style: auto-format 21 files to fix ruff-format pre-commit failures

.pre-commit-config.yaml

@@ -6,4 +6,4 @@ repos:
         args: ["--fix", "--show-fixes", "--output-format=full"]
         exclude: ^.*\.(ipynb)$|^verl/.*$
       - id: ruff-format
-        exclude: ^verl/.*$
+        exclude: ^.*\.(ipynb)$|^verl/.*$

examples/archive/sft/run_sft_model.py

@@ -30,7 +30,9 @@
     agent_args = {
         "tools": ["python"],
         "parser_name": "qwen",
-        "system_prompt": ('You are an expert mathematician and programmer. Your goal is to solve challenging math problems, like those from the AIME competition, by breaking them down into logical steps and using Python code for calculations. Strive for clarity and efficiency.\n\nFollow this process for every problem:\n1.  **Analyze the Problem**: Read the question carefully. Identify the key information, constraints, and what is being asked.\n2.  **Think Step-by-Step**: In the `<think>` block, outline your plan. Decompose the problem into the smallest, most logical steps. **You must not write code or perform calculations in this block.** Your goal is to create a plan that will be executed by the Python tool.\n3.  **Write Python Code**: In the `<tool_call>` block, write an efficient Python script to execute your plan. The tool expects a JSON object with `name` and `arguments` keys. The `arguments` should be a dictionary with a single `code` key. Ensure the code is self-contained, runs quickly, and prints the final result.\n4.  **State the Final Answer**: After receiving the `<tool_result>`, verify it. Then, state the final answer clearly and concisely in the format \\boxed{answer}.\n\nHere is an example:\nQuestion: What is the largest prime factor of 25! ?\n<think>The problem asks for the largest prime factor of 25 factorial. The largest prime factor of n! is the largest prime number less than or equal to n. In this case, n=25. I will write a Python script to find the largest prime number less than or equal to 25.</think>\n<tool_call>\n{"name": "python", "arguments": {"code": "import math\\ndef is_prime(n):\\n    if n <= 1:\\n        return False\\n    for i in range(2, int(math.sqrt(n)) + 1):\\n        if n % i == 0:\\n            return False\\n    return True\\n\\ndef largest_prime_up_to(n):\\n    for i in range(n, 1, -1):\\n        if is_prime(i):\\n            return i\\n    return None\\n\\nprint(largest_prime_up_to(25))"}}\n</tool_call>\n<tool_result>\n23\n</tool_result>\nThe largest prime factor of 25! is the largest prime number less than or equal to 25. The answer is \\boxed{23}.'),
+        "system_prompt": (
+            'You are an expert mathematician and programmer. Your goal is to solve challenging math problems, like those from the AIME competition, by breaking them down into logical steps and using Python code for calculations. Strive for clarity and efficiency.\n\nFollow this process for every problem:\n1.  **Analyze the Problem**: Read the question carefully. Identify the key information, constraints, and what is being asked.\n2.  **Think Step-by-Step**: In the `<think>` block, outline your plan. Decompose the problem into the smallest, most logical steps. **You must not write code or perform calculations in this block.** Your goal is to create a plan that will be executed by the Python tool.\n3.  **Write Python Code**: In the `<tool_call>` block, write an efficient Python script to execute your plan. The tool expects a JSON object with `name` and `arguments` keys. The `arguments` should be a dictionary with a single `code` key. Ensure the code is self-contained, runs quickly, and prints the final result.\n4.  **State the Final Answer**: After receiving the `<tool_result>`, verify it. Then, state the final answer clearly and concisely in the format \\boxed{answer}.\n\nHere is an example:\nQuestion: What is the largest prime factor of 25! ?\n<think>The problem asks for the largest prime factor of 25 factorial. The largest prime factor of n! is the largest prime number less than or equal to n. In this case, n=25. I will write a Python script to find the largest prime number less than or equal to 25.</think>\n<tool_call>\n{"name": "python", "arguments": {"code": "import math\\ndef is_prime(n):\\n    if n <= 1:\\n        return False\\n    for i in range(2, int(math.sqrt(n)) + 1):\\n        if n % i == 0:\\n            return False\\n    return True\\n\\ndef largest_prime_up_to(n):\\n    for i in range(n, 1, -1):\\n        if is_prime(i):\\n            return i\\n    return None\\n\\nprint(largest_prime_up_to(25))"}}\n</tool_call>\n<tool_result>\n23\n</tool_result>\nThe largest prime factor of 25! is the largest prime number less than or equal to 25. The answer is \\boxed{23}.'
+        ),
     }
     env_args = {
         "tools": ["python"],

examples/fully_async/deepresearch/refine_agent.py

@@ -70,7 +70,9 @@ async def end_request(self, success: bool, latency: float):
             # Log periodically
             if (self.total_completed + self.total_failed) % 50 == 0:
                 avg_latency = self.total_latency / max(1, self.total_completed)
-                logger.info(f"[STATS] In-flight: {self.in_flight}, Completed: {self.total_completed}, Failed: {self.total_failed}, Latency(avg/min/max): {avg_latency:.2f}s/{self.min_latency:.2f}s/{self.max_latency:.2f}s")
+                logger.info(
+                    f"[STATS] In-flight: {self.in_flight}, Completed: {self.total_completed}, Failed: {self.total_failed}, Latency(avg/min/max): {avg_latency:.2f}s/{self.min_latency:.2f}s/{self.max_latency:.2f}s"
+                )
 
     async def get_stats(self) -> dict:
         async with self._lock:

examples/fully_async/deepresearch/search_agent.py

@@ -242,7 +242,28 @@ async def run(self, question):
             final_answer = extract_boxed_answer(content)
 
         # Aggregate metrics across all tool calls
-        aggregated_metrics = {"num_turns": num_turns, "total_parse_tool_args_error": sum(m.get("parse_tool_args_error", 0) for m in metrics), "total_tool_return_error": sum(m.get("tool_return_error", 0) for m in metrics), "total_tool_calls": sum(m.get("tool_calls", 0) for m in metrics), "total_tool_wait_time": sum(m.get("tool_wait_time", 0) for m in metrics), "total_refine_time": sum(m.get("refine_time", 0) for m in metrics), "avg_refine_time": sum(m.get("refine_time", 0) for m in metrics) / max(sum(m.get("tool_calls", 0) for m in metrics), 1), "total_query_length": sum(m.get("query_length", 0) for m in metrics), "avg_query_length": sum(m.get("query_length", 0) for m in metrics) / max(sum(m.get("tool_calls", 0) for m in metrics), 1), "total_generation_time": total_generation_time, "total_completion_tokens": total_completion_tokens, "total_tool_tokens": sum(m.get("tool_tokens", 0) for m in metrics), "avg_completion_tokens_per_turn": total_completion_tokens / max(num_turns, 1), "avg_tool_tokens_per_call": sum(m.get("tool_tokens", 0) for m in metrics) / max(sum(m.get("tool_calls", 0) for m in metrics), 1), "duplicate_search_detected": duplicate_search_detected, "excessive_parallel_calls": excessive_parallel_calls, "tool_error_detected": tool_error_detected, "refine_error_detected": refine_error_detected, "overlong": overlong, "merged_step": len(trajectory.merge())}
+        aggregated_metrics = {
+            "num_turns": num_turns,
+            "total_parse_tool_args_error": sum(m.get("parse_tool_args_error", 0) for m in metrics),
+            "total_tool_return_error": sum(m.get("tool_return_error", 0) for m in metrics),
+            "total_tool_calls": sum(m.get("tool_calls", 0) for m in metrics),
+            "total_tool_wait_time": sum(m.get("tool_wait_time", 0) for m in metrics),
+            "total_refine_time": sum(m.get("refine_time", 0) for m in metrics),
+            "avg_refine_time": sum(m.get("refine_time", 0) for m in metrics) / max(sum(m.get("tool_calls", 0) for m in metrics), 1),
+            "total_query_length": sum(m.get("query_length", 0) for m in metrics),
+            "avg_query_length": sum(m.get("query_length", 0) for m in metrics) / max(sum(m.get("tool_calls", 0) for m in metrics), 1),
+            "total_generation_time": total_generation_time,
+            "total_completion_tokens": total_completion_tokens,
+            "total_tool_tokens": sum(m.get("tool_tokens", 0) for m in metrics),
+            "avg_completion_tokens_per_turn": total_completion_tokens / max(num_turns, 1),
+            "avg_tool_tokens_per_call": sum(m.get("tool_tokens", 0) for m in metrics) / max(sum(m.get("tool_calls", 0) for m in metrics), 1),
+            "duplicate_search_detected": duplicate_search_detected,
+            "excessive_parallel_calls": excessive_parallel_calls,
+            "tool_error_detected": tool_error_detected,
+            "refine_error_detected": refine_error_detected,
+            "overlong": overlong,
+            "merged_step": len(trajectory.merge()),
+        }
 
         if OVERLONG_FILTER and overlong:
             for seq in trajectory.sequences:

rllm-model-gateway/src/rllm_model_gateway/client.py

@@ -60,9 +60,7 @@ def get_session_info(self, session_id: str) -> dict[str, Any]:
         resp.raise_for_status()
         return resp.json()
 
-    def list_sessions(
-        self, since: float | None = None, limit: int | None = None
-    ) -> list[dict[str, Any]]:
+    def list_sessions(self, since: float | None = None, limit: int | None = None) -> list[dict[str, Any]]:
         params: dict[str, Any] = {}
         if since is not None:
             params["since"] = since
@@ -90,9 +88,7 @@ def get_session_traces(
             params["since"] = since
         if limit is not None:
             params["limit"] = limit
-        resp = self._http.get(
-            f"{self.gateway_url}/sessions/{session_id}/traces", params=params
-        )
+        resp = self._http.get(f"{self.gateway_url}/sessions/{session_id}/traces", params=params)
         resp.raise_for_status()
         data = resp.json()
         return [TraceRecord(**t) for t in data]
@@ -188,9 +184,7 @@ async def get_session_info(self, session_id: str) -> dict[str, Any]:
         resp.raise_for_status()
         return resp.json()
 
-    async def list_sessions(
-        self, since: float | None = None, limit: int | None = None
-    ) -> list[dict[str, Any]]:
+    async def list_sessions(self, since: float | None = None, limit: int | None = None) -> list[dict[str, Any]]:
         params: dict[str, Any] = {}
         if since is not None:
             params["since"] = since
@@ -218,9 +212,7 @@ async def get_session_traces(
             params["since"] = since
         if limit is not None:
             params["limit"] = limit
-        resp = await self._http.get(
-            f"{self.gateway_url}/sessions/{session_id}/traces", params=params
-        )
+        resp = await self._http.get(f"{self.gateway_url}/sessions/{session_id}/traces", params=params)
         resp.raise_for_status()
         data = resp.json()
         return [TraceRecord(**t) for t in data]

rllm-model-gateway/src/rllm_model_gateway/middleware.py

@@ -67,17 +67,13 @@ async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
 
         # Inject sampling parameters into POST request bodies (chat completions, etc.)
         method = scope.get("method", "").upper()
-        needs_injection = (
-            self.add_logprobs or self.add_return_token_ids or self.sessions is not None
-        )
+        needs_injection = self.add_logprobs or self.add_return_token_ids or self.sessions is not None
         if method == "POST" and needs_injection:
             await self._inject_params(scope, receive, send, session_id)
         else:
             await self.app(scope, receive, send)
 
-    async def _inject_params(
-        self, scope: Scope, receive: Receive, send: Send, session_id: str | None = None
-    ) -> None:
+    async def _inject_params(self, scope: Scope, receive: Receive, send: Send, session_id: str | None = None) -> None:
         """Read body, inject sampling params, then forward with mutated body."""
         body_parts: list[bytes] = []
         more = True
@@ -94,9 +90,7 @@ async def _inject_params(
                     # Record whether the client originally requested logprobs
                     # so the proxy can strip them from the response if not.
                     state = scope["state"]
-                    state["originally_requested_logprobs"] = (
-                        "logprobs" in payload and payload["logprobs"]
-                    )
+                    state["originally_requested_logprobs"] = "logprobs" in payload and payload["logprobs"]
                     self._mutate(payload, session_id)
                     raw = json.dumps(payload).encode("utf-8")
             except (json.JSONDecodeError, UnicodeDecodeError):

rllm-model-gateway/src/rllm_model_gateway/server.py

@@ -359,10 +359,7 @@ def _load_config(args: argparse.Namespace) -> GatewayConfig:
     # Workers from CLI --worker flags (WorkerConfig validator auto-splits URLs)
     worker_urls = getattr(args, "worker", None) or []
     if worker_urls:
-        data["workers"] = [
-            {"url": raw_url, "worker_id": str(i)}
-            for i, raw_url in enumerate(worker_urls)
-        ]
+        data["workers"] = [{"url": raw_url, "worker_id": str(i)} for i, raw_url in enumerate(worker_urls)]
 
     return GatewayConfig(**data)
 
@@ -373,9 +370,7 @@ def _load_config(args: argparse.Namespace) -> GatewayConfig:
 
 
 def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="rllm-model-gateway: lightweight LLM call proxy for RL training"
-    )
+    parser = argparse.ArgumentParser(description="rllm-model-gateway: lightweight LLM call proxy for RL training")
     parser.add_argument("--host", type=str, default=None)
     parser.add_argument("--port", type=int, default=None)
     parser.add_argument("--config", type=str, default=None, help="Path to YAML config")
@@ -398,9 +393,7 @@ def main() -> None:
 
     import uvicorn
 
-    uvicorn.run(
-        app, host=config.host, port=config.port, log_level=config.log_level.lower()
-    )
+    uvicorn.run(app, host=config.host, port=config.port, log_level=config.log_level.lower())
 
 
 if __name__ == "__main__":

rllm-model-gateway/src/rllm_model_gateway/session_manager.py

@@ -23,9 +23,7 @@ def __init__(self, store: TraceStore) -> None:
         self._created_at: dict[str, float] = {}
         self._sampling_params: dict[str, dict[str, Any]] = {}
 
-    def ensure_session(
-        self, session_id: str, metadata: dict[str, Any] | None = None
-    ) -> str:
+    def ensure_session(self, session_id: str, metadata: dict[str, Any] | None = None) -> str:
         """Ensure a session exists (create if needed).  Returns session_id."""
         if session_id not in self._created_at:
             self._created_at[session_id] = time.time()

rllm/agents/miniwob_agent.py

@@ -38,7 +38,17 @@ def image_to_jpg_base64_url(image: np.ndarray | Image.Image) -> str:
 
 
 class MiniWobAgent(BaseAgent):
-    def __init__(self, chat_mode: bool = False, use_html: bool = True, use_axtree: bool = True, use_screenshot: bool = False, use_accumulate_thinking: bool = True, cot_prompt: bool = False, use_full_conversation: bool = True, use_reward_shaping: bool = False):
+    def __init__(
+        self,
+        chat_mode: bool = False,
+        use_html: bool = True,
+        use_axtree: bool = True,
+        use_screenshot: bool = False,
+        use_accumulate_thinking: bool = True,
+        cot_prompt: bool = False,
+        use_full_conversation: bool = True,
+        use_reward_shaping: bool = False,
+    ):
         self.chat_mode: bool = chat_mode
         self.use_html: bool = use_html
         self.use_axtree: bool = use_axtree
@@ -217,7 +227,12 @@ def get_user_msgs(self, user_obs) -> list[dict[str, str]]:
         user_msgs.append({"type": "text", "text": self._get_action_space_description()})
 
         # Add next action prompt
-        user_msgs.append({"type": "text", "text": "# Next action\nThe task has not been completed yet. You will now think step by step and produce your next best action. Reflect on your past actions, any resulting error message, and the current state of the page before deciding on your next action. The content must be in the same format as shown before in the Action Space. You can plan ahead but only 1 immediate action is needed."})
+        user_msgs.append(
+            {
+                "type": "text",
+                "text": "# Next action\nThe task has not been completed yet. You will now think step by step and produce your next best action. Reflect on your past actions, any resulting error message, and the current state of the page before deciding on your next action. The content must be in the same format as shown before in the Action Space. You can plan ahead but only 1 immediate action is needed.",
+            }
+        )
 
         return user_msgs
 

rllm/environments/tools/mcp_env.py

@@ -403,9 +403,7 @@ def _ensure_connection_managers(self) -> list[str]:
                     existing_spec = MCPEnvironment._server_specs.get(server_name)
                     if existing_spec is not None:
                         if existing_spec != server_spec:
-                            raise ValueError(
-                                f"MCP server '{server_name}' is already initialized with a different configuration"
-                            )
+                            raise ValueError(f"MCP server '{server_name}' is already initialized with a different configuration")
                         continue
 
                     manager = MCPConnectionManager(
@@ -460,27 +458,18 @@ def _build_tool_routing(self) -> dict[str, str]:
             explicit_server_name = self.tool_name_to_server_name.get(public_tool_name)
             if explicit_server_name is not None:
                 if explicit_server_name not in candidate_servers:
-                    raise ValueError(
-                        f"Tool '{public_tool_name}' is not provided by mapped MCP server '{explicit_server_name}'"
-                    )
+                    raise ValueError(f"Tool '{public_tool_name}' is not provided by mapped MCP server '{explicit_server_name}'")
                 resolved[public_tool_name] = explicit_server_name
             elif len(candidate_servers) == 1:
                 resolved[public_tool_name] = next(iter(candidate_servers))
             else:
-                raise ValueError(
-                    f"Tool '{public_tool_name}' is provided by multiple MCP servers {sorted(candidate_servers)}. "
-                    "Supply 'tool_name_to_server_name' to disambiguate."
-                )
+                raise ValueError(f"Tool '{public_tool_name}' is provided by multiple MCP servers {sorted(candidate_servers)}. Supply 'tool_name_to_server_name' to disambiguate.")
 
         for public_tool_name, mapped_server_name in self.tool_name_to_server_name.items():
             if mapped_server_name not in self.mcp_servers:
-                raise ValueError(
-                    f"Tool mapping for '{public_tool_name}' references unknown MCP server '{mapped_server_name}'"
-                )
+                raise ValueError(f"Tool mapping for '{public_tool_name}' references unknown MCP server '{mapped_server_name}'")
             if public_tool_name not in discovered_tool_servers:
-                raise ValueError(
-                    f"Tool mapping for '{public_tool_name}' does not match any discovered tool on the configured MCP servers"
-                )
+                raise ValueError(f"Tool mapping for '{public_tool_name}' does not match any discovered tool on the configured MCP servers")
 
         return resolved
 

rllm/experimental/engine/remote_runtime/agentcore_runtime.py

@@ -112,9 +112,7 @@ async def _run_one(self, sub: TaskSubmission, timeout: float) -> RemoteTaskResul
             raw_result=result,
         )
 
-    async def execute_tasks(
-        self, submissions: list[TaskSubmission], timeout: float | None = None
-    ) -> list[RemoteTaskResult]:
+    async def execute_tasks(self, submissions: list[TaskSubmission], timeout: float | None = None) -> list[RemoteTaskResult]:
         """Submit all tasks concurrently via asyncio.gather.
 
         Each task invokes then polls in sequence; all tasks run in parallel.

rllm/experimental/engine/remote_runtime/protocol.py

@@ -48,9 +48,7 @@ def initialize(self) -> None:
         """Client setup from config."""
         ...
 
-    async def execute_tasks(
-        self, submissions: list[TaskSubmission], timeout: float | None = None
-    ) -> list[RemoteTaskResult]:
+    async def execute_tasks(self, submissions: list[TaskSubmission], timeout: float | None = None) -> list[RemoteTaskResult]:
         """Submit tasks concurrently and gather results. Returns one result per submission."""
         ...