello"),))
+
+
 @pytest.mark.asyncio
 async def test_incorrect_belief_collector_message(agent, mock_settings):
     """Test that incorrect message format triggers an exception."""
@@ -128,7 +148,8 @@ def test_add_belief_sets_event(agent):
     agent._wake_bdi_loop = MagicMock()
 
     belief = Belief(name="test_belief", arguments=["a", "b"])
-    agent._apply_beliefs([belief])
+    belief_changes = BeliefMessage(replace=[belief])
+    agent._apply_belief_changes(belief_changes)
 
     assert agent.bdi_agent.call.called
     agent._wake_bdi_loop.set.assert_called()
@@ -137,7 +158,7 @@ def test_add_belief_sets_event(agent):
 def test_apply_beliefs_empty_returns(agent):
     """Line: if not beliefs: return"""
     agent._wake_bdi_loop = MagicMock()
-    agent._apply_beliefs([])
+    agent._apply_belief_changes(BeliefMessage())
     agent.bdi_agent.call.assert_not_called()
     agent._wake_bdi_loop.set.assert_not_called()
 
@@ -220,8 +241,9 @@ def test_replace_belief_calls_remove_all(agent):
     agent._remove_all_with_name = MagicMock()
     agent._wake_bdi_loop = MagicMock()
 
-    belief = Belief(name="user_said", arguments=["Hello"], replace=True)
-    agent._apply_beliefs([belief])
+    belief = Belief(name="user_said", arguments=["Hello"])
+    belief_changes = BeliefMessage(replace=[belief])
+    agent._apply_belief_changes(belief_changes)
 
     agent._remove_all_with_name.assert_called_with("user_said")
 

test/unit/agents/bdi/test_bdi_program_manager.py

@@ -1,6 +1,6 @@
 import asyncio
-import json
 import sys
+import uuid
 from unittest.mock import AsyncMock
 
 import pytest
@@ -8,31 +8,45 @@ import pytest
 from control_backend.agents.bdi.bdi_program_manager import BDIProgramManager
 from control_backend.core.agent_system import InternalMessage
 from control_backend.schemas.belief_message import BeliefMessage
-from control_backend.schemas.program import Program
+from control_backend.schemas.program import BasicNorm, Goal, Phase, Plan, Program
 
 # Fix Windows Proactor loop for zmq
 if sys.platform.startswith("win"):
     asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
 
 
-def make_valid_program_json(norm="N1", goal="G1"):
-    return json.dumps(
-        {
-            "phases": [
-                {
-                    "id": "phase1",
-                    "label": "Phase 1",
-                    "triggers": [],
-                    "norms": [{"id": "n1", "label": "Norm 1", "norm": norm}],
-                    "goals": [
-                        {"id": "g1", "label": "Goal 1", "description": goal, "achieved": False}
-                    ],
-                }
-            ]
-        }
-    )
+def make_valid_program_json(norm="N1", goal="G1") -> str:
+    return Program(
+        phases=[
+            Phase(
+                id=uuid.uuid4(),
+                name="Basic Phase",
+                norms=[
+                    BasicNorm(
+                        id=uuid.uuid4(),
+                        name=norm,
+                        norm=norm,
+                    ),
+                ],
+                goals=[
+                    Goal(
+                        id=uuid.uuid4(),
+                        name=goal,
+                        plan=Plan(
+                            id=uuid.uuid4(),
+                            name="Goal Plan",
+                            steps=[],
+                        ),
+                        can_fail=False,
+                    ),
+                ],
+                triggers=[],
+            ),
+        ],
+    ).model_dump_json()
 
 
+@pytest.mark.skip(reason="Functionality being rebuilt.")
 @pytest.mark.asyncio
 async def test_send_to_bdi():
     manager = BDIProgramManager(name="program_manager_test")
@@ -73,5 +87,5 @@ async def test_receive_programs_valid_and_invalid():
     # Only valid Program should have triggered _send_to_bdi
     assert manager._send_to_bdi.await_count == 1
     forwarded: Program = manager._send_to_bdi.await_args[0][0]
-    assert forwarded.phases[0].norms[0].norm == "N1"
-    assert forwarded.phases[0].goals[0].description == "G1"
+    assert forwarded.phases[0].norms[0].name == "N1"
+    assert forwarded.phases[0].goals[0].name == "G1"

test/unit/agents/bdi/test_belief_collector.py

@@ -86,7 +86,7 @@ async def test_send_beliefs_to_bdi(agent):
     sent: InternalMessage = agent.send.call_args.args[0]
     assert sent.to == settings.agent_settings.bdi_core_name
     assert sent.thread == "beliefs"
-    assert json.loads(sent.body)["beliefs"] == [belief.model_dump() for belief in beliefs]
+    assert json.loads(sent.body)["create"] == [belief.model_dump() for belief in beliefs]
 
 
 @pytest.mark.asyncio

test/unit/agents/bdi/test_text_belief_extractor.py

@@ -0,0 +1,346 @@
+import json
+import uuid
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import httpx
+import pytest
+
+from control_backend.agents.bdi import TextBeliefExtractorAgent
+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.program import (
+    ConditionalNorm,
+    LLMAction,
+    Phase,
+    Plan,
+    Program,
+    SemanticBelief,
+    Trigger,
+)
+
+
+@pytest.fixture
+def agent():
+    agent = TextBeliefExtractorAgent("text_belief_agent")
+    agent.send = AsyncMock()
+    agent._query_llm = AsyncMock()
+    return agent
+
+
+@pytest.fixture
+def sample_program():
+    return Program(
+        phases=[
+            Phase(
+                name="Some phase",
+                id=uuid.uuid4(),
+                norms=[
+                    ConditionalNorm(
+                        name="Some norm",
+                        id=uuid.uuid4(),
+                        norm="Use nautical terms.",
+                        critical=False,
+                        condition=SemanticBelief(
+                            name="is_pirate",
+                            id=uuid.uuid4(),
+                            description="The user is a pirate. Perhaps because they say "
+                            "they are, or because they speak like a pirate "
+                            'with terms like "arr".',
+                        ),
+                    ),
+                ],
+                goals=[],
+                triggers=[
+                    Trigger(
+                        name="Some trigger",
+                        id=uuid.uuid4(),
+                        condition=SemanticBelief(
+                            name="no_more_booze",
+                            id=uuid.uuid4(),
+                            description="There is no more alcohol.",
+                        ),
+                        plan=Plan(
+                            name="Some plan",
+                            id=uuid.uuid4(),
+                            steps=[
+                                LLMAction(
+                                    name="Some action",
+                                    id=uuid.uuid4(),
+                                    goal="Suggest eating chocolate instead.",
+                                ),
+                            ],
+                        ),
+                    ),
+                ],
+            ),
+        ],
+    )
+
+
+def make_msg(sender: str, body: str, thread: str | None = None) -> InternalMessage:
+    return InternalMessage(to="unused", sender=sender, body=body, thread=thread)
+
+
+@pytest.mark.asyncio
+async def test_handle_message_ignores_other_agents(agent):
+    msg = make_msg("unknown", "some data", None)
+
+    await agent.handle_message(msg)
+
+    agent.send.assert_not_called()  # noqa  # `agent.send` has no such property, but we mock it.
+
+
+@pytest.mark.asyncio
+async def test_handle_message_from_transcriber(agent, mock_settings):
+    transcription = "hello world"
+    msg = make_msg(mock_settings.agent_settings.transcription_name, transcription, None)
+
+    await agent.handle_message(msg)
+
+    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]}, "type": "belief_extraction_text"}
+
+
+@pytest.mark.asyncio
+async def test_process_user_said(agent, mock_settings):
+    transcription = "this is a test"
+
+    await agent._user_said(transcription)
+
+    agent.send.assert_awaited_once()  # noqa  # `agent.send` has no such property, but we mock it.
+    sent: InternalMessage = agent.send.call_args.args[0]  # noqa
+    assert sent.to == mock_settings.agent_settings.bdi_belief_collector_name
+    assert sent.thread == "beliefs"
+    parsed = json.loads(sent.body)
+    assert parsed["beliefs"]["user_said"] == [transcription]
+
+
+@pytest.mark.asyncio
+async def test_query_llm():
+    mock_response = MagicMock()
+    mock_response.json.return_value = {
+        "choices": [
+            {
+                "message": {
+                    "content": "null",
+                }
+            }
+        ]
+    }
+    mock_client = AsyncMock()
+    mock_client.post.return_value = mock_response
+    mock_async_client = MagicMock()
+    mock_async_client.__aenter__.return_value = mock_client
+    mock_async_client.__aexit__.return_value = None
+
+    with patch(
+        "control_backend.agents.bdi.text_belief_extractor_agent.httpx.AsyncClient",
+        return_value=mock_async_client,
+    ):
+        agent = TextBeliefExtractorAgent("text_belief_agent")
+
+        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(agent):
+    agent._query_llm.return_value = None
+    res = await agent._retry_query_llm("hello world", {"type": "null"})
+
+    agent._query_llm.assert_called_once()
+    assert res is None
+
+
+@pytest.mark.asyncio
+async def test_retry_query_llm_success_after_failure(agent):
+    agent._query_llm.side_effect = [KeyError(), "real value"]
+    res = await agent._retry_query_llm("hello world", {"type": "string"})
+
+    assert agent._query_llm.call_count == 2
+    assert res == "real value"
+
+
+@pytest.mark.asyncio
+async def test_retry_query_llm_failures(agent):
+    agent._query_llm.side_effect = [KeyError(), KeyError(), KeyError(), "real value"]
+    res = await agent._retry_query_llm("hello world", {"type": "string"})
+
+    assert agent._query_llm.call_count == 3
+    assert res is None
+
+
+@pytest.mark.asyncio
+async def test_retry_query_llm_fail_immediately(agent):
+    agent._query_llm.side_effect = [KeyError(), "real value"]
+    res = await agent._retry_query_llm("hello world", {"type": "string"}, tries=1)
+
+    assert agent._query_llm.call_count == 1
+    assert res is None
+
+
+@pytest.mark.asyncio
+async def test_extracting_beliefs_from_program(agent, sample_program):
+    assert len(agent.available_beliefs) == 0
+    await agent.handle_message(
+        InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=settings.agent_settings.bdi_program_manager_name,
+            body=sample_program.model_dump_json(),
+        ),
+    )
+    assert len(agent.available_beliefs) == 2
+
+
+@pytest.mark.asyncio
+async def test_handle_invalid_program(agent, sample_program):
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    assert len(agent.available_beliefs) == 2
+
+    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"}),
+        ),
+    )
+
+    assert len(agent.available_beliefs) == 2
+
+
+@pytest.mark.asyncio
+async def test_handle_robot_response(agent):
+    initial_length = len(agent.conversation.messages)
+    response = "Hi, I'm Pepper. What's your name?"
+
+    await agent.handle_message(
+        InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=settings.agent_settings.llm_name,
+            body=response,
+        ),
+    )
+
+    assert len(agent.conversation.messages) == initial_length + 1
+    assert agent.conversation.messages[-1].role == "assistant"
+    assert agent.conversation.messages[-1].content == response
+
+
+@pytest.mark.asyncio
+async def test_simulated_real_turn_with_beliefs(agent, sample_program):
+    """Test sending user message to extract beliefs from."""
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+
+    # Send a user message with the belief that there's no more booze
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": True}
+    assert len(agent.conversation.messages) == 0
+    await agent.handle_message(
+        InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=settings.agent_settings.transcription_name,
+            body="We're all out of schnaps.",
+        ),
+    )
+    assert len(agent.conversation.messages) == 1
+
+    # There should be a belief set and sent to the BDI core, as well as the user_said belief
+    assert agent.send.call_count == 2
+
+    # First should be the beliefs message
+    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, sample_program):
+    """Test a user message to extract beliefs from, but no beliefs are formed."""
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+
+    # Send a user message with no new beliefs
+    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,
+            sender=settings.agent_settings.transcription_name,
+            body="Hello there!",
+        ),
+    )
+
+    # Only the user_said belief should've been sent
+    agent.send.assert_called_once()
+
+
+@pytest.mark.asyncio
+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.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.beliefs["is_pirate"] = True
+
+    # Send a user message with the belief the user is a pirate, still
+    agent._query_llm.return_value = {"is_pirate": True, "no_more_booze": None}
+    await agent.handle_message(
+        InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=settings.agent_settings.transcription_name,
+            body="Arr, nice to meet you, matey.",
+        ),
+    )
+
+    # Only the user_said belief should've been sent, as no beliefs have changed
+    agent.send.assert_called_once()
+
+
+@pytest.mark.asyncio
+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.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+    agent.beliefs["no_more_booze"] = True
+
+    # Send a user message with the belief the user is a pirate, still
+    agent._query_llm.return_value = {"is_pirate": None, "no_more_booze": False}
+    await agent.handle_message(
+        InternalMessage(
+            to=settings.agent_settings.text_belief_extractor_name,
+            sender=settings.agent_settings.transcription_name,
+            body="I found an untouched barrel of wine!",
+        ),
+    )
+
+    # Both user_said and belief change should've been sent
+    assert agent.send.call_count == 2
+
+    # Agent's current beliefs should've changed
+    assert not agent.beliefs["no_more_booze"]
+
+
+@pytest.mark.asyncio
+async def test_llm_failure_handling(agent, sample_program):
+    """
+    Check that the agent handles failures gracefully without crashing.
+    """
+    agent._query_llm.side_effect = httpx.HTTPError("")
+    agent.available_beliefs.append(sample_program.phases[0].norms[0].condition)
+    agent.available_beliefs.append(sample_program.phases[0].triggers[0].condition)
+
+    belief_changes = await agent._infer_turn()
+
+    assert len(belief_changes) == 0

test/unit/agents/bdi/test_text_extractor.py

@@ -1,65 +0,0 @@
-import json
-from unittest.mock import AsyncMock
-
-import pytest
-
-from control_backend.agents.bdi import (
-    TextBeliefExtractorAgent,
-)
-from control_backend.core.agent_system import InternalMessage
-
-
-@pytest.fixture
-def agent():
-    agent = TextBeliefExtractorAgent("text_belief_agent")
-    agent.send = AsyncMock()
-    return agent
-
-
-def make_msg(sender: str, body: str, thread: str | None = None) -> InternalMessage:
-    return InternalMessage(to="unused", sender=sender, body=body, thread=thread)
-
-
-@pytest.mark.asyncio
-async def test_handle_message_ignores_other_agents(agent):
-    msg = make_msg("unknown", "some data", None)
-
-    await agent.handle_message(msg)
-
-    agent.send.assert_not_called()  # noqa  # `agent.send` has no such property, but we mock it.
-
-
-@pytest.mark.asyncio
-async def test_handle_message_from_transcriber(agent, mock_settings):
-    transcription = "hello world"
-    msg = make_msg(mock_settings.agent_settings.transcription_name, transcription, None)
-
-    await agent.handle_message(msg)
-
-    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]}, "type": "belief_extraction_text"}
-
-
-@pytest.mark.asyncio
-async def test_process_transcription_demo(agent, mock_settings):
-    transcription = "this is a test"
-
-    await agent._process_transcription_demo(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
-async def test_setup_initializes_beliefs(agent):
-    """Covers the setup method and ensures beliefs are initialized."""
-    await agent.setup()
-    assert agent.beliefs == {"mood": ["X"], "car": ["Y"]}

test/unit/agents/communication/test_ri_communication_agent.py

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

test/unit/agents/llm/test_llm_agent.py

@@ -66,7 +66,7 @@ async def test_llm_processing_success(mock_httpx_client, mock_settings):
     # "Hello world." constitutes one sentence/chunk based on punctuation split
     # The agent should call send once with the full sentence
     assert agent.send.called
-    args = agent.send.call_args[0][0]
+    args = agent.send.call_args_list[0][0][0]
     assert args.to == mock_settings.agent_settings.bdi_core_name
     assert "Hello world." in args.body
 
@@ -197,6 +197,9 @@ async def test_query_llm_yields_final_tail_chunk(mock_settings):
     agent = LLMAgent("llm_agent")
     agent.send = AsyncMock()
 
+    agent.logger = MagicMock()
+    agent.logger.llm = MagicMock()
+
     # Patch _stream_query_llm to yield tokens that do NOT end with punctuation
     async def fake_stream(messages):
         yield "Hello"

test/unit/agents/user_interrupt/test_user_interrupt.py

@@ -0,0 +1,146 @@
+import asyncio
+import json
+from unittest.mock import AsyncMock, MagicMock
+
+import pytest
+
+from control_backend.agents.user_interrupt.user_interrupt_agent import UserInterruptAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
+from control_backend.schemas.ri_message import RIEndpoint
+
+
+@pytest.fixture
+def agent():
+    agent = UserInterruptAgent(name="user_interrupt_agent")
+    agent.send = AsyncMock()
+    agent.logger = MagicMock()
+    agent.sub_socket = AsyncMock()
+    return agent
+
+
+@pytest.mark.asyncio
+async def test_send_to_speech_agent(agent):
+    """Verify speech command format."""
+    await agent._send_to_speech_agent("Hello World")
+
+    agent.send.assert_awaited_once()
+    sent_msg: InternalMessage = agent.send.call_args.args[0]
+
+    assert sent_msg.to == settings.agent_settings.robot_speech_name
+    body = json.loads(sent_msg.body)
+    assert body["data"] == "Hello World"
+    assert body["is_priority"] is True
+
+
+@pytest.mark.asyncio
+async def test_send_to_gesture_agent(agent):
+    """Verify gesture command format."""
+    await agent._send_to_gesture_agent("wave_hand")
+
+    agent.send.assert_awaited_once()
+    sent_msg: InternalMessage = agent.send.call_args.args[0]
+
+    assert sent_msg.to == settings.agent_settings.robot_gesture_name
+    body = json.loads(sent_msg.body)
+    assert body["data"] == "wave_hand"
+    assert body["is_priority"] is True
+    assert body["endpoint"] == RIEndpoint.GESTURE_SINGLE.value
+
+
+@pytest.mark.asyncio
+async def test_send_to_program_manager(agent):
+    """Verify belief update format."""
+    context_str = "2"
+
+    await agent._send_to_program_manager(context_str)
+
+    agent.send.assert_awaited_once()
+    sent_msg: InternalMessage = agent.send.call_args.args[0]
+
+    assert sent_msg.to == settings.agent_settings.bdi_program_manager_name
+    assert sent_msg.thread == "belief_override_id"
+
+    body = json.loads(sent_msg.body)
+
+    assert body["belief"] == context_str
+
+
+@pytest.mark.asyncio
+async def test_receive_loop_routing_success(agent):
+    """
+    Test that the loop correctly:
+    1. Receives 'button_pressed' topic from ZMQ
+    2. Parses the JSON payload to find 'type' and 'context'
+    3. Calls the correct handler method based on 'type'
+    """
+    # Prepare JSON payloads as bytes
+    payload_speech = json.dumps({"type": "speech", "context": "Hello Speech"}).encode()
+    payload_gesture = json.dumps({"type": "gesture", "context": "Hello Gesture"}).encode()
+    payload_override = json.dumps({"type": "override", "context": "Hello Override"}).encode()
+
+    agent.sub_socket.recv_multipart.side_effect = [
+        (b"button_pressed", payload_speech),
+        (b"button_pressed", payload_gesture),
+        (b"button_pressed", payload_override),
+        asyncio.CancelledError,  # Stop the infinite loop
+    ]
+
+    agent._send_to_speech_agent = AsyncMock()
+    agent._send_to_gesture_agent = AsyncMock()
+    agent._send_to_program_manager = AsyncMock()
+
+    try:
+        await agent._receive_button_event()
+    except asyncio.CancelledError:
+        pass
+
+    await asyncio.sleep(0)
+
+    # Speech
+    agent._send_to_speech_agent.assert_awaited_once_with("Hello Speech")
+
+    # Gesture
+    agent._send_to_gesture_agent.assert_awaited_once_with("Hello Gesture")
+
+    # Override
+    agent._send_to_program_manager.assert_awaited_once_with("Hello Override")
+
+    assert agent._send_to_speech_agent.await_count == 1
+    assert agent._send_to_gesture_agent.await_count == 1
+    assert agent._send_to_program_manager.await_count == 1
+
+
+@pytest.mark.asyncio
+async def test_receive_loop_unknown_type(agent):
+    """Test that unknown 'type' values in the JSON log a warning and do not crash."""
+
+    # Prepare a payload with an unknown type
+    payload_unknown = json.dumps({"type": "unknown_thing", "context": "some_data"}).encode()
+
+    agent.sub_socket.recv_multipart.side_effect = [
+        (b"button_pressed", payload_unknown),
+        asyncio.CancelledError,
+    ]
+
+    agent._send_to_speech_agent = AsyncMock()
+    agent._send_to_gesture_agent = AsyncMock()
+    agent._send_to_belief_collector = AsyncMock()
+
+    try:
+        await agent._receive_button_event()
+    except asyncio.CancelledError:
+        pass
+
+    await asyncio.sleep(0)
+
+    # Ensure no handlers were called
+    agent._send_to_speech_agent.assert_not_called()
+    agent._send_to_gesture_agent.assert_not_called()
+    agent._send_to_belief_collector.assert_not_called()
+
+    agent.logger.warning.assert_called_with(
+        "Received button press with unknown type '%s' (context: '%s').",
+        "unknown_thing",
+        "some_data",
+    )

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

@@ -1,4 +1,5 @@
 import json
+import uuid
 from unittest.mock import AsyncMock
 
 import pytest
@@ -6,7 +7,7 @@ from fastapi import FastAPI
 from fastapi.testclient import TestClient
 
 from control_backend.api.v1.endpoints import program
-from control_backend.schemas.program import Program
+from control_backend.schemas.program import BasicNorm, Goal, Phase, Plan, Program
 
 
 @pytest.fixture
@@ -25,29 +26,37 @@ def client(app):
 
 def make_valid_program_dict():
     """Helper to create a valid Program JSON structure."""
-    return {
-        "phases": [
-            {
-                "id": "phase1",
-                "label": "basephase",
-                "norms": [{"id": "n1", "label": "norm", "norm": "be nice"}],
-                "goals": [
-                    {"id": "g1", "label": "goal", "description": "test goal", "achieved": False}
+    # Converting to JSON using Pydantic because it knows how to convert a UUID object
+    program_json_str = Program(
+        phases=[
+            Phase(
+                id=uuid.uuid4(),
+                name="Basic Phase",
+                norms=[
+                    BasicNorm(
+                        id=uuid.uuid4(),
+                        name="Some norm",
+                        norm="Do normal.",
+                    ),
                 ],
-                "triggers": [
-                    {
-                        "id": "t1",
-                        "label": "trigger",
-                        "type": "keywords",
-                        "keywords": [
-                            {"id": "kw1", "keyword": "keyword1"},
-                            {"id": "kw2", "keyword": "keyword2"},
-                        ],
-                    },
+                goals=[
+                    Goal(
+                        id=uuid.uuid4(),
+                        name="Some goal",
+                        plan=Plan(
+                            id=uuid.uuid4(),
+                            name="Goal Plan",
+                            steps=[],
+                        ),
+                        can_fail=False,
+                    ),
                 ],
-            }
-        ]
-    }
+                triggers=[],
+            ),
+        ],
+    ).model_dump_json()
+    # Converting back to a dict because that's what's expected
+    return json.loads(program_json_str)
 
 
 def test_receive_program_success(client):
@@ -71,7 +80,8 @@ def test_receive_program_success(client):
     sent_bytes = args[0][1]
     sent_obj = json.loads(sent_bytes.decode())
 
-    expected_obj = Program.model_validate(program_dict).model_dump()
+    # Converting to JSON using Pydantic because it knows how to handle UUIDs
+    expected_obj = json.loads(Program.model_validate(program_dict).model_dump_json())
     assert sent_obj == expected_obj
 
 

test/unit/schemas/test_ui_program_message.py

@@ -1,49 +1,65 @@
+import uuid
+
 import pytest
 from pydantic import ValidationError
 
 from control_backend.schemas.program import (
+    BasicNorm,
+    ConditionalNorm,
     Goal,
-    KeywordTrigger,
-    Norm,
+    InferredBelief,
+    KeywordBelief,
+    LogicalOperator,
     Phase,
+    Plan,
     Program,
-    TriggerKeyword,
+    SemanticBelief,
+    Trigger,
 )
 
 
-def base_norm() -> Norm:
-    return Norm(
-        id="norm1",
-        label="testNorm",
+def base_norm() -> BasicNorm:
+    return BasicNorm(
+        id=uuid.uuid4(),
+        name="testNormName",
         norm="testNormNorm",
+        critical=False,
     )
 
 
 def base_goal() -> Goal:
     return Goal(
-        id="goal1",
-        label="testGoal",
-        description="testGoalDescription",
-        achieved=False,
+        id=uuid.uuid4(),
+        name="testGoalName",
+        plan=Plan(
+            id=uuid.uuid4(),
+            name="testGoalPlanName",
+            steps=[],
+        ),
+        can_fail=False,
     )
 
 
-def base_trigger() -> KeywordTrigger:
-    return KeywordTrigger(
-        id="trigger1",
-        label="testTrigger",
-        type="keywords",
-        keywords=[
-            TriggerKeyword(id="keyword1", keyword="testKeyword1"),
-            TriggerKeyword(id="keyword1", keyword="testKeyword2"),
-        ],
+def base_trigger() -> Trigger:
+    return Trigger(
+        id=uuid.uuid4(),
+        name="testTriggerName",
+        condition=KeywordBelief(
+            id=uuid.uuid4(),
+            name="testTriggerKeywordBeliefTriggerName",
+            keyword="Keyword",
+        ),
+        plan=Plan(
+            id=uuid.uuid4(),
+            name="testTriggerPlanName",
+            steps=[],
+        ),
     )
 
 
 def base_phase() -> Phase:
     return Phase(
-        id="phase1",
-        label="basephase",
+        id=uuid.uuid4(),
         norms=[base_norm()],
         goals=[base_goal()],
         triggers=[base_trigger()],
@@ -58,7 +74,7 @@ def invalid_program() -> dict:
     # wrong types inside phases list (not Phase objects)
     return {
         "phases": [
-            {"id": "phase1"},  # incomplete
+            {"id": uuid.uuid4()},  # incomplete
             {"not_a_phase": True},
         ]
     }
@@ -77,11 +93,112 @@ def test_valid_deepprogram():
     # validate nested components directly
     phase = validated.phases[0]
     assert isinstance(phase.goals[0], Goal)
-    assert isinstance(phase.triggers[0], KeywordTrigger)
-    assert isinstance(phase.norms[0], Norm)
+    assert isinstance(phase.triggers[0], Trigger)
+    assert isinstance(phase.norms[0], BasicNorm)
 
 
 def test_invalid_program():
     bad = invalid_program()
     with pytest.raises(ValidationError):
         Program.model_validate(bad)
+
+
+def test_conditional_norm_parsing():
+    """
+    Check that pydantic is able to preserve the type of the norm, that it doesn't lose its
+    "condition" field when serializing and deserializing.
+    """
+    norm = ConditionalNorm(
+        name="testNormName",
+        id=uuid.uuid4(),
+        norm="testNormNorm",
+        critical=False,
+        condition=KeywordBelief(
+            name="testKeywordBelief",
+            id=uuid.uuid4(),
+            keyword="testKeywordBelief",
+        ),
+    )
+    program = Program(
+        phases=[
+            Phase(
+                name="Some phase",
+                id=uuid.uuid4(),
+                norms=[norm],
+                goals=[],
+                triggers=[],
+            ),
+        ],
+    )
+
+    parsed_program = Program.model_validate_json(program.model_dump_json())
+    parsed_norm = parsed_program.phases[0].norms[0]
+
+    assert hasattr(parsed_norm, "condition")
+    assert isinstance(parsed_norm, ConditionalNorm)
+
+
+def test_belief_type_parsing():
+    """
+    Check that pydantic is able to discern between the different types of beliefs when serializing
+    and deserializing.
+    """
+    keyword_belief = KeywordBelief(
+        name="testKeywordBelief",
+        id=uuid.uuid4(),
+        keyword="something",
+    )
+    semantic_belief = SemanticBelief(
+        name="testSemanticBelief",
+        id=uuid.uuid4(),
+        description="something",
+    )
+    inferred_belief = InferredBelief(
+        name="testInferredBelief",
+        id=uuid.uuid4(),
+        operator=LogicalOperator.OR,
+        left=keyword_belief,
+        right=semantic_belief,
+    )
+
+    program = Program(
+        phases=[
+            Phase(
+                name="Some phase",
+                id=uuid.uuid4(),
+                norms=[],
+                goals=[],
+                triggers=[
+                    Trigger(
+                        name="testTriggerKeywordTrigger",
+                        id=uuid.uuid4(),
+                        condition=keyword_belief,
+                        plan=Plan(name="testTriggerPlanName", id=uuid.uuid4(), steps=[]),
+                    ),
+                    Trigger(
+                        name="testTriggerSemanticTrigger",
+                        id=uuid.uuid4(),
+                        condition=semantic_belief,
+                        plan=Plan(name="testTriggerPlanName", id=uuid.uuid4(), steps=[]),
+                    ),
+                    Trigger(
+                        name="testTriggerInferredTrigger",
+                        id=uuid.uuid4(),
+                        condition=inferred_belief,
+                        plan=Plan(name="testTriggerPlanName", id=uuid.uuid4(), steps=[]),
+                    ),
+                ],
+            ),
+        ],
+    )
+
+    parsed_program = Program.model_validate_json(program.model_dump_json())
+
+    parsed_keyword_belief = parsed_program.phases[0].triggers[0].condition
+    assert isinstance(parsed_keyword_belief, KeywordBelief)
+
+    parsed_semantic_belief = parsed_program.phases[0].triggers[1].condition
+    assert isinstance(parsed_semantic_belief, SemanticBelief)
+
+    parsed_inferred_belief = parsed_program.phases[0].triggers[2].condition
+    assert isinstance(parsed_inferred_belief, InferredBelief)