Merge branch 'dev' into feat/pause-functionality

ref: N25B-350

.env.example

@@ -1,20 +0,0 @@
-# Example .env file. To use, make a copy, call it ".env" (i.e. removing the ".example" suffix), then you edit values.
-
-# The hostname of the Robot Interface. Change if the Control Backend and Robot Interface are running on different computers.
-RI_HOST="localhost"
-
-# URL for the local LLM API. Must be an API that implements the OpenAI Chat Completions API, but most do.
-LLM_SETTINGS__LOCAL_LLM_URL="http://localhost:1234/v1/chat/completions"
-
-# Name of the local LLM model to use.
-LLM_SETTINGS__LOCAL_LLM_MODEL="gpt-oss"
-
-# Number of non-speech chunks to wait before speech ended. A chunk is approximately 31 ms. Increasing this number allows longer pauses in speech, but also increases response time.
-BEHAVIOUR_SETTINGS__VAD_NON_SPEECH_PATIENCE_CHUNKS=3
-
-# Timeout in milliseconds for socket polling. Increase this number if network latency/jitter is high, often the case when using Wi-Fi. Perhaps 500 ms. A symptom of this issue is transcriptions getting cut off.
-BEHAVIOUR_SETTINGS__SOCKET_POLLER_TIMEOUT_MS=100
-
-
-
-# For an exhaustive list of options, see the control_backend.core.config module in the docs.

README.md

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

pyproject.toml

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

src/control_backend/agents/actuation/robot_gesture_agent.py

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

src/control_backend/agents/bdi/asl_ast.py

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

src/control_backend/agents/bdi/asl_gen.py

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

src/control_backend/agents/bdi/astv2.py

@@ -0,0 +1,272 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from enum import StrEnum
+
+
+class AstNode(ABC):
+    """
+    Abstract base class for all elements of an AgentSpeak program.
+    """
+
+    @abstractmethod
+    def _to_agentspeak(self) -> str:
+        """
+        Generates the AgentSpeak code string.
+        """
+        pass
+
+    def __str__(self) -> str:
+        return self._to_agentspeak()
+
+
+class AstExpression(AstNode, ABC):
+    """
+    Intermediate class for anything that can be used in a logical expression.
+    """
+
+    def __and__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.AND, _coalesce_expr(other))
+
+    def __or__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.OR, _coalesce_expr(other))
+
+    def __invert__(self) -> AstLogicalExpression:
+        if isinstance(self, AstLogicalExpression):
+            self.negated = not self.negated
+            return self
+        return AstLogicalExpression(self, negated=True)
+
+
+type ExprCoalescible = AstExpression | str | int | float
+
+
+def _coalesce_expr(value: ExprCoalescible) -> AstExpression:
+    if isinstance(value, AstExpression):
+        return value
+    if isinstance(value, str):
+        return AstString(value)
+    if isinstance(value, (int, float)):
+        return AstNumber(value)
+    raise TypeError(f"Cannot coalesce type {type(value)} into an AstTerm.")
+
+
+@dataclass
+class AstTerm(AstExpression, ABC):
+    """
+    Base class for terms appearing inside literals.
+    """
+
+    def __ge__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.GREATER_EQUALS, _coalesce_expr(other))
+
+    def __gt__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.GREATER_THAN, _coalesce_expr(other))
+
+    def __le__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.LESS_EQUALS, _coalesce_expr(other))
+
+    def __lt__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.LESS_THAN, _coalesce_expr(other))
+
+    def __eq__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.EQUALS, _coalesce_expr(other))
+
+    def __ne__(self, other: ExprCoalescible) -> AstBinaryOp:
+        return AstBinaryOp(self, BinaryOperatorType.NOT_EQUALS, _coalesce_expr(other))
+
+
+@dataclass
+class AstAtom(AstTerm):
+    """
+    Grounded expression in all lowercase.
+    """
+
+    value: str
+
+    def _to_agentspeak(self) -> str:
+        return self.value.lower()
+
+
+@dataclass
+class AstVar(AstTerm):
+    """
+    Ungrounded variable expression. First letter capitalized.
+    """
+
+    name: str
+
+    def _to_agentspeak(self) -> str:
+        return self.name.capitalize()
+
+
+@dataclass
+class AstNumber(AstTerm):
+    value: int | float
+
+    def _to_agentspeak(self) -> str:
+        return str(self.value)
+
+
+@dataclass
+class AstString(AstTerm):
+    value: str
+
+    def _to_agentspeak(self) -> str:
+        return f'"{self.value}"'
+
+
+@dataclass
+class AstLiteral(AstTerm):
+    functor: str
+    terms: list[AstTerm] = field(default_factory=list)
+
+    def _to_agentspeak(self) -> str:
+        if not self.terms:
+            return self.functor
+        args = ", ".join(map(str, self.terms))
+        return f"{self.functor}({args})"
+
+
+class BinaryOperatorType(StrEnum):
+    AND = "&"
+    OR = "|"
+    GREATER_THAN = ">"
+    LESS_THAN = "<"
+    EQUALS = "=="
+    NOT_EQUALS = "\\=="
+    GREATER_EQUALS = ">="
+    LESS_EQUALS = "<="
+
+
+@dataclass
+class AstBinaryOp(AstExpression):
+    left: AstExpression
+    operator: BinaryOperatorType
+    right: AstExpression
+
+    def __post_init__(self):
+        self.left = _as_logical(self.left)
+        self.right = _as_logical(self.right)
+
+    def _to_agentspeak(self) -> str:
+        l_str = str(self.left)
+        r_str = str(self.right)
+
+        assert isinstance(self.left, AstLogicalExpression)
+        assert isinstance(self.right, AstLogicalExpression)
+
+        if isinstance(self.left.expression, AstBinaryOp) or self.left.negated:
+            l_str = f"({l_str})"
+        if isinstance(self.right.expression, AstBinaryOp) or self.right.negated:
+            r_str = f"({r_str})"
+
+        return f"{l_str} {self.operator.value} {r_str}"
+
+
+@dataclass
+class AstLogicalExpression(AstExpression):
+    expression: AstExpression
+    negated: bool = False
+
+    def _to_agentspeak(self) -> str:
+        expr_str = str(self.expression)
+        if isinstance(self.expression, AstBinaryOp) and self.negated:
+            expr_str = f"({expr_str})"
+        return f"{'not ' if self.negated else ''}{expr_str}"
+
+
+def _as_logical(expr: AstExpression) -> AstLogicalExpression:
+    if isinstance(expr, AstLogicalExpression):
+        return expr
+    return AstLogicalExpression(expr)
+
+
+class StatementType(StrEnum):
+    EMPTY = ""
+    DO_ACTION = "."
+    ACHIEVE_GOAL = "!"
+    # TEST_GOAL = "?"  # TODO
+    ADD_BELIEF = "+"
+    REMOVE_BELIEF = "-"
+
+
+@dataclass
+class AstStatement(AstNode):
+    """
+    A statement that can appear inside a plan.
+    """
+
+    type: StatementType
+    expression: AstExpression
+
+    def _to_agentspeak(self) -> str:
+        return f"{self.type.value}{self.expression}"
+
+
+@dataclass
+class AstRule(AstNode):
+    result: AstExpression
+    condition: AstExpression | None = None
+
+    def __post_init__(self):
+        if self.condition is not None:
+            self.condition = _as_logical(self.condition)
+
+    def _to_agentspeak(self) -> str:
+        if not self.condition:
+            return f"{self.result}."
+        return f"{self.result} :- {self.condition}."
+
+
+class TriggerType(StrEnum):
+    ADDED_BELIEF = "+"
+    # REMOVED_BELIEF = "-"  # TODO
+    # MODIFIED_BELIEF = "^"  # TODO
+    ADDED_GOAL = "+!"
+    # REMOVED_GOAL = "-!"  # TODO
+
+
+@dataclass
+class AstPlan(AstNode):
+    type: TriggerType
+    trigger_literal: AstExpression
+    context: list[AstExpression]
+    body: list[AstStatement]
+
+    def _to_agentspeak(self) -> str:
+        assert isinstance(self.trigger_literal, AstLiteral)
+
+        indent = " " * 6
+        colon = "  :   "
+        arrow = "  <-  "
+
+        lines = []
+
+        lines.append(f"{self.type.value}{self.trigger_literal}")
+
+        if self.context:
+            lines.append(colon + f" &\n{indent}".join(str(c) for c in self.context))
+
+        if self.body:
+            lines.append(arrow + f";\n{indent}".join(str(s) for s in self.body) + ".")
+
+        lines.append("")
+
+        return "\n".join(lines)
+
+
+@dataclass
+class AstProgram(AstNode):
+    rules: list[AstRule] = field(default_factory=list)
+    plans: list[AstPlan] = field(default_factory=list)
+
+    def _to_agentspeak(self) -> str:
+        lines = []
+        lines.extend(map(str, self.rules))
+
+        lines.extend(["", ""])
+        lines.extend(map(str, self.plans))
+
+        return "\n".join(lines)

src/control_backend/agents/bdi/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
 
@@ -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
@@ -145,22 +145,29 @@ 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)
 
-    def _add_belief(self, name: str, args: Iterable[str] = []):
+        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.
 
@@ -168,9 +175,13 @@ class BDICoreAgent(BaseAgent):
         :param args: Arguments for the belief.
         """
         # new_args = (agentspeak.Literal(arg) for arg in args)  # TODO: Eventually support multiple
-        merged_args = DELIMITER.join(arg for arg in args)
-        new_args = (agentspeak.Literal(merged_args),)
-        term = agentspeak.Literal(name, new_args)
+        args = args or []
+        if args:
+            merged_args = DELIMITER.join(arg for arg in args)
+            new_args = (agentspeak.Literal(merged_args),)
+            term = agentspeak.Literal(name, new_args)
+        else:
+            term = agentspeak.Literal(name)
 
         self.bdi_agent.call(
             agentspeak.Trigger.addition,
@@ -238,8 +249,7 @@ class BDICoreAgent(BaseAgent):
         @self.actions.add(".reply", 3)
         def _reply(agent: "BDICoreAgent", term, intention):
             """
-            Sends text to the LLM (AgentSpeak action).
-            Example: .reply("Hello LLM!", "Some norm", "Some goal")
+            Let the LLM generate a response to a user's utterance with the current norms and goals.
             """
             message_text = agentspeak.grounded(term.args[0], intention.scope)
             norms = agentspeak.grounded(term.args[1], intention.scope)
@@ -252,15 +262,71 @@ class BDICoreAgent(BaseAgent):
             asyncio.create_task(self._send_to_llm(str(message_text), str(norms), str(goals)))
             yield
 
-    async def _send_to_llm(self, text: str, norms: str = None, goals: str = None):
+        @self.actions.add(".reply_with_goal", 3)
+        def _reply_with_goal(agent: "BDICoreAgent", term, intention):
+            """
+            Let the LLM generate a response to a user's utterance with the current norms and a
+            specific goal.
+            """
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+            norms = agentspeak.grounded(term.args[1], intention.scope)
+            goal = agentspeak.grounded(term.args[2], intention.scope)
+
+            self.logger.debug(
+                '"reply_with_goal" action called with message=%s, norms=%s, goal=%s',
+                message_text,
+                norms,
+                goal,
+            )
+            # asyncio.create_task(self._send_to_llm(str(message_text), norms, str(goal)))
+            yield
+
+        @self.actions.add(".say", 1)
+        def _say(agent: "BDICoreAgent", term, intention):
+            """
+            Make the robot say the given text instantly.
+            """
+            message_text = agentspeak.grounded(term.args[0], intention.scope)
+
+            self.logger.debug('"say" action called with text=%s', message_text)
+
+            # speech_command = SpeechCommand(data=message_text)
+            # speech_message = InternalMessage(
+            #     to=settings.agent_settings.robot_speech_name,
+            #     sender=settings.agent_settings.bdi_core_name,
+            #     body=speech_command.model_dump_json(),
+            # )
+            # asyncio.create_task(agent.send(speech_message))
+            yield
+
+        @self.actions.add(".gesture", 2)
+        def _gesture(agent: "BDICoreAgent", term, intention):
+            """
+            Make the robot perform the given gesture instantly.
+            """
+            gesture_type = agentspeak.grounded(term.args[0], intention.scope)
+            gesture_name = agentspeak.grounded(term.args[1], intention.scope)
+
+            self.logger.debug(
+                '"gesture" action called with type=%s, name=%s',
+                gesture_type,
+                gesture_name,
+            )
+
+            # gesture = Gesture(type=gesture_type, name=gesture_name)
+            # gesture_message = InternalMessage(
+            #     to=settings.agent_settings.robot_gesture_name,
+            #     sender=settings.agent_settings.bdi_core_name,
+            #     body=gesture.model_dump_json(),
+            # )
+            # asyncio.create_task(agent.send(gesture_message))
+            yield
+
+    async def _send_to_llm(self, text: str, norms: str, goals: str):
         """
         Sends a text query to the LLM agent asynchronously.
         """
-        prompt = LLMPromptMessage(
-            text=text,
-            norms=norms.split("\n") if norms else [],
-            goals=goals.split("\n") if norms else [],
-        )
+        prompt = LLMPromptMessage(text=text, norms=norms.split("\n"), goals=goals.split("\n"))
         msg = InternalMessage(
             to=settings.agent_settings.llm_name,
             sender=self.name,

src/control_backend/agents/bdi/bdi_program_manager.py

@@ -1,12 +1,598 @@
+import uuid
+from collections.abc import Iterable
+
 import zmq
 from pydantic import ValidationError
+from slugify import slugify
 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.belief_message import Belief, BeliefMessage
-from control_backend.schemas.program import Program
+from control_backend.schemas.program import (
+    Action,
+    BasicBelief,
+    BasicNorm,
+    Belief,
+    ConditionalNorm,
+    GestureAction,
+    Goal,
+    InferredBelief,
+    KeywordBelief,
+    LLMAction,
+    LogicalOperator,
+    Phase,
+    Plan,
+    Program,
+    ProgramElement,
+    SemanticBelief,
+    SpeechAction,
+    Trigger,
+)
+
+test_program = Program(
+    phases=[
+        Phase(
+            norms=[
+                BasicNorm(norm="Talk like a pirate", id=uuid.uuid4()),
+                ConditionalNorm(
+                    condition=InferredBelief(
+                        left=KeywordBelief(keyword="Arr", id=uuid.uuid4()),
+                        right=SemanticBelief(
+                            description="testing", name="semantic belief", id=uuid.uuid4()
+                        ),
+                        operator=LogicalOperator.OR,
+                        name="Talking to a pirate",
+                        id=uuid.uuid4(),
+                    ),
+                    norm="Use nautical terms",
+                    id=uuid.uuid4(),
+                ),
+                ConditionalNorm(
+                    condition=SemanticBelief(
+                        description="We are talking to a child",
+                        name="talking to child",
+                        id=uuid.uuid4(),
+                    ),
+                    norm="Do not use cuss words",
+                    id=uuid.uuid4(),
+                ),
+            ],
+            triggers=[
+                Trigger(
+                    condition=InferredBelief(
+                        left=KeywordBelief(keyword="key", id=uuid.uuid4()),
+                        right=InferredBelief(
+                            left=KeywordBelief(keyword="key2", id=uuid.uuid4()),
+                            right=SemanticBelief(
+                                description="Decode this", name="semantic belief 2", id=uuid.uuid4()
+                            ),
+                            operator=LogicalOperator.OR,
+                            name="test trigger inferred inner",
+                            id=uuid.uuid4(),
+                        ),
+                        operator=LogicalOperator.OR,
+                        name="test trigger inferred outer",
+                        id=uuid.uuid4(),
+                    ),
+                    plan=Plan(
+                        steps=[
+                            SpeechAction(text="Testing trigger", id=uuid.uuid4()),
+                            Goal(
+                                name="Testing trigger",
+                                plan=Plan(
+                                    steps=[LLMAction(goal="Do something", id=uuid.uuid4())],
+                                    id=uuid.uuid4(),
+                                ),
+                                id=uuid.uuid4(),
+                            ),
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                )
+            ],
+            goals=[
+                Goal(
+                    name="Determine user age",
+                    plan=Plan(
+                        steps=[LLMAction(goal="Determine the age of the user.", id=uuid.uuid4())],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Find the user's name",
+                    plan=Plan(
+                        steps=[
+                            Goal(
+                                name="Greet the user",
+                                plan=Plan(
+                                    steps=[LLMAction(goal="Greet the user.", id=uuid.uuid4())],
+                                    id=uuid.uuid4(),
+                                ),
+                                can_fail=False,
+                                id=uuid.uuid4(),
+                            ),
+                            Goal(
+                                name="Ask for name",
+                                plan=Plan(
+                                    steps=[
+                                        LLMAction(goal="Obtain the user's name.", id=uuid.uuid4())
+                                    ],
+                                    id=uuid.uuid4(),
+                                ),
+                                id=uuid.uuid4(),
+                            ),
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Tell a joke",
+                    plan=Plan(
+                        steps=[LLMAction(goal="Tell a joke.", id=uuid.uuid4())], id=uuid.uuid4()
+                    ),
+                    id=uuid.uuid4(),
+                ),
+            ],
+            id=uuid.uuid4(),
+        ),
+        Phase(
+            id=uuid.uuid4(),
+            norms=[
+                BasicNorm(norm="Use very gentle speech.", id=uuid.uuid4()),
+                ConditionalNorm(
+                    condition=SemanticBelief(
+                        description="We are talking to a child",
+                        name="talking to child",
+                        id=uuid.uuid4(),
+                    ),
+                    norm="Do not use cuss words",
+                    id=uuid.uuid4(),
+                ),
+            ],
+            triggers=[
+                Trigger(
+                    condition=InferredBelief(
+                        left=KeywordBelief(keyword="help", id=uuid.uuid4()),
+                        right=SemanticBelief(
+                            description="User is stuck", name="stuck", id=uuid.uuid4()
+                        ),
+                        operator=LogicalOperator.OR,
+                        name="help_or_stuck",
+                        id=uuid.uuid4(),
+                    ),
+                    plan=Plan(
+                        steps=[
+                            Goal(
+                                name="Unblock user",
+                                plan=Plan(
+                                    steps=[
+                                        LLMAction(
+                                            goal="Provide a step-by-step path to "
+                                            "resolve the user's issue.",
+                                            id=uuid.uuid4(),
+                                        )
+                                    ],
+                                    id=uuid.uuid4(),
+                                ),
+                                id=uuid.uuid4(),
+                            ),
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+            ],
+            goals=[
+                Goal(
+                    name="Clarify intent",
+                    plan=Plan(
+                        steps=[
+                            LLMAction(
+                                goal="Ask 1-2 targeted questions to clarify the "
+                                "user's intent, then proceed.",
+                                id=uuid.uuid4(),
+                            )
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Provide solution",
+                    plan=Plan(
+                        steps=[
+                            LLMAction(
+                                goal="Deliver a solution to complete the user's goal.",
+                                id=uuid.uuid4(),
+                            )
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+                Goal(
+                    name="Summarize next steps",
+                    plan=Plan(
+                        steps=[
+                            LLMAction(
+                                goal="Summarize what the user should do next.", id=uuid.uuid4()
+                            )
+                        ],
+                        id=uuid.uuid4(),
+                    ),
+                    id=uuid.uuid4(),
+                ),
+            ],
+        ),
+    ]
+)
+
+
+def do_things():
+    print(AgentSpeakGenerator().generate(test_program))
+
+
+class AgentSpeakGenerator:
+    """
+    Converts Pydantic representation of behavior programs into AgentSpeak(L) code string.
+    """
+
+    arrow_prefix = f"{' ' * 2}<-{' ' * 2}"
+    colon_prefix = f"{' ' * 2}:{' ' * 3}"
+    indent_prefix = " " * 6
+
+    def generate(self, program: Program) -> str:
+        lines = []
+        lines.append("")
+
+        lines += self._generate_initial_beliefs(program)
+
+        lines += self._generate_basic_flow(program)
+
+        lines += self._generate_phase_transitions(program)
+
+        lines += self._generate_norms(program)
+
+        lines += self._generate_belief_inference(program)
+
+        lines += self._generate_goals(program)
+
+        lines += self._generate_triggers(program)
+
+        return "\n".join(lines)
+
+    def _generate_initial_beliefs(self, program: Program) -> Iterable[str]:
+        yield "// --- Initial beliefs and agent startup ---"
+
+        yield "phase(start)."
+
+        yield ""
+
+        yield "+started"
+        yield f"{self.colon_prefix}phase(start)"
+        yield f"{self.arrow_prefix}phase({program.phases[0].id if program.phases else 'end'})."
+
+        yield from ["", ""]
+
+    def _generate_basic_flow(self, program: Program) -> Iterable[str]:
+        yield "// --- Basic flow ---"
+
+        for phase in program.phases:
+            yield from self._generate_basic_flow_per_phase(phase)
+
+        yield from ["", ""]
+
+    def _generate_basic_flow_per_phase(self, phase: Phase) -> Iterable[str]:
+        yield "+user_said(Message)"
+        yield f"{self.colon_prefix}phase({phase.id})"
+
+        goals = phase.goals
+        if goals:
+            yield f"{self.arrow_prefix}{self._slugify(goals[0], include_prefix=True)}"
+            for goal in goals[1:]:
+                yield f"{self.indent_prefix}{self._slugify(goal, include_prefix=True)}"
+
+        yield f"{self.indent_prefix if goals else self.arrow_prefix}!transition_phase."
+
+    def _generate_phase_transitions(self, program: Program) -> Iterable[str]:
+        yield "// --- Phase transitions ---"
+
+        if len(program.phases) == 0:
+            yield from ["", ""]
+            return
+
+        # TODO: remove outdated things
+
+        for i in range(-1, len(program.phases)):
+            predecessor = program.phases[i] if i >= 0 else None
+            successor = program.phases[i + 1] if i < len(program.phases) - 1 else None
+            yield from self._generate_phase_transition(predecessor, successor)
+
+        yield from self._generate_phase_transition(None, None)  # to avoid failing plan
+
+        yield from ["", ""]
+
+    def _generate_phase_transition(
+        self, phase: Phase | None = None, next_phase: Phase | None = None
+    ) -> Iterable[str]:
+        yield "+!transition_phase"
+
+        if phase is None and next_phase is None:  # base case true to avoid failing plan
+            yield f"{self.arrow_prefix}true."
+            return
+
+        yield f"{self.colon_prefix}phase({phase.id if phase else 'start'})"
+        yield f"{self.arrow_prefix}-+phase({next_phase.id if next_phase else 'end'})."
+
+    def _generate_norms(self, program: Program) -> Iterable[str]:
+        yield "// --- Norms ---"
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if type(norm) is BasicNorm:
+                    yield f"{self._slugify(norm)} :- phase({phase.id})."
+                if type(norm) is ConditionalNorm:
+                    yield (
+                        f"{self._slugify(norm)} :- phase({phase.id}) & "
+                        f"{self._slugify(norm.condition)}."
+                    )
+
+        yield from ["", ""]
+
+    def _generate_belief_inference(self, program: Program) -> Iterable[str]:
+        yield "// --- Belief inference rules ---"
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if not isinstance(norm, ConditionalNorm):
+                    continue
+
+                yield from self._belief_inference_recursive(norm.condition)
+
+            for trigger in phase.triggers:
+                yield from self._belief_inference_recursive(trigger.condition)
+
+        yield from ["", ""]
+
+    def _belief_inference_recursive(self, belief: Belief) -> Iterable[str]:
+        if type(belief) is KeywordBelief:
+            yield (
+                f"{self._slugify(belief)} :- user_said(Message) & "
+                f'.substring(Message, "{belief.keyword}", Pos) & Pos >= 0.'
+            )
+        if type(belief) is InferredBelief:
+            yield (
+                f"{self._slugify(belief)} :- {self._slugify(belief.left)} "
+                f"{'&' if belief.operator == LogicalOperator.AND else '|'} "
+                f"{self._slugify(belief.right)}."
+            )
+
+            yield from self._belief_inference_recursive(belief.left)
+            yield from self._belief_inference_recursive(belief.right)
+
+    def _generate_goals(self, program: Program) -> Iterable[str]:
+        yield "// --- Goals ---"
+
+        for phase in program.phases:
+            previous_goal: Goal | None = None
+            for goal in phase.goals:
+                yield from self._generate_goal_plan_recursive(goal, phase, previous_goal)
+                previous_goal = goal
+
+        yield from ["", ""]
+
+    def _generate_goal_plan_recursive(
+        self, goal: Goal, phase: Phase, previous_goal: Goal | None = None
+    ) -> Iterable[str]:
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+
+        # Context
+        yield f"{self.colon_prefix}phase({phase.id}) &"
+        yield f"{self.indent_prefix}not responded_this_turn &"
+        yield f"{self.indent_prefix}not achieved_{self._slugify(goal)} &"
+        if previous_goal:
+            yield f"{self.indent_prefix}achieved_{self._slugify(previous_goal)}"
+        else:
+            yield f"{self.indent_prefix}true"
+
+        extra_goals_to_generate = []
+
+        steps = goal.plan.steps
+
+        if len(steps) == 0:
+            yield f"{self.arrow_prefix}true."
+            return
+
+        first_step = steps[0]
+        yield (
+            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
+            f"{'.' if len(steps) == 1 and goal.can_fail else ';'}"
+        )
+        if isinstance(first_step, Goal):
+            extra_goals_to_generate.append(first_step)
+
+        for step in steps[1:-1]:
+            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
+            if isinstance(step, Goal):
+                extra_goals_to_generate.append(step)
+
+        if len(steps) > 1:
+            last_step = steps[-1]
+            yield (
+                f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}"
+                f"{'.' if goal.can_fail else ';'}"
+            )
+            if isinstance(last_step, Goal):
+                extra_goals_to_generate.append(last_step)
+
+        if not goal.can_fail:
+            yield f"{self.indent_prefix}+achieved_{self._slugify(goal)}."
+
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+        yield f"{self.arrow_prefix}true."
+
+        yield ""
+
+        extra_previous_goal: Goal | None = None
+        for extra_goal in extra_goals_to_generate:
+            yield from self._generate_goal_plan_recursive(extra_goal, phase, extra_previous_goal)
+            extra_previous_goal = extra_goal
+
+    def _generate_triggers(self, program: Program) -> Iterable[str]:
+        yield "// --- Triggers ---"
+
+        for phase in program.phases:
+            for trigger in phase.triggers:
+                yield from self._generate_trigger_plan(trigger, phase)
+
+        yield from ["", ""]
+
+    def _generate_trigger_plan(self, trigger: Trigger, phase: Phase) -> Iterable[str]:
+        belief_name = self._slugify(trigger.condition)
+
+        yield f"+{belief_name}"
+        yield f"{self.colon_prefix}phase({phase.id})"
+
+        extra_goals_to_generate = []
+
+        steps = trigger.plan.steps
+
+        if len(steps) == 0:
+            yield f"{self.arrow_prefix}true."
+            return
+
+        first_step = steps[0]
+        yield (
+            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
+            f"{'.' if len(steps) == 1 else ';'}"
+        )
+        if isinstance(first_step, Goal):
+            extra_goals_to_generate.append(first_step)
+
+        for step in steps[1:-1]:
+            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
+            if isinstance(step, Goal):
+                extra_goals_to_generate.append(step)
+
+        if len(steps) > 1:
+            last_step = steps[-1]
+            yield f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}."
+            if isinstance(last_step, Goal):
+                extra_goals_to_generate.append(last_step)
+
+        yield ""
+
+        extra_previous_goal: Goal | None = None
+        for extra_goal in extra_goals_to_generate:
+            yield from self._generate_trigger_plan_recursive(extra_goal, phase, extra_previous_goal)
+            extra_previous_goal = extra_goal
+
+    def _generate_trigger_plan_recursive(
+        self, goal: Goal, phase: Phase, previous_goal: Goal | None = None
+    ) -> Iterable[str]:
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+
+        extra_goals_to_generate = []
+
+        steps = goal.plan.steps
+
+        if len(steps) == 0:
+            yield f"{self.arrow_prefix}true."
+            return
+
+        first_step = steps[0]
+        yield (
+            f"{self.arrow_prefix}{self._slugify(first_step, include_prefix=True)}"
+            f"{'.' if len(steps) == 1 and goal.can_fail else ';'}"
+        )
+        if isinstance(first_step, Goal):
+            extra_goals_to_generate.append(first_step)
+
+        for step in steps[1:-1]:
+            yield f"{self.indent_prefix}{self._slugify(step, include_prefix=True)};"
+            if isinstance(step, Goal):
+                extra_goals_to_generate.append(step)
+
+        if len(steps) > 1:
+            last_step = steps[-1]
+            yield (
+                f"{self.indent_prefix}{self._slugify(last_step, include_prefix=True)}"
+                f"{'.' if goal.can_fail else ';'}"
+            )
+            if isinstance(last_step, Goal):
+                extra_goals_to_generate.append(last_step)
+
+        if not goal.can_fail:
+            yield f"{self.indent_prefix}+achieved_{self._slugify(goal)}."
+
+        yield f"+{self._slugify(goal, include_prefix=True)}"
+        yield f"{self.arrow_prefix}true."
+
+        yield ""
+
+        extra_previous_goal: Goal | None = None
+        for extra_goal in extra_goals_to_generate:
+            yield from self._generate_goal_plan_recursive(extra_goal, phase, extra_previous_goal)
+            extra_previous_goal = extra_goal
+
+    def _slugify(self, element: ProgramElement, include_prefix: bool = False) -> str:
+        def base_slugify_call(text: str):
+            return slugify(text, separator="_", stopwords=["a", "the"])
+
+        if type(element) is KeywordBelief:
+            return f'keyword_said("{element.keyword}")'
+
+        if type(element) is SemanticBelief:
+            name = element.name
+            return f"semantic_{base_slugify_call(name if name else element.description)}"
+
+        if isinstance(element, BasicNorm):
+            return f'norm("{element.norm}")'
+
+        if isinstance(element, Goal):
+            return f"{'!' if include_prefix else ''}{base_slugify_call(element.name)}"
+
+        if isinstance(element, SpeechAction):
+            return f'.say("{element.text}")'
+
+        if isinstance(element, GestureAction):
+            return f'.gesture("{element.gesture}")'
+
+        if isinstance(element, LLMAction):
+            return f'!generate_response_with_goal("{element.goal}")'
+
+        if isinstance(element, Action.__value__):
+            raise NotImplementedError(
+                "Have not implemented an ASL string representation for this action."
+            )
+
+        if element.name == "":
+            raise ValueError("Name must be initialized for this type of ProgramElement.")
+
+        return base_slugify_call(element.name)
+
+    def _extract_basic_beliefs_from_program(self, program: Program) -> list[BasicBelief]:
+        beliefs = []
+
+        for phase in program.phases:
+            for norm in phase.norms:
+                if isinstance(norm, ConditionalNorm):
+                    beliefs += self._extract_basic_beliefs_from_belief(norm.condition)
+
+            for trigger in phase.triggers:
+                beliefs += self._extract_basic_beliefs_from_belief(trigger.condition)
+
+        return beliefs
+
+    def _extract_basic_beliefs_from_belief(self, belief: Belief) -> list[BasicBelief]:
+        if isinstance(belief, InferredBelief):
+            return self._extract_basic_beliefs_from_belief(
+                belief.left
+            ) + self._extract_basic_beliefs_from_belief(belief.right)
+        return [belief]
 
 
 class BDIProgramManager(BaseAgent):
@@ -25,55 +611,40 @@ class BDIProgramManager(BaseAgent):
         super().__init__(**kwargs)
         self.sub_socket = None
 
-    async def _send_to_bdi(self, program: Program):
-        """
-        Convert a received program into BDI beliefs and send them to the BDI Core Agent.
-
-        Currently, it takes the **first phase** of the program and extracts:
-        -   **Norms**: Constraints or rules the agent must follow.
-        -   **Goals**: Objectives the agent must achieve.
-
-        These are sent as a ``BeliefMessage`` with ``replace=True``, meaning they will
-        overwrite any existing norms/goals of the same name in the BDI agent.
-
-        :param program: The program object received from the API.
-        """
-        first_phase = program.phases[0]
-        norms_belief = Belief(
-            name="norms",
-            arguments=[norm.norm for norm in first_phase.norms],
-            replace=True,
-        )
-        goals_belief = Belief(
-            name="goals",
-            arguments=[goal.description for goal in first_phase.goals],
-            replace=True,
-        )
-        program_beliefs = BeliefMessage(beliefs=[norms_belief, goals_belief])
-
-        message = InternalMessage(
-            to=settings.agent_settings.bdi_core_name,
-            sender=self.name,
-            body=program_beliefs.model_dump_json(),
-            thread="beliefs",
-        )
-        await self.send(message)
-        self.logger.debug("Sent new norms and goals to the BDI agent.")
-
-    async def _send_clear_llm_history(self):
-        """
-        Clear the LLM Agent's conversation history.
-
-        Sends an empty history to the LLM Agent to reset its state.
-        """
-        message = InternalMessage(
-            to=settings.agent_settings.llm_name,
-            sender=self.name,
-            body="clear_history",
-            threads="clear history message",
-        )
-        await self.send(message)
-        self.logger.debug("Sent message to LLM agent to clear history.")
+    # async def _send_to_bdi(self, program: Program):
+    #     """
+    #     Convert a received program into BDI beliefs and send them to the BDI Core Agent.
+    #
+    #     Currently, it takes the **first phase** of the program and extracts:
+    #     -   **Norms**: Constraints or rules the agent must follow.
+    #     -   **Goals**: Objectives the agent must achieve.
+    #
+    #     These are sent as a ``BeliefMessage`` with ``replace=True``, meaning they will
+    #     overwrite any existing norms/goals of the same name in the BDI agent.
+    #
+    #     :param program: The program object received from the API.
+    #     """
+    #     first_phase = program.phases[0]
+    #     norms_belief = Belief(
+    #         name="norms",
+    #         arguments=[norm.norm for norm in first_phase.norms],
+    #         replace=True,
+    #     )
+    #     goals_belief = Belief(
+    #         name="goals",
+    #         arguments=[goal.description for goal in first_phase.goals],
+    #         replace=True,
+    #     )
+    #     program_beliefs = BeliefMessage(beliefs=[norms_belief, goals_belief])
+    #
+    #     message = InternalMessage(
+    #         to=settings.agent_settings.bdi_core_name,
+    #         sender=self.name,
+    #         body=program_beliefs.model_dump_json(),
+    #         thread="beliefs",
+    #     )
+    #     await self.send(message)
+    #     self.logger.debug("Sent new norms and goals to the BDI agent.")
 
     async def _receive_programs(self):
         """
@@ -81,20 +652,18 @@ class BDIProgramManager(BaseAgent):
 
         It listens to the ``program`` topic on the internal ZMQ SUB socket.
         When a program is received, it is validated and forwarded to BDI via :meth:`_send_to_bdi`.
-        Additionally, the LLM history is cleared via :meth:`_send_clear_llm_history`.
         """
         while True:
             topic, body = await self.sub_socket.recv_multipart()
 
             try:
                 program = Program.model_validate_json(body)
-                await self._send_to_bdi(program)
-                await self._send_clear_llm_history()
-
             except ValidationError:
                 self.logger.exception("Received an invalid program.")
                 continue
 
+            await self._send_to_bdi(program)
+
     async def setup(self):
         """
         Initialize the agent.
@@ -109,3 +678,7 @@ class BDIProgramManager(BaseAgent):
         self.sub_socket.subscribe("program")
 
         self.add_behavior(self._receive_programs())
+
+
+if __name__ == "__main__":
+    do_things()

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
@@ -38,7 +41,7 @@ class RICommunicationAgent(BaseAgent):
     def __init__(
         self,
         name: str,
-        address=settings.zmq_settings.ri_communication_address,
+        address=settings.zmq_settings.ri_command_address,
         bind=False,
     ):
         super().__init__(name)
@@ -168,7 +171,7 @@ class RICommunicationAgent(BaseAgent):
             bind = port_data["bind"]
 
             if not bind:
-                addr = f"tcp://{settings.ri_host}:{port}"
+                addr = f"tcp://localhost:{port}"
             else:
                 addr = f"tcp://*:{port}"
 
@@ -298,3 +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

@@ -52,10 +52,6 @@ class LLMAgent(BaseAgent):
                 await self._process_bdi_message(prompt_message)
             except ValidationError:
                 self.logger.debug("Prompt message from BDI core is invalid.")
-        elif msg.sender == settings.agent_settings.bdi_program_manager_name:
-            if msg.body == "clear_history":
-                self.logger.debug("Clearing conversation history.")
-                self.history.clear()
         else:
             self.logger.debug("Message ignored (not from BDI core.")
 
@@ -68,11 +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):
         """
@@ -87,6 +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]:
@@ -176,7 +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):
@@ -103,11 +110,12 @@ class VADAgent(BaseAgent):
 
         self._connect_audio_in_socket()
 
-        audio_out_address = self._connect_audio_out_socket()
-        if audio_out_address is None:
+        audio_out_port = self._connect_audio_out_socket()
+        if audio_out_port is None:
             self.logger.error("Could not bind output socket, stopping.")
             await self.stop()
             return
+        audio_out_address = f"tcp://localhost:{audio_out_port}"
 
         # Connect to internal communication socket
         self.program_sub_socket = azmq.Context.instance().socket(zmq.SUB)
@@ -160,14 +168,13 @@ class VADAgent(BaseAgent):
             self.audio_in_socket.connect(self.audio_in_address)
         self.audio_in_poller = SocketPoller[bytes](self.audio_in_socket)
 
-    def _connect_audio_out_socket(self) -> str | None:
+    def _connect_audio_out_socket(self) -> int | None:
         """
-        Returns the address that was bound to, or None if binding failed.
+        Returns the port bound, or None if binding failed.
         """
         try:
             self.audio_out_socket = azmq.Context.instance().socket(zmq.PUB)
-            self.audio_out_socket.bind(settings.zmq_settings.vad_pub_address)
-            return settings.zmq_settings.vad_pub_address
+            return self.audio_out_socket.bind_to_random_port("tcp://localhost", max_tries=100)
         except zmq.ZMQBindError:
             self.logger.error("Failed to bind an audio output socket after 100 tries.")
             self.audio_out_socket = None
@@ -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

@@ -6,7 +6,12 @@ from zmq.asyncio import Context
 from control_backend.agents import BaseAgent
 from control_backend.core.agent_system import InternalMessage
 from control_backend.core.config import settings
-from control_backend.schemas.ri_message import GestureCommand, RIEndpoint, SpeechCommand
+from control_backend.schemas.ri_message import (
+    GestureCommand,
+    PauseCommand,
+    RIEndpoint,
+    SpeechCommand,
+)
 
 
 class UserInterruptAgent(BaseAgent):
@@ -71,6 +76,12 @@ class UserInterruptAgent(BaseAgent):
                     "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').",
@@ -130,6 +141,38 @@ class UserInterruptAgent(BaseAgent):
             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.

src/control_backend/core/config.py

@@ -1,12 +1,3 @@
-"""
-An exhaustive overview of configurable options. All of these can be set using environment variables
-by nesting with double underscores (__). Start from the ``Settings`` class.
-
-For example, ``settings.ri_host`` becomes ``RI_HOST``, and
-``settings.zmq_settings.ri_communication_address`` becomes
-``ZMQ_SETTINGS__RI_COMMUNICATION_ADDRESS``.
-"""
-
 from pydantic import BaseModel
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -17,17 +8,16 @@ class ZMQSettings(BaseModel):
 
     :ivar internal_pub_address: Address for the internal PUB socket.
     :ivar internal_sub_address: Address for the internal SUB socket.
-    :ivar ri_communication_address: Address for the endpoint that the Robot Interface connects to.
-    :ivar vad_pub_address: Address that the VAD agent binds to and publishes audio segments to.
+    :ivar ri_command_address: Address for sending commands to the Robot Interface.
+    :ivar ri_communication_address: Address for receiving communication from the Robot Interface.
+    :ivar vad_agent_address: Address for the Voice Activity Detection (VAD) agent.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     internal_pub_address: str = "tcp://localhost:5560"
     internal_sub_address: str = "tcp://localhost:5561"
+    ri_command_address: str = "tcp://localhost:0000"
     ri_communication_address: str = "tcp://*:5555"
     internal_gesture_rep_adress: str = "tcp://localhost:7788"
-    vad_pub_address: str = "inproc://vad_stream"
 
 
 class AgentSettings(BaseModel):
@@ -46,8 +36,6 @@ class AgentSettings(BaseModel):
     :ivar robot_speech_name: Name of the Robot Speech Agent.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     # agent names
     bdi_core_name: str = "bdi_core_agent"
     bdi_belief_collector_name: str = "belief_collector_agent"
@@ -77,10 +65,9 @@ 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.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     sleep_s: float = 1.0
     comm_setup_max_retries: int = 5
     socket_poller_timeout_ms: int = 100
@@ -96,6 +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):
     """
@@ -103,12 +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.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     local_llm_url: str = "http://localhost:1234/v1/chat/completions"
     local_llm_model: str = "gpt-oss"
+    chat_temperature: float = 1.0
+    code_temperature: float = 0.3
+    n_parallel: int = 4
 
 
 class VADSettings(BaseModel):
@@ -120,8 +115,6 @@ class VADSettings(BaseModel):
     :ivar sample_rate_hz: Sample rate in Hz for the VAD model.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     repo_or_dir: str = "snakers4/silero-vad"
     model_name: str = "silero_vad"
     sample_rate_hz: int = 16000
@@ -135,8 +128,6 @@ class SpeechModelSettings(BaseModel):
     :ivar openai_model_name: Model name for OpenAI-based speech recognition.
     """
 
-    # ATTENTION: When adding/removing settings, make sure to update the .env.example file
-
     # model identifiers for speech recognition
     mlx_model_name: str = "mlx-community/whisper-small.en-mlx"
     openai_model_name: str = "small.en"
@@ -148,7 +139,6 @@ class Settings(BaseSettings):
 
     :ivar app_title: Title of the application.
     :ivar ui_url: URL of the frontend UI.
-    :ivar ri_host: The hostname of the Robot Interface.
     :ivar zmq_settings: ZMQ configuration.
     :ivar agent_settings: Agent name configuration.
     :ivar behaviour_settings: Behavior configuration.
@@ -161,8 +151,6 @@ class Settings(BaseSettings):
 
     ui_url: str = "http://localhost:5173"
 
-    ri_host: str = "localhost"
-
     zmq_settings: ZMQSettings = ZMQSettings()
 
     agent_settings: AgentSettings = AgentSettings()

src/control_backend/main.py

@@ -39,10 +39,11 @@ from control_backend.agents.communication import RICommunicationAgent
 # LLM Agents
 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
-
-# Other backend imports
 from control_backend.api.v1.router import api_router
 from control_backend.core.config import settings
 from control_backend.logging import setup_logging
@@ -141,6 +142,12 @@ async def lifespan(app: FastAPI):
                 "name": settings.agent_settings.bdi_program_manager_name,
             },
         ),
+        "TestPauseAgent": (
+            TestPauseAgent,
+            {
+                "name": "pause_test_agent",
+            },
+        ),
         "UserInterruptAgent": (
             UserInterruptAgent,
             {

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/program.py

@@ -1,64 +1,202 @@
-from pydantic import BaseModel
+from enum import Enum
+from typing import Literal
+
+from pydantic import UUID4, BaseModel
 
 
-class Norm(BaseModel):
+class ProgramElement(BaseModel):
     """
-    Represents a behavioral norm.
+    Represents a basic element of our behavior program.
 
+    :ivar name: The researcher-assigned name of the element.
     :ivar id: Unique identifier.
-    :ivar label: Human-readable label.
-    :ivar norm: The actual norm text describing the behavior.
     """
 
-    id: str
-    label: str
-    norm: str
+    name: str
+    id: UUID4
 
 
-class Goal(BaseModel):
+class LogicalOperator(Enum):
+    AND = "AND"
+    OR = "OR"
+
+
+type Belief = KeywordBelief | SemanticBelief | InferredBelief
+type BasicBelief = KeywordBelief | SemanticBelief
+
+
+class KeywordBelief(ProgramElement):
     """
-    Represents an objective to be achieved.
+    Represents a belief that is set when the user spoken text contains a certain keyword.
 
-    :ivar id: Unique identifier.
-    :ivar label: Human-readable label.
-    :ivar description: Detailed description of the goal.
-    :ivar achieved: Status flag indicating if the goal has been met.
+    :ivar keyword: The keyword on which this belief gets set.
     """
 
-    id: str
-    label: str
-    description: str
-    achieved: bool
-
-
-class TriggerKeyword(BaseModel):
-    id: str
+    name: str = ""
     keyword: str
 
 
-class KeywordTrigger(BaseModel):
-    id: str
-    label: str
-    type: str
-    keywords: list[TriggerKeyword]
+class SemanticBelief(ProgramElement):
+    """
+    Represents a belief that is set by semantic LLM validation.
+
+    :ivar description: Description of how to form the belief, used by the LLM.
+    """
+
+    name: str = ""
+    description: str
 
 
-class Phase(BaseModel):
+class InferredBelief(ProgramElement):
+    """
+    Represents a belief that gets formed by combining two beliefs with a logical AND or OR.
+
+    These beliefs can also be :class:`InferredBelief`, leading to arbitrarily deep nesting.
+
+    :ivar operator: The logical operator to apply.
+    :ivar left: The left part of the logical expression.
+    :ivar right: The right part of the logical expression.
+    """
+
+    name: str = ""
+    operator: LogicalOperator
+    left: Belief
+    right: Belief
+
+
+class Norm(ProgramElement):
+    name: str = ""
+    norm: str
+    critical: bool = False
+
+
+class BasicNorm(Norm):
+    """
+    Represents a behavioral norm.
+
+    :ivar norm: The actual norm text describing the behavior.
+    :ivar critical: When true, this norm should absolutely not be violated (checked separately).
+    """
+
+    pass
+
+
+class ConditionalNorm(Norm):
+    """
+    Represents a norm that is only active when a condition is met (i.e., a certain belief holds).
+
+    :ivar condition: When to activate this norm.
+    """
+
+    condition: Belief
+
+
+type PlanElement = Goal | Action
+
+
+class Plan(ProgramElement):
+    """
+    Represents a list of steps to execute. Each of these steps can be a goal (with its own plan)
+    or a simple action.
+
+    :ivar steps: The actions or subgoals to execute, in order.
+    """
+
+    name: str = ""
+    steps: list[PlanElement]
+
+
+class Goal(ProgramElement):
+    """
+    Represents an objective to be achieved. To reach the goal, we should execute
+    the corresponding plan. If we can fail to achieve a goal after executing the plan,
+    for example when the achieving of the goal is dependent on the user's reply, this means
+    that the achieved status will be set from somewhere else in the program.
+
+    :ivar plan: The plan to execute.
+    :ivar can_fail: Whether we can fail to achieve the goal after executing the plan.
+    """
+
+    plan: Plan
+    can_fail: bool = True
+
+
+type Action = SpeechAction | GestureAction | LLMAction
+
+
+class SpeechAction(ProgramElement):
+    """
+    Represents the action of the robot speaking a literal text.
+
+    :ivar text: The text to speak.
+    """
+
+    name: str = ""
+    text: str
+
+
+class Gesture(BaseModel):
+    """
+    Represents a gesture to be performed. Can be either a single gesture,
+    or a random gesture from a category (tag).
+
+    :ivar type: The type of the gesture, "tag" or "single".
+    :ivar name: The name of the single gesture or tag.
+    """
+
+    type: Literal["tag", "single"]
+    name: str
+
+
+class GestureAction(ProgramElement):
+    """
+    Represents the action of the robot performing a gesture.
+
+    :ivar gesture: The gesture to perform.
+    """
+
+    name: str = ""
+    gesture: Gesture
+
+
+class LLMAction(ProgramElement):
+    """
+    Represents the action of letting an LLM generate a reply based on its chat history
+    and an additional goal added in the prompt.
+
+    :ivar goal: The extra (temporary) goal to add to the LLM.
+    """
+
+    name: str = ""
+    goal: str
+
+
+class Trigger(ProgramElement):
+    """
+    Represents a belief-based trigger. When a belief is set, the corresponding plan is executed.
+
+    :ivar condition: When to activate the trigger.
+    :ivar plan: The plan to execute.
+    """
+
+    name: str = ""
+    condition: Belief
+    plan: Plan
+
+
+class Phase(ProgramElement):
     """
     A distinct phase within a program, containing norms, goals, and triggers.
 
-    :ivar id: Unique identifier.
-    :ivar label: Human-readable label.
     :ivar norms: List of norms active in this phase.
     :ivar goals: List of goals to pursue in this phase.
     :ivar triggers: List of triggers that define transitions out of this phase.
     """
 
-    id: str
-    label: str
-    norms: list[Norm]
+    name: str = ""
+    norms: list[BasicNorm | ConditionalNorm]
     goals: list[Goal]
-    triggers: list[KeywordTrigger]
+    triggers: list[Trigger]
 
 
 class Program(BaseModel):

src/control_backend/schemas/ri_message.py

@@ -14,6 +14,7 @@ class RIEndpoint(str, Enum):
     GESTURE_TAG = "actuate/gesture/tag"
     PING = "ping"
     NEGOTIATE_PORTS = "negotiate/ports"
+    PAUSE = "pause"
 
 
 class RIMessage(BaseModel):
@@ -64,3 +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/integration/agents/perception/vad_agent/test_vad_agent.py

@@ -91,7 +91,7 @@ def test_out_socket_creation(zmq_context):
     assert per_vad_agent.audio_out_socket is not None
 
     zmq_context.return_value.socket.assert_called_once_with(zmq.PUB)
-    zmq_context.return_value.socket.return_value.bind.assert_called_once_with("inproc://vad_stream")
+    zmq_context.return_value.socket.return_value.bind_to_random_port.assert_called_once()
 
 
 @pytest.mark.asyncio

test/unit/agents/actuation/test_robot_gesture_agent.py

@@ -73,7 +73,7 @@ async def test_setup_connect(zmq_context, mocker):
 async def test_handle_message_sends_valid_gesture_command():
     """Internal message with valid gesture tag is forwarded to robot pub socket."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     payload = {
@@ -91,7 +91,7 @@ async def test_handle_message_sends_valid_gesture_command():
 async def test_handle_message_sends_non_gesture_command():
     """Internal message with non-gesture endpoint is not forwarded by this agent."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     payload = {"endpoint": "some_other_endpoint", "data": "invalid_tag_not_in_list"}
@@ -107,7 +107,7 @@ async def test_handle_message_sends_non_gesture_command():
 async def test_handle_message_rejects_invalid_gesture_tag():
     """Internal message with invalid gesture tag is not forwarded."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     # Use a tag that's not in gesture_data
@@ -123,7 +123,7 @@ async def test_handle_message_rejects_invalid_gesture_tag():
 async def test_handle_message_invalid_payload():
     """Invalid payload is caught and does not send."""
     pubsocket = AsyncMock()
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.pubsocket = pubsocket
 
     msg = InternalMessage(to="robot", sender="tester", body=json.dumps({"bad": "data"}))
@@ -142,12 +142,12 @@ async def test_zmq_command_loop_valid_gesture_payload():
     async def recv_once():
         # stop after first iteration
         agent._running = False
-        return b"command", json.dumps(command).encode("utf-8")
+        return (b"command", json.dumps(command).encode("utf-8"))
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -165,12 +165,12 @@ async def test_zmq_command_loop_valid_non_gesture_payload():
 
     async def recv_once():
         agent._running = False
-        return b"command", json.dumps(command).encode("utf-8")
+        return (b"command", json.dumps(command).encode("utf-8"))
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -188,12 +188,12 @@ async def test_zmq_command_loop_invalid_gesture_tag():
 
     async def recv_once():
         agent._running = False
-        return b"command", json.dumps(command).encode("utf-8")
+        return (b"command", json.dumps(command).encode("utf-8"))
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -210,12 +210,12 @@ async def test_zmq_command_loop_invalid_json():
 
     async def recv_once():
         agent._running = False
-        return b"command", b"{not_json}"
+        return (b"command", b"{not_json}")
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -232,12 +232,12 @@ async def test_zmq_command_loop_ignores_send_gestures_topic():
 
     async def recv_once():
         agent._running = False
-        return b"send_gestures", b"{}"
+        return (b"send_gestures", b"{}")
 
     fake_socket.recv_multipart = recv_once
     fake_socket.send_json = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.subsocket = fake_socket
     agent.pubsocket = fake_socket
     agent._running = True
@@ -259,9 +259,7 @@ async def test_fetch_gestures_loop_without_amount():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent(
-        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
-    )
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -289,9 +287,7 @@ async def test_fetch_gestures_loop_with_amount():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent(
-        "robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"], address=""
-    )
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no", "wave", "point"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -319,7 +315,7 @@ async def test_fetch_gestures_loop_with_integer_request():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -344,7 +340,7 @@ async def test_fetch_gestures_loop_with_invalid_json():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -369,7 +365,7 @@ async def test_fetch_gestures_loop_with_non_integer_json():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent._running = True
 
@@ -385,7 +381,7 @@ async def test_fetch_gestures_loop_with_non_integer_json():
 def test_gesture_data_attribute():
     """Test that gesture_data returns the expected list."""
     gesture_data = ["hello", "yes", "no", "wave"]
-    agent = RobotGestureAgent("robot_gesture", gesture_data=gesture_data, address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=gesture_data)
 
     assert agent.gesture_data == gesture_data
     assert isinstance(agent.gesture_data, list)
@@ -402,7 +398,7 @@ async def test_stop_closes_sockets():
     pubsocket = MagicMock()
     subsocket = MagicMock()
     repsocket = MagicMock()
-    agent = RobotGestureAgent("robot_gesture", address="")
+    agent = RobotGestureAgent("robot_gesture")
     agent.pubsocket = pubsocket
     agent.subsocket = subsocket
     agent.repsocket = repsocket
@@ -419,7 +415,7 @@ async def test_stop_closes_sockets():
 async def test_initialization_with_custom_gesture_data():
     """Agent can be initialized with custom gesture data."""
     custom_gestures = ["custom1", "custom2", "custom3"]
-    agent = RobotGestureAgent("robot_gesture", gesture_data=custom_gestures, address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=custom_gestures)
 
     assert agent.gesture_data == custom_gestures
 
@@ -436,7 +432,7 @@ async def test_fetch_gestures_loop_handles_exception():
     fake_repsocket.recv = recv_once
     fake_repsocket.send = AsyncMock()
 
-    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"], address="")
+    agent = RobotGestureAgent("robot_gesture", gesture_data=["hello", "yes", "no"])
     agent.repsocket = fake_repsocket
     agent.logger = MagicMock()
     agent._running = True

test/unit/agents/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")
@@ -63,7 +77,6 @@ async def test_receive_programs_valid_and_invalid():
     manager = BDIProgramManager(name="program_manager_test")
     manager.sub_socket = sub
     manager._send_to_bdi = AsyncMock()
-    manager._send_clear_llm_history = AsyncMock()
 
     try:
         # Will give StopAsyncIteration when the predefined `sub.recv_multipart` side-effects run out
@@ -74,26 +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"
-
-    # Verify history clear was triggered
-    assert manager._send_clear_llm_history.await_count == 1
-
-
-@pytest.mark.asyncio
-async def test_send_clear_llm_history(mock_settings):
-    # Ensure the mock returns a string for the agent name (just like in your LLM tests)
-    mock_settings.agent_settings.llm_agent_name = "llm_agent"
-
-    manager = BDIProgramManager(name="program_manager_test")
-    manager.send = AsyncMock()
-
-    await manager._send_clear_llm_history()
-
-    assert manager.send.await_count == 1
-    msg: InternalMessage = manager.send.await_args[0][0]
-
-    # Verify the content and recipient
-    assert msg.body == "clear_history"
-    assert msg.to == "llm_agent"
+    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/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
 
@@ -265,23 +265,3 @@ async def test_stream_query_llm_skips_non_data_lines(mock_httpx_client, mock_set
 
     # Only the valid 'data:' line should yield content
     assert tokens == ["Hi"]
-
-
-@pytest.mark.asyncio
-async def test_clear_history_command(mock_settings):
-    """Test that the 'clear_history' message clears the agent's memory."""
-    # setup LLM to have some history
-    mock_settings.agent_settings.bdi_program_manager_name = "bdi_program_manager_agent"
-    agent = LLMAgent("llm_agent")
-    agent.history = [
-        {"role": "user", "content": "Old conversation context"},
-        {"role": "assistant", "content": "Old response"},
-    ]
-    assert len(agent.history) == 2
-    msg = InternalMessage(
-        to="llm_agent",
-        sender=mock_settings.agent_settings.bdi_program_manager_name,
-        body="clear_history",
-    )
-    await agent.handle_message(msg)
-    assert len(agent.history) == 0

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

@@ -7,15 +7,6 @@ import zmq
 from control_backend.agents.perception.vad_agent import VADAgent
 
 
-# We don't want to use real ZMQ in unit tests, for example because it can give errors when sockets
-# aren't closed properly.
-@pytest.fixture(autouse=True)
-def mock_zmq():
-    with patch("zmq.asyncio.Context") as mock:
-        mock.instance.return_value = MagicMock()
-        yield mock
-
-
 @pytest.fixture
 def audio_out_socket():
     return AsyncMock()
@@ -149,10 +140,12 @@ async def test_vad_model_load_failure_stops_agent(vad_agent):
         # Patch stop to an AsyncMock so we can check it was awaited
         vad_agent.stop = AsyncMock()
 
-        await vad_agent.setup()
+        result = await vad_agent.setup()
 
         # Assert stop was called
         vad_agent.stop.assert_awaited_once()
+        # Assert setup returned None
+        assert result is None
 
 
 @pytest.mark.asyncio
@@ -162,7 +155,7 @@ async def test_audio_out_bind_failure_sets_none_and_logs(vad_agent, caplog):
     audio_out_socket is set to None, None is returned, and an error is logged.
     """
     mock_socket = MagicMock()
-    mock_socket.bind.side_effect = zmq.ZMQBindError()
+    mock_socket.bind_to_random_port.side_effect = zmq.ZMQBindError()
     with patch("control_backend.agents.perception.vad_agent.azmq.Context.instance") as mock_ctx:
         mock_ctx.return_value.socket.return_value = mock_socket
 

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)

uv.lock

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