format

Author: ollmer

src/agentlab/actions.py

@@ -27,7 +27,6 @@ class FunctionSpec(BaseModel):
     parameters: dict
 
 
-
 class ToolCall(BaseModel):
     id: str = Field(default_factory=lambda: uuid4().hex)
     name: str
@@ -106,7 +105,9 @@ def parse_action(cls, llm_output: str) -> ToolCall:
         if "<action>" in llm_output:
             content_dict, valid, retry_message = parse_html_tags_raise(llm_output, keys=["action"])
             if not valid or "action" not in content_dict:
-                raise ValueError(f"Invalid action: llm_output: {llm_output}, retry_message: {retry_message}")
+                raise ValueError(
+                    f"Invalid action: llm_output: {llm_output}, retry_message: {retry_message}"
+                )
             action_str = content_dict["action"]
         else:
             action_str = llm_output

src/agentlab/agents/react_toolcall_agent.py

@@ -138,7 +138,7 @@ def get_action(self, obs: dict) -> tuple[ToolCall, dict]:
         action = self.action_from_message(message)
         return action, {"think": thoughts}
 
-    def thoughts_from_message(self, message) -> str:
+    def thoughts_from_message(self, message: Message) -> str:
         thoughts = []
         if reasoning := message.get("reasoning_content"):
             logger.info(colored(f"LLM reasoning:\n{reasoning}", "yellow"))
@@ -153,7 +153,7 @@ def thoughts_from_message(self, message) -> str:
             thoughts.append(message.content)
         return "\n\n".join(thoughts)
 
-    def action_from_message(self, message) -> ToolCall:
+    def action_from_message(self, message: Message) -> ToolCall:
         if message.tool_calls:
             if len(message.tool_calls) > 1:
                 logger.warning("Multiple tool calls found in LLM response, using the first one.")
@@ -162,7 +162,7 @@ def action_from_message(self, message) -> ToolCall:
             args = json.loads(tool_call.function.arguments)
             action = ToolCall(id=tool_call.id, name=name, arguments=args)
             self.last_tool_call_id = action.id
-            logger.info(f"Parsed tool call action: {action}")
+            logger.info(colored(f"Parsed tool call: {action}", "magenta"))
         else:
             raise ValueError(f"No tool call found in LLM response: {message}")
         return action

src/agentlab/backends/browser/env.py

@@ -17,6 +17,7 @@ def final_step():
     """
     pass
 
+
 class BrowserEnv(AbstractEnv):
     def __init__(
         self, task_name: str, task: AbstractWebTask, backend: BrowserBackend, seed: int = 0

src/agentlab/backends/browser/mcp_playwright.py

@@ -44,7 +44,9 @@ def step(self, action: ToolCall) -> dict:
         }
 
     def page_html(self) -> str:
-        contents = self.call_tool("browser_evaluate", {"function": "document.documentElement.outerHTML"})
+        contents = self.call_tool(
+            "browser_evaluate", {"function": "document.documentElement.outerHTML"}
+        )
         raw_response = "\n".join([c.text for c in contents if c.type == "text"])
         try:
             _, half_response = raw_response.split("### Result", maxsplit=1)

src/agentlab/experiments/loop.py

@@ -412,7 +412,11 @@ def run(self):
                 logger.debug(f"Starting step {step_info.step}.")
                 step_info.profiling.agent_start = time.time()
                 action, step_info.agent_info = agent.get_action(step_info.obs.copy())
-                step_info.action = action.model_dump_json(indent=2) if isinstance(action, BaseModel) else str(action)
+                step_info.action = (
+                    action.model_dump_json(indent=2)
+                    if isinstance(action, BaseModel)
+                    else str(action)
+                )
                 step_info.profiling.agent_stop = time.time()
                 if step_info.agent_info.get("think", None):
                     logger.info(f"Agent thought: {step_info.agent_info['think']}")