diff --git a/runtime_manager/worker_main.py b/runtime_manager/worker_main.py
index 7ef463b17768..8349ff9d4138 100644
--- a/runtime_manager/worker_main.py
+++ b/runtime_manager/worker_main.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import ast
+import hashlib
 import json
 import os
 import re
@@ -252,6 +253,7 @@ def command_reader() -> None:
     )
     listener.start()
 
+    llm_debug_hook_registrations: list[tuple[Any, str, Any]] = []
     tool_started_at: dict[str, float] = {}
     seen_artifact_ids: set[str] = set()
     tool_lock = threading.Lock()
@@ -372,6 +374,9 @@ def on_thinking(message: str | None) -> None:
         )
         register_gateway_notify(approval_session_key, approval_notify)
         runtime_llm_config = _resolve_runtime_llm_config(request)
+        model = runtime_llm_config["model"]
+        provider = runtime_llm_config["provider"]
+        base_url = runtime_llm_config["base_url"]
 
         system_prompt = _compose_effective_system_prompt(
             request,
@@ -410,6 +415,12 @@ def on_thinking(message: str | None) -> None:
         )
         _apply_runtime_llm_config_to_agent(agent, runtime_llm_config)
         _AGENT_HOLDER["agent"] = agent
+        llm_debug_hook_registrations = _register_runtime_llm_debug_hooks(
+            run_id=run_id,
+            request=request,
+            system_prompt=system_prompt,
+            emit_fn=emit,
+        )
 
         emit({"event": "run.running", "run_id": run_id, "timestamp": time.time()})
         result = agent.run_conversation(
@@ -463,6 +474,7 @@ def on_thinking(message: str | None) -> None:
         )
         return 1
     finally:
+        _unregister_runtime_llm_debug_hooks(llm_debug_hook_registrations)
         try:
             unregister_gateway_notify(approval_session_key)
         except Exception:
@@ -615,6 +627,245 @@ def _base_url_host(value: Any) -> str:
     return parsed.netloc or parsed.path.split("/", 1)[0]
 
 
+def _register_runtime_llm_debug_hooks(
+    *,
+    run_id: str,
+    request: dict[str, Any],
+    system_prompt: str | None,
+    emit_fn,
+) -> list[tuple[Any, str, Any]]:
+    try:
+        from hermes_cli.plugins import get_plugin_manager
+    except Exception:
+        return []
+
+    manager = get_plugin_manager()
+    registrations: list[tuple[Any, str, Any]] = []
+
+    def on_pre_api_request(**kwargs: Any) -> None:
+        event = _llm_request_metadata_event(
+            kwargs,
+            run_id=run_id,
+            request=request,
+            system_prompt=system_prompt,
+        )
+        if event:
+            emit_fn(event)
+
+    def on_post_api_request(**kwargs: Any) -> None:
+        event = _llm_response_metadata_event(kwargs, run_id=run_id)
+        if event:
+            emit_fn(event)
+
+    for hook_name, callback in (
+        ("pre_api_request", on_pre_api_request),
+        ("post_api_request", on_post_api_request),
+    ):
+        hooks = getattr(manager, "_hooks", None)
+        if not isinstance(hooks, dict):
+            return registrations
+        hooks.setdefault(hook_name, []).append(callback)
+        registrations.append((manager, hook_name, callback))
+    return registrations
+
+
+def _unregister_runtime_llm_debug_hooks(registrations: list[tuple[Any, str, Any]]) -> None:
+    for manager, hook_name, callback in registrations:
+        try:
+            hooks = getattr(manager, "_hooks", None)
+            callbacks = hooks.get(hook_name) if isinstance(hooks, dict) else None
+            if isinstance(callbacks, list) and callback in callbacks:
+                callbacks.remove(callback)
+        except Exception:
+            continue
+
+
+def _llm_request_metadata_event(
+    hook_kwargs: dict[str, Any],
+    *,
+    run_id: str,
+    request: dict[str, Any],
+    system_prompt: str | None,
+) -> dict[str, Any]:
+    body = _hook_request_body(hook_kwargs)
+    tools = body.get("tools")
+    if not isinstance(tools, list):
+        tools = []
+
+    prompt_text = system_prompt or ""
+    skills = _normalize_string_list(request.get("skills"))
+    return {
+        "event": "llm.request.metadata",
+        "run_id": run_id,
+        "timestamp": time.time(),
+        "api_request_id": str(hook_kwargs.get("api_request_id") or ""),
+        "turn_id": str(hook_kwargs.get("turn_id") or ""),
+        "api_call_count": _coerce_positive_int(hook_kwargs.get("api_call_count")),
+        "provider": str(hook_kwargs.get("provider") or ""),
+        "model": str(hook_kwargs.get("model") or ""),
+        "base_url_host": _base_url_host(hook_kwargs.get("base_url")),
+        "api_mode": str(hook_kwargs.get("api_mode") or ""),
+        "message_count": _coerce_positive_int(hook_kwargs.get("message_count")),
+        "request_message_count": _message_count_from_body(body),
+        "request_char_count": _coerce_positive_int(hook_kwargs.get("request_char_count")),
+        "approx_input_tokens": _coerce_positive_int(hook_kwargs.get("approx_input_tokens")),
+        "max_tokens": _coerce_positive_int(hook_kwargs.get("max_tokens")),
+        "skills_count": len(skills),
+        "skill_names": skills,
+        "skill_prompt_presence": {
+            skill: bool(skill and skill in prompt_text)
+            for skill in skills
+        },
+        "system_prompt_chars": len(prompt_text),
+        "system_prompt_sha256": _sha256_hex(prompt_text) if prompt_text else "",
+        "enabled_toolsets": _normalize_string_list(request.get("enabled_toolsets")),
+        "disabled_toolsets": _normalize_string_list(request.get("disabled_toolsets")),
+        "tool_count": _coerce_positive_int(hook_kwargs.get("tool_count")),
+        "tools_count": len(tools),
+        "tool_names": _tool_names_from_schemas(tools),
+        "tool_choice": _summarize_tool_choice(body),
+    }
+
+
+def _llm_response_metadata_event(
+    hook_kwargs: dict[str, Any],
+    *,
+    run_id: str,
+) -> dict[str, Any]:
+    assistant_message = hook_kwargs.get("assistant_message")
+    return {
+        "event": "llm.response.metadata",
+        "run_id": run_id,
+        "timestamp": time.time(),
+        "api_request_id": str(hook_kwargs.get("api_request_id") or ""),
+        "turn_id": str(hook_kwargs.get("turn_id") or ""),
+        "api_call_count": _coerce_positive_int(hook_kwargs.get("api_call_count")),
+        "provider": str(hook_kwargs.get("provider") or ""),
+        "model": str(hook_kwargs.get("model") or ""),
+        "base_url_host": _base_url_host(hook_kwargs.get("base_url")),
+        "api_mode": str(hook_kwargs.get("api_mode") or ""),
+        "api_duration": _coerce_nonnegative_float(hook_kwargs.get("api_duration")),
+        "finish_reason": str(hook_kwargs.get("finish_reason") or ""),
+        "response_model": str(hook_kwargs.get("response_model") or ""),
+        "message_count": _coerce_positive_int(hook_kwargs.get("message_count")),
+        "assistant_content_chars": _coerce_positive_int(
+            hook_kwargs.get("assistant_content_chars")
+        ),
+        "assistant_tool_calls_count": _coerce_positive_int(
+            hook_kwargs.get("assistant_tool_call_count")
+        ),
+        "assistant_tool_names": _tool_names_from_calls(
+            getattr(assistant_message, "tool_calls", None)
+        ),
+        "usage": hook_kwargs.get("usage") if isinstance(hook_kwargs.get("usage"), dict) else {},
+    }
+
+
+def _hook_request_body(hook_kwargs: dict[str, Any]) -> dict[str, Any]:
+    request_payload = hook_kwargs.get("request")
+    if not isinstance(request_payload, dict):
+        return {}
+    body = request_payload.get("body")
+    return body if isinstance(body, dict) else {}
+
+
+def _message_count_from_body(body: dict[str, Any]) -> int:
+    for key in ("messages", "input"):
+        value = body.get(key)
+        if isinstance(value, list):
+            return len(value)
+    return 0
+
+
+def _tool_names_from_schemas(tools: list[Any], *, limit: int = 32) -> list[str]:
+    names: list[str] = []
+    for item in tools:
+        name = _tool_name_from_schema(item)
+        if name:
+            names.append(name)
+        if len(names) >= limit:
+            break
+    return names
+
+
+def _tool_name_from_schema(value: Any) -> str:
+    if not isinstance(value, dict):
+        return ""
+    function = value.get("function")
+    if isinstance(function, dict) and isinstance(function.get("name"), str):
+        return function["name"]
+    for key in ("name", "tool_name"):
+        item = value.get(key)
+        if isinstance(item, str) and item.strip():
+            return item.strip()
+    return ""
+
+
+def _tool_names_from_calls(value: Any, *, limit: int = 32) -> list[str]:
+    if not isinstance(value, list):
+        return []
+    names: list[str] = []
+    for item in value:
+        name = _tool_name_from_call(item)
+        if name:
+            names.append(name)
+        if len(names) >= limit:
+            break
+    return names
+
+
+def _tool_name_from_call(value: Any) -> str:
+    function = getattr(value, "function", None)
+    name = getattr(function, "name", None)
+    if isinstance(name, str) and name.strip():
+        return name.strip()
+    if isinstance(value, dict):
+        function = value.get("function")
+        if isinstance(function, dict) and isinstance(function.get("name"), str):
+            return function["name"].strip()
+        if isinstance(value.get("name"), str):
+            return value["name"].strip()
+    return ""
+
+
+def _summarize_tool_choice(body: dict[str, Any]) -> str:
+    if "tool_choice" not in body:
+        return "omitted"
+    value = body.get("tool_choice")
+    if value is None:
+        return "null"
+    if isinstance(value, str):
+        return value.strip() or "empty"
+    if isinstance(value, dict):
+        function = value.get("function")
+        name = function.get("name") if isinstance(function, dict) else None
+        if isinstance(name, str) and name.strip():
+            return f"function:{name.strip()}"
+        item_type = value.get("type")
+        if isinstance(item_type, str) and item_type.strip():
+            return f"type:{item_type.strip()}"
+        return "object"
+    return type(value).__name__
+
+
+def _sha256_hex(value: str) -> str:
+    return hashlib.sha256(value.encode("utf-8", errors="replace")).hexdigest()
+
+
+def _coerce_nonnegative_float(value: Any) -> float:
+    if isinstance(value, bool) or value is None:
+        return 0.0
+    if isinstance(value, (int, float)):
+        return float(value) if value >= 0 else 0.0
+    if isinstance(value, str):
+        try:
+            parsed = float(value.strip())
+        except ValueError:
+            return 0.0
+        return parsed if parsed >= 0 else 0.0
+    return 0.0
+
+
 def _safe_status_event_text(value: Any, *, limit: int = 512) -> str:
     text = str(value or "")
     text = _SENSITIVE_STATUS_VALUE_PATTERN.sub(r"\1\2<redacted>", text)
diff --git a/tests/runtime_manager/test_registry.py b/tests/runtime_manager/test_registry.py
index 890b2e46fd65..54c423cba1d3 100644
--- a/tests/runtime_manager/test_registry.py
+++ b/tests/runtime_manager/test_registry.py
@@ -177,6 +177,104 @@ def __init__(self):
     }
 
 
+def test_runtime_worker_llm_request_metadata_is_safe_and_actionable():
+    from runtime_manager.worker_main import _llm_request_metadata_event
+
+    event = _llm_request_metadata_event(
+        {
+            "api_request_id": "turn-1:api:1",
+            "turn_id": "turn-1",
+            "api_call_count": 1,
+            "provider": "custom",
+            "model": "qwen3.6-35b-a3b",
+            "base_url": "https://models.example/v1",
+            "api_mode": "chat_completions",
+            "message_count": 3,
+            "request_char_count": 12345,
+            "approx_input_tokens": 6789,
+            "max_tokens": 8192,
+            "tool_count": 6,
+            "request": {
+                "body": {
+                    "messages": [
+                        {"role": "system", "content": "SECRET PROMPT"},
+                        {"role": "user", "content": "diagnose"},
+                    ],
+                    "tools": [
+                        {
+                            "type": "function",
+                            "function": {
+                                "name": "terminal",
+                                "parameters": {"properties": {"command": {"description": "SECRET SCHEMA"}}},
+                            },
+                        },
+                        {"type": "function", "function": {"name": "read_file"}},
+                    ],
+                    "tool_choice": "auto",
+                    "api_key": "secret-key",
+                }
+            },
+        },
+        run_id="run-1",
+        request={
+            "skills": ["dba-diagnose-oracle"],
+            "enabled_toolsets": ["terminal", "file"],
+        },
+        system_prompt="BASE\n\ndba-diagnose-oracle\n\nSECRET PROMPT",
+    )
+
+    assert event["event"] == "llm.request.metadata"
+    assert event["run_id"] == "run-1"
+    assert event["skills_count"] == 1
+    assert event["skill_names"] == ["dba-diagnose-oracle"]
+    assert event["skill_prompt_presence"] == {"dba-diagnose-oracle": True}
+    assert event["system_prompt_chars"] > 0
+    assert len(event["system_prompt_sha256"]) == 64
+    assert event["tools_count"] == 2
+    assert event["tool_names"] == ["terminal", "read_file"]
+    assert event["tool_choice"] == "auto"
+    assert event["enabled_toolsets"] == ["terminal", "file"]
+    encoded = json.dumps(event, ensure_ascii=False)
+    assert "SECRET PROMPT" not in encoded
+    assert "SECRET SCHEMA" not in encoded
+    assert "secret-key" not in encoded
+
+
+def test_runtime_worker_llm_response_metadata_is_safe_and_actionable():
+    from types import SimpleNamespace
+
+    from runtime_manager.worker_main import _llm_response_metadata_event
+
+    tool_call = SimpleNamespace(function=SimpleNamespace(name="terminal"))
+    event = _llm_response_metadata_event(
+        {
+            "api_request_id": "turn-1:api:1",
+            "turn_id": "turn-1",
+            "api_call_count": 1,
+            "provider": "custom",
+            "model": "qwen3.6-35b-a3b",
+            "base_url": "https://models.example/v1",
+            "api_mode": "chat_completions",
+            "api_duration": 1.25,
+            "finish_reason": "tool_calls",
+            "response_model": "qwen3.6-35b-a3b",
+            "message_count": 3,
+            "assistant_content_chars": 0,
+            "assistant_tool_call_count": 1,
+            "assistant_message": SimpleNamespace(tool_calls=[tool_call]),
+            "usage": {"prompt_tokens": 100, "completion_tokens": 20},
+        },
+        run_id="run-1",
+    )
+
+    assert event["event"] == "llm.response.metadata"
+    assert event["run_id"] == "run-1"
+    assert event["finish_reason"] == "tool_calls"
+    assert event["assistant_tool_calls_count"] == 1
+    assert event["assistant_tool_names"] == ["terminal"]
+    assert event["usage"] == {"prompt_tokens": 100, "completion_tokens": 20}
+
+
 def test_runtime_worker_projects_compression_status_as_structured_event():
     from runtime_manager.worker_main import _compression_event_from_status