Format Python code with black

python/agent_runtime/agent.py

@@ -13,6 +13,7 @@
 @dataclass
 class Observation:
     """Represents an agent's observation of the world."""
+
     timestamp: datetime
     data: Dict[str, Any]
     source: str = "world"
@@ -21,6 +22,7 @@ class Observation:
 @dataclass
 class Action:
     """Represents an action the agent wants to take."""
+
     tool_name: str
     parameters: Dict[str, Any]
     reasoning: Optional[str] = None
@@ -29,6 +31,7 @@ class Action:
 @dataclass
 class AgentState:
     """Internal state of an agent."""
+
     agent_id: str
     goals: List[str] = field(default_factory=list)
     observations: List[Observation] = field(default_factory=list)
@@ -87,7 +90,7 @@ def perceive(self, observation: Dict[str, Any], source: str = "world") -> None:
 
         # Keep only recent observations to manage memory
         if len(self.state.observations) > self.memory_capacity:
-            self.state.observations = self.state.observations[-self.memory_capacity:]
+            self.state.observations = self.state.observations[-self.memory_capacity :]
 
         logger.debug(f"Agent {self.state.agent_id} received observation from {source}")
 
@@ -113,9 +116,7 @@ def decide_action(self) -> Optional[Action]:
 
             if action:
                 self.state.action_history.append(action)
-                logger.info(
-                    f"Agent {self.state.agent_id} decided action: {action.tool_name}"
-                )
+                logger.info(f"Agent {self.state.agent_id} decided action: {action.tool_name}")
 
             return action
 

python/agent_runtime/tool_dispatcher.py

@@ -13,6 +13,7 @@
 @dataclass
 class ToolSchema:
     """Schema definition for a tool."""
+
     name: str
     description: str
     parameters: Dict[str, Any]  # JSON schema for parameters
@@ -154,11 +155,13 @@ def export_schemas_json(self) -> str:
         """
         schemas = []
         for schema in self.schemas.values():
-            schemas.append({
-                "name": schema.name,
-                "description": schema.description,
-                "parameters": schema.parameters,
-                "returns": schema.returns,
-            })
+            schemas.append(
+                {
+                    "name": schema.name,
+                    "description": schema.description,
+                    "parameters": schema.parameters,
+                    "returns": schema.returns,
+                }
+            )
 
         return json.dumps(schemas, indent=2)

python/backends/base.py

@@ -10,6 +10,7 @@
 @dataclass
 class BackendConfig:
     """Configuration for LLM backend."""
+
     model_path: str
     temperature: float = 0.7
     max_tokens: int = 512
@@ -20,6 +21,7 @@ class BackendConfig:
 @dataclass
 class GenerationResult:
     """Result from LLM generation."""
+
     text: str
     tokens_used: int
     finish_reason: str  # "stop", "length", "error"

python/backends/llama_cpp_backend.py

@@ -37,7 +37,9 @@ def _load_model(self) -> None:
             logger.info("Model loaded successfully")
 
         except ImportError:
-            logger.error("llama-cpp-python not installed. Install with: pip install llama-cpp-python")
+            logger.error(
+                "llama-cpp-python not installed. Install with: pip install llama-cpp-python"
+            )
             raise
         except Exception as e:
             logger.error(f"Failed to load model: {e}")