fix(agent): fix agent loop hanging and simplify LLM module

- Fix agent loop getting stuck by adding hard stop mechanism
- Add _force_stop flag for immediate task cancellation across threads
- Use thread-safe loop.call_soon_threadsafe for cross-thread cancellation
- Remove request_queue.py (eliminated threading/queue complexity causing hangs)
- Simplify llm.py: direct acompletion calls, cleaner streaming
- Reduce retry wait times to prevent long hangs during retries
- Make timeouts configurable (llm_max_retries, memory_compressor_timeout, sandbox_execution_timeout)
- Keep essential token tracking (input/output/cached tokens, cost, requests)
- Maintain Anthropic prompt caching for system messages

strix.spec

@@ -111,7 +111,6 @@ hiddenimports = [
     'strix.llm.llm',
     'strix.llm.config',
     'strix.llm.utils',
-    'strix.llm.request_queue',
     'strix.llm.memory_compressor',
     'strix.runtime',
     'strix.runtime.runtime',

strix/agents/base_agent.py

@@ -79,6 +79,7 @@ class BaseAgent(metaclass=AgentMeta):
         with contextlib.suppress(Exception):
             self.llm.set_agent_identity(self.state.agent_name, self.state.agent_id)
         self._current_task: asyncio.Task[Any] | None = None
+        self._force_stop = False
 
         from strix.telemetry.tracer import get_global_tracer
 
@@ -156,6 +157,11 @@ class BaseAgent(metaclass=AgentMeta):
             return self._handle_sandbox_error(e, tracer)
 
         while True:
+            if self._force_stop:
+                self._force_stop = False
+                await self._enter_waiting_state(tracer, was_cancelled=True)
+                continue
+
             self._check_agent_messages(self.state)
 
             if self.state.is_waiting_for_input():
@@ -246,7 +252,8 @@ class BaseAgent(metaclass=AgentMeta):
                     continue
 
     async def _wait_for_input(self) -> None:
-        import asyncio
+        if self._force_stop:
+            return
 
         if self.state.has_waiting_timeout():
             self.state.resume_from_waiting()
@@ -339,6 +346,7 @@ class BaseAgent(metaclass=AgentMeta):
 
     async def _process_iteration(self, tracer: Optional["Tracer"]) -> bool:
         final_response = None
+
         async for response in self.llm.generate(self.state.get_conversation_history()):
             final_response = response
             if tracer and response.content:
@@ -584,6 +592,11 @@ class BaseAgent(metaclass=AgentMeta):
         return True
 
     def cancel_current_execution(self) -> None:
+        self._force_stop = True
         if self._current_task and not self._current_task.done():
+            try:
+                loop = self._current_task.get_loop()
+                loop.call_soon_threadsafe(self._current_task.cancel)
+            except RuntimeError:
                 self._current_task.cancel()
         self._current_task = None

strix/config/config.py

@@ -16,9 +16,9 @@ class Config:
     litellm_base_url = None
     ollama_api_base = None
     strix_reasoning_effort = "high"
+    strix_llm_max_retries = "5"
+    strix_memory_compressor_timeout = "30"
     llm_timeout = "300"
-    llm_rate_limit_delay = "4.0"
-    llm_rate_limit_concurrent = "1"
 
     # Tool & Feature Configuration
     perplexity_api_key = None
@@ -27,7 +27,7 @@ class Config:
     # Runtime Configuration
     strix_image = "ghcr.io/usestrix/strix-sandbox:0.1.10"
     strix_runtime_backend = "docker"
-    strix_sandbox_execution_timeout = "500"
+    strix_sandbox_execution_timeout = "120"
     strix_sandbox_connect_timeout = "10"
 
     # Telemetry

strix/llm/llm.py

@@ -1,23 +1,16 @@
 import asyncio
-import logging
 from collections.abc import AsyncIterator
 from dataclasses import dataclass
-from enum import Enum
 from typing import Any
 
 import litellm
-from jinja2 import (
+from jinja2 import Environment, FileSystemLoader, select_autoescape
-    Environment,
+from litellm import acompletion, completion_cost, stream_chunk_builder, supports_reasoning
-    FileSystemLoader,
-    select_autoescape,
-)
-from litellm import completion_cost, stream_chunk_builder, supports_reasoning
 from litellm.utils import supports_prompt_caching, supports_vision
 
 from strix.config import Config
 from strix.llm.config import LLMConfig
 from strix.llm.memory_compressor import MemoryCompressor
-from strix.llm.request_queue import get_global_queue
 from strix.llm.utils import (
     _truncate_to_first_function,
     fix_incomplete_tool_call,
@@ -28,37 +21,9 @@ from strix.tools import get_tools_prompt
 from strix.utils.resource_paths import get_strix_resource_path
 
 
-MAX_RETRIES = 5
-RETRY_MULTIPLIER = 8
-RETRY_MIN = 8
-RETRY_MAX = 64
-
-
-def _should_retry(exception: Exception) -> bool:
-    status_code = None
-    if hasattr(exception, "status_code"):
-        status_code = exception.status_code
-    elif hasattr(exception, "response") and hasattr(exception.response, "status_code"):
-        status_code = exception.response.status_code
-    if status_code is not None:
-        return bool(litellm._should_retry(status_code))
-    return True
-
-
-logger = logging.getLogger(__name__)
-
 litellm.drop_params = True
 litellm.modify_params = True
 
-_LLM_API_KEY = Config.get("llm_api_key")
-_LLM_API_BASE = (
-    Config.get("llm_api_base")
-    or Config.get("openai_api_base")
-    or Config.get("litellm_base_url")
-    or Config.get("ollama_api_base")
-)
-_STRIX_REASONING_EFFORT = Config.get("strix_reasoning_effort")
-
 
 class LLMRequestFailedError(Exception):
     def __init__(self, message: str, details: str | None = None):
@@ -67,20 +32,11 @@ class LLMRequestFailedError(Exception):
         self.details = details
 
 
-class StepRole(str, Enum):
-    AGENT = "agent"
-    USER = "user"
-    SYSTEM = "system"
-
-
 @dataclass
 class LLMResponse:
     content: str
     tool_invocations: list[dict[str, Any]] | None = None
-    scan_id: str | None = None
+    thinking_blocks: list[dict[str, Any]] | None = None
-    step_number: int = 1
-    role: StepRole = StepRole.AGENT
-    thinking_blocks: list[dict[str, Any]] | None = None  # For reasoning models.
 
 
 @dataclass
@@ -88,76 +44,63 @@ class RequestStats:
     input_tokens: int = 0
     output_tokens: int = 0
     cached_tokens: int = 0
-    cache_creation_tokens: int = 0
     cost: float = 0.0
     requests: int = 0
-    failed_requests: int = 0
 
     def to_dict(self) -> dict[str, int | float]:
         return {
             "input_tokens": self.input_tokens,
             "output_tokens": self.output_tokens,
             "cached_tokens": self.cached_tokens,
-            "cache_creation_tokens": self.cache_creation_tokens,
             "cost": round(self.cost, 4),
             "requests": self.requests,
-            "failed_requests": self.failed_requests,
         }
 
 
 class LLM:
-    def __init__(
+    def __init__(self, config: LLMConfig, agent_name: str | None = None):
-        self, config: LLMConfig, agent_name: str | None = None, agent_id: str | None = None
-    ):
         self.config = config
         self.agent_name = agent_name
-        self.agent_id = agent_id
+        self.agent_id: str | None = None
         self._total_stats = RequestStats()
-        self._last_request_stats = RequestStats()
+        self.memory_compressor = MemoryCompressor(model_name=config.model_name)
+        self.system_prompt = self._load_system_prompt(agent_name)
 
-        if _STRIX_REASONING_EFFORT:
+        reasoning = Config.get("strix_reasoning_effort")
-            self._reasoning_effort = _STRIX_REASONING_EFFORT
+        if reasoning:
-        elif self.config.scan_mode == "quick":
+            self._reasoning_effort = reasoning
+        elif config.scan_mode == "quick":
             self._reasoning_effort = "medium"
         else:
             self._reasoning_effort = "high"
 
-        self.memory_compressor = MemoryCompressor(
+    def _load_system_prompt(self, agent_name: str | None) -> str:
-            model_name=self.config.model_name,
+        if not agent_name:
-            timeout=self.config.timeout,
+            return ""
-        )
 
-        if agent_name:
+        try:
             prompt_dir = get_strix_resource_path("agents", agent_name)
             skills_dir = get_strix_resource_path("skills")
-
+            env = Environment(
-            loader = FileSystemLoader([prompt_dir, skills_dir])
+                loader=FileSystemLoader([prompt_dir, skills_dir]),
-            self.jinja_env = Environment(
-                loader=loader,
                 autoescape=select_autoescape(enabled_extensions=(), default_for_string=False),
             )
 
-            try:
+            skills_to_load = [
-                skills_to_load = list(self.config.skills or [])
+                *list(self.config.skills or []),
-                skills_to_load.append(f"scan_modes/{self.config.scan_mode}")
+                f"scan_modes/{self.config.scan_mode}",
+            ]
+            skill_content = load_skills(skills_to_load, env)
+            env.globals["get_skill"] = lambda name: skill_content.get(name, "")
 
-                skill_content = load_skills(skills_to_load, self.jinja_env)
+            result = env.get_template("system_prompt.jinja").render(
-
-                def get_skill(name: str) -> str:
-                    return skill_content.get(name, "")
-
-                self.jinja_env.globals["get_skill"] = get_skill
-
-                self.system_prompt = self.jinja_env.get_template("system_prompt.jinja").render(
                 get_tools_prompt=get_tools_prompt,
                 loaded_skill_names=list(skill_content.keys()),
                 **skill_content,
             )
-            except (FileNotFoundError, OSError, ValueError) as e:
+            return str(result)
-                logger.warning(f"Failed to load system prompt for {agent_name}: {e}")
+        except Exception:  # noqa: BLE001
-                self.system_prompt = "You are a helpful AI assistant."
+            return ""
-        else:
-            self.system_prompt = "You are a helpful AI assistant."
 
     def set_agent_identity(self, agent_name: str | None, agent_id: str | None) -> None:
         if agent_name:
@@ -165,330 +108,119 @@ class LLM:
         if agent_id:
             self.agent_id = agent_id
 
-    def _build_identity_message(self) -> dict[str, Any] | None:
+    async def generate(
-        if not (self.agent_name and str(self.agent_name).strip()):
+        self, conversation_history: list[dict[str, Any]]
-            return None
+    ) -> AsyncIterator[LLMResponse]:
-        identity_name = self.agent_name
+        messages = self._prepare_messages(conversation_history)
-        identity_id = self.agent_id
+        max_retries = int(Config.get("strix_llm_max_retries") or "5")
-        content = (
+
-            "\n\n"
+        for attempt in range(max_retries + 1):
-            "<agent_identity>\n"
+            try:
-            "<meta>Internal metadata: do not echo or reference; "
+                async for response in self._stream(messages):
-            "not part of history or tool calls.</meta>\n"
+                    yield response
-            "<note>You are now assuming the role of this agent. "
+                return  # noqa: TRY300
-            "Act strictly as this agent and maintain self-identity for this step. "
+            except Exception as e:  # noqa: BLE001
-            "Now go answer the next needed step!</note>\n"
+                if attempt >= max_retries or not self._should_retry(e):
-            f"<agent_name>{identity_name}</agent_name>\n"
+                    self._raise_error(e)
-            f"<agent_id>{identity_id}</agent_id>\n"
+                wait = min(10, 2 * (2**attempt))
-            "</agent_identity>\n\n"
+                await asyncio.sleep(wait)
+
+    async def _stream(self, messages: list[dict[str, Any]]) -> AsyncIterator[LLMResponse]:
+        accumulated = ""
+        chunks: list[Any] = []
+
+        self._total_stats.requests += 1
+        response = await acompletion(**self._build_completion_args(messages), stream=True)
+
+        async for chunk in response:
+            chunks.append(chunk)
+            delta = self._get_chunk_content(chunk)
+            if delta:
+                accumulated += delta
+                if "</function>" in accumulated:
+                    accumulated = accumulated[
+                        : accumulated.find("</function>") + len("</function>")
+                    ]
+                yield LLMResponse(content=accumulated)
+
+        if chunks:
+            self._update_usage_stats(stream_chunk_builder(chunks))
+
+        accumulated = fix_incomplete_tool_call(_truncate_to_first_function(accumulated))
+        yield LLMResponse(
+            content=accumulated,
+            tool_invocations=parse_tool_invocations(accumulated),
+            thinking_blocks=self._extract_thinking(chunks),
         )
-        return {"role": "user", "content": content}
-
-    def _add_cache_control_to_content(
-        self, content: str | list[dict[str, Any]]
-    ) -> str | list[dict[str, Any]]:
-        if isinstance(content, str):
-            return [{"type": "text", "text": content, "cache_control": {"type": "ephemeral"}}]
-        if isinstance(content, list) and content:
-            last_item = content[-1]
-            if isinstance(last_item, dict) and last_item.get("type") == "text":
-                return content[:-1] + [{**last_item, "cache_control": {"type": "ephemeral"}}]
-        return content
-
-    def _is_anthropic_model(self) -> bool:
-        if not self.config.model_name:
-            return False
-        model_lower = self.config.model_name.lower()
-        return any(provider in model_lower for provider in ["anthropic/", "claude"])
-
-    def _calculate_cache_interval(self, total_messages: int) -> int:
-        if total_messages <= 1:
-            return 10
-
-        max_cached_messages = 3
-        non_system_messages = total_messages - 1
-
-        interval = 10
-        while non_system_messages // interval > max_cached_messages:
-            interval += 10
-
-        return interval
-
-    def _prepare_cached_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
-        if (
-            not self.config.enable_prompt_caching
-            or not supports_prompt_caching(self.config.model_name)
-            or not messages
-        ):
-            return messages
-
-        if not self._is_anthropic_model():
-            return messages
-
-        cached_messages = list(messages)
-
-        if cached_messages and cached_messages[0].get("role") == "system":
-            system_message = cached_messages[0].copy()
-            system_message["content"] = self._add_cache_control_to_content(
-                system_message["content"]
-            )
-            cached_messages[0] = system_message
-
-        total_messages = len(cached_messages)
-        if total_messages > 1:
-            interval = self._calculate_cache_interval(total_messages)
-
-            cached_count = 0
-            for i in range(interval, total_messages, interval):
-                if cached_count >= 3:
-                    break
-
-                if i < len(cached_messages):
-                    message = cached_messages[i].copy()
-                    message["content"] = self._add_cache_control_to_content(message["content"])
-                    cached_messages[i] = message
-                    cached_count += 1
-
-        return cached_messages
 
     def _prepare_messages(self, conversation_history: list[dict[str, Any]]) -> list[dict[str, Any]]:
         messages = [{"role": "system", "content": self.system_prompt}]
 
-        identity_message = self._build_identity_message()
+        if self.agent_name:
-        if identity_message:
+            messages.append(
-            messages.append(identity_message)
-
-        compressed_history = list(self.memory_compressor.compress_history(conversation_history))
-
-        conversation_history.clear()
-        conversation_history.extend(compressed_history)
-        messages.extend(compressed_history)
-
-        return self._prepare_cached_messages(messages)
-
-    async def _stream_and_accumulate(
-        self,
-        messages: list[dict[str, Any]],
-        scan_id: str | None,
-        step_number: int,
-    ) -> AsyncIterator[LLMResponse]:
-        accumulated_content = ""
-        chunks: list[Any] = []
-
-        async for chunk in self._stream_request(messages):
-            chunks.append(chunk)
-            delta = self._extract_chunk_delta(chunk)
-            if delta:
-                accumulated_content += delta
-
-                if "</function>" in accumulated_content:
-                    function_end = accumulated_content.find("</function>") + len("</function>")
-                    accumulated_content = accumulated_content[:function_end]
-
-                yield LLMResponse(
-                    scan_id=scan_id,
-                    step_number=step_number,
-                    role=StepRole.AGENT,
-                    content=accumulated_content,
-                    tool_invocations=None,
-                )
-
-        if chunks:
-            complete_response = stream_chunk_builder(chunks)
-            self._update_usage_stats(complete_response)
-
-        accumulated_content = _truncate_to_first_function(accumulated_content)
-        if "</function>" in accumulated_content:
-            function_end = accumulated_content.find("</function>") + len("</function>")
-            accumulated_content = accumulated_content[:function_end]
-
-        accumulated_content = fix_incomplete_tool_call(accumulated_content)
-
-        tool_invocations = parse_tool_invocations(accumulated_content)
-
-        # Extract thinking blocks from the complete response if available
-        thinking_blocks = None
-        if chunks and self._should_include_reasoning_effort():
-            complete_response = stream_chunk_builder(chunks)
-            if (
-                hasattr(complete_response, "choices")
-                and complete_response.choices
-                and hasattr(complete_response.choices[0], "message")
-            ):
-                message = complete_response.choices[0].message
-                if hasattr(message, "thinking_blocks") and message.thinking_blocks:
-                    thinking_blocks = message.thinking_blocks
-
-        yield LLMResponse(
-            scan_id=scan_id,
-            step_number=step_number,
-            role=StepRole.AGENT,
-            content=accumulated_content,
-            tool_invocations=tool_invocations if tool_invocations else None,
-            thinking_blocks=thinking_blocks,
-        )
-
-    def _raise_llm_error(self, e: Exception) -> None:
-        error_map: list[tuple[type, str]] = [
-            (litellm.RateLimitError, "Rate limit exceeded"),
-            (litellm.AuthenticationError, "Invalid API key"),
-            (litellm.NotFoundError, "Model not found"),
-            (litellm.ContextWindowExceededError, "Context too long"),
-            (litellm.ContentPolicyViolationError, "Content policy violation"),
-            (litellm.ServiceUnavailableError, "Service unavailable"),
-            (litellm.Timeout, "Request timed out"),
-            (litellm.UnprocessableEntityError, "Unprocessable entity"),
-            (litellm.InternalServerError, "Internal server error"),
-            (litellm.APIConnectionError, "Connection error"),
-            (litellm.UnsupportedParamsError, "Unsupported parameters"),
-            (litellm.BudgetExceededError, "Budget exceeded"),
-            (litellm.APIResponseValidationError, "Response validation error"),
-            (litellm.JSONSchemaValidationError, "JSON schema validation error"),
-            (litellm.InvalidRequestError, "Invalid request"),
-            (litellm.BadRequestError, "Bad request"),
-            (litellm.APIError, "API error"),
-            (litellm.OpenAIError, "OpenAI error"),
-        ]
-
-        from strix.telemetry import posthog
-
-        for error_type, message in error_map:
-            if isinstance(e, error_type):
-                posthog.error(f"llm_{error_type.__name__}", message)
-                raise LLMRequestFailedError(f"LLM request failed: {message}", str(e)) from e
-
-        posthog.error("llm_unknown_error", type(e).__name__)
-        raise LLMRequestFailedError(f"LLM request failed: {type(e).__name__}", str(e)) from e
-
-    async def generate(
-        self,
-        conversation_history: list[dict[str, Any]],
-        scan_id: str | None = None,
-        step_number: int = 1,
-    ) -> AsyncIterator[LLMResponse]:
-        messages = self._prepare_messages(conversation_history)
-
-        last_error: Exception | None = None
-        for attempt in range(MAX_RETRIES):
-            try:
-                async for response in self._stream_and_accumulate(messages, scan_id, step_number):
-                    yield response
-                return  # noqa: TRY300
-            except Exception as e:  # noqa: BLE001
-                last_error = e
-                if not _should_retry(e) or attempt == MAX_RETRIES - 1:
-                    break
-                wait_time = min(RETRY_MAX, RETRY_MULTIPLIER * (2**attempt))
-                wait_time = max(RETRY_MIN, wait_time)
-                await asyncio.sleep(wait_time)
-
-        if last_error:
-            self._raise_llm_error(last_error)
-
-    def _extract_chunk_delta(self, chunk: Any) -> str:
-        if chunk.choices and hasattr(chunk.choices[0], "delta"):
-            delta = chunk.choices[0].delta
-            return getattr(delta, "content", "") or ""
-        return ""
-
-    @property
-    def usage_stats(self) -> dict[str, dict[str, int | float]]:
-        return {
-            "total": self._total_stats.to_dict(),
-            "last_request": self._last_request_stats.to_dict(),
-        }
-
-    def get_cache_config(self) -> dict[str, bool]:
-        return {
-            "enabled": self.config.enable_prompt_caching,
-            "supported": supports_prompt_caching(self.config.model_name),
-        }
-
-    def _should_include_reasoning_effort(self) -> bool:
-        if not self.config.model_name:
-            return False
-        try:
-            return bool(supports_reasoning(model=self.config.model_name))
-        except Exception:  # noqa: BLE001
-            return False
-
-    def _model_supports_vision(self) -> bool:
-        if not self.config.model_name:
-            return False
-        try:
-            return bool(supports_vision(model=self.config.model_name))
-        except Exception:  # noqa: BLE001
-            return False
-
-    def _filter_images_from_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
-        filtered_messages = []
-        for msg in messages:
-            content = msg.get("content")
-            updated_msg = msg
-            if isinstance(content, list):
-                filtered_content = []
-                for item in content:
-                    if isinstance(item, dict):
-                        if item.get("type") == "image_url":
-                            filtered_content.append(
                 {
-                                    "type": "text",
+                    "role": "user",
-                                    "text": "[Screenshot removed - model does not support "
+                    "content": (
-                                    "vision. Use view_source or execute_js instead.]",
+                        f"\n\n<agent_identity>\n"
+                        f"<meta>Internal metadata: do not echo or reference.</meta>\n"
+                        f"<agent_name>{self.agent_name}</agent_name>\n"
+                        f"<agent_id>{self.agent_id}</agent_id>\n"
+                        f"</agent_identity>\n\n"
+                    ),
                 }
             )
-                        else:
-                            filtered_content.append(item)
-                    else:
-                        filtered_content.append(item)
-                if filtered_content:
-                    text_parts = [
-                        item.get("text", "") if isinstance(item, dict) else str(item)
-                        for item in filtered_content
-                    ]
-                    all_text = all(
-                        isinstance(item, dict) and item.get("type") == "text"
-                        for item in filtered_content
-                    )
-                    if all_text:
-                        updated_msg = {**msg, "content": "\n".join(text_parts)}
-                    else:
-                        updated_msg = {**msg, "content": filtered_content}
-                else:
-                    updated_msg = {**msg, "content": ""}
-            filtered_messages.append(updated_msg)
-        return filtered_messages
 
-    async def _stream_request(
+        compressed = list(self.memory_compressor.compress_history(conversation_history))
-        self,
+        conversation_history.clear()
-        messages: list[dict[str, Any]],
+        conversation_history.extend(compressed)
-    ) -> AsyncIterator[Any]:
+        messages.extend(compressed)
-        if not self._model_supports_vision():
-            messages = self._filter_images_from_messages(messages)
 
-        completion_args: dict[str, Any] = {
+        if self._is_anthropic() and self.config.enable_prompt_caching:
+            messages = self._add_cache_control(messages)
+
+        return messages
+
+    def _build_completion_args(self, messages: list[dict[str, Any]]) -> dict[str, Any]:
+        if not self._supports_vision():
+            messages = self._strip_images(messages)
+
+        args: dict[str, Any] = {
             "model": self.config.model_name,
             "messages": messages,
             "timeout": self.config.timeout,
             "stream_options": {"include_usage": True},
+            "stop": ["</function>"],
         }
 
-        if _LLM_API_KEY:
+        if api_key := Config.get("llm_api_key"):
-            completion_args["api_key"] = _LLM_API_KEY
+            args["api_key"] = api_key
-        if _LLM_API_BASE:
+        if api_base := (
-            completion_args["api_base"] = _LLM_API_BASE
+            Config.get("llm_api_base")
+            or Config.get("openai_api_base")
+            or Config.get("litellm_base_url")
+        ):
+            args["api_base"] = api_base
+        if self._supports_reasoning():
+            args["reasoning_effort"] = self._reasoning_effort
 
-        completion_args["stop"] = ["</function>"]
+        return args
 
-        if self._should_include_reasoning_effort():
+    def _get_chunk_content(self, chunk: Any) -> str:
-            completion_args["reasoning_effort"] = self._reasoning_effort
+        if chunk.choices and hasattr(chunk.choices[0], "delta"):
+            return getattr(chunk.choices[0].delta, "content", "") or ""
+        return ""
 
-        queue = get_global_queue()
+    def _extract_thinking(self, chunks: list[Any]) -> list[dict[str, Any]] | None:
-        self._total_stats.requests += 1
+        if not chunks or not self._supports_reasoning():
-        self._last_request_stats = RequestStats(requests=1)
+            return None
-
+        try:
-        async for chunk in queue.stream_request(completion_args):
+            resp = stream_chunk_builder(chunks)
-            yield chunk
+            if resp.choices and hasattr(resp.choices[0].message, "thinking_blocks"):
+                blocks: list[dict[str, Any]] = resp.choices[0].message.thinking_blocks
+                return blocks
+        except Exception:  # noqa: BLE001, S110  # nosec B110
+            pass
+        return None
 
     def _update_usage_stats(self, response: Any) -> None:
         try:
@@ -497,45 +229,88 @@ class LLM:
                 output_tokens = getattr(response.usage, "completion_tokens", 0)
 
                 cached_tokens = 0
-                cache_creation_tokens = 0
-
                 if hasattr(response.usage, "prompt_tokens_details"):
                     prompt_details = response.usage.prompt_tokens_details
                     if hasattr(prompt_details, "cached_tokens"):
                         cached_tokens = prompt_details.cached_tokens or 0
 
-                if hasattr(response.usage, "cache_creation_input_tokens"):
-                    cache_creation_tokens = response.usage.cache_creation_input_tokens or 0
-
             else:
                 input_tokens = 0
                 output_tokens = 0
                 cached_tokens = 0
-                cache_creation_tokens = 0
 
             try:
                 cost = completion_cost(response) or 0.0
-            except Exception as e:  # noqa: BLE001
+            except Exception:  # noqa: BLE001
-                logger.warning(f"Failed to calculate cost: {e}")
                 cost = 0.0
 
             self._total_stats.input_tokens += input_tokens
             self._total_stats.output_tokens += output_tokens
             self._total_stats.cached_tokens += cached_tokens
-            self._total_stats.cache_creation_tokens += cache_creation_tokens
             self._total_stats.cost += cost
 
-            self._last_request_stats.input_tokens = input_tokens
+        except Exception:  # noqa: BLE001, S110  # nosec B110
-            self._last_request_stats.output_tokens = output_tokens
+            pass
-            self._last_request_stats.cached_tokens = cached_tokens
-            self._last_request_stats.cache_creation_tokens = cache_creation_tokens
-            self._last_request_stats.cost = cost
 
-            if cached_tokens > 0:
+    def _should_retry(self, e: Exception) -> bool:
-                logger.info(f"Cache hit: {cached_tokens} cached tokens, {input_tokens} new tokens")
+        code = getattr(e, "status_code", None) or getattr(
-            if cache_creation_tokens > 0:
+            getattr(e, "response", None), "status_code", None
-                logger.info(f"Cache creation: {cache_creation_tokens} tokens written to cache")
+        )
+        return code is None or litellm._should_retry(code)
 
-            logger.info(f"Usage stats: {self.usage_stats}")
+    def _raise_error(self, e: Exception) -> None:
-        except Exception as e:  # noqa: BLE001
+        from strix.telemetry import posthog
-            logger.warning(f"Failed to update usage stats: {e}")
+
+        posthog.error("llm_error", type(e).__name__)
+        raise LLMRequestFailedError(f"LLM request failed: {type(e).__name__}", str(e)) from e
+
+    def _is_anthropic(self) -> bool:
+        if not self.config.model_name:
+            return False
+        return any(p in self.config.model_name.lower() for p in ["anthropic/", "claude"])
+
+    def _supports_vision(self) -> bool:
+        try:
+            return bool(supports_vision(model=self.config.model_name))
+        except Exception:  # noqa: BLE001
+            return False
+
+    def _supports_reasoning(self) -> bool:
+        try:
+            return bool(supports_reasoning(model=self.config.model_name))
+        except Exception:  # noqa: BLE001
+            return False
+
+    def _strip_images(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        result = []
+        for msg in messages:
+            content = msg.get("content")
+            if isinstance(content, list):
+                text_parts = []
+                for item in content:
+                    if isinstance(item, dict) and item.get("type") == "text":
+                        text_parts.append(item.get("text", ""))
+                    elif isinstance(item, dict) and item.get("type") == "image_url":
+                        text_parts.append("[Image removed - model doesn't support vision]")
+                result.append({**msg, "content": "\n".join(text_parts)})
+            else:
+                result.append(msg)
+        return result
+
+    def _add_cache_control(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        if not messages or not supports_prompt_caching(self.config.model_name):
+            return messages
+
+        result = list(messages)
+
+        if result[0].get("role") == "system":
+            content = result[0]["content"]
+            result[0] = {
+                **result[0],
+                "content": [
+                    {"type": "text", "text": content, "cache_control": {"type": "ephemeral"}}
+                ]
+                if isinstance(content, str)
+                else content,
+            }
+        return result

strix/llm/memory_compressor.py

@@ -86,7 +86,7 @@ def _extract_message_text(msg: dict[str, Any]) -> str:
 def _summarize_messages(
     messages: list[dict[str, Any]],
     model: str,
-    timeout: int = 600,
+    timeout: int = 30,
 ) -> dict[str, Any]:
     if not messages:
         empty_summary = "<context_summary message_count='0'>{text}</context_summary>"
@@ -148,11 +148,11 @@ class MemoryCompressor:
         self,
         max_images: int = 3,
         model_name: str | None = None,
-        timeout: int = 600,
+        timeout: int | None = None,
     ):
         self.max_images = max_images
         self.model_name = model_name or Config.get("strix_llm")
-        self.timeout = timeout
+        self.timeout = timeout or int(Config.get("strix_memory_compressor_timeout") or "30")
 
         if not self.model_name:
             raise ValueError("STRIX_LLM environment variable must be set and not empty")

strix/llm/request_queue.py

@@ -1,58 +0,0 @@
-import asyncio
-import threading
-import time
-from collections.abc import AsyncIterator
-from typing import Any
-
-from litellm import acompletion
-from litellm.types.utils import ModelResponseStream
-
-from strix.config import Config
-
-
-class LLMRequestQueue:
-    def __init__(self) -> None:
-        self.delay_between_requests = float(Config.get("llm_rate_limit_delay") or "4.0")
-        self.max_concurrent = int(Config.get("llm_rate_limit_concurrent") or "1")
-        self._semaphore = threading.BoundedSemaphore(self.max_concurrent)
-        self._last_request_time = 0.0
-        self._lock = threading.Lock()
-
-    async def stream_request(
-        self, completion_args: dict[str, Any]
-    ) -> AsyncIterator[ModelResponseStream]:
-        try:
-            while not self._semaphore.acquire(timeout=0.2):
-                await asyncio.sleep(0.1)
-
-            with self._lock:
-                now = time.time()
-                time_since_last = now - self._last_request_time
-                sleep_needed = max(0, self.delay_between_requests - time_since_last)
-                self._last_request_time = now + sleep_needed
-
-            if sleep_needed > 0:
-                await asyncio.sleep(sleep_needed)
-
-            async for chunk in self._stream_request(completion_args):
-                yield chunk
-        finally:
-            self._semaphore.release()
-
-    async def _stream_request(
-        self, completion_args: dict[str, Any]
-    ) -> AsyncIterator[ModelResponseStream]:
-        response = await acompletion(**completion_args, stream=True)
-
-        async for chunk in response:
-            yield chunk
-
-
-_global_queue: LLMRequestQueue | None = None
-
-
-def get_global_queue() -> LLMRequestQueue:
-    global _global_queue  # noqa: PLW0603
-    if _global_queue is None:
-        _global_queue = LLMRequestQueue()
-    return _global_queue

strix/telemetry/tracer.py

@@ -430,10 +430,8 @@ class Tracer:
             "input_tokens": 0,
             "output_tokens": 0,
             "cached_tokens": 0,
-            "cache_creation_tokens": 0,
             "cost": 0.0,
             "requests": 0,
-            "failed_requests": 0,
         }
 
         for agent_instance in _agent_instances.values():
@@ -442,10 +440,8 @@ class Tracer:
                 total_stats["input_tokens"] += agent_stats.input_tokens
                 total_stats["output_tokens"] += agent_stats.output_tokens
                 total_stats["cached_tokens"] += agent_stats.cached_tokens
-                total_stats["cache_creation_tokens"] += agent_stats.cache_creation_tokens
                 total_stats["cost"] += agent_stats.cost
                 total_stats["requests"] += agent_stats.requests
-                total_stats["failed_requests"] += agent_stats.failed_requests
 
         total_stats["cost"] = round(total_stats["cost"], 4)
 

strix/tools/executor.py

@@ -20,7 +20,7 @@ from .registry import (
 )
 
 
-SANDBOX_EXECUTION_TIMEOUT = float(Config.get("strix_sandbox_execution_timeout") or "500")
+SANDBOX_EXECUTION_TIMEOUT = float(Config.get("strix_sandbox_execution_timeout") or "120")
 SANDBOX_CONNECT_TIMEOUT = float(Config.get("strix_sandbox_connect_timeout") or "10")