refactor: remove SPADE dependencies

Did not look at tests yet, this is a very non-final commit.

ref: N25B-300

docs/conf.py

@@ -4,34 +4,35 @@
 # https://www.sphinx-doc.org/en/master/usage/configuration.html
 import os
 import sys
+
 sys.path.insert(0, os.path.abspath("../src"))
 
 # -- Project information -----------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
 
-project = 'control_backend'
-copyright = '2025, Author'
-author = 'Author'
+project = "control_backend"
+copyright = "2025, Author"
+author = "Author"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
 
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.viewcode',
-    'sphinx.ext.todo',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.todo",
 ]
 
-templates_path = ['_templates']
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+templates_path = ["_templates"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
-language = 'en'
+language = "en"
 
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
-html_theme = 'sphinx_rtd_theme'
-html_static_path = ['_static']
+html_theme = "sphinx_rtd_theme"
+html_static_path = ["_static"]
 
 # -- Options for todo extension ----------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/extensions/todo.html#configuration

pyproject.toml

@@ -21,8 +21,6 @@ dependencies = [
     "pyyaml>=6.0.3",
     "pyzmq>=27.1.0",
     "silero-vad>=6.0.0",
-    "spade>=4.1.0",
-    "spade-bdi>=0.3.2",
     "sphinx>=7.3.7",
     "sphinx-rtd-theme>=3.0.2",
     "torch>=2.8.0",

src/control_backend/agents/actuation/robot_speech_agent.py

@@ -1,11 +1,10 @@
 import json
 
-import spade.agent
 import zmq
-from spade.behaviour import CyclicBehaviour
-from zmq.asyncio import Context
+import zmq.asyncio as azmq
 
 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 SpeechCommand
 
@@ -18,57 +17,21 @@ class RobotSpeechAgent(BaseAgent):
 
     def __init__(
         self,
-        jid: str,
-        password: str,
-        port: int = settings.agent_settings.default_spade_port,
-        verify_security: bool = False,
+        name: str,
         address=settings.zmq_settings.ri_command_address,
         bind=False,
     ):
-        super().__init__(jid, password, port, verify_security)
+        super().__init__(name)
         self.address = address
         self.bind = bind
 
-    class SendZMQCommandsBehaviour(CyclicBehaviour):
-        """Behaviour for sending commands received from the UI."""
-
-        async def run(self):
-            """
-            Run the command publishing loop indefinetely.
-            """
-            assert self.agent is not None
-            # Get a message internally (with topic command)
-            topic, body = await self.agent.subsocket.recv_multipart()
-
-            # Try to get body
-            try:
-                body = json.loads(body)
-                message = SpeechCommand.model_validate(body)
-
-                # Send to the robot.
-                await self.agent.pubsocket.send_json(message.model_dump())
-            except Exception as e:
-                self.agent.logger.error("Error processing message: %s", e)
-
-    class SendSpadeCommandsBehaviour(CyclicBehaviour):
-        """Behaviour for sending commands received from other Python agents."""
-
-        async def run(self):
-            message: spade.agent.Message = await self.receive(timeout=0.1)
-            if message and message.to == self.agent.jid:
-                try:
-                    speech_command = SpeechCommand.model_validate_json(message.body)
-                    await self.agent.pubsocket.send_json(speech_command.model_dump())
-                except Exception as e:
-                    self.agent.logger.error("Error processing message: %s", e)
-
     async def setup(self):
         """
         Setup the robot speech command agent
         """
-        self.logger.info("Setting up %s", self.jid)
+        self.logger.info("Setting up %s", self.name)
 
-        context = Context.instance()
+        context = azmq.Context.instance()
 
         # To the robot
         self.pubsocket = context.socket(zmq.PUB)
@@ -82,9 +45,38 @@ class RobotSpeechAgent(BaseAgent):
         self.subsocket.connect(settings.zmq_settings.internal_sub_address)
         self.subsocket.setsockopt(zmq.SUBSCRIBE, b"command")
 
-        # Add behaviour to our agent
-        commands_behaviour = self.SendZMQCommandsBehaviour()
-        self.add_behaviour(commands_behaviour)
-        self.add_behaviour(self.SendSpadeCommandsBehaviour())
+        await self.add_background_task(self._zmq_command_loop())
 
-        self.logger.info("Finished setting up %s", self.jid)
+        self.logger.info("Finished setting up %s", self.name)
+
+    async def stop(self):
+        if self.subsocket:
+            self.subsocket.close()
+        if self.pubsocket:
+            self.pubsocket.close()
+        await super().stop()
+
+    async def handle_message(self, msg: InternalMessage):
+        """
+        Handle commands received from other Python agents.
+        """
+        try:
+            speech_command = SpeechCommand.model_validate_json(msg.body)
+            await self.pubsocket.send_json(speech_command.model_dump())
+        except Exception:
+            self.logger.exception("Error processing internal message.")
+
+    async def _zmq_command_loop(self):
+        """
+        Handle commands from the UI.
+        """
+        while self._running:
+            try:
+                _, body = await self.subsocket.recv_multipart()
+
+                body = json.loads(body)
+                message = SpeechCommand.model_validate(body)
+
+                await self.pubsocket.send_json(message.model_dump())
+            except Exception:
+                self.logger.exception("Error processing ZMQ message.")

src/control_backend/agents/base.py

@@ -1,12 +1,11 @@
 import logging
 
-from spade.agent import Agent
+from control_backend.core.agent_system import BaseAgent as CoreBaseAgent
 
 
-class BaseAgent(Agent):
+class BaseAgent(CoreBaseAgent):
     """
     Base agent class for our agents to inherit from.
-    This ensures that all agents have a logger.
     """
 
     logger: logging.Logger

src/control_backend/agents/bdi/bdi_core_agent/bdi_core_agent.py

@@ -1,67 +1,121 @@
-import logging
+import asyncio
+import json
+from collections.abc import Iterable
 
 import agentspeak
-from spade.behaviour import OneShotBehaviour
-from spade.message import Message
-from spade_bdi.bdi import BDIAgent
+import agentspeak.runtime
+import agentspeak.stdlib
 
+from control_backend.agents.base import BaseAgent
+from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-
-from .behaviours.belief_setter_behaviour import BeliefSetterBehaviour
-from .behaviours.receive_llm_resp_behaviour import ReceiveLLMResponseBehaviour
+from control_backend.schemas.ri_message import SpeechCommand
 
 
-class BDICoreAgent(BDIAgent):
-    """
-    This is the Brain agent that does the belief inference with AgentSpeak.
-    This is a continous process that happens automatically in the background.
-    This class contains all the actions that can be called from AgentSpeak plans.
-    It has the BeliefSetter behaviour and can aks and recieve requests from the LLM agent.
-    """
-
-    logger = logging.getLogger(__package__).getChild(__name__)
+class BDICoreAgent(BaseAgent):
+    def __init__(self, name: str, asl: str):
+        super().__init__(name)
+        self.asl_file = asl
+        self.env = agentspeak.runtime.Environment()
+        self.bdi_agent = None
+        self.actions = agentspeak.stdlib.actions
 
     async def setup(self) -> None:
-        """
-        Initializes belief behaviors and message routing.
-        """
-        self.logger.info("BDICoreAgent setup started.")
+        self.logger.debug("Setup started.")
 
-        self.add_behaviour(BeliefSetterBehaviour())
-        self.add_behaviour(ReceiveLLMResponseBehaviour())
+        self._add_custom_actions()
 
-        self.logger.info("BDICoreAgent setup complete.")
+        await self._load_asl()
 
-    def add_custom_actions(self, actions) -> None:
+        # Start the BDI cycle loop
+        await self.add_background_task(self._bdi_loop())
+        self.logger.debug("Setup complete.")
+
+    async def _load_asl(self):
+        try:
+            with open(self.asl_file) as source:
+                self.bdi_agent = self.env.build_agent(source, self.actions)
+        except FileNotFoundError:
+            self.logger.warning(f"Could not find the specified ASL file at {self.asl_file}.")
+            self.bdi_agent = agentspeak.runtime.Agent(self.env, self.name)
+
+    async def _bdi_loop(self):
+        """Runs the AgentSpeak BDI loop."""
+        while self._running:
+            assert self.bdi_agent is not None
+            self.bdi_agent.step()
+            await asyncio.sleep(0.01)
+
+    async def handle_message(self, msg: InternalMessage):
         """
-        Registers custom AgentSpeak actions callable from plans.
+        Route incoming messages (Beliefs or LLM responses).
         """
+        sender = msg.sender
 
-        @actions.add(".reply", 1)
-        def _reply(agent: "BDICoreAgent", term, intention):
+        match sender:
+            case settings.agent_settings.bdi_belief_collector_name:
+                self.logger.debug("Processing message from belief collector.")
+                try:
+                    if msg.thread == "beliefs":
+                        beliefs = json.loads(msg.body)
+                        self._add_beliefs(beliefs)
+                except Exception as e:
+                    self.logger.error(f"Error processing belief: {e}")
+            case settings.agent_settings.llm_name:
+                content = msg.body
+                self.logger.info("Received LLM response: %s", content)
+
+                # Forward to Robot Speech Agent
+                cmd = SpeechCommand(data=content)
+                out_msg = InternalMessage(
+                    to=settings.agent_settings.robot_speech_name,
+                    sender=self.name,
+                    body=cmd.model_dump_json(),
+                )
+                await self.send(out_msg)
+
+    # TODO: test way of adding beliefs
+    def _add_beliefs(self, beliefs: dict[str, list[str]]):
+        if not beliefs:
+            return
+
+        for belief_name, args in beliefs.items():
+            self._add_belief(belief_name, args)
+
+            if belief_name == "user_said":
+                self._add_belief("user_said")
+
+    def _add_belief(self, belief_name: str, arguments: Iterable[str] = []):
+        args = (agentspeak.Literal(arg) for arg in arguments)
+        literal_belief = agentspeak.Literal(belief_name, args)
+
+        assert self.bdi_agent is not None
+
+        self.bdi_agent.call(
+            agentspeak.Trigger.addition,
+            agentspeak.GoalType.belief,
+            literal_belief,
+            agentspeak.runtime.Intention(),
+        )
+        self.logger.debug(f"Added belief {belief_name}({','.join(arguments)})")
+
+    def _add_custom_actions(self) -> None:
+        """Add any custom actions here."""
+
+        @self.actions.add(".reply", 1)
+        def _reply(agent, term, intention):
             """
-            Sends text to the LLM (AgentSpeak action).
-            Example: .reply("Hello LLM!")
+            Sends text to the LLM.
             """
             message_text = agentspeak.grounded(term.args[0], intention.scope)
-            self.logger.debug("Reply action sending: %s", message_text)
 
-            self._send_to_llm(str(message_text))
+            asyncio.create_task(self._send_to_llm(str(message_text)))
             yield
 
-    def _send_to_llm(self, text: str):
+    async def _send_to_llm(self, text: str):
         """
-        Sends a text query to the LLM Agent asynchronously.
+        Sends a text query to the LLM agent asynchronously.
         """
-
-        class SendBehaviour(OneShotBehaviour):
-            async def run(self) -> None:
-                msg = Message(
-                    to=settings.agent_settings.llm_name + "@" + settings.agent_settings.host,
-                    body=text,
-                )
-
-                await self.send(msg)
-                self.agent.logger.info("Message sent to LLM agent: %s", text)
-
-        self.add_behaviour(SendBehaviour())
+        msg = InternalMessage(to=settings.agent_settings.llm_name, sender=self.name, body=text)
+        await self.send(msg)
+        self.logger.info("Message sent to LLM agent: %s", text)

src/control_backend/agents/bdi/bdi_core_agent/behaviours/belief_setter_behaviour.py

@@ -1,85 +0,0 @@
-import json
-
-from spade.agent import Message
-from spade.behaviour import CyclicBehaviour
-from spade_bdi.bdi import BDIAgent
-
-from control_backend.core.config import settings
-
-
-class BeliefSetterBehaviour(CyclicBehaviour):
-    """
-    This is the behaviour that the BDI agent runs. This behaviour waits for incoming
-    message and updates the agent's beliefs accordingly.
-    """
-
-    agent: BDIAgent
-
-    async def run(self):
-        """Polls for messages and processes them."""
-        msg = await self.receive()
-        self.agent.logger.debug(
-            "Received message from %s with thread '%s' and body: %s",
-            msg.sender,
-            msg.thread,
-            msg.body,
-        )
-        self._process_message(msg)
-
-    def _process_message(self, message: Message):
-        """Routes the message to the correct processing function based on the sender."""
-        sender = message.sender.node  # removes host from jid and converts to str
-        self.agent.logger.debug("Processing message from sender: %s", sender)
-
-        match sender:
-            case settings.agent_settings.bdi_belief_collector_name:
-                self.agent.logger.debug(
-                    "Message is from the belief collector agent. Processing as belief message."
-                )
-                self._process_belief_message(message)
-            case _:
-                self.agent.logger.debug("Not the belief agent, discarding message")
-                pass
-
-    def _process_belief_message(self, message: Message):
-        if not message.body:
-            self.agent.logger.debug("Ignoring message with empty body from %s", message.sender.node)
-            return
-
-        match message.thread:
-            case "beliefs":
-                try:
-                    beliefs: dict[str, list[str]] = json.loads(message.body)
-                    self._set_beliefs(beliefs)
-                except json.JSONDecodeError:
-                    self.agent.logger.error(
-                        "Could not decode beliefs from JSON. Message body: '%s'",
-                        message.body,
-                        exc_info=True,
-                    )
-            case _:
-                pass
-
-    def _set_beliefs(self, beliefs: dict[str, list[str]]):
-        """Removes previous values for beliefs and updates them with the provided values."""
-        if self.agent.bdi is None:
-            self.agent.logger.warning("Cannot set beliefs; agent's BDI is not yet initialized.")
-            return
-
-        if not beliefs:
-            self.agent.logger.debug("Received an empty set of beliefs. No beliefs were updated.")
-            return
-
-        # Set new beliefs (outdated beliefs are automatically removed)
-        for belief, arguments in beliefs.items():
-            self.agent.logger.debug("Setting belief %s with arguments %s", belief, arguments)
-            self.agent.bdi.set_belief(belief, *arguments)
-
-            # Special case: if there's a new user message, flag that we haven't responded yet
-            if belief == "user_said":
-                self.agent.bdi.set_belief("new_message")
-                self.agent.logger.debug(
-                    "Detected 'user_said' belief, also setting 'new_message' belief."
-                )
-
-        self.agent.logger.info("Successfully updated %d beliefs.", len(beliefs))

src/control_backend/agents/bdi/bdi_core_agent/behaviours/receive_llm_resp_behaviour.py

@@ -1,37 +0,0 @@
-from spade.behaviour import CyclicBehaviour
-from spade.message import Message
-
-from control_backend.core.config import settings
-from control_backend.schemas.ri_message import SpeechCommand
-
-
-class ReceiveLLMResponseBehaviour(CyclicBehaviour):
-    """
-    Adds behavior to receive responses from the LLM Agent.
-    """
-
-    async def run(self):
-        msg = await self.receive()
-
-        sender = msg.sender.node
-        match sender:
-            case settings.agent_settings.llm_name:
-                content = msg.body
-                self.agent.logger.info("Received LLM response: %s", content)
-
-                speech_command = SpeechCommand(data=content)
-
-                message = Message(
-                    to=settings.agent_settings.robot_speech_name
-                    + "@"
-                    + settings.agent_settings.host,
-                    sender=self.agent.jid,
-                    body=speech_command.model_dump_json(),
-                )
-
-                self.agent.logger.debug("Sending message: %s", message)
-
-                await self.send(message)
-            case _:
-                self.agent.logger.debug("Discarding message from %s", sender)
-                pass

src/control_backend/agents/bdi/belief_collector_agent/behaviours/belief_collector_behaviour.py

@@ -1,92 +0,0 @@
-import json
-from json import JSONDecodeError
-
-from spade.agent import Message
-from spade.behaviour import CyclicBehaviour
-
-from control_backend.core.config import settings
-
-
-class BeliefCollectorBehaviour(CyclicBehaviour):
-    """
-    Continuously collects beliefs/emotions from extractor agents:
-    Then we send a unified belief packet to the BDI agent.
-    """
-
-    async def run(self):
-        msg = await self.receive()
-        await self._process_message(msg)
-
-    async def _process_message(self, msg: Message):
-        sender_node = msg.sender.node
-
-        # Parse JSON payload
-        try:
-            payload = json.loads(msg.body)
-        except JSONDecodeError as e:
-            self.agent.logger.warning(
-                "BeliefCollector: failed to parse JSON from %s. Body=%r Error=%s",
-                sender_node,
-                msg.body,
-                e,
-            )
-            return
-
-        msg_type = payload.get("type")
-
-        # Prefer explicit 'type' field
-        if msg_type == "belief_extraction_text" or sender_node == "bel_text_agent_mock":
-            self.agent.logger.debug(
-                "Message routed to _handle_belief_text (sender=%s)", sender_node
-            )
-            await self._handle_belief_text(payload, sender_node)
-        # This is not implemented yet, but we keep the structure for future use
-        elif msg_type == "emotion_extraction_text" or sender_node == "emo_text_agent_mock":
-            self.agent.logger.debug("Message routed to _handle_emo_text (sender=%s)", sender_node)
-            await self._handle_emo_text(payload, sender_node)
-        else:
-            self.agent.logger.warning(
-                "Unrecognized message (sender=%s, type=%r). Ignoring.", sender_node, msg_type
-            )
-
-    async def _handle_belief_text(self, payload: dict, origin: str):
-        """
-        Expected payload:
-         {
-            "type": "belief_extraction_text",
-            "beliefs": {"user_said": ["Can you help me?"]}
-
-        }
-
-        """
-        beliefs = payload.get("beliefs", {})
-
-        if not beliefs:
-            self.agent.logger.debug("Received empty beliefs set.")
-            return
-
-        self.agent.logger.debug("Forwarding %d beliefs.", len(beliefs))
-        for belief_name, belief_list in beliefs.items():
-            for belief in belief_list:
-                self.agent.logger.debug("  - %s %s", belief_name, str(belief))
-
-        await self._send_beliefs_to_bdi(beliefs, origin=origin)
-
-    async def _handle_emo_text(self, payload: dict, origin: str):
-        """TODO: implement (after we have emotional recogntion)"""
-        pass
-
-    async def _send_beliefs_to_bdi(self, beliefs: list[str], origin: str | None = None):
-        """
-        Sends a unified belief packet to the BDI agent.
-        """
-        if not beliefs:
-            return
-
-        to_jid = f"{settings.agent_settings.bdi_core_name}@{settings.agent_settings.host}"
-
-        msg = Message(to=to_jid, sender=self.agent.jid, thread="beliefs")
-        msg.body = json.dumps(beliefs)
-
-        await self.send(msg)
-        self.agent.logger.info("Sent %d belief(s) to BDI core.", len(beliefs))

src/control_backend/agents/bdi/belief_collector_agent/belief_collector_agent.py

@@ -1,11 +1,88 @@
-from control_backend.agents.base import BaseAgent
+import json
 
-from .behaviours.belief_collector_behaviour import BeliefCollectorBehaviour
+from control_backend.agents.base import BaseAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
 
 
 class BDIBeliefCollectorAgent(BaseAgent):
+    """
+    Continuously collects beliefs/emotions from extractor agents and forwards a
+    unified belief packet to the BDI agent.
+    """
+
     async def setup(self):
-        self.logger.info("BDIBeliefCollectorAgent starting (%s)", self.jid)
-        # Attach the continuous collector behaviour (listens and forwards to BDI)
-        self.add_behaviour(BeliefCollectorBehaviour())
-        self.logger.info("BDIBeliefCollectorAgent ready.")
+        self.logger.info("Setting up %s", self.name)
+
+    async def handle_message(self, msg: InternalMessage):
+        sender_node = msg.sender
+
+        # Parse JSON payload
+        try:
+            payload = json.loads(msg.body)
+        except Exception as e:
+            self.logger.warning(
+                "BeliefCollector: failed to parse JSON from %s. Body=%r Error=%s",
+                sender_node,
+                msg.body,
+                e,
+            )
+            return
+
+        msg_type = payload.get("type")
+
+        # Prefer explicit 'type' field
+        if msg_type == "belief_extraction_text":
+            self.logger.debug("Message routed to _handle_belief_text (sender=%s)", sender_node)
+            await self._handle_belief_text(payload, sender_node)
+        # This is not implemented yet, but we keep the structure for future use
+        elif msg_type == "emotion_extraction_text":
+            self.logger.debug("Message routed to _handle_emo_text (sender=%s)", sender_node)
+            await self._handle_emo_text(payload, sender_node)
+        else:
+            self.logger.warning(
+                "Unrecognized message (sender=%s, type=%r). Ignoring.", sender_node, msg_type
+            )
+
+    async def _handle_belief_text(self, payload: dict, origin: str):
+        """
+        Expected payload:
+        {
+            "type": "belief_extraction_text",
+            "beliefs": {"user_said": ["Can you help me?"]}
+
+        }
+        """
+        beliefs = payload.get("beliefs", {})
+
+        if not beliefs:
+            self.logger.debug("Received empty beliefs set.")
+            return
+
+        self.logger.debug("Forwarding %d beliefs.", len(beliefs))
+        for belief_name, belief_list in beliefs.items():
+            for belief in belief_list:
+                self.logger.debug("  - %s %s", belief_name, str(belief))
+
+        await self._send_beliefs_to_bdi(beliefs, origin=origin)
+
+    async def _handle_emo_text(self, payload: dict, origin: str):
+        """TODO: implement (after we have emotional recognition)"""
+        pass
+
+    async def _send_beliefs_to_bdi(self, beliefs: dict, origin: str | None = None):
+        """
+        Sends a unified belief packet to the BDI agent.
+        """
+        if not beliefs:
+            return
+
+        msg = InternalMessage(
+            to=settings.agent_settings.bdi_core_name,
+            sender=self.name,
+            body=json.dumps(beliefs),
+            thread="beliefs",
+        )
+
+        await self.send(msg)
+        self.logger.info("Sent %d belief(s) to BDI core.", len(beliefs))

src/control_backend/agents/bdi/text_belief_extractor_agent/behaviours/text_belief_extractor_behaviour.py

@@ -1,104 +0,0 @@
-import json
-import logging
-
-from spade.behaviour import CyclicBehaviour
-from spade.message import Message
-
-from control_backend.core.config import settings
-
-
-class TextBeliefExtractorBehaviour(CyclicBehaviour):
-    logger = logging.getLogger(__name__)
-
-    # TODO: LLM prompt nog hardcoded
-    llm_instruction_prompt = """
-    You are an information extraction assistent for a BDI agent. Your task is to extract values \
-    from a user's text to bind a list of ungrounded beliefs. Rules:
-    You will receive a JSON object with "beliefs" (a list of ungrounded AgentSpeak beliefs) \
-    and "text" (user's transcript).
-    Analyze the text to find values that sematically match the variables (X,Y,Z) in the beliefs.
-    A single piece of text might contain multiple instances that match a belief.
-    Respond ONLY with a single JSON object.
-    The JSON object's keys should be the belief functors (e.g., "weather").
-    The value for each key must be a list of lists.
-    Each inner list must contain the extracted arguments (as strings) for one instance \
-    of that belief.
-    CRITICAL: If no information in the text matches a belief, DO NOT include that key \
-    in your response.
-    """
-
-    # on_start agent receives message containing the beliefs to look out for and
-    # sets up the LLM with instruction prompt
-    # async def on_start(self):
-    #    msg = await self.receive(timeout=0.1)
-    #    self.beliefs = dict uit message
-    #    send instruction prompt to LLM
-
-    beliefs: dict[str, list[str]]
-    beliefs = {"mood": ["X"], "car": ["Y"]}
-
-    async def run(self):
-        msg = await self.receive()
-        if msg is None:
-            return
-
-        sender = msg.sender.node
-        match sender:
-            case settings.agent_settings.transcription_name:
-                self.logger.debug("Received text from transcriber: %s", msg.body)
-                await self._process_transcription_demo(msg.body)
-            case _:
-                self.logger.info("Discarding message from %s", sender)
-                pass
-
-    async def _process_transcription(self, text: str):
-        text_prompt = f"Text: {text}"
-
-        beliefs_prompt = "These are the beliefs to be bound:\n"
-        for belief, values in self.beliefs.items():
-            beliefs_prompt += f"{belief}({', '.join(values)})\n"
-
-        prompt = text_prompt + beliefs_prompt
-        self.logger.info(prompt)
-        # prompt_msg = Message(to="LLMAgent@whatever")
-        # response = self.send(prompt_msg)
-
-        # Mock response; response is beliefs in JSON format, it parses do dict[str,list[list[str]]]
-        response = '{"mood": [["happy"]]}'
-        # Verify by trying to parse
-        try:
-            json.loads(response)
-            belief_message = Message()
-
-            belief_message.to = (
-                settings.agent_settings.bdi_belief_collector_name
-                + "@"
-                + settings.agent_settings.host
-            )
-            belief_message.body = response
-            belief_message.thread = "beliefs"
-
-            await self.send(belief_message)
-            self.agent.logger.info("Sent beliefs to BDI.")
-        except json.JSONDecodeError:
-            # Parsing failed, so the response is in the wrong format, log warning
-            self.agent.logger.warning("Received LLM response in incorrect format.")
-
-    async def _process_transcription_demo(self, txt: str):
-        """
-        Demo version to process the transcription input to beliefs. For the demo only the belief
-        'user_said' is relevant, so this function simply makes a dict with key: "user_said",
-        value: txt and passes this to the Belief Collector agent.
-        """
-        belief = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
-        payload = json.dumps(belief)
-        belief_msg = Message()
-
-        belief_msg.to = (
-            settings.agent_settings.bdi_belief_collector_name + "@" + settings.agent_settings.host
-        )
-        belief_msg.body = payload
-        belief_msg.thread = "beliefs"
-
-        await self.send(belief_msg)
-        self.logger.info("Sent %d beliefs to the belief collector.", len(belief["beliefs"]))

src/control_backend/agents/bdi/text_belief_extractor_agent/text_belief_extractor_agent.py

@@ -1,8 +1,38 @@
-from control_backend.agents.base import BaseAgent
+import json
 
-from .behaviours.text_belief_extractor_behaviour import TextBeliefExtractorBehaviour
+from control_backend.agents.base import BaseAgent
+from control_backend.core.agent_system import InternalMessage
+from control_backend.core.config import settings
 
 
 class TextBeliefExtractorAgent(BaseAgent):
     async def setup(self):
-        self.add_behaviour(TextBeliefExtractorBehaviour())
+        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"]}
+
+    async def handle_message(self, msg: InternalMessage):
+        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):
+        """
+        Demo version to process the transcription input to beliefs.
+        """
+        # For demo, just wrapping user text as user_said belief
+        belief = {"beliefs": {"user_said": [txt]}, "type": "belief_extraction_text"}
+        payload = json.dumps(belief)
+
+        belief_msg = InternalMessage(
+            to=settings.agent_settings.bdi_belief_collector_name,
+            sender=self.name,
+            body=payload,
+            thread="beliefs",
+        )
+
+        await self.send(belief_msg)
+        self.logger.info("Sent %d beliefs to the belief collector.", len(belief["beliefs"]))

src/control_backend/agents/communication/ri_communication_agent.py

@@ -1,8 +1,8 @@
 import asyncio
 import json
 
-import zmq.asyncio
-from spade.behaviour import CyclicBehaviour
+import zmq
+import zmq.asyncio as azmq
 from zmq.asyncio import Context
 
 from control_backend.agents import BaseAgent
@@ -12,109 +12,38 @@ from ..actuation.robot_speech_agent import RobotSpeechAgent
 
 
 class RICommunicationAgent(BaseAgent):
-    req_socket: zmq.Socket
-    _address = ""
-    _bind = True
-    connected = False
-
     def __init__(
         self,
-        jid: str,
-        password: str,
-        port: int = settings.agent_settings.default_spade_port,
-        verify_security: bool = False,
+        name: str,
         address=settings.zmq_settings.ri_command_address,
         bind=False,
     ):
-        super().__init__(jid, password, port, verify_security)
+        super().__init__(name)
         self._address = address
         self._bind = bind
-        self._req_socket: zmq.asyncio.Socket | None = None
-        self.pub_socket: zmq.asyncio.Socket | None = None
+        self._req_socket: azmq.Socket | None = None
+        self.pub_socket: azmq.Socket | None = None
+        self.connected = False
 
-    class ListenBehaviour(CyclicBehaviour):
-        async def run(self):
-            """
-            Run the listening (ping) loop indefinetely.
-            """
-            assert self.agent is not None
+    async def setup(self):
+        """
+        Try to set up the communication agent, we have `behaviour_settings.comm_setup_max_retries`
+        retries in case we don't have a response yet.
+        """
+        self.logger.info("Setting up %s", self.name)
 
-            if not self.agent.connected:
-                await asyncio.sleep(settings.behaviour_settings.sleep_s)
-                return
+        # Bind request socket
+        await self._setup_sockets()
 
-            # We need to listen and sent pings.
-            message = {"endpoint": "ping", "data": {"id": "e.g. some reference id"}}
-            seconds_to_wait_total = settings.behaviour_settings.sleep_s
-            try:
-                await asyncio.wait_for(
-                    self.agent._req_socket.send_json(message), timeout=seconds_to_wait_total / 2
-                )
-            except TimeoutError:
-                self.agent.logger.debug(
-                    "Waited too long to send message - "
-                    "we probably dont have any receivers... but let's check!"
-                )
+        if await self._negotiate_connection():
+            self.connected = True
+            await self.add_background_task(self._listen_loop())
+        else:
+            self.logger.warning("Failed to negotiate connection during setup.")
 
-            # Wait up to {seconds_to_wait_total/2} seconds for a reply
-            try:
-                message = await asyncio.wait_for(
-                    self.agent._req_socket.recv_json(), timeout=seconds_to_wait_total / 2
-                )
+        self.logger.info("Finished setting up %s", self.name)
 
-            # We didnt get a reply
-            except TimeoutError:
-                self.agent.logger.info(
-                    f"No ping retrieved in {seconds_to_wait_total} seconds, "
-                    "sending UI disconnection event and attempting to restart."
-                )
-
-                # Make sure we dont retry receiving messages untill we're setup.
-                self.agent.connected = False
-                self.agent.remove_behaviour(self)
-
-                # Tell UI we're disconnected.
-                topic = b"ping"
-                data = json.dumps(False).encode()
-                if self.agent.pub_socket is None:
-                    self.agent.logger.warning(
-                        "Communication agent pub socket not correctly initialized."
-                    )
-                else:
-                    try:
-                        await asyncio.wait_for(
-                            self.agent.pub_socket.send_multipart([topic, data]), 5
-                        )
-                    except TimeoutError:
-                        self.agent.logger.warning(
-                            f"Initial connection ping for router timed out in {self.agent.name}."
-                        )
-
-                # Try to reboot.
-                self.agent.logger.debug("Restarting communication agent.")
-                await self.agent.setup()
-
-            self.agent.logger.debug(f'Received message "{message}" from RI.')
-            if "endpoint" not in message:
-                self.agent.logger.warning(
-                    "No received endpoint in message, expected ping endpoint."
-                )
-                return
-
-            # See what endpoint we received
-            match message["endpoint"]:
-                case "ping":
-                    topic = b"ping"
-                    data = json.dumps(True).encode()
-                    if self.agent.pub_socket is not None:
-                        await self.agent.pub_socket.send_multipart([topic, data])
-                    await asyncio.sleep(settings.behaviour_settings.sleep_s)
-                case _:
-                    self.agent.logger.debug(
-                        "Received message with topic different than ping, while ping expected."
-                    )
-
-    async def setup_sockets(self, force=False):
+    async def _setup_sockets(self, force=False):
         """
         Sets up request socket for communication agent.
         """
@@ -130,21 +59,13 @@ class RICommunicationAgent(BaseAgent):
             self.pub_socket = Context.instance().socket(zmq.PUB)
             self.pub_socket.connect(settings.zmq_settings.internal_pub_address)
 
-    async def setup(self, max_retries: int = settings.behaviour_settings.comm_setup_max_retries):
-        """
-        Try to set up the communication agent, we have `behaviour_settings.comm_setup_max_retries`
-        retries in case we don't have a response yet.
-        """
-        self.logger.info("Setting up %s", self.jid)
-
-        # Bind request socket
-        await self.setup_sockets()
-
+    async def _negotiate_connection(
+        self, max_retries: int = settings.behaviour_settings.comm_setup_max_retries
+    ):
         retries = 0
-        # Let's try a certain amount of times before failing connection
         while retries < max_retries:
-            # Make sure the socket is properly setup.
             if self._req_socket is None:
+                retries += 1
                 continue
 
             # Send our message and receive one back
@@ -156,7 +77,6 @@ class RICommunicationAgent(BaseAgent):
                 received_message = await asyncio.wait_for(
                     self._req_socket.recv_json(), timeout=retry_frequency
                 )
-
             except TimeoutError:
                 self.logger.warning(
                     "No connection established in %d seconds (attempt %d/%d)",
@@ -166,7 +86,6 @@ class RICommunicationAgent(BaseAgent):
                 )
                 retries += 1
                 continue
-
             except Exception as e:
                 self.logger.warning("Unexpected error during negotiation: %s", e)
                 retries += 1
@@ -187,64 +106,129 @@ class RICommunicationAgent(BaseAgent):
 
             # At this point, we have a valid response
             try:
-                for port_data in received_message["data"]:
-                    id = port_data["id"]
-                    port = port_data["port"]
-                    bind = port_data["bind"]
-
-                    if not bind:
-                        addr = f"tcp://localhost:{port}"
-                    else:
-                        addr = f"tcp://*:{port}"
-
-                    match id:
-                        case "main":
-                            if addr != self._address:
-                                if not bind:
-                                    self._req_socket.connect(addr)
-                                else:
-                                    self._req_socket.bind(addr)
-                        case "actuation":
-                            ri_commands_agent = RobotSpeechAgent(
-                                settings.agent_settings.robot_speech_name
-                                + "@"
-                                + settings.agent_settings.host,
-                                settings.agent_settings.robot_speech_name,
-                                address=addr,
-                                bind=bind,
-                            )
-                            await ri_commands_agent.start()
-                        case _:
-                            self.logger.warning("Unhandled negotiation id: %s", id)
-
+                await self._handle_negotiation_response(received_message)
+                # Let UI know that we're connected
+                topic = b"ping"
+                data = json.dumps(True).encode()
+                if self.pub_socket:
+                    await self.pub_socket.send_multipart([topic, data])
+                return True
             except Exception as e:
                 self.logger.warning("Error unpacking negotiation data: %s", e)
                 retries += 1
                 await asyncio.sleep(1)
                 continue
 
-            # setup succeeded
-            break
+        return False
 
-        else:
-            self.logger.warning("Failed to set up %s after %d retries", self.name, max_retries)
-            return
+    async def _handle_negotiation_response(self, received_message):
+        for port_data in received_message["data"]:
+            id = port_data["id"]
+            port = port_data["port"]
+            bind = port_data["bind"]
 
-        # Set up ping behaviour
-        listen_behaviour = self.ListenBehaviour()
-        self.add_behaviour(listen_behaviour)
+            if not bind:
+                addr = f"tcp://localhost:{port}"
+            else:
+                addr = f"tcp://*:{port}"
 
-        # Let UI know that we're connected
+            match id:
+                case "main":
+                    if addr != self._address:
+                        assert self._req_socket is not None
+                        if not bind:
+                            self._req_socket.connect(addr)
+                        else:
+                            self._req_socket.bind(addr)
+                case "actuation":
+                    ri_commands_agent = RobotSpeechAgent(
+                        settings.agent_settings.robot_speech_name,
+                        address=addr,
+                        bind=bind,
+                    )
+                    await ri_commands_agent.start()
+                case _:
+                    self.logger.warning("Unhandled negotiation id: %s", id)
+
+    async def stop(self):
+        if self._req_socket:
+            self._req_socket.close()
+        if self.pub_socket:
+            self.pub_socket.close()
+        await super().stop()
+
+    async def _listen_loop(self):
+        """
+        Run the listening (ping) loop indefinitely.
+        """
+        while self._running:
+            if not self.connected:
+                await asyncio.sleep(settings.behaviour_settings.sleep_s)
+                continue
+
+            # We need to listen and send pings.
+            message = {"endpoint": "ping", "data": {"id": "e.g. some reference id"}}
+            seconds_to_wait_total = settings.behaviour_settings.sleep_s
+            try:
+                assert self._req_socket is not None
+                await asyncio.wait_for(
+                    self._req_socket.send_json(message), timeout=seconds_to_wait_total / 2
+                )
+            except TimeoutError:
+                self.logger.debug(
+                    "Waited too long to send message - "
+                    "we probably dont have any receivers... but let's check!"
+                )
+
+            # Wait up to {seconds_to_wait_total/2} seconds for a reply
+            try:
+                assert self._req_socket is not None
+                message = await asyncio.wait_for(
+                    self._req_socket.recv_json(), timeout=seconds_to_wait_total / 2
+                )
+
+                self.logger.debug(f'Received message "{message}" from RI.')
+                if "endpoint" not in message:
+                    self.logger.warning("No received endpoint in message, expected ping endpoint.")
+                    continue
+
+                # See what endpoint we received
+                match message["endpoint"]:
+                    case "ping":
+                        topic = b"ping"
+                        data = json.dumps(True).encode()
+                        if self.pub_socket is not None:
+                            await self.pub_socket.send_multipart([topic, data])
+                        await asyncio.sleep(settings.behaviour_settings.sleep_s)
+                    case _:
+                        self.logger.debug(
+                            "Received message with topic different than ping, while ping expected."
+                        )
+            # We didnt get a reply
+            except TimeoutError:
+                self.logger.info(
+                    f"No ping retrieved in {seconds_to_wait_total} seconds, "
+                    "sending UI disconnection event and attempting to restart."
+                )
+                await self._handle_disconnection()
+                continue
+            except Exception:
+                self.logger.error("Error while waiting for ping message.", exc_info=True)
+                raise
+
+    async def _handle_disconnection(self):
+        self.connected = False
+
+        # Tell UI we're disconnected.
         topic = b"ping"
-        data = json.dumps(True).encode()
-        if self.pub_socket is None:
-            self.logger.warning("Communication agent pub socket not correctly initialized.")
-        else:
+        data = json.dumps(False).encode()
+        if self.pub_socket:
             try:
                 await asyncio.wait_for(self.pub_socket.send_multipart([topic, data]), 5)
             except TimeoutError:
-                self.logger.warning("Initial connection ping for router timed out in com_ri_agent.")
+                self.logger.warning("Connection ping for router timed out.")
 
-        # Make sure to start listening now that we're connected.
-        self.connected = True
-        self.logger.info("Finished setting up %s", self.jid)
+        # Try to reboot/renegotiate
+        self.logger.debug("Restarting communication negotiation.")
+        if await self._negotiate_connection(max_retries=1):
+            self.connected = True

src/control_backend/agents/llm/llm_agent.py

@@ -3,10 +3,9 @@ import re
 from collections.abc import AsyncGenerator
 
 import httpx
-from spade.behaviour import CyclicBehaviour
-from spade.message import Message
 
 from control_backend.agents import BaseAgent
+from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 
 from .llm_instructions import LLMInstructions
@@ -19,143 +18,114 @@ class LLMAgent(BaseAgent):
     and responds with processed LLM output.
     """
 
-    class ReceiveMessageBehaviour(CyclicBehaviour):
-        """
-        Cyclic behaviour to continuously listen for incoming messages from
-        the BDI Core Agent and handle them.
-        """
-
-        async def run(self):
-            """
-            Receives SPADE messages and processes only those originating from the
-            configured BDI agent.
-            """
-            msg = await self.receive()
-
-            sender = msg.sender.node
-            self.agent.logger.debug(
-                "Received message: %s from %s",
-                msg.body,
-                sender,
-            )
-
-            if sender == settings.agent_settings.bdi_core_name:
-                self.agent.logger.debug("Processing message from BDI Core Agent")
-                await self._process_bdi_message(msg)
-            else:
-                self.agent.logger.debug("Message ignored (not from BDI Core Agent)")
-
-        async def _process_bdi_message(self, message: Message):
-            """
-            Forwards user text from the BDI to the LLM and replies with the generated text in chunks
-            separated by punctuation.
-            """
-            user_text = message.body
-            # Consume the streaming generator and send a reply for every chunk
-            async for chunk in self._query_llm(user_text):
-                await self._reply(chunk)
-            self.agent.logger.debug(
-                "Finished processing BDI message. Response sent in chunks to BDI Core Agent."
-            )
-
-        async def _reply(self, msg: str):
-            """
-            Sends a response message back to the BDI Core Agent.
-            """
-            reply = Message(
-                to=settings.agent_settings.bdi_core_name + "@" + settings.agent_settings.host,
-                body=msg,
-            )
-            await self.send(reply)
-
-        async def _query_llm(self, prompt: str) -> AsyncGenerator[str]:
-            """
-            Sends a chat completion request to the local LLM service and streams the response by
-            yielding fragments separated by punctuation like.
-
-            :param prompt: Input text prompt to pass to the LLM.
-            :yield: Fragments of the LLM-generated content.
-            """
-            instructions = LLMInstructions(
-                "- Be friendly and respectful.\n"
-                "- Make the conversation feel natural and engaging.\n"
-                "- Speak like a pirate.\n"
-                "- When the user asks what you can do, tell them.",
-                "- Try to learn the user's name during conversation.\n"
-                "- Suggest playing a game of asking yes or no questions where you think of a word "
-                "and the user must guess it.",
-            )
-            messages = [
-                {
-                    "role": "developer",
-                    "content": instructions.build_developer_instruction(),
-                },
-                {
-                    "role": "user",
-                    "content": prompt,
-                },
-            ]
-
-            try:
-                current_chunk = ""
-                async for token in self._stream_query_llm(messages):
-                    current_chunk += token
-
-                    # Stream the message in chunks separated by punctuation.
-                    # We include the delimiter in the emitted chunk for natural flow.
-                    pattern = re.compile(r".*?(?:,|;|:|—|–|\.{3}|…|\.|\?|!)\s*", re.DOTALL)
-                    for m in pattern.finditer(current_chunk):
-                        chunk = m.group(0)
-                        if chunk:
-                            yield current_chunk
-                            current_chunk = ""
-
-                # Yield any remaining tail
-                if current_chunk:
-                    yield current_chunk
-            except httpx.HTTPError as err:
-                self.agent.logger.error("HTTP error.", exc_info=err)
-                yield "LLM service unavailable."
-            except Exception as err:
-                self.agent.logger.error("Unexpected error.", exc_info=err)
-                yield "Error processing the request."
-
-        async def _stream_query_llm(self, messages) -> AsyncGenerator[str]:
-            """Raises httpx.HTTPError when the API gives an error."""
-            async with httpx.AsyncClient(timeout=None) as client:
-                async with client.stream(
-                    "POST",
-                    settings.llm_settings.local_llm_url,
-                    json={
-                        "model": settings.llm_settings.local_llm_model,
-                        "messages": messages,
-                        "temperature": 0.3,
-                        "stream": True,
-                    },
-                ) as response:
-                    response.raise_for_status()
-
-                    async for line in response.aiter_lines():
-                        if not line or not line.startswith("data: "):
-                            continue
-
-                        data = line[len("data: ") :]
-                        if data.strip() == "[DONE]":
-                            break
-
-                        try:
-                            event = json.loads(data)
-                            delta = event.get("choices", [{}])[0].get("delta", {}).get("content")
-                            if delta:
-                                yield delta
-                        except json.JSONDecodeError:
-                            self.agent.logger.error("Failed to parse LLM response: %s", data)
-
     async def setup(self):
+        self.logger.info("Setting up %s.", self.name)
+
+    async def handle_message(self, msg: InternalMessage):
+        if msg.sender == settings.agent_settings.bdi_core_name:
+            self.logger.debug("Processing message from BDI core.")
+            await self._process_bdi_message(msg)
+        else:
+            self.logger.debug("Message ignored (not from BDI core.")
+
+    async def _process_bdi_message(self, message: InternalMessage):
+        user_text = message.body
+        async for chunk in self._query_llm(user_text):
+            await self._send_reply(chunk)
+            self.logger.debug(
+                "Finished processing BDI message. Response sent in chunks to BDI core."
+            )
+
+    async def _send_reply(self, msg: str):
         """
-        Sets up the SPADE behaviour to filter and process messages from the
-        BDI Core Agent.
+        Sends a response message back to the BDI Core Agent.
         """
-        behaviour = self.ReceiveMessageBehaviour()
-        self.add_behaviour(behaviour)
-        self.logger.info("LLMAgent setup complete")
+        reply = InternalMessage(
+            to=settings.agent_settings.bdi_core_name + "@" + settings.agent_settings.host,
+            sender=self.name,
+            body=msg,
+        )
+        await self.send(reply)
+
+    async def _query_llm(self, prompt: str) -> AsyncGenerator[str]:
+        """
+        Sends a chat completion request to the local LLM service and streams the response by
+        yielding fragments separated by punctuation like.
+
+        :param prompt: Input text prompt to pass to the LLM.
+        :yield: Fragments of the LLM-generated content.
+        """
+        instructions = LLMInstructions(
+            "- Be friendly and respectful.\n"
+            "- Make the conversation feel natural and engaging.\n"
+            "- Speak like a pirate.\n"
+            "- When the user asks what you can do, tell them.",
+            "- Try to learn the user's name during conversation.\n"
+            "- Suggest playing a game of asking yes or no questions where you think of a word "
+            "and the user must guess it.",
+        )
+        messages = [
+            {
+                "role": "developer",
+                "content": instructions.build_developer_instruction(),
+            },
+            {
+                "role": "user",
+                "content": prompt,
+            },
+        ]
+
+        try:
+            current_chunk = ""
+            async for token in self._stream_query_llm(messages):
+                current_chunk += token
+
+                # Stream the message in chunks separated by punctuation.
+                # We include the delimiter in the emitted chunk for natural flow.
+                pattern = re.compile(r".*?(?:,|;|:|—|–|\.{3}|…|\.|\?|!)\s*", re.DOTALL)
+                for m in pattern.finditer(current_chunk):
+                    chunk = m.group(0)
+                    if chunk:
+                        yield current_chunk
+                        current_chunk = ""
+
+            # Yield any remaining tail
+            if current_chunk:
+                yield current_chunk
+        except httpx.HTTPError as err:
+            self.logger.error("HTTP error.", exc_info=err)
+            yield "LLM service unavailable."
+        except Exception as err:
+            self.logger.error("Unexpected error.", exc_info=err)
+            yield "Error processing the request."
+
+    async def _stream_query_llm(self, messages) -> AsyncGenerator[str]:
+        """Raises httpx.HTTPError when the API gives an error."""
+        async with httpx.AsyncClient(timeout=None) as client:
+            async with client.stream(
+                "POST",
+                settings.llm_settings.local_llm_url,
+                json={
+                    "model": settings.llm_settings.local_llm_model,
+                    "messages": messages,
+                    "temperature": 0.3,
+                    "stream": True,
+                },
+            ) as response:
+                response.raise_for_status()
+
+                async for line in response.aiter_lines():
+                    if not line or not line.startswith("data: "):
+                        continue
+
+                    data = line[len("data: ") :]
+                    if data.strip() == "[DONE]":
+                        break
+
+                    try:
+                        event = json.loads(data)
+                        delta = event.get("choices", [{}])[0].get("delta", {}).get("content")
+                        if delta:
+                            yield delta
+                    except json.JSONDecodeError:
+                        self.logger.error("Failed to parse LLM response: %s", data)

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

@@ -3,10 +3,9 @@ import asyncio
 import numpy as np
 import zmq
 import zmq.asyncio as azmq
-from spade.behaviour import CyclicBehaviour
-from spade.message import Message
 
 from control_backend.agents import BaseAgent
+from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
 
 from .speech_recognizer import SpeechRecognizer
@@ -19,53 +18,31 @@ class TranscriptionAgent(BaseAgent):
     """
 
     def __init__(self, audio_in_address: str):
-        jid = settings.agent_settings.transcription_name + "@" + settings.agent_settings.host
-        super().__init__(jid, settings.agent_settings.transcription_name)
+        super().__init__(settings.agent_settings.transcription_name)
 
         self.audio_in_address = audio_in_address
         self.audio_in_socket: azmq.Socket | None = None
+        self.speech_recognizer = None
+        self._concurrency = None
 
-    class TranscribingBehaviour(CyclicBehaviour):
-        def __init__(self, audio_in_socket: azmq.Socket):
-            super().__init__()
-            max_concurrent_tasks = settings.behaviour_settings.transcription_max_concurrent_tasks
-            self.audio_in_socket = audio_in_socket
-            self.speech_recognizer = SpeechRecognizer.best_type()
-            self._concurrency = asyncio.Semaphore(max_concurrent_tasks)
+    async def setup(self):
+        self.logger.info("Setting up %s", self.name)
 
-        def warmup(self):
-            """Load the transcription model into memory to speed up the first transcription."""
-            self.speech_recognizer.load_model()
+        self._connect_audio_in_socket()
 
-        async def _transcribe(self, audio: np.ndarray) -> str:
-            async with self._concurrency:
-                return await asyncio.to_thread(self.speech_recognizer.recognize_speech, audio)
+        # Initialize recognizer and semaphore
+        max_concurrent_tasks = settings.behaviour_settings.transcription_max_concurrent_tasks
+        self._concurrency = asyncio.Semaphore(max_concurrent_tasks)
+        self.speech_recognizer = SpeechRecognizer.best_type()
+        self.speech_recognizer.load_model()  # Warmup
 
-        async def _share_transcription(self, transcription: str):
-            """Share a transcription to the other agents that depend on it."""
-            receiver_jids = [
-                settings.agent_settings.text_belief_extractor_name
-                + "@"
-                + settings.agent_settings.host,
-            ]  # Set message receivers here
+        # Start background loop
+        await self.add_background_task(self._transcribing_loop())
 
-            for receiver_jid in receiver_jids:
-                message = Message(to=receiver_jid, body=transcription)
-                await self.send(message)
-
-        async def run(self) -> None:
-            audio = await self.audio_in_socket.recv()
-            audio = np.frombuffer(audio, dtype=np.float32)
-            speech = await self._transcribe(audio)
-            if not speech:
-                self.agent.logger.info("Nothing transcribed.")
-                return
-
-            self.agent.logger.info("Transcribed speech: %s", speech)
-
-            await self._share_transcription(speech)
+        self.logger.info("Finished setting up %s", self.name)
 
     async def stop(self):
+        assert self.audio_in_socket is not None
         self.audio_in_socket.close()
         self.audio_in_socket = None
         return await super().stop()
@@ -75,13 +52,37 @@ class TranscriptionAgent(BaseAgent):
         self.audio_in_socket.setsockopt_string(zmq.SUBSCRIBE, "")
         self.audio_in_socket.connect(self.audio_in_address)
 
-    async def setup(self):
-        self.logger.info("Setting up %s", self.jid)
+    async def _transcribe(self, audio: np.ndarray) -> str:
+        assert self._concurrency is not None and self.speech_recognizer is not None
+        async with self._concurrency:
+            return await asyncio.to_thread(self.speech_recognizer.recognize_speech, audio)
 
-        self._connect_audio_in_socket()
+    async def _share_transcription(self, transcription: str):
+        """Share a transcription to the other agents that depend on it."""
+        receiver_names = [
+            settings.agent_settings.text_belief_extractor_name,
+        ]
 
-        transcribing = self.TranscribingBehaviour(self.audio_in_socket)
-        transcribing.warmup()
-        self.add_behaviour(transcribing)
+        for receiver_name in receiver_names:
+            message = InternalMessage(
+                to=receiver_name,
+                sender=self.name,
+                body=transcription,
+            )
+            await self.send(message)
 
-        self.logger.info("Finished setting up %s", self.jid)
+    async def _transcribing_loop(self) -> None:
+        while self._running:
+            try:
+                assert self.audio_in_socket is not None
+                audio_data = await self.audio_in_socket.recv()
+                audio = np.frombuffer(audio_data, dtype=np.float32)
+                speech = await self._transcribe(audio)
+                if not speech:
+                    self.logger.info("Nothing transcribed.")
+                    continue
+
+                self.logger.info("Transcribed speech: %s", speech)
+                await self._share_transcription(speech)
+            except Exception as e:
+                self.logger.error(f"Error in transcription loop: {e}")

src/control_backend/agents/perception/vad_agent.py

@@ -1,8 +1,9 @@
+import asyncio
+
 import numpy as np
 import torch
 import zmq
 import zmq.asyncio as azmq
-from spade.behaviour import CyclicBehaviour
 
 from control_backend.agents import BaseAgent
 from control_backend.core.config import settings
@@ -26,7 +27,7 @@ class SocketPoller[T]:
         :param timeout_ms: A timeout in milliseconds to wait for data.
         """
         self.socket = socket
-        self.poller = zmq.Poller()
+        self.poller = azmq.Poller()
         self.poller.register(self.socket, zmq.POLLIN)
         self.timeout_ms = timeout_ms
 
@@ -38,81 +39,12 @@ class SocketPoller[T]:
         :return: Data from the socket or None.
         """
         timeout_ms = timeout_ms or self.timeout_ms
-        socks = dict(self.poller.poll(timeout_ms))
+        socks = dict(await self.poller.poll(timeout_ms))
         if socks.get(self.socket) == zmq.POLLIN:
             return await self.socket.recv()
         return None
 
 
-class StreamingBehaviour(CyclicBehaviour):
-    def __init__(self, audio_in_socket: azmq.Socket, audio_out_socket: azmq.Socket):
-        super().__init__()
-        self.audio_in_poller = SocketPoller[bytes](audio_in_socket)
-        self.model, _ = torch.hub.load(
-            repo_or_dir=settings.vad_settings.repo_or_dir,
-            model=settings.vad_settings.model_name,
-            force_reload=False,
-        )
-        self.audio_out_socket = audio_out_socket
-
-        self.audio_buffer = np.array([], dtype=np.float32)
-        self.i_since_speech = (
-            settings.behaviour_settings.vad_initial_since_speech
-        )  # Used to allow small pauses in speech
-        self._ready = False
-
-    async def reset(self):
-        """Clears the ZeroMQ queue and tells this behavior to start."""
-        discarded = 0
-        # Poll for the shortest amount of time possible to clear the queue
-        while await self.audio_in_poller.poll(1) is not None:
-            discarded += 1
-        self.agent.logger.info(f"Discarded {discarded} audio packets before starting.")
-        self._ready = True
-
-    async def run(self) -> None:
-        if not self._ready:
-            return
-
-        data = await self.audio_in_poller.poll()
-        if data is None:
-            if len(self.audio_buffer) > 0:
-                self.agent.logger.debug(
-                    "No audio data received. Discarding buffer until new data arrives."
-                )
-                self.audio_buffer = np.array([], dtype=np.float32)
-                self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
-            return
-
-        # copy otherwise Torch will be sad that it's immutable
-        chunk = np.frombuffer(data, dtype=np.float32).copy()
-        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
-        prob_threshold = settings.behaviour_settings.vad_prob_threshold
-
-        if prob > prob_threshold:
-            if self.i_since_speech > non_speech_patience:
-                self.agent.logger.debug("Speech started.")
-            self.audio_buffer = np.append(self.audio_buffer, chunk)
-            self.i_since_speech = 0
-            return
-        self.i_since_speech += 1
-
-        # prob < 0.5, so speech maybe ended. Wait a bit more before to be more certain
-        if self.i_since_speech <= non_speech_patience:
-            self.audio_buffer = np.append(self.audio_buffer, chunk)
-            return
-
-        # Speech probably ended. Make sure we have a usable amount of data.
-        if len(self.audio_buffer) >= 3 * len(chunk):
-            self.agent.logger.debug("Speech ended.")
-            await self.audio_out_socket.send(self.audio_buffer[: -2 * len(chunk)].tobytes())
-
-        # 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
-
-
 class VADAgent(BaseAgent):
     """
     An agent which listens to an audio stream, does Voice Activity Detection (VAD), and sends
@@ -120,16 +52,54 @@ class VADAgent(BaseAgent):
     """
 
     def __init__(self, audio_in_address: str, audio_in_bind: bool):
-        jid = settings.agent_settings.vad_name + "@" + settings.agent_settings.host
-        super().__init__(jid, settings.agent_settings.vad_name)
+        super().__init__(settings.agent_settings.vad_name)
 
         self.audio_in_address = audio_in_address
         self.audio_in_bind = audio_in_bind
 
         self.audio_in_socket: azmq.Socket | None = None
         self.audio_out_socket: azmq.Socket | None = None
+        self.audio_in_poller: SocketPoller | None = None
 
-        self.streaming_behaviour: StreamingBehaviour | None = None
+        self.audio_buffer = np.array([], dtype=np.float32)
+        self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
+        self._ready = False
+        self.model = None
+
+    async def setup(self):
+        self.logger.info("Setting up %s", self.jid)
+
+        self._connect_audio_in_socket()
+
+        audio_out_port = self._connect_audio_out_socket()
+        if audio_out_port is None:
+            self.logger.error("Could not bind output socket, stopping.")
+            await self.stop()
+            return
+        audio_out_address = f"tcp://localhost:{audio_out_port}"
+
+        # Initialize VAD model
+        try:
+            self.model, _ = torch.hub.load(
+                repo_or_dir=settings.vad_settings.repo_or_dir,
+                model=settings.vad_settings.model_name,
+                force_reload=False,
+            )
+        except Exception:
+            self.logger.exception("Failed to load VAD model.")
+            await self.stop()
+            return
+
+        # Warmup/reset
+        await self.reset_stream()
+
+        await self.add_background_task(self._streaming_loop())
+
+        # Start agents dependent on the output audio fragments here
+        transcriber = TranscriptionAgent(audio_out_address)
+        await transcriber.start()
+
+        self.logger.info("Finished setting up %s", self.jid)
 
     async def stop(self):
         """
@@ -141,7 +111,7 @@ class VADAgent(BaseAgent):
         if self.audio_out_socket is not None:
             self.audio_out_socket.close()
             self.audio_out_socket = None
-        return await super().stop()
+        await super().stop()
 
     def _connect_audio_in_socket(self):
         self.audio_in_socket = azmq.Context.instance().socket(zmq.SUB)
@@ -156,28 +126,67 @@ class VADAgent(BaseAgent):
         """Returns the port bound, or None if binding failed."""
         try:
             self.audio_out_socket = azmq.Context.instance().socket(zmq.PUB)
-            return self.audio_out_socket.bind_to_random_port("tcp://*", max_tries=100)
+            return self.audio_out_socket.bind_to_random_port("tcp://localhost", max_tries=100)
         except zmq.ZMQBindError:
             self.logger.error("Failed to bind an audio output socket after 100 tries.")
             self.audio_out_socket = None
             return None
 
-    async def setup(self):
-        self.logger.info("Setting up %s", self.jid)
+    async def reset_stream(self):
+        """
+        Clears the ZeroMQ queue and sets ready state.
+        """
+        discarded = 0
+        assert self.audio_in_poller is not None
+        while await self.audio_in_poller.poll(1) is not None:
+            discarded += 1
+        self.logger.info(f"Discarded {discarded} audio packets before starting.")
+        self._ready = True
 
-        self._connect_audio_in_socket()
+    async def _streaming_loop(self):
+        while self._running:
+            if not self._ready:
+                await asyncio.sleep(0.1)
+                continue
 
-        audio_out_port = self._connect_audio_out_socket()
-        if audio_out_port is None:
-            await self.stop()
-            return
-        audio_out_address = f"tcp://localhost:{audio_out_port}"
+            assert self.audio_in_poller is not None
+            data = await self.audio_in_poller.poll()
+            if data is None:
+                if len(self.audio_buffer) > 0:
+                    self.logger.debug(
+                        "No audio data received. Discarding buffer until new data arrives."
+                    )
+                    self.audio_buffer = np.array([], dtype=np.float32)
+                    self.i_since_speech = settings.behaviour_settings.vad_initial_since_speech
+                continue
 
-        self.streaming_behaviour = StreamingBehaviour(self.audio_in_socket, self.audio_out_socket)
-        self.add_behaviour(self.streaming_behaviour)
+            # copy otherwise Torch will be sad that it's immutable
+            chunk = np.frombuffer(data, dtype=np.float32).copy()
+            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
+            prob_threshold = settings.behaviour_settings.vad_prob_threshold
 
-        # Start agents dependent on the output audio fragments here
-        transcriber = TranscriptionAgent(audio_out_address)
-        await transcriber.start()
+            if prob > prob_threshold:
+                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
+                continue
 
-        self.logger.info("Finished setting up %s", self.jid)
+            self.i_since_speech += 1
+
+            # prob < threshold, so speech maybe ended. Wait a bit more before to be more certain
+            if self.i_since_speech <= non_speech_patience:
+                self.audio_buffer = np.append(self.audio_buffer, chunk)
+                continue
+
+            # Speech probably ended. Make sure we have a usable amount of data.
+            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 the speech has ended.
+            # Prepend the last chunk that had no speech, for a more fluent boundary
+            self.audio_buffer = chunk

src/control_backend/core/agent_system.py

@@ -0,0 +1,87 @@
+import asyncio
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any
+
+# Central directory to resolve agent names to instances
+_agent_directory: dict[str, "BaseAgent"] = {}
+
+
+@dataclass
+class InternalMessage:
+    to: str
+    sender: str
+    body: str
+    thread: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+class AgentDirectory:
+    @staticmethod
+    def register(name: str, agent: "BaseAgent"):
+        _agent_directory[name] = agent
+
+    @staticmethod
+    def get(name: str) -> "BaseAgent | None":
+        return _agent_directory.get(name)
+
+
+class BaseAgent(ABC):
+    logger: logging.Logger
+
+    def __init__(self, name: str):
+        self.name = name
+        self.jid = name  # present for backwards compatibility
+        self.inbox: asyncio.Queue[InternalMessage] = asyncio.Queue()
+        self._tasks: set[asyncio.Task] = set()
+        self._running = False
+
+        # Register immediately
+        AgentDirectory.register(name, self)
+
+    @abstractmethod
+    async def setup(self):
+        """Override this to initialize resources."""
+        pass
+
+    async def start(self):
+        """Starts the agent and its loops."""
+        self.logger.info(f"Starting agent {self.name}")
+        self._running = True
+        await self.setup()
+
+        # Start processing inbox
+        await self.add_background_task(self._process_inbox())
+
+    async def stop(self):
+        """Stops the agent."""
+        self._running = False
+        for task in self._tasks:
+            task.cancel()
+        self.logger.info(f"Agent {self.name} stopped")
+
+    async def send(self, message: InternalMessage):
+        target = AgentDirectory.get(message.to)
+        if target:
+            await target.inbox.put(message)
+        else:
+            self.logger.warning(f"Attempted to send message to unknown agent: {message.to}")
+
+    async def _process_inbox(self):
+        """Default loop: equivalent to a CyclicBehaviour receiving messages."""
+        while self._running:
+            msg = await self.inbox.get()
+            await self.handle_message(msg)
+
+    async def handle_message(self, msg: InternalMessage):
+        """Override this to handle incoming messages."""
+        raise NotImplementedError
+
+    async def add_background_task(self, coro):
+        """Helper to run cyclic behaviors."""
+        task = asyncio.create_task(coro)
+        self._tasks.add(task)
+        task.add_done_callback(self._tasks.discard)
+
+        # await asyncio.sleep(1)

src/control_backend/main.py

@@ -7,7 +7,6 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from zmq.asyncio import Context
 
-# Act agents
 # BDI agents
 from control_backend.agents.bdi import (
     BDIBeliefCollectorAgent,
@@ -60,7 +59,6 @@ async def lifespan(app: FastAPI):
     # --- APPLICATION STARTUP ---
     setup_logging()
     logger.info("%s is starting up.", app.title)
-    logger.warning("testing extra", extra={"extra1": "one", "extra2": "two"})
 
     # Initiate sockets
     proxy_thread = threading.Thread(target=setup_sockets)
@@ -75,14 +73,12 @@ async def lifespan(app: FastAPI):
 
     # --- Initialize Agents ---
     logger.info("Initializing and starting agents.")
+
     agents_to_start = {
         "RICommunicationAgent": (
             RICommunicationAgent,
             {
                 "name": settings.agent_settings.ri_communication_name,
-                "jid": f"{settings.agent_settings.ri_communication_name}"
-                f"@{settings.agent_settings.host}",
-                "password": settings.agent_settings.ri_communication_name,
                 "address": settings.zmq_settings.ri_communication_address,
                 "bind": True,
             },
@@ -91,16 +87,12 @@ async def lifespan(app: FastAPI):
             LLMAgent,
             {
                 "name": settings.agent_settings.llm_name,
-                "jid": f"{settings.agent_settings.llm_name}@{settings.agent_settings.host}",
-                "password": settings.agent_settings.llm_name,
             },
         ),
         "BDICoreAgent": (
             BDICoreAgent,
             {
                 "name": settings.agent_settings.bdi_core_name,
-                "jid": f"{settings.agent_settings.bdi_core_name}@{settings.agent_settings.host}",
-                "password": settings.agent_settings.bdi_core_name,
                 "asl": "src/control_backend/agents/bdi/bdi_core_agent/rules.asl",
             },
         ),
@@ -108,18 +100,12 @@ async def lifespan(app: FastAPI):
             BDIBeliefCollectorAgent,
             {
                 "name": settings.agent_settings.bdi_belief_collector_name,
-                "jid": f"{settings.agent_settings.bdi_belief_collector_name}@"
-                f"{settings.agent_settings.host}",
-                "password": settings.agent_settings.bdi_belief_collector_name,
             },
         ),
         "TextBeliefExtractorAgent": (
             TextBeliefExtractorAgent,
             {
                 "name": settings.agent_settings.text_belief_extractor_name,
-                "jid": f"{settings.agent_settings.text_belief_extractor_name}@"
-                f"{settings.agent_settings.host}",
-                "password": settings.agent_settings.text_belief_extractor_name,
             },
         ),
         "VADAgent": (
@@ -128,22 +114,25 @@ async def lifespan(app: FastAPI):
         ),
     }
 
+    agents = []
+
     vad_agent = None
 
     for name, (agent_class, kwargs) in agents_to_start.items():
         try:
             logger.debug("Starting agent: %s", name)
-            agent_instance = agent_class(**{k: v for k, v in kwargs.items() if k != "name"})
+            agent_instance = agent_class(**kwargs)
             await agent_instance.start()
             if isinstance(agent_instance, VADAgent):
                 vad_agent = agent_instance
+            agents.append(agent_instance)
             logger.info("Agent '%s' started successfully.", name)
         except Exception as e:
             logger.error("Failed to start agent '%s': %s", name, e, exc_info=True)
-            # Consider if the application should continue if an agent fails to start.
             raise
 
-    await vad_agent.streaming_behaviour.reset()
+    assert vad_agent is not None
+    await vad_agent.reset_stream()
 
     logger.info("Application startup complete.")
 

test/unit/agents/bdi/bdi_core_agent/behaviours/test_belief_setter.py

@@ -3,7 +3,6 @@ import logging
 from unittest.mock import AsyncMock, MagicMock, call
 
 import pytest
-
 from control_backend.agents.bdi.bdi_core_agent.behaviours.belief_setter_behaviour import (
     BeliefSetterBehaviour,
 )

test/unit/agents/bdi/belief_collector_agent/behaviours/test_continuous_collect.py

@@ -2,7 +2,6 @@ import json
 from unittest.mock import AsyncMock, MagicMock
 
 import pytest
-
 from control_backend.agents.bdi.belief_collector_agent.behaviours.belief_collector_behaviour import (  # noqa: E501
     BeliefCollectorBehaviour,
 )

uv.lock

@@ -6,78 +6,6 @@ resolution-markers = [
     "python_full_version < '3.14'",
 ]
 
-[[package]]
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