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/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)