forked from Rockachopa/Timmy-time-dashboard
feat: Lightning interface, swarm routing, sovereignty audit, embodiment prep
Lightning Backend Interface: - Abstract LightningBackend with pluggable implementations - MockBackend for development (auto-settle invoices) - LndBackend stub with gRPC integration path documented - Backend factory for runtime selection via LIGHTNING_BACKEND env Intelligent Swarm Routing: - CapabilityManifest for agent skill declarations - Task scoring based on keywords + capabilities + bid price - RoutingDecision audit logging to SQLite - Agent stats tracking (wins, consideration rate) Sovereignty Audit: - Comprehensive audit report (docs/SOVEREIGNTY_AUDIT.md) - 9.2/10 sovereignty score - Documented all external dependencies and local alternatives Substrate-Agnostic Agent Interface: - TimAgent abstract base class - Perception/Action/Memory/Communication types - OllamaAdapter implementation - Foundation for future embodiment (robot, VR) Tests: - 36 new tests for Lightning and routing - 472 total tests passing - Maintained 0 warning policy
This commit is contained in:
Alexander Payne
0
src/agent_core/__init__.py
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0
src/agent_core/__init__.py
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368
src/agent_core/interface.py
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368
src/agent_core/interface.py
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@@ -0,0 +1,368 @@
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"""TimAgent Interface — The substrate-agnostic agent contract.
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This is the foundation for embodiment. Whether Timmy runs on:
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- A server with Ollama (today)
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- A Raspberry Pi with sensors
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- A Boston Dynamics Spot robot
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- A VR avatar
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The interface remains constant. Implementation varies.
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Architecture:
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perceive() → reason → act()
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↑ ↓
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←←← remember() ←←←←←←┘
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All methods return effects that can be logged, audited, and replayed.
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"""
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from enum import Enum, auto
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from typing import Any, Optional
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import uuid
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class PerceptionType(Enum):
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"""Types of sensory input an agent can receive."""
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TEXT = auto() # Natural language
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IMAGE = auto() # Visual input
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AUDIO = auto() # Sound/speech
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SENSOR = auto() # Temperature, distance, etc.
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MOTION = auto() # Accelerometer, gyroscope
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NETWORK = auto() # API calls, messages
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INTERNAL = auto() # Self-monitoring (battery, temp)
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class ActionType(Enum):
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"""Types of actions an agent can perform."""
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TEXT = auto() # Generate text response
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SPEAK = auto() # Text-to-speech
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MOVE = auto() # Physical movement
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GRIP = auto() # Manipulate objects
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CALL = auto() # API/network call
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EMIT = auto() # Signal/light/sound
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SLEEP = auto() # Power management
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class AgentCapability(Enum):
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"""High-level capabilities a TimAgent may possess."""
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REASONING = "reasoning"
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CODING = "coding"
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WRITING = "writing"
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ANALYSIS = "analysis"
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VISION = "vision"
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SPEECH = "speech"
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NAVIGATION = "navigation"
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MANIPULATION = "manipulation"
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LEARNING = "learning"
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COMMUNICATION = "communication"
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@dataclass(frozen=True)
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class AgentIdentity:
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"""Immutable identity for an agent instance.
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This persists across sessions and substrates. If Timmy moves
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from cloud to robot, the identity follows.
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"""
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id: str
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name: str
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version: str
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created_at: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
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@classmethod
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def generate(cls, name: str, version: str = "1.0.0") -> "AgentIdentity":
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"""Generate a new unique identity."""
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return cls(
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id=str(uuid.uuid4()),
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name=name,
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version=version,
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)
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@dataclass
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class Perception:
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"""A sensory input to the agent.
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Substrate-agnostic representation. A camera image and a
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LiDAR point cloud are both Perception instances.
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"""
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type: PerceptionType
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data: Any # Content depends on type
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timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
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source: str = "unknown" # e.g., "camera_1", "microphone", "user_input"
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metadata: dict = field(default_factory=dict)
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@classmethod
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def text(cls, content: str, source: str = "user") -> "Perception":
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"""Factory for text perception."""
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return cls(
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type=PerceptionType.TEXT,
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data=content,
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source=source,
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)
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@classmethod
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def sensor(cls, kind: str, value: float, unit: str = "") -> "Perception":
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"""Factory for sensor readings."""
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return cls(
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type=PerceptionType.SENSOR,
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data={"kind": kind, "value": value, "unit": unit},
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source=f"sensor_{kind}",
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)
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@dataclass
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class Action:
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"""An action the agent intends to perform.
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Actions are effects — they describe what should happen,
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not how. The substrate implements the "how."
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"""
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type: ActionType
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payload: Any # Action-specific data
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timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
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confidence: float = 1.0 # 0-1, agent's certainty
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deadline: Optional[str] = None # When action must complete
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@classmethod
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def respond(cls, text: str, confidence: float = 1.0) -> "Action":
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"""Factory for text response action."""
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return cls(
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type=ActionType.TEXT,
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payload=text,
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confidence=confidence,
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)
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@classmethod
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def move(cls, vector: tuple[float, float, float], speed: float = 1.0) -> "Action":
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"""Factory for movement action (x, y, z meters)."""
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return cls(
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type=ActionType.MOVE,
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payload={"vector": vector, "speed": speed},
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)
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@dataclass
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class Memory:
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"""A stored experience or fact.
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Memories are substrate-agnostic. A conversation history
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and a video recording are both Memory instances.
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"""
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id: str
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content: Any
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created_at: str
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access_count: int = 0
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last_accessed: Optional[str] = None
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importance: float = 0.5 # 0-1, for pruning decisions
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tags: list[str] = field(default_factory=list)
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def touch(self) -> None:
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"""Mark memory as accessed."""
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self.access_count += 1
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self.last_accessed = datetime.now(timezone.utc).isoformat()
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@dataclass
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class Communication:
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"""A message to/from another agent or human."""
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sender: str
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recipient: str
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content: Any
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timestamp: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
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protocol: str = "direct" # e.g., "http", "websocket", "speech"
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encrypted: bool = False
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class TimAgent(ABC):
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"""Abstract base class for all Timmy agent implementations.
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This is the substrate-agnostic interface. Implementations:
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- OllamaAgent: LLM-based reasoning (today)
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- RobotAgent: Physical embodiment (future)
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- SimulationAgent: Virtual environment (future)
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Usage:
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agent = OllamaAgent(identity) # Today's implementation
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perception = Perception.text("Hello Timmy")
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memory = agent.perceive(perception)
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action = agent.reason("How should I respond?")
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result = agent.act(action)
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agent.remember(memory) # Store for future
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"""
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def __init__(self, identity: AgentIdentity) -> None:
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self._identity = identity
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self._capabilities: set[AgentCapability] = set()
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self._state: dict[str, Any] = {}
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@property
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def identity(self) -> AgentIdentity:
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"""Return this agent's immutable identity."""
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return self._identity
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@property
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def capabilities(self) -> set[AgentCapability]:
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"""Return set of supported capabilities."""
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return self._capabilities.copy()
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def has_capability(self, capability: AgentCapability) -> bool:
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"""Check if agent supports a capability."""
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return capability in self._capabilities
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@abstractmethod
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def perceive(self, perception: Perception) -> Memory:
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"""Process sensory input and create a memory.
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This is the entry point for all agent interaction.
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A text message, camera frame, or temperature reading
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all enter through perceive().
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Args:
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perception: Sensory input
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Returns:
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Memory: Stored representation of the perception
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"""
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pass
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@abstractmethod
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def reason(self, query: str, context: list[Memory]) -> Action:
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"""Reason about a situation and decide on action.
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This is where "thinking" happens. The agent uses its
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substrate-appropriate reasoning (LLM, neural net, rules)
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to decide what to do.
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Args:
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query: What to reason about
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context: Relevant memories for context
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Returns:
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Action: What the agent decides to do
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"""
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pass
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@abstractmethod
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def act(self, action: Action) -> Any:
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"""Execute an action in the substrate.
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This is where the abstract action becomes concrete:
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- TEXT → Generate LLM response
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- MOVE → Send motor commands
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- SPEAK → Call TTS engine
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Args:
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action: The action to execute
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Returns:
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Result of the action (substrate-specific)
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"""
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pass
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@abstractmethod
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def remember(self, memory: Memory) -> None:
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"""Store a memory for future retrieval.
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The storage mechanism depends on substrate:
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- Cloud: SQLite, vector DB
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- Robot: Local flash storage
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- Hybrid: Synced with conflict resolution
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Args:
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memory: Experience to store
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"""
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pass
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@abstractmethod
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def recall(self, query: str, limit: int = 5) -> list[Memory]:
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"""Retrieve relevant memories.
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Args:
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query: What to search for
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limit: Maximum memories to return
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Returns:
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List of relevant memories, sorted by relevance
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"""
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pass
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@abstractmethod
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def communicate(self, message: Communication) -> bool:
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"""Send/receive communication with another agent.
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Args:
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message: Message to send
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Returns:
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True if communication succeeded
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"""
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pass
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def get_state(self) -> dict[str, Any]:
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"""Get current agent state for monitoring/debugging."""
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return {
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"identity": self._identity,
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"capabilities": list(self._capabilities),
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"state": self._state.copy(),
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}
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def shutdown(self) -> None:
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"""Graceful shutdown. Persist state, close connections."""
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# Override in subclass for cleanup
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pass
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class AgentEffect:
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"""Log entry for agent actions — for audit and replay.
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The complete history of an agent's life can be captured
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as a sequence of AgentEffects. This enables:
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- Debugging: What did the agent see and do?
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- Audit: Why did it make that decision?
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- Replay: Reconstruct agent state from log
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- Training: Learn from agent experiences
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"""
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def __init__(self, log_path: Optional[str] = None) -> None:
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self._effects: list[dict] = []
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self._log_path = log_path
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def log_perceive(self, perception: Perception, memory_id: str) -> None:
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"""Log a perception event."""
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self._effects.append({
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"type": "perceive",
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"perception_type": perception.type.name,
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"source": perception.source,
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"memory_id": memory_id,
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"timestamp": datetime.now(timezone.utc).isoformat(),
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})
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def log_reason(self, query: str, action_type: ActionType) -> None:
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"""Log a reasoning event."""
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self._effects.append({
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"type": "reason",
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"query": query,
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"action_type": action_type.name,
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"timestamp": datetime.now(timezone.utc).isoformat(),
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})
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def log_act(self, action: Action, result: Any) -> None:
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"""Log an action event."""
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self._effects.append({
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"type": "act",
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"action_type": action.type.name,
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"confidence": action.confidence,
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"result_type": type(result).__name__,
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"timestamp": datetime.now(timezone.utc).isoformat(),
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})
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def export(self) -> list[dict]:
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"""Export effect log for analysis."""
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return self._effects.copy()
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259
src/agent_core/ollama_adapter.py
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src/agent_core/ollama_adapter.py
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"""Ollama-based implementation of TimAgent interface.
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This adapter wraps the existing Timmy Ollama agent to conform
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to the substrate-agnostic TimAgent interface. It's the bridge
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between the old codebase and the new embodiment-ready architecture.
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Usage:
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from agent_core import AgentIdentity, Perception
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from agent_core.ollama_adapter import OllamaAgent
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identity = AgentIdentity.generate("Timmy")
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agent = OllamaAgent(identity)
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perception = Perception.text("Hello!")
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memory = agent.perceive(perception)
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action = agent.reason("How should I respond?", [memory])
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result = agent.act(action)
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"""
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from typing import Any, Optional
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|
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from agent_core.interface import (
|
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AgentCapability,
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AgentIdentity,
|
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Perception,
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PerceptionType,
|
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Action,
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ActionType,
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Memory,
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Communication,
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TimAgent,
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AgentEffect,
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)
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from timmy.agent import create_timmy
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|
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|
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class OllamaAgent(TimAgent):
|
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"""TimAgent implementation using local Ollama LLM.
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This is the production agent for Timmy Time v2. It uses
|
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Ollama for reasoning and SQLite for memory persistence.
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Capabilities:
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- REASONING: LLM-based inference
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- CODING: Code generation and analysis
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- WRITING: Long-form content creation
|
||||
- ANALYSIS: Data processing and insights
|
||||
- COMMUNICATION: Multi-agent messaging
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
identity: AgentIdentity,
|
||||
model: Optional[str] = None,
|
||||
effect_log: Optional[str] = None,
|
||||
) -> None:
|
||||
"""Initialize Ollama-based agent.
|
||||
|
||||
Args:
|
||||
identity: Agent identity (persistent across sessions)
|
||||
model: Ollama model to use (default from config)
|
||||
effect_log: Path to log agent effects (optional)
|
||||
"""
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||||
super().__init__(identity)
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||||
|
||||
# Initialize underlying Ollama agent
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self._timmy = create_timmy(model=model)
|
||||
|
||||
# Set capabilities based on what Ollama can do
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self._capabilities = {
|
||||
AgentCapability.REASONING,
|
||||
AgentCapability.CODING,
|
||||
AgentCapability.WRITING,
|
||||
AgentCapability.ANALYSIS,
|
||||
AgentCapability.COMMUNICATION,
|
||||
}
|
||||
|
||||
# Effect logging for audit/replay
|
||||
self._effect_log = AgentEffect(effect_log) if effect_log else None
|
||||
|
||||
# Simple in-memory working memory (short term)
|
||||
self._working_memory: list[Memory] = []
|
||||
self._max_working_memory = 10
|
||||
|
||||
def perceive(self, perception: Perception) -> Memory:
|
||||
"""Process perception and store in memory.
|
||||
|
||||
For text perceptions, we might do light preprocessing
|
||||
(summarization, keyword extraction) before storage.
|
||||
"""
|
||||
# Create memory from perception
|
||||
memory = Memory(
|
||||
id=f"mem_{len(self._working_memory)}",
|
||||
content={
|
||||
"type": perception.type.name,
|
||||
"data": perception.data,
|
||||
"source": perception.source,
|
||||
},
|
||||
created_at=perception.timestamp,
|
||||
tags=self._extract_tags(perception),
|
||||
)
|
||||
|
||||
# Add to working memory
|
||||
self._working_memory.append(memory)
|
||||
if len(self._working_memory) > self._max_working_memory:
|
||||
self._working_memory.pop(0) # FIFO eviction
|
||||
|
||||
# Log effect
|
||||
if self._effect_log:
|
||||
self._effect_log.log_perceive(perception, memory.id)
|
||||
|
||||
return memory
|
||||
|
||||
def reason(self, query: str, context: list[Memory]) -> Action:
|
||||
"""Use LLM to reason and decide on action.
|
||||
|
||||
This is where the Ollama agent does its work. We construct
|
||||
a prompt from the query and context, then interpret the
|
||||
response as an action.
|
||||
"""
|
||||
# Build context string from memories
|
||||
context_str = self._format_context(context)
|
||||
|
||||
# Construct prompt
|
||||
prompt = f"""You are {self._identity.name}, an AI assistant.
|
||||
|
||||
Context from previous interactions:
|
||||
{context_str}
|
||||
|
||||
Current query: {query}
|
||||
|
||||
Respond naturally and helpfully."""
|
||||
|
||||
# Run LLM inference
|
||||
result = self._timmy.run(prompt, stream=False)
|
||||
response_text = result.content if hasattr(result, "content") else str(result)
|
||||
|
||||
# Create text response action
|
||||
action = Action.respond(response_text, confidence=0.9)
|
||||
|
||||
# Log effect
|
||||
if self._effect_log:
|
||||
self._effect_log.log_reason(query, action.type)
|
||||
|
||||
return action
|
||||
|
||||
def act(self, action: Action) -> Any:
|
||||
"""Execute action in the Ollama substrate.
|
||||
|
||||
For text actions, the "execution" is just returning the
|
||||
text (already generated during reasoning). For future
|
||||
action types (MOVE, SPEAK), this would trigger the
|
||||
appropriate Ollama tool calls.
|
||||
"""
|
||||
result = None
|
||||
|
||||
if action.type == ActionType.TEXT:
|
||||
result = action.payload
|
||||
elif action.type == ActionType.SPEAK:
|
||||
# Would call TTS here
|
||||
result = {"spoken": action.payload, "tts_engine": "pyttsx3"}
|
||||
elif action.type == ActionType.CALL:
|
||||
# Would make API call
|
||||
result = {"status": "not_implemented", "payload": action.payload}
|
||||
else:
|
||||
result = {"error": f"Action type {action.type} not supported by OllamaAgent"}
|
||||
|
||||
# Log effect
|
||||
if self._effect_log:
|
||||
self._effect_log.log_act(action, result)
|
||||
|
||||
return result
|
||||
|
||||
def remember(self, memory: Memory) -> None:
|
||||
"""Store memory persistently.
|
||||
|
||||
For now, working memory is sufficient. In the future,
|
||||
this would write to SQLite or vector DB for long-term
|
||||
memory across sessions.
|
||||
"""
|
||||
# Mark as accessed to update importance
|
||||
memory.touch()
|
||||
|
||||
# TODO: Persist to SQLite for long-term memory
|
||||
# This would integrate with the existing briefing system
|
||||
pass
|
||||
|
||||
def recall(self, query: str, limit: int = 5) -> list[Memory]:
|
||||
"""Retrieve relevant memories.
|
||||
|
||||
Simple keyword matching for now. Future: vector similarity.
|
||||
"""
|
||||
query_lower = query.lower()
|
||||
scored = []
|
||||
|
||||
for memory in self._working_memory:
|
||||
score = 0
|
||||
content_str = str(memory.content).lower()
|
||||
|
||||
# Simple keyword overlap
|
||||
query_words = set(query_lower.split())
|
||||
content_words = set(content_str.split())
|
||||
overlap = len(query_words & content_words)
|
||||
score += overlap
|
||||
|
||||
# Boost recent memories
|
||||
score += memory.importance
|
||||
|
||||
scored.append((score, memory))
|
||||
|
||||
# Sort by score descending
|
||||
scored.sort(key=lambda x: x[0], reverse=True)
|
||||
|
||||
# Return top N
|
||||
return [m for _, m in scored[:limit]]
|
||||
|
||||
def communicate(self, message: Communication) -> bool:
|
||||
"""Send message to another agent.
|
||||
|
||||
This would use the swarm comms layer for inter-agent
|
||||
messaging. For now, it's a stub.
|
||||
"""
|
||||
# TODO: Integrate with swarm.comms
|
||||
return True
|
||||
|
||||
def _extract_tags(self, perception: Perception) -> list[str]:
|
||||
"""Extract searchable tags from perception."""
|
||||
tags = [perception.type.name, perception.source]
|
||||
|
||||
if perception.type == PerceptionType.TEXT:
|
||||
# Simple keyword extraction
|
||||
text = str(perception.data).lower()
|
||||
keywords = ["code", "bug", "help", "question", "task"]
|
||||
for kw in keywords:
|
||||
if kw in text:
|
||||
tags.append(kw)
|
||||
|
||||
return tags
|
||||
|
||||
def _format_context(self, memories: list[Memory]) -> str:
|
||||
"""Format memories into context string for prompt."""
|
||||
if not memories:
|
||||
return "No previous context."
|
||||
|
||||
parts = []
|
||||
for mem in memories[-5:]: # Last 5 memories
|
||||
if isinstance(mem.content, dict):
|
||||
data = mem.content.get("data", "")
|
||||
parts.append(f"- {data}")
|
||||
else:
|
||||
parts.append(f"- {mem.content}")
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
def get_effect_log(self) -> Optional[list[dict]]:
|
||||
"""Export effect log if logging is enabled."""
|
||||
if self._effect_log:
|
||||
return self._effect_log.export()
|
||||
return None
|
||||
Reference in New Issue
Block a user