fix:: lint-errors

Author: theonlychant

src/gaia/agents/base/agent.py

@@ -1004,12 +1004,14 @@ def _parse_llm_response(self, response: str) -> Dict[str, Any]:
                             f"str or dict, got {type(arguments_raw).__name__}"
                         )
 
-                    parsed_calls.append({
-                        "thought": "",
-                        "goal": "",
-                        "tool": name,
-                        "tool_args": tool_args,
-                    })
+                    parsed_calls.append(
+                        {
+                            "thought": "",
+                            "goal": "",
+                            "tool": name,
+                            "tool_args": tool_args,
+                        }
+                    )
                 logger.debug(
                     "[PARSE] Native tool_calls: returning %d parsed calls",
                     len(parsed_calls),
@@ -2573,7 +2575,7 @@ def process_query(
                             "This agent prefers either a single tool call per response, "
                             "or a structured JSON 'plan' containing an ordered array of steps. "
                             "Please either: (A) output a single tool call JSON object, "
-                            "or (B) output a JSON plan in the format: {\"plan\": [{\"tool\": \"name\", \"tool_args\": {...}}]}. "
+                            'or (B) output a JSON plan in the format: {"plan": [{"tool": "name", "tool_args": {...}}]}. '
                             "If you don't need to call a tool, answer in plain text."
                         ),
                     }
@@ -2601,7 +2603,9 @@ def process_query(
             # sequentially in this same LLM turn (one LLM turn -> N tool turns).
             if isinstance(parsed, list):
                 # Record assistant turn containing multiple tool_calls
-                conversation.append({"role": "assistant", "content": {"tool_calls": parsed}})
+                conversation.append(
+                    {"role": "assistant", "content": {"tool_calls": parsed}}
+                )
                 # Preserve raw assistant response for history
                 messages.append({"role": "assistant", "content": response})
 
@@ -2611,7 +2615,9 @@ def process_query(
 
                     tool_name = call["tool"]
                     tool_args = call["tool_args"]
-                    logger.debug(f"Sequential native tool call: {tool_name} {tool_args}")
+                    logger.debug(
+                        f"Sequential native tool call: {tool_name} {tool_args}"
+                    )
 
                     # Display the tool call in real-time
                     self.console.print_tool_usage(tool_name)
@@ -2658,11 +2664,19 @@ def process_query(
                     self.console.pretty_print_json(tool_result, "Result")
                     self.console.print_tool_complete()
 
-                    previous_outputs.append({"tool": tool_name, "args": tool_args, "result": truncated_result})
+                    previous_outputs.append(
+                        {
+                            "tool": tool_name,
+                            "args": tool_args,
+                            "result": truncated_result,
+                        }
+                    )
                     step_results.append(tool_result)
 
                     # Share tool output with subsequent LLM calls
-                    messages.append(self._create_tool_message(tool_name, truncated_result))
+                    messages.append(
+                        self._create_tool_message(tool_name, truncated_result)
+                    )
 
                     # Error handling
                     is_error = isinstance(tool_result, dict) and (
@@ -2681,7 +2695,9 @@ def process_query(
                             or tool_result.get("suggested_fix")
                             or f"Command failed with return code {tool_result.get('return_code')}"
                         )
-                        logger.warning(f"Tool execution error in sequential calls (count: {error_count}): {last_error}")
+                        logger.warning(
+                            f"Tool execution error in sequential calls (count: {error_count}): {last_error}"
+                        )
                         if not tool_result.get("error_displayed"):
                             self.console.print_error(last_error)
                         self.execution_state = self.STATE_ERROR_RECOVERY

tests/unit/test_agent_parallel_tool_calls.py

@@ -1,5 +1,4 @@
 import json
-import pytest
 
 from gaia.agents.base.agent import Agent
 from gaia.agents.base.tools import _TOOL_REGISTRY
@@ -53,6 +52,8 @@ def _register_tools(self):
     result = agent.process_query("execute both tools", max_steps=6)
 
     # Verify both tool results were appended to conversation
-    tool_names = [m.get("name") for m in result["conversation"] if m.get("role") == "tool"]
+    tool_names = [
+        m.get("name") for m in result["conversation"] if m.get("role") == "tool"
+    ]
     assert "tool_one" in tool_names
     assert "tool_two" in tool_names

tests/unit/test_agent_parallel_tool_calls_extra.py

@@ -1,5 +1,4 @@
 import json
-import pytest
 
 from gaia.agents.base.agent import Agent
 from gaia.agents.base.tools import _TOOL_REGISTRY
@@ -43,7 +42,11 @@ def t_ok2(z=""):
     # make send_messages return envelope
     responses = [type("R", (), {"text": json.dumps(envelope), "stats": {}})()]
 
-    monkeypatch.setattr(agent.chat, "send_messages", lambda messages, system_prompt, tools: responses.pop(0))
+    monkeypatch.setattr(
+        agent.chat,
+        "send_messages",
+        lambda messages, system_prompt, tools: responses.pop(0),
+    )
 
     result = agent.process_query("run three tools", max_steps=10)
 
@@ -55,7 +58,10 @@ def t_ok2(z=""):
     # Find the errtool result and ensure it's an error
     err_entry = next((t for t in tool_entries if t.get("name") == "errtool"), None)
     assert err_entry is not None
-    assert isinstance(err_entry.get("content"), dict) and err_entry["content"].get("status") == "error"
+    assert (
+        isinstance(err_entry.get("content"), dict)
+        and err_entry["content"].get("status") == "error"
+    )
 
 
 def test_plan_then_native_tool_calls(monkeypatch):
@@ -100,13 +106,27 @@ def test_notimplementederror_recovery_message(monkeypatch):
     agent = _make_agent(monkeypatch)
 
     # Make the parser raise NotImplementedError
-    monkeypatch.setattr(agent, "_parse_llm_response", lambda r: (_ for _ in ()).throw(NotImplementedError("multiple")))
+    monkeypatch.setattr(
+        agent,
+        "_parse_llm_response",
+        lambda r: (_ for _ in ()).throw(NotImplementedError("multiple")),
+    )
 
     # Make send_messages return something (will be ignored by parser)
-    monkeypatch.setattr(agent.chat, "send_messages", lambda messages, system_prompt, tools: type("R", (), {"text": "{\"bad\":1}", "stats": {}})())
+    monkeypatch.setattr(
+        agent.chat,
+        "send_messages",
+        lambda messages, system_prompt, tools: type(
+            "R", (), {"text": '{"bad":1}', "stats": {}}
+        )(),
+    )
 
     result = agent.process_query("trigger parse error", max_steps=3)
 
     # Last user message in conversation should instruct about multiple tool calls
     user_msgs = [m for m in result["conversation"] if m.get("role") == "user"]
-    assert any("MULTIPLE tool calls" in str(m.get("content")) or "single tool call" in str(m.get("content")) for m in user_msgs)
+    assert any(
+        "MULTIPLE tool calls" in str(m.get("content"))
+        or "single tool call" in str(m.get("content"))
+        for m in user_msgs
+    )