EvoScientist/EvoScientist.py

@@ -19,6 +19,7 @@ Usage:
 import json
 import logging
 import os
+from collections.abc import Sequence
 from pathlib import Path
 from typing import TYPE_CHECKING
 
@@ -304,9 +305,12 @@ def _inject_subagent_middleware(
     path doesn't fall back to the global-writing ``_ensure_chat_model()``.
     """
     from .middleware import (
+        DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD,
         ContextOverflowMapperMiddleware,
         ErrorNormalizationMiddleware,
+        RepetitiveToolCallGuardMiddleware,
         ToolErrorHandlerMiddleware,
+        ToolProtocolGuardMiddleware,
         create_context_editing_middleware,
         create_memory_lifecycle_middleware,
         create_memory_middleware,
@@ -315,6 +319,16 @@ def _inject_subagent_middleware(
     )
 
     cfg = cfg if cfg is not None else _ensure_config()
+    repetitive_tool_call_threshold = getattr(
+        cfg,
+        "repetitive_tool_call_threshold",
+        DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD,
+    )
+    if not isinstance(repetitive_tool_call_threshold, int):
+        repetitive_tool_call_threshold = DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD
+    max_consecutive_tool_errors = getattr(cfg, "max_consecutive_tool_errors", 3)
+    if not isinstance(max_consecutive_tool_errors, int):
+        max_consecutive_tool_errors = 3
     memory_controls = MemoryControls.from_config(cfg)
     memory_dir = str(_paths_mod.MEMORIES_DIR)
     memory_scheduler = default_memory_scheduler()
@@ -339,6 +353,11 @@ def _inject_subagent_middleware(
             # them into a non-dataclass envelope wrapper before
             # anything downstream sees them.
             ErrorNormalizationMiddleware(),
+            RepetitiveToolCallGuardMiddleware(
+                threshold=repetitive_tool_call_threshold,
+                max_consecutive_errors=max_consecutive_tool_errors,
+            ),
+            ToolProtocolGuardMiddleware(),
             # Subagents share the main agent's model: use the threaded
             # ``chat_model`` on the pure path, else defer to the factory's
             # ``_ensure_chat_model()`` fallback (when ``chat_model=None``).
@@ -654,6 +673,7 @@ def _get_default_middleware(
     tool_selector_threshold: int | None = None,
     memory_max_inline_profile_chars: int | None = None,
     enable_background_execution: bool = True,
+    enable_legacy_model_fallback: bool = True,
 ):
     """Build the default middleware list.
 
@@ -675,11 +695,14 @@ def _get_default_middleware(
             Async sub-agent factories pass their deployed agent name here.
     """
     from .middleware import (
+        DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD,
         ConfigurableModelMiddleware,
         ContextOverflowMapperMiddleware,
         ErrorNormalizationMiddleware,
         ModelFallbackMiddleware,
+        RepetitiveToolCallGuardMiddleware,
         ToolErrorHandlerMiddleware,
+        ToolProtocolGuardMiddleware,
         create_code_interpreter_middleware,
         create_context_editing_middleware,
         create_memory_lifecycle_middleware,
@@ -692,6 +715,16 @@ def _get_default_middleware(
     )
 
     cfg = cfg if cfg is not None else _ensure_config()
+    repetitive_tool_call_threshold = getattr(
+        cfg,
+        "repetitive_tool_call_threshold",
+        DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD,
+    )
+    if not isinstance(repetitive_tool_call_threshold, int):
+        repetitive_tool_call_threshold = DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD
+    max_consecutive_tool_errors = getattr(cfg, "max_consecutive_tool_errors", 3)
+    if not isinstance(max_consecutive_tool_errors, int):
+        max_consecutive_tool_errors = 3
     if cfg.model_fallbacks:
         load_fallback_chain(cfg.model_fallbacks)
     model = chat_model if chat_model is not None else _ensure_chat_model()
@@ -741,6 +774,15 @@ def _get_default_middleware(
             from .llm import get_chat_model
 
             tool_selector_model = get_chat_model(model=aux_model, provider=aux_provider)
+    selector_middlewares = create_tool_selector_middleware(
+        **(
+            {"threshold": tool_selector_threshold}
+            if tool_selector_threshold is not None
+            else {}
+        ),
+        model=tool_selector_model,
+        track_stream_selection=not for_async_subagent,
+    )
     mw = [
         # Outermost — catches provider-SDK exceptions from the model
         # call (including exceptions surfaced through inner
@@ -749,18 +791,15 @@ def _get_default_middleware(
         ErrorNormalizationMiddleware(),
         ConfigurableModelMiddleware(),
         create_context_editing_middleware(model),
-        ModelFallbackMiddleware(),
+        *([ModelFallbackMiddleware()] if enable_legacy_model_fallback else []),
+        RepetitiveToolCallGuardMiddleware(
+            threshold=repetitive_tool_call_threshold,
+            max_consecutive_errors=max_consecutive_tool_errors,
+        ),
         ContextOverflowMapperMiddleware(),
         ToolErrorHandlerMiddleware(),
-        *create_tool_selector_middleware(
-            **(
-                {"threshold": tool_selector_threshold}
-                if tool_selector_threshold is not None
-                else {}
-            ),
-            model=tool_selector_model,
-            track_stream_selection=not for_async_subagent,
-        ),
+        *selector_middlewares,
+        ToolProtocolGuardMiddleware(),
         # Interpreter prompt must land before runtime/memory context, so this
         # middleware sits ahead of runtime_context in the stack.
         create_code_interpreter_middleware(
@@ -897,6 +936,8 @@ def create_cli_agent(
     memory_max_inline_profile_chars: int | None = None,
     enable_subagents: bool = True,
     enable_background_execution: bool = True,
+    main_agent_outer_middlewares: Sequence[AgentMiddleware] | None = None,
+    main_agent_route_middleware: AgentMiddleware | None = None,
 ) -> "CompiledStateGraph":
     """Create agent with checkpointer for CLI multi-turn support.
 
@@ -933,6 +974,12 @@ def create_cli_agent(
         enable_background_execution: Whether local background-process tools are
             installed. Embedding hosts should disable this when process execution
             is provided by an external backend.
+        main_agent_outer_middlewares: Optional host-owned middleware installed
+            only on the top-level agent, outside EvoScientist's default chain.
+        main_agent_route_middleware: Optional host-owned route middleware placed
+            after ConfigurableModelMiddleware and before tool selection. When
+            provided, EvoScientist's legacy model fallback is disabled for the
+            top-level agent so the host is the only fallback authority.
     """
     import os as _os
 
@@ -1017,7 +1064,22 @@ def create_cli_agent(
         tool_selector_threshold=tool_selector_threshold,
         memory_max_inline_profile_chars=memory_max_inline_profile_chars,
         enable_background_execution=enable_background_execution,
+        enable_legacy_model_fallback=main_agent_route_middleware is None,
     )
+    if main_agent_route_middleware is not None:
+        configurable_index = next(
+            (
+                index
+                for index, middleware in enumerate(mw)
+                if getattr(middleware, "name", "") == "configurable_model"
+            ),
+            None,
+        )
+        if configurable_index is None:
+            raise RuntimeError("ConfigurableModelMiddleware route slot is unavailable")
+        mw.insert(configurable_index + 1, main_agent_route_middleware)
+    if main_agent_outer_middlewares:
+        mw = [*main_agent_outer_middlewares, *mw]
 
     # HITL on main agent only — passing `interrupt_on=` to create_deep_agent
     # would propagate it to every subagent, breaking parallel execute calls

EvoScientist/config/settings.py

@@ -262,6 +262,13 @@ class EvoScientistConfig:
     # Lower (e.g., 5000) if you want a tighter safety net against runaway loops.
     recursion_limit: int = 1_000_000
 
+    # Number of consecutive model rounds with the same structured tool name and
+    # arguments that activates provider-facing loop repair. Set 0 to disable.
+    repetitive_tool_call_threshold: int = 2
+    # Number of consecutive deterministic tool errors allowed before the next
+    # model call is blocked. Transient provider/network errors are not counted.
+    max_consecutive_tool_errors: int = 3
+
     # Memory Settings
     # Profile memory injects and maintains `/memories/profile/...` files.
     memory_profile_enabled: bool = True
@@ -473,6 +480,14 @@ class EvoScientistConfig:
     stt_compute_type: str = "int8"  # "int8" | "float16" | "float32"
 
     def __post_init__(self) -> None:
+        for field_name in (
+            "repetitive_tool_call_threshold",
+            "max_consecutive_tool_errors",
+        ):
+            value = getattr(self, field_name)
+            if not isinstance(value, int) or isinstance(value, bool) or value < 0:
+                raise ValueError(f"{field_name} must be a non-negative integer")
+
         # A non-positive or non-int sandbox_execute_timeout (e.g. a hand-edited
         # config file value — load_config does not coerce file values — or a
         # 0/negative env value) would raise inside CustomSandboxBackend.__init__
@@ -735,6 +750,11 @@ def set_config_value(key: str, value: Any) -> bool:
 
     if key == "sandbox_execute_timeout" and value <= 0:
         return False
+    if key in {
+        "repetitive_tool_call_threshold",
+        "max_consecutive_tool_errors",
+    } and (isinstance(value, bool) or value < 0):
+        return False
     if key == "memory_skill_synthesis_time":
         value = _normalize_hhmm(value)
         if value is None:
@@ -813,6 +833,10 @@ _ENV_MAPPINGS = {
     "langgraph_dev_file_persistence": "EVOSCIENTIST_LANGGRAPH_DEV_FILE_PERSISTENCE",
     "langgraph_dev_jobs_per_worker": "EVOSCIENTIST_LANGGRAPH_DEV_JOBS_PER_WORKER",
     "recursion_limit": "EVOSCIENTIST_RECURSION_LIMIT",
+    "repetitive_tool_call_threshold": (
+        "EVOSCIENTIST_REPETITIVE_TOOL_CALL_THRESHOLD"
+    ),
+    "max_consecutive_tool_errors": "EVOSCIENTIST_MAX_CONSECUTIVE_TOOL_ERRORS",
     "memory_profile_enabled": "EVOSCIENTIST_MEMORY_PROFILE_ENABLED",
     "memory_observations_enabled": "EVOSCIENTIST_MEMORY_OBSERVATIONS_ENABLED",
     "memory_observation_writer": "EVOSCIENTIST_MEMORY_OBSERVATION_WRITER",

EvoScientist/llm/errors.py

@@ -32,6 +32,98 @@ from typing import Any
 # ---------------------------------------------------------------------------
 
 
+class AgentControlError(Exception):
+    """Host-defined terminal control error that must bypass model fallback."""
+
+    non_fallbackable = True
+
+    def __init__(
+        self,
+        code: str,
+        message: str,
+        *,
+        status_code: int = 403,
+        retryable: bool = False,
+    ) -> None:
+        super().__init__(message)
+        self.code = code
+        self.message = message
+        self.status_code = status_code
+        self.retryable = retryable
+
+    def model_dump(self) -> dict[str, Any]:
+        return {
+            "error": type(self).__name__,
+            "code": self.code,
+            "message": self.message,
+            "status_code": self.status_code,
+            "retryable": self.retryable,
+        }
+
+
+class ModelToolProtocolError(AgentControlError):
+    """A completed model response contained an invalid tool-call protocol."""
+
+    def __init__(
+        self,
+        reason: str,
+        *,
+        provider: str | None = None,
+        model: str | None = None,
+        route_key: str | None = None,
+        config_generation: int | None = None,
+        api_mode: str | None = None,
+        endpoint: str | None = None,
+        tool_call_transport: str | None = None,
+        call_id: str | None = None,
+        call_diagnostic: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(
+            "MODEL_TOOL_PROTOCOL_INVALID",
+            "The model returned an invalid structured tool call.",
+            status_code=502,
+            retryable=False,
+        )
+        self.reason = reason
+        self.provider = provider
+        self.model = model
+        self.route_key = route_key
+        self.config_generation = config_generation
+        self.api_mode = api_mode
+        self.endpoint = endpoint
+        self.tool_call_transport = tool_call_transport
+        self.call_id = call_id
+        # Internal-only, redacted structure for server logs. Deliberately omitted
+        # from model_dump() so it never becomes part of the public SSE contract.
+        self.call_diagnostic = dict(call_diagnostic or {})
+        self.fallbackable = True
+        self.recoverable = True
+
+    def model_dump(self) -> dict[str, Any]:
+        payload = super().model_dump()
+        payload.update(
+            {
+                "reason": self.reason,
+                "fallbackable": self.fallbackable,
+                "recoverable": self.recoverable,
+            }
+        )
+        for key in (
+            "provider",
+            "model",
+            "route_key",
+            "config_generation",
+            "api_mode",
+            "endpoint",
+            "tool_call_transport",
+            "call_id",
+        ):
+            value = getattr(self, key)
+            if value is not None:
+                payload[key] = value
+        return payload
+
+
 class ProviderStreamError(Exception):
     """Envelope-shaped wrapper for a provider SDK exception raised
     inside a chat model call.

EvoScientist/llm/patches.py

@@ -284,71 +284,288 @@ def _stable_tool_call_id(message: Any, message_index: int, call_index: int) -> s
     return "call_" + hashlib.sha256(seed.encode("utf-8")).hexdigest()[:24]
 
 
-def _ensure_openai_tool_call_ids(messages: list[Any]) -> list[Any]:
-    """Copy messages and repair missing AI/ToolMessage call identifiers."""
-    import copy
-    from collections import deque
+def _tool_message_match_index(
+    tool_messages: list[Any],
+    used_indexes: set[int],
+    *,
+    call_id: str,
+    call_name: str,
+) -> int | None:
+    """Find the best unused result for one assistant tool call."""
 
-    pending_call_ids: deque[str] = deque()
-    normalized: list[Any] = []
+    def _matches(index: int, *, require_id: bool, require_name: bool) -> bool:
+        if index in used_indexes:
+            return False
+        message = tool_messages[index]
+        result_id = str(getattr(message, "tool_call_id", "") or "")
+        result_name = str(getattr(message, "name", "") or "")
+        if require_id and result_id != call_id:
+            return False
+        if not require_id and result_id:
+            return False
+        return not require_name or not result_name or result_name == call_name
 
-    for message_index, message in enumerate(messages):
-        message_type = getattr(message, "type", None)
-        if message_type == "ai":
-            tool_calls = list(getattr(message, "tool_calls", None) or [])
-            if not tool_calls:
-                normalized.append(message)
+    if call_id:
+        for require_name in (True, False):
+            for index in range(len(tool_messages)):
+                if _matches(index, require_id=True, require_name=require_name):
+                    return index
+        for require_name in (True, False):
+            for index in range(len(tool_messages)):
+                if _matches(index, require_id=False, require_name=require_name):
+                    return index
+        return None
+
+    # A result-side identifier is more authoritative than a generated fallback.
+    for require_name in (True, False):
+        for index, message in enumerate(tool_messages):
+            if index in used_indexes:
                 continue
+            result_id = str(getattr(message, "tool_call_id", "") or "")
+            result_name = str(getattr(message, "name", "") or "")
+            if result_id and (
+                not require_name or not result_name or result_name == call_name
+            ):
+                return index
+    for require_name in (True, False):
+        for index in range(len(tool_messages)):
+            if _matches(index, require_id=False, require_name=require_name):
+                return index
+    return None
 
-            copied = copy.copy(message)
-            normalized_calls: list[dict[str, Any]] = []
-            for call_index, original_call in enumerate(tool_calls):
-                call = dict(original_call)
-                call_id = str(call.get("id") or "") or _stable_tool_call_id(
-                    message, message_index, call_index
-                )
-                call["id"] = call_id
-                normalized_calls.append(call)
-                pending_call_ids.append(call_id)
-            copied.tool_calls = normalized_calls
 
-            if isinstance(copied.content, list):
-                call_index = 0
-                blocks: list[Any] = []
-                for original_block in copied.content:
-                    if not isinstance(original_block, dict):
-                        blocks.append(original_block)
-                        continue
-                    block = dict(original_block)
-                    if block.get("type") in {"tool_call", "function_call"}:
-                        if call_index < len(normalized_calls):
-                            block["id"] = normalized_calls[call_index]["id"]
-                        call_index += 1
-                    blocks.append(block)
-                copied.content = blocks
-            normalized.append(copied)
+def _copy_ai_message_with_tool_pairs(
+    message: Any,
+    message_index: int,
+    tool_messages: list[Any],
+) -> tuple[Any | None, list[Any]]:
+    """Return a replay-safe assistant message and its matched tool results."""
+    import copy
+
+    copied = copy.copy(message)
+    additional_kwargs = dict(getattr(message, "additional_kwargs", None) or {})
+    # Parsed tool_calls are canonical. Raw copies can otherwise re-introduce an
+    # invalid call after invalid_tool_calls has been cleared.
+    additional_kwargs.pop("tool_calls", None)
+    copied.additional_kwargs = additional_kwargs
+    copied.invalid_tool_calls = []
+
+    original_calls = list(getattr(message, "tool_calls", None) or [])
+    used_results: set[int] = set()
+    matched_calls: list[dict[str, Any]] = []
+    matched_result_indexes: list[int] = []
+    original_to_matched_call: dict[int, tuple[str, str]] = {}
+
+    for call_index, original_call in enumerate(original_calls):
+        call = dict(original_call)
+        call_id = str(call.get("id") or "")
+        call_name = str(call.get("name") or "").strip()
+        # A missing name is structurally unreplayable. Never infer it from
+        # arguments or retain its paired ToolMessage in provider history.
+        if not call_name:
+            continue
+        call["name"] = call_name
+        result_index = _tool_message_match_index(
+            tool_messages,
+            used_results,
+            call_id=call_id,
+            call_name=call_name,
+        )
+        # A historical client-side function call is only replayable together
+        # with its result. Incomplete calls are discarded instead of asking the
+        # provider to continue a broken tool turn.
+        if result_index is None:
+            continue
+        if not call_id:
+            result_id = str(
+                getattr(tool_messages[result_index], "tool_call_id", "") or ""
+            )
+            call_id = result_id or _stable_tool_call_id(
+                message, message_index, call_index
+            )
+        call["id"] = call_id
+        matched_calls.append(call)
+        matched_result_indexes.append(result_index)
+        original_to_matched_call[call_index] = (call_id, call_name)
+        used_results.add(result_index)
+
+    copied.tool_calls = matched_calls
+    if isinstance(copied.content, list):
+        original_call_index = 0
+        blocks: list[Any] = []
+        for original_block in copied.content:
+            if not isinstance(original_block, dict):
+                blocks.append(original_block)
+                continue
+            block = dict(original_block)
+            if block.get("type") in {"tool_call", "function_call"}:
+                matched_call = original_to_matched_call.get(original_call_index)
+                original_call_index += 1
+                if matched_call is None:
+                    continue
+                call_id, call_name = matched_call
+                # LangChain content blocks use id; the Responses converter later
+                # maps it to call_id.
+                block["id"] = call_id
+                block["name"] = call_name
+                if isinstance(block.get("function"), dict):
+                    block["function"] = {**block["function"], "name": call_name}
+            blocks.append(block)
+        copied.content = blocks
+
+    matched_results: list[Any] = []
+    result_to_call_id = {
+        result_index: matched_calls[index]["id"]
+        for index, result_index in enumerate(matched_result_indexes)
+    }
+    for result_index, result in enumerate(tool_messages):
+        call_id = result_to_call_id.get(result_index)
+        if call_id is None:
+            continue
+        copied_result = copy.copy(result)
+        copied_result.tool_call_id = call_id
+        matched_results.append(copied_result)
+
+    had_tool_protocol = bool(original_calls) or bool(
+        getattr(message, "invalid_tool_calls", None)
+    )
+    if not matched_calls and had_tool_protocol:
+        replayable_content = _flatten_message_content(copied.content)
+        if not replayable_content:
+            return None, matched_results
+
+    return copied, matched_results
+
+
+def _sanitize_openai_tool_history(messages: list[Any]) -> list[Any]:
+    """Copy history while retaining only complete, replayable tool turns."""
+
+    normalized: list[Any] = []
+    index = 0
+    while index < len(messages):
+        message = messages[index]
+        message_type = getattr(message, "type", None)
+        if message_type == "tool":
+            # A tool result without its immediately preceding assistant call is
+            # invalid for both Chat Completions and Responses APIs.
+            index += 1
+            continue
+        if message_type != "ai":
+            normalized.append(message)
+            index += 1
             continue
 
-        if message_type == "tool":
-            tool_call_id = str(getattr(message, "tool_call_id", "") or "")
-            if tool_call_id:
-                try:
-                    pending_call_ids.remove(tool_call_id)
-                except ValueError:
-                    pass
-                normalized.append(message)
-                continue
-            if pending_call_ids:
-                copied = copy.copy(message)
-                copied.tool_call_id = pending_call_ids.popleft()
-                normalized.append(copied)
-                continue
-
-        normalized.append(message)
+        next_index = index + 1
+        tool_messages: list[Any] = []
+        while (
+            next_index < len(messages)
+            and getattr(messages[next_index], "type", None) == "tool"
+        ):
+            tool_messages.append(messages[next_index])
+            next_index += 1
+        copied, matched_results = _copy_ai_message_with_tool_pairs(
+            message,
+            index,
+            tool_messages,
+        )
+        if copied is not None:
+            normalized.append(copied)
+        normalized.extend(matched_results)
+        index = next_index
 
     return normalized
 
 
+def _ensure_openai_tool_call_ids(messages: list[Any]) -> list[Any]:
+    """Backward-compatible alias for replay-safe tool history normalization."""
+
+    return _sanitize_openai_tool_history(messages)
+
+
+def _has_assistant_tool_protocol(messages: list[Any]) -> bool:
+    """Return whether history contains assistant-side tool protocol state."""
+
+    for message in messages:
+        if getattr(message, "type", None) != "ai":
+            continue
+        if getattr(message, "tool_calls", None) or getattr(
+            message, "invalid_tool_calls", None
+        ):
+            return True
+        additional_kwargs = getattr(message, "additional_kwargs", None) or {}
+        if additional_kwargs.get("tool_calls"):
+            return True
+        content = getattr(message, "content", None)
+        if isinstance(content, list) and any(
+            isinstance(block, dict)
+            and block.get("type") in {"tool_call", "function_call"}
+            for block in content
+        ):
+            return True
+    return False
+
+
+def _validate_openai_tool_history(messages: list[Any]) -> None:
+    """Raise when sanitized history still contains an invalid tool protocol."""
+
+    available_call_ids: set[str] = set()
+    for message in messages:
+        message_type = getattr(message, "type", None)
+        if message_type == "ai":
+            if getattr(message, "invalid_tool_calls", None):
+                raise ValueError("invalid_tool_calls must not be replayed")
+            response_call_ids: set[str] = set()
+            response_calls: dict[str, str] = {}
+            for call in getattr(message, "tool_calls", None) or []:
+                call_name = str(call.get("name") or "").strip()
+                if not call_name:
+                    raise ValueError("assistant tool call is missing a name")
+                call_id = str(call.get("id") or "").strip()
+                if not call_id:
+                    raise ValueError("assistant tool call is missing an id")
+                if call_id in response_call_ids or call_id in available_call_ids:
+                    raise ValueError(
+                        "assistant tool call id is duplicated while outstanding"
+                    )
+                response_call_ids.add(call_id)
+                available_call_ids.add(call_id)
+                response_calls[call_id] = call_name
+            content = getattr(message, "content", None)
+            content_call_ids: set[str] = set()
+            if isinstance(content, list):
+                for block in content:
+                    if not isinstance(block, dict) or block.get("type") not in {
+                        "tool_call",
+                        "function_call",
+                    }:
+                        continue
+                    block_id = str(
+                        block.get("id") or block.get("call_id") or ""
+                    ).strip()
+                    block_name = block.get("name") or block.get("tool_name")
+                    function = block.get("function")
+                    if not block_name and isinstance(function, dict):
+                        block_name = function.get("name")
+                    block_name = str(block_name or "").strip()
+                    if (
+                        not block_id
+                        or block_id in content_call_ids
+                        or response_calls.get(block_id) != block_name
+                    ):
+                        raise ValueError(
+                            "assistant content block does not match parsed tool call"
+                        )
+                    content_call_ids.add(block_id)
+        elif message_type == "tool":
+            call_id = str(getattr(message, "tool_call_id", "") or "")
+            if not call_id or call_id not in available_call_ids:
+                raise ValueError("tool result does not match a prior tool call")
+            available_call_ids.remove(call_id)
+
+    if available_call_ids:
+        raise ValueError("assistant tool call is missing its tool result")
+
+
 def _sanitize_messages(messages: list[Any], hoist_tool_media: bool = True) -> list[Any]:
     """Flatten list content for OpenAI-compatible APIs, preserving media.
 
@@ -364,7 +581,9 @@ def _sanitize_messages(messages: list[Any], hoist_tool_media: bool = True) -> li
 
     from langchain_core.messages import HumanMessage
 
-    messages = _ensure_openai_tool_call_ids(messages)
+    sanitize_tool_history = _has_assistant_tool_protocol(messages)
+    if sanitize_tool_history:
+        messages = _sanitize_openai_tool_history(messages)
     out: list[Any] = []
     pending_media: list[Any] = []  # media hoisted out of a run of tool messages
 
@@ -403,6 +622,8 @@ def _sanitize_messages(messages: list[Any], hoist_tool_media: bool = True) -> li
             msg.content = flat
             out.append(msg)
     _flush()  # conversation may end with tool messages
+    if sanitize_tool_history:
+        _validate_openai_tool_history(out)
     return out
 
 

EvoScientist/middleware/__init__.py

@@ -29,16 +29,25 @@ from .memory_lifecycle import (
     default_memory_scheduler,
 )
 from .model_fallback import ModelFallbackMiddleware, load_fallback_chain
+from .repetitive_tool_guard import (
+    DEFAULT_MAX_CONSECUTIVE_TOOL_ERRORS,
+    DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD,
+    RepetitiveToolCallGuardMiddleware,
+    collapse_repetitive_tool_rounds,
+)
 from .runtime_context import RuntimeContextMiddleware, create_runtime_context_middleware
 from .scheduler import (
     SchedulerMiddleware,
     create_scheduler_middleware,
 )
 from .tool_error_handler import ToolErrorHandlerMiddleware
+from .tool_protocol_guard import ToolProtocolGuardMiddleware
 from .tool_selector import create_tool_selector_middleware
 from .utils import disable_thinking
 
 __all__ = [
+    "DEFAULT_MAX_CONSECUTIVE_TOOL_ERRORS",
+    "DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD",
     "AskUserMiddleware",
     "AskUserRequest",
     "AskUserWidgetResult",
@@ -50,9 +59,12 @@ __all__ = [
     "EvoMemoryMiddleware",
     "ModelFallbackMiddleware",
     "Question",
+    "RepetitiveToolCallGuardMiddleware",
     "RuntimeContextMiddleware",
     "SchedulerMiddleware",
     "ToolErrorHandlerMiddleware",
+    "ToolProtocolGuardMiddleware",
+    "collapse_repetitive_tool_rounds",
     "compute_context_editing_trigger",
     "create_code_interpreter_middleware",
     "create_context_editing_middleware",

EvoScientist/middleware/error_normalization.py

@@ -20,9 +20,9 @@ recognized provider SDK client, wraps the exception in a
 envelope pre-baked on its instance attributes.
 
 Contract: the wrap decision is based on the **model**, not the
-exception. Every exception raised inside a call to a recognized
-provider model gets wrapped — SDK exceptions, httpx errors,
-langchain-wrapper failures, and even builtins like ``RuntimeError``.
+exception, after platform and graph control signals have been excluded.
+Provider SDK exceptions, httpx errors, langchain-wrapper failures, and
+even builtins like ``RuntimeError`` get wrapped for a recognized model.
 At the middleware boundary we can tell which provider was in use, but
 not the exception's precise origin; a uniform envelope is more useful
 to the WebUI than gambling on the exception class. If the model isn't
@@ -138,11 +138,15 @@ def _normalize(request: ModelRequest, exc: BaseException) -> ProviderStreamError
       **outside** the user middleware stack to compress history and
       retry. Wrapping it here would change the type and break that
       self-healing fallback.
+    - ``AgentControlError`` — a platform-owned typed decision. Gateway route
+      fallback and canonical error mapping depend on its concrete type and
+      structured fields, so it must never become a provider incident.
     - Models we don't recognize as a provider SDK.
     """
     from langchain_core.exceptions import ContextOverflowError
 
     from ..llm.errors import (
+        AgentControlError,
         ProviderStreamError,
         _extract_error_type,
         _extract_provider_code,
@@ -157,6 +161,12 @@ def _normalize(request: ModelRequest, exc: BaseException) -> ProviderStreamError
     if isinstance(exc, ProviderStreamError):
         return None
 
+    # Platform control errors are raised by inner middleware after the provider
+    # response has already been interpreted. Wrapping them would erase routing,
+    # retry and recovery semantics such as ModelToolProtocolError.fallbackable.
+    if isinstance(exc, AgentControlError):
+        return None
+
     # LangGraph control-flow / structural signals must propagate
     # untouched, regardless of which caller invoked us.
     if _should_pass_through(exc):

EvoScientist/middleware/model_fallback.py

@@ -48,6 +48,8 @@ _MALFORMED_REQUEST_PATTERNS: list[str] = [
     "invalid_request_error",
     "invalid request",
     "malformed",
+    "repetitive tool calls",
+    "identical name and arguments",
 ]
 """Substrings that identify a malformed request (client-side bug)."""
 
@@ -215,6 +217,9 @@ def _is_non_fallbackable(exc: Exception) -> str | None:
     """
     from langchain_core.exceptions import ContextOverflowError
 
+    if getattr(exc, "non_fallbackable", False):
+        return f"platform control error: {getattr(exc, 'code', type(exc).__name__)}"
+
     if isinstance(exc, ContextOverflowError):
         return "context length exceeded"
 

EvoScientist/middleware/repetitive_tool_guard.py

@@ -0,0 +1,350 @@
+"""Detect deterministic tool loops and compact only provider-facing history."""
+
+from __future__ import annotations
+
+import json
+import logging
+import re
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from dataclasses import dataclass
+from typing import Any
+
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    ModelRequest,
+    ModelResponse,
+)
+
+from ..llm.errors import AgentControlError
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD = 2
+DEFAULT_MAX_CONSECUTIVE_TOOL_ERRORS = 3
+
+_TRANSIENT_PATTERNS = (
+    "timeout",
+    "timed out",
+    "cancelled",
+    "canceled",
+    "connection",
+    "rate limit",
+    "too many requests",
+    "temporarily unavailable",
+    "service unavailable",
+    "overloaded",
+    "bad gateway",
+    "gateway timeout",
+    "http 500",
+    "http 502",
+    "http 503",
+    "http 504",
+)
+_DETERMINISTIC_PATTERNS: tuple[tuple[str, tuple[str, ...]], ...] = (
+    (
+        "INVALID_ARGUMENTS",
+        ("invalid argument", "validation error", "schema", "bad input"),
+    ),
+    ("UNKNOWN_TOOL", ("not a valid tool", "unknown tool", "tool not found")),
+    ("UNSUPPORTED", ("not supported", "unsupported", "not implemented")),
+    (
+        "POLICY_DENIED",
+        ("permission denied", "forbidden", "policy denied", "not allowed"),
+    ),
+)
+_SAFE_CODE_RE = re.compile(r"^[A-Za-z][A-Za-z0-9_.:-]{0,95}$")
+_DETERMINISTIC_CODE_MARKERS = (
+    "INVALID",
+    "VALIDATION",
+    "SCHEMA",
+    "UNKNOWN_TOOL",
+    "NOT_FOUND",
+    "UNSUPPORTED",
+    "NOT_IMPLEMENTED",
+    "POLICY",
+    "PERMISSION",
+    "FORBIDDEN",
+    "DENIED",
+)
+
+
+@dataclass(frozen=True, slots=True)
+class RepetitiveToolHistoryRepair:
+    messages: list[Any]
+    blocked_tool_names: frozenset[str]
+    removed_rounds: int
+    tail_repetitions: int = 0
+    tail_consecutive_errors: int = 0
+
+
+@dataclass(frozen=True, slots=True)
+class _ToolRound:
+    messages: tuple[Any, ...]
+    signature: tuple[tuple[str, str, str], ...]
+    tool_names: frozenset[str]
+    deterministic_error: bool
+
+
+def _canonical_tool_args(value: Any) -> str:
+    if isinstance(value, str):
+        try:
+            value = json.loads(value)
+        except json.JSONDecodeError:
+            return value.strip()
+    try:
+        return json.dumps(
+            value,
+            ensure_ascii=False,
+            sort_keys=True,
+            separators=(",", ":"),
+            default=str,
+        )
+    except (TypeError, ValueError):
+        return repr(value)
+
+
+def _deterministic_result_code(message: Any) -> str | None:
+    additional = getattr(message, "additional_kwargs", None)
+    additional = additional if isinstance(additional, Mapping) else {}
+    raw_code = additional.get("error_code") or additional.get("code")
+    status = str(getattr(message, "status", "") or "").lower()
+    content = str(getattr(message, "content", "") or "")
+    lowered = content.lower()
+
+    if any(pattern in lowered for pattern in _TRANSIENT_PATTERNS):
+        return None
+    if isinstance(raw_code, str) and _SAFE_CODE_RE.fullmatch(raw_code.strip()):
+        normalized = raw_code.strip().upper()
+        if any(
+            pattern.replace(" ", "_") in normalized for pattern in _TRANSIENT_PATTERNS
+        ):
+            return None
+        if any(marker in normalized for marker in _DETERMINISTIC_CODE_MARKERS):
+            return normalized
+        return None
+    is_error = status == "error" or lowered.startswith("error:")
+    if not is_error:
+        return None
+    for code, patterns in _DETERMINISTIC_PATTERNS:
+        if any(pattern in lowered for pattern in patterns):
+            return code
+    return None
+
+
+def _parse_tool_round(
+    messages: Sequence[Any], start: int
+) -> tuple[_ToolRound, int] | None:
+    assistant = messages[start]
+    if getattr(assistant, "type", None) != "ai":
+        return None
+    raw_calls = list(getattr(assistant, "tool_calls", None) or [])
+    calls = [call for call in raw_calls if isinstance(call, Mapping)]
+    if not calls or len(calls) != len(raw_calls):
+        return None
+
+    end = start + 1
+    results: list[Any] = []
+    while end < len(messages) and getattr(messages[end], "type", None) == "tool":
+        results.append(messages[end])
+        end += 1
+    if not results:
+        return None
+    results_by_id = {
+        str(getattr(result, "tool_call_id", "") or "").strip(): result
+        for result in results
+        if str(getattr(result, "tool_call_id", "") or "").strip()
+    }
+
+    signature: list[tuple[str, str, str]] = []
+    tool_names: set[str] = set()
+    for index, call in enumerate(calls):
+        name = str(call.get("name") or "").strip()
+        call_id = str(call.get("id") or "").strip()
+        if not name or not call_id:
+            return None
+        result = results_by_id.get(call_id)
+        if result is None and index < len(results):
+            candidate = results[index]
+            if not str(getattr(candidate, "tool_call_id", "") or "").strip():
+                result = candidate
+        if result is None:
+            return None
+        result_code = _deterministic_result_code(result)
+        if result_code is None:
+            return _ToolRound(
+                messages=(assistant, *results),
+                signature=(),
+                tool_names=frozenset(),
+                deterministic_error=False,
+            ), end
+        signature.append((name, _canonical_tool_args(call.get("args")), result_code))
+        tool_names.add(name)
+
+    return (
+        _ToolRound(
+            messages=(assistant, *results),
+            signature=tuple(signature),
+            tool_names=frozenset(tool_names),
+            deterministic_error=True,
+        ),
+        end,
+    )
+
+
+def collapse_repetitive_tool_rounds(
+    messages: Sequence[Any],
+    *,
+    threshold: int = DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD,
+) -> RepetitiveToolHistoryRepair:
+    """Build a provider-only projection while preserving audit history.
+
+    Only the middle rounds of three-or-more identical deterministic error
+    groups are omitted. The first and last observations remain, and callers
+    must never persist this projection back to a checkpoint.
+    """
+    if not isinstance(threshold, int) or isinstance(threshold, bool) or threshold < 0:
+        raise ValueError("repetitive tool call threshold must be non-negative")
+    original = list(messages)
+    segments: list[Any | _ToolRound] = []
+    index = 0
+    while index < len(original):
+        parsed = _parse_tool_round(original, index)
+        if parsed is None:
+            segments.append(original[index])
+            index += 1
+            continue
+        tool_round, index = parsed
+        segments.append(tool_round)
+
+    tail_repetitions = 0
+    tail_consecutive_errors = 0
+    if segments and isinstance(segments[-1], _ToolRound):
+        tail = segments[-1]
+        if tail.deterministic_error:
+            cursor = len(segments) - 1
+            while cursor >= 0 and isinstance(segments[cursor], _ToolRound):
+                current = segments[cursor]
+                if not current.deterministic_error:
+                    break
+                tail_consecutive_errors += 1
+                cursor -= 1
+            cursor = len(segments) - 1
+            while cursor >= 0 and isinstance(segments[cursor], _ToolRound):
+                current = segments[cursor]
+                if (
+                    not current.deterministic_error
+                    or current.signature != tail.signature
+                ):
+                    break
+                tail_repetitions += 1
+                cursor -= 1
+
+    projected: list[Any] = []
+    removed_rounds = 0
+    index = 0
+    while index < len(segments):
+        segment = segments[index]
+        if not isinstance(segment, _ToolRound) or not segment.deterministic_error:
+            if isinstance(segment, _ToolRound):
+                projected.extend(segment.messages)
+            else:
+                projected.append(segment)
+            index += 1
+            continue
+        end = index + 1
+        while (
+            end < len(segments)
+            and isinstance(segments[end], _ToolRound)
+            and segments[end].deterministic_error
+            and segments[end].signature == segment.signature
+        ):
+            end += 1
+        group = segments[index:end]
+        should_compact = threshold > 0 and len(group) >= threshold and len(group) > 2
+        if should_compact:
+            projected.extend(group[0].messages)
+            projected.extend(group[-1].messages)
+            removed_rounds += len(group) - 2
+        else:
+            for item in group:
+                projected.extend(item.messages)
+        index = end
+
+    blocked = (
+        segments[-1].tool_names
+        if tail_repetitions and isinstance(segments[-1], _ToolRound)
+        else frozenset()
+    )
+    return RepetitiveToolHistoryRepair(
+        messages=projected,
+        blocked_tool_names=blocked,
+        removed_rounds=removed_rounds,
+        tail_repetitions=tail_repetitions,
+        tail_consecutive_errors=tail_consecutive_errors,
+    )
+
+
+class RepetitiveToolCallGuardMiddleware(AgentMiddleware):
+    """Stop deterministic loops before another model request is made."""
+
+    name = "repetitive_tool_call_guard"
+
+    def __init__(
+        self,
+        *,
+        threshold: int = DEFAULT_REPETITIVE_TOOL_CALL_THRESHOLD,
+        max_consecutive_errors: int = DEFAULT_MAX_CONSECUTIVE_TOOL_ERRORS,
+    ) -> None:
+        super().__init__()
+        for name, value in {
+            "threshold": threshold,
+            "max_consecutive_errors": max_consecutive_errors,
+        }.items():
+            if not isinstance(value, int) or isinstance(value, bool) or value < 0:
+                raise ValueError(f"{name} must be a non-negative integer")
+        self.threshold = threshold
+        self.max_consecutive_errors = max_consecutive_errors
+
+    def _prepare_request(self, request: ModelRequest) -> ModelRequest:
+        repair = collapse_repetitive_tool_rounds(
+            request.messages,
+            threshold=self.threshold,
+        )
+        if self.threshold and repair.tail_repetitions >= self.threshold:
+            raise AgentControlError(
+                "MODEL_TOOL_LOOP_DETECTED",
+                "A deterministic repeated tool-call loop was stopped.",
+                status_code=422,
+                retryable=False,
+            )
+        if (
+            self.max_consecutive_errors
+            and repair.tail_consecutive_errors >= self.max_consecutive_errors
+        ):
+            raise AgentControlError(
+                "MODEL_TOOL_ERROR_LIMIT",
+                "Too many consecutive deterministic tool errors were stopped.",
+                status_code=422,
+                retryable=False,
+            )
+        if repair.removed_rounds:
+            logger.info(
+                "Compacted deterministic tool errors for provider projection: removed_rounds=%d",
+                repair.removed_rounds,
+            )
+            return request.override(messages=repair.messages)
+        return request
+
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelResponse:
+        return handler(self._prepare_request(request))
+
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelResponse:
+        return await handler(self._prepare_request(request))

EvoScientist/middleware/tool_protocol_guard.py

@@ -0,0 +1,361 @@
+"""Validate completed model tool calls before they can reach ToolNode."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from typing import Any
+
+from langchain.agents.middleware.types import (
+    AgentMiddleware,
+    ExtendedModelResponse,
+    ModelRequest,
+    ModelResponse,
+)
+from langchain_core.messages import AIMessage
+from langchain_core.tools import BaseTool
+
+from ..llm.errors import ModelToolProtocolError, _provider_from_model
+
+_TOOL_BLOCK_TYPES = frozenset({"tool_call", "function_call"})
+_MAX_DIAGNOSTIC_KEYS = 16
+_MAX_DIAGNOSTIC_KEY_CHARS = 64
+
+
+def _tool_name(tool: BaseTool | Mapping[str, Any] | Any) -> str | None:
+    if isinstance(tool, BaseTool):
+        return tool.name.strip() or None
+    if isinstance(tool, Mapping):
+        value = tool.get("name")
+        if not value and isinstance(tool.get("function"), Mapping):
+            value = tool["function"].get("name")
+        if isinstance(value, str) and value.strip():
+            return value.strip()
+        return None
+    value = getattr(tool, "name", None)
+    return value.strip() if isinstance(value, str) and value.strip() else None
+
+
+def _ai_messages(response: Any) -> list[AIMessage]:
+    """Extract final AI messages from every LangChain middleware response shape."""
+    if isinstance(response, AIMessage):
+        return [response]
+    if isinstance(response, ExtendedModelResponse):
+        response = response.model_response
+    elif not isinstance(response, ModelResponse):
+        nested = getattr(response, "model_response", None)
+        if nested is not None:
+            response = nested
+    result = getattr(response, "result", None)
+    if not isinstance(result, Sequence) or isinstance(result, str | bytes):
+        return []
+    return [message for message in result if isinstance(message, AIMessage)]
+
+
+def _block_identity(block: Mapping[str, Any]) -> tuple[str, str]:
+    call_id = str(block.get("id") or block.get("call_id") or "").strip()
+    name = block.get("name") or block.get("tool_name")
+    function = block.get("function")
+    if not name and isinstance(function, Mapping):
+        name = function.get("name")
+    return call_id, str(name or "").strip()
+
+
+def _value_digest(value: Any) -> str:
+    try:
+        encoded = json.dumps(
+            value,
+            ensure_ascii=False,
+            sort_keys=True,
+            separators=(",", ":"),
+            default=lambda item: f"<{type(item).__name__}>",
+        ).encode("utf-8")
+    except (TypeError, ValueError):
+        encoded = f"<{type(value).__name__}:unserializable>".encode()
+    return "sha256:" + hashlib.sha256(encoded).hexdigest()[:16]
+
+
+def _argument_diagnostic(value: Any, *, present: bool) -> dict[str, Any]:
+    if not present:
+        return {"args_present": False, "args_type": "missing"}
+    if isinstance(value, Mapping):
+        keys = sorted(str(key)[:_MAX_DIAGNOSTIC_KEY_CHARS] for key in value)
+        return {
+            "args_present": True,
+            "args_type": "object",
+            "args_key_count": len(keys),
+            "args_keys": keys[:_MAX_DIAGNOSTIC_KEYS],
+            "args_keys_truncated": len(keys) > _MAX_DIAGNOSTIC_KEYS,
+            "args_digest": _value_digest(value),
+        }
+    if isinstance(value, Sequence) and not isinstance(value, str | bytes):
+        value_type = "array"
+    elif isinstance(value, str):
+        value_type = "string"
+    elif value is None:
+        value_type = "null"
+    else:
+        value_type = type(value).__name__
+    return {
+        "args_present": True,
+        "args_type": value_type,
+        "args_digest": _value_digest(value),
+    }
+
+
+def _summarize_call(call: Any) -> dict[str, Any]:
+    if not isinstance(call, Mapping):
+        return {"call_type": type(call).__name__}
+    function = call.get("function")
+    function = function if isinstance(function, Mapping) else {}
+    call_id = str(call.get("id") or call.get("call_id") or "").strip()
+    name = call.get("name") or call.get("tool_name") or function.get("name")
+    name = str(name or "").strip()
+    if "args" in call:
+        args = call.get("args")
+        args_present = True
+    elif "arguments" in call:
+        args = call.get("arguments")
+        args_present = True
+    elif "arguments" in function:
+        args = function.get("arguments")
+        args_present = True
+    else:
+        args = None
+        args_present = False
+    summary = {
+        "call_type": "object",
+        "name": name or "<missing>",
+        "id_present": bool(call_id),
+        **_argument_diagnostic(args, present=args_present),
+    }
+    if call_id:
+        summary["id_fingerprint"] = _value_digest(call_id)
+    return summary
+
+
+def _raw_openai_call(message: AIMessage, call_index: int) -> Any | None:
+    additional = getattr(message, "additional_kwargs", None)
+    additional = additional if isinstance(additional, Mapping) else {}
+    raw_calls = additional.get("tool_calls")
+    if (
+        isinstance(raw_calls, Sequence)
+        and not isinstance(raw_calls, str | bytes)
+        and call_index < len(raw_calls)
+    ):
+        return raw_calls[call_index]
+    return None
+
+
+def _call_diagnostic(
+    message: AIMessage,
+    call: Any,
+    *,
+    source: str,
+    call_index: int,
+    call_count: int,
+) -> dict[str, Any]:
+    diagnostic = {
+        "source": source,
+        "call_index": call_index,
+        "call_count": call_count,
+        **_summarize_call(call),
+    }
+    raw_call = _raw_openai_call(message, call_index)
+    diagnostic["raw_openai_call_available"] = raw_call is not None
+    if raw_call is not None:
+        diagnostic["raw_openai_call"] = _summarize_call(raw_call)
+    return diagnostic
+
+
+def _route_metadata(request: ModelRequest) -> dict[str, Any]:
+    model = request.model
+    metadata = getattr(model, "metadata", None)
+    metadata = metadata if isinstance(metadata, Mapping) else {}
+    provider = metadata.get("route_provider") or _provider_from_model(model)
+    model_id = metadata.get("route_model")
+    if not model_id:
+        model_id = (
+            getattr(model, "model_name", None)
+            or getattr(model, "model", None)
+            or getattr(model, "model_id", None)
+        )
+    generation = metadata.get("route_config_generation")
+    try:
+        config_generation = int(generation) if generation is not None else None
+    except (TypeError, ValueError):
+        config_generation = None
+    return {
+        "provider": str(provider) if provider else None,
+        "model": str(model_id) if model_id else None,
+        "route_key": str(metadata.get("route_key"))
+        if metadata.get("route_key")
+        else None,
+        "config_generation": config_generation,
+        "api_mode": str(metadata.get("route_api_mode"))
+        if metadata.get("route_api_mode")
+        else None,
+        "endpoint": str(metadata.get("route_endpoint"))
+        if metadata.get("route_endpoint")
+        else None,
+        "tool_call_transport": str(metadata.get("route_tool_call_transport"))
+        if metadata.get("route_tool_call_transport")
+        else None,
+    }
+
+
+def _raise_protocol_error(
+    request: ModelRequest,
+    reason: str,
+    *,
+    call_id: str | None = None,
+    call_diagnostic: dict[str, Any] | None = None,
+) -> None:
+    raise ModelToolProtocolError(
+        reason,
+        call_id=call_id or None,
+        call_diagnostic=call_diagnostic,
+        **_route_metadata(request),
+    )
+
+
+def _validate_message(
+    message: AIMessage,
+    request: ModelRequest,
+    allowed_names: frozenset[str],
+) -> None:
+    invalid_calls = list(getattr(message, "invalid_tool_calls", None) or [])
+    if invalid_calls:
+        invalid = invalid_calls[0]
+        call_id = str(invalid.get("id") or "") if isinstance(invalid, Mapping) else ""
+        _raise_protocol_error(
+            request,
+            "invalid_final_call",
+            call_id=call_id,
+            call_diagnostic=_call_diagnostic(
+                message,
+                invalid,
+                source="invalid_tool_calls",
+                call_index=0,
+                call_count=len(invalid_calls),
+            ),
+        )
+
+    parsed_by_id: dict[str, str] = {}
+    parsed_calls = list(getattr(message, "tool_calls", None) or [])
+    for call_index, raw_call in enumerate(parsed_calls):
+        diagnostic = _call_diagnostic(
+            message,
+            raw_call,
+            source="parsed_tool_calls",
+            call_index=call_index,
+            call_count=len(parsed_calls),
+        )
+        if not isinstance(raw_call, Mapping):
+            _raise_protocol_error(
+                request, "invalid_final_call", call_diagnostic=diagnostic
+            )
+        call_id = str(raw_call.get("id") or raw_call.get("call_id") or "").strip()
+        name = str(raw_call.get("name") or "").strip()
+        if not name:
+            _raise_protocol_error(
+                request,
+                "missing_name",
+                call_id=call_id,
+                call_diagnostic=diagnostic,
+            )
+        if name not in allowed_names:
+            _raise_protocol_error(
+                request,
+                "unknown_name",
+                call_id=call_id,
+                call_diagnostic=diagnostic,
+            )
+        if not call_id:
+            _raise_protocol_error(request, "missing_id", call_diagnostic=diagnostic)
+        if call_id in parsed_by_id:
+            _raise_protocol_error(
+                request,
+                "duplicate_id",
+                call_id=call_id,
+                call_diagnostic=diagnostic,
+            )
+        args = raw_call.get("args")
+        if not isinstance(args, Mapping):
+            _raise_protocol_error(
+                request,
+                "invalid_args",
+                call_id=call_id,
+                call_diagnostic=diagnostic,
+            )
+        parsed_by_id[call_id] = name
+
+    content = getattr(message, "content", None)
+    if not isinstance(content, list):
+        return
+    seen_block_ids: set[str] = set()
+    tool_blocks = [
+        block
+        for block in content
+        if isinstance(block, Mapping) and block.get("type") in _TOOL_BLOCK_TYPES
+    ]
+    for block_index, block in enumerate(tool_blocks):
+        diagnostic = _call_diagnostic(
+            message,
+            block,
+            source="content_blocks",
+            call_index=block_index,
+            call_count=len(tool_blocks),
+        )
+        call_id, name = _block_identity(block)
+        if not call_id:
+            _raise_protocol_error(request, "missing_id", call_diagnostic=diagnostic)
+        if call_id in seen_block_ids:
+            _raise_protocol_error(
+                request,
+                "duplicate_id",
+                call_id=call_id,
+                call_diagnostic=diagnostic,
+            )
+        seen_block_ids.add(call_id)
+        parsed_name = parsed_by_id.get(call_id)
+        if parsed_name is None or (name and name != parsed_name):
+            _raise_protocol_error(
+                request,
+                "inconsistent_block",
+                call_id=call_id,
+                call_diagnostic=diagnostic,
+            )
+
+
+class ToolProtocolGuardMiddleware(AgentMiddleware):
+    """Fail closed on malformed final tool calls using the actual request tools."""
+
+    name = "tool_protocol_guard"
+
+    @staticmethod
+    def _validate(response: Any, request: ModelRequest) -> None:
+        allowed_names = frozenset(
+            name for tool in request.tools if (name := _tool_name(tool)) is not None
+        )
+        for message in _ai_messages(response):
+            _validate_message(message, request, allowed_names)
+
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelResponse:
+        response = handler(request)
+        self._validate(response, request)
+        return response
+
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelResponse:
+        response = await handler(request)
+        self._validate(response, request)
+        return response

EvoScientist/middleware/tool_selector.py

@@ -48,6 +48,7 @@ DEFAULT_ALWAYS_INCLUDE_TOOLS: frozenset[str] = frozenset(
         "read_memory",
         "record_observation",
         "search_observations",
+        "write_todos",
     }
 )
 
@@ -276,6 +277,15 @@ def create_tool_selector_middleware(
 
         model = _ensure_chat_model()
     safe_model = disable_thinking(model)
+    safe_model = safe_model.model_copy(
+        update={
+            "tags": [*(safe_model.tags or []), "metering:tool_selector"],
+            "metadata": {
+                **(safe_model.metadata or {}),
+                "metering_scope": "tool_selector",
+            },
+        }
+    )
 
     system_prompt = (
         "You are selecting tools for a scientific research agent. "

EvoScientist/stream/emitter.py

@@ -7,6 +7,15 @@ All events contain a type and associated data dict.
 from dataclasses import dataclass
 from typing import Any
 
+STREAM_PROTOCOL_CAPABILITIES = frozenset(
+    {
+        "task_snapshot_v1",
+        "complete_tool_call_v1",
+        "correlated_tool_call_id_v1",
+        "final_invalid_tool_call_v1",
+    }
+)
+
 
 @dataclass
 class StreamEvent:
@@ -158,6 +167,14 @@ class StreamEventEmitter:
             },
         )
 
+    @staticmethod
+    def task_snapshot(source: str, items: list[dict[str, Any]]) -> StreamEvent:
+        """Emit the complete root-agent task state without product-specific IDs."""
+        return StreamEvent(
+            "task_snapshot",
+            {"type": "task_snapshot", "source": source, "items": items},
+        )
+
     @staticmethod
     def interrupt(
         interrupt_id: str,
@@ -213,6 +230,19 @@ class StreamEventEmitter:
         )
 
     @staticmethod
-    def error(message: str) -> StreamEvent:
+    def error(
+        message: str,
+        *,
+        code: str | None = None,
+        recoverable: bool | None = None,
+        details: dict[str, Any] | None = None,
+    ) -> StreamEvent:
         """Error event."""
-        return StreamEvent("error", {"type": "error", "message": message})
+        data: dict[str, Any] = {"type": "error", "message": message}
+        if code is not None:
+            data["code"] = code
+        if recoverable is not None:
+            data["recoverable"] = recoverable
+        if details is not None:
+            data["details"] = details
+        return StreamEvent("error", data)

EvoScientist/stream/events.py

@@ -16,7 +16,7 @@ from typing import Any, TypeAlias
 from langchain_core._api import LangChainBetaWarning
 from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, ToolMessage
 from langgraph.graph import END
-from langgraph.types import Command, Interrupt
+from langgraph.types import Command, Interrupt, Overwrite
 
 from ..memory.worker_activity import clear_completed_memory_activity_counts
 from .emitter import StreamEventEmitter
@@ -95,9 +95,10 @@ async def _clear_interrupted_graph_state(
     no output and leaves the messages channel unchanged. From the user's side the
     conversation looks like it lost all history because the agent stops responding.
 
-    The fix: ``aupdate_state(config, None, as_node=END)`` clears all pending tasks
-    and writes a checkpoint whose ``next`` is the empty tuple, without touching
-    any channel values (message history is preserved).
+    Recovery first removes malformed/incomplete tool protocol from the messages
+    channel, then ``aupdate_state(config, None, as_node=END)`` clears pending
+    tasks and writes a checkpoint whose ``next`` is the empty tuple. Completed
+    tool call/result pairs and all non-tool history are preserved.
 
     Critically, this only runs when the stuck state is *not* a legitimate
     human-in-the-loop interrupt. The agent pauses via ``interrupt()`` /
@@ -114,10 +115,9 @@ async def _clear_interrupted_graph_state(
     _log = logging.getLogger(__name__)
     try:
         snapshot = await agent.aget_state(config)
-        # Only act when the graph is genuinely stuck (non-empty next tuple)...
-        if not snapshot or not getattr(snapshot, "next", None):
+        if not snapshot:
             return
-        # ...and not parked at a real human-in-the-loop interrupt.
+        # Never alter a real human-in-the-loop pause.
         if _snapshot_has_pending_interrupt(snapshot):
             _log.debug(
                 "Leaving interrupted graph state intact for thread %s: "
@@ -127,6 +127,13 @@ async def _clear_interrupted_graph_state(
             )
             return
 
+        await _repair_malformed_tool_history(agent, config, snapshot=snapshot)
+
+        # Only force END when the graph is genuinely stuck. Message repair also
+        # applies to failures that already left next empty.
+        if not getattr(snapshot, "next", None):
+            return
+
         stuck_at = snapshot.next
         await agent.aupdate_state(config, None, as_node=END)
         _log.debug(
@@ -142,6 +149,49 @@ async def _clear_interrupted_graph_state(
         )
 
 
+async def _repair_malformed_tool_history(
+    agent: Any,
+    config: dict[str, Any],
+    *,
+    snapshot: Any | None = None,
+) -> bool:
+    """Rewrite a checkpoint's messages to a replay-safe tool history.
+
+    Only structurally invalid protocol is removed. Completed tool call/result
+    pairs, including repeated successes and repeated errors, are audit and
+    billing facts and must remain in persistent history.
+    """
+
+    import logging
+
+    from ..llm.patches import _sanitize_openai_tool_history
+
+    _log = logging.getLogger(__name__)
+    if snapshot is None:
+        snapshot = await agent.aget_state(config)
+    if not snapshot or _snapshot_has_pending_interrupt(snapshot):
+        return False
+    values = getattr(snapshot, "values", None)
+    if not isinstance(values, Mapping):
+        return False
+    messages = values.get("messages")
+    if not isinstance(messages, list):
+        return False
+
+    repaired = _sanitize_openai_tool_history(messages)
+    if repaired == messages:
+        return False
+
+    await agent.aupdate_state(config, {"messages": Overwrite(repaired)})
+    _log.warning(
+        "Repaired structurally invalid tool history for thread %s: messages %d -> %d",
+        config.get("configurable", {}).get("thread_id", "?"),
+        len(messages),
+        len(repaired),
+    )
+    return True
+
+
 @dataclass(frozen=True)
 class _SubagentInfo:
     path: tuple[str, ...]
@@ -217,7 +267,12 @@ class _V3EventProcessor:
             tuple[tuple[str, ...], str], tuple[str, dict[str, Any]]
         ] = {}
         self._emitted_tool_calls: set[tuple[tuple[str, ...], str]] = set()
+        self._pending_tool_calls: dict[
+            tuple[tuple[str, ...], str], tuple[str, dict[str, Any]]
+        ] = {}
         self._emitted_interrupts: set[str] = set()
+        self._pending_invalid_tool_calls: dict[str, tuple[str, str]] = {}
+        self._last_task_snapshot: tuple[tuple[str, str], ...] | None = None
         self._selector = _ToolSelectionSuppressor(emitter)
 
     @staticmethod
@@ -245,13 +300,26 @@ class _V3EventProcessor:
         if method == "tools":
             return self._process_tool_event(namespace, _event_data(event), subagent)
         if method == "updates":
-            return self._process_update_event(_event_data(event))
+            return self._process_update_event(
+                _event_data(event), namespace=namespace, source="update"
+            )
         if method == "values":
             events: list[dict[str, Any]] = []
             params = event.get("params") or {}
             interrupts = params.get("interrupts") or ()
             if interrupts:
-                events.extend(self._process_update_event({"__interrupt__": interrupts}))
+                events.extend(
+                    self._process_update_event(
+                        {"__interrupt__": interrupts},
+                        namespace=namespace,
+                        source="values",
+                    )
+                )
+            events.extend(
+                self._process_update_event(
+                    _event_data(event), namespace=namespace, source="values"
+                )
+            )
             if self._process_value_message_snapshots and not namespace:
                 events.extend(self._process_value_messages(_event_data(event)))
             return events
@@ -390,6 +458,7 @@ class _V3EventProcessor:
                 inp, out = _usage_counts(usage) if usage is not None else (0, 0)
                 if inp or out:
                     events.append(self.emitter.usage_stats(inp, out).data)
+            events.extend(self._flush_invalid_tool_calls())
             return events
         return []
 
@@ -415,14 +484,12 @@ class _V3EventProcessor:
         if tool_call is None:
             return events
         tool_name, args, tool_call_id = tool_call
-        events.extend(
-            self._emit_tool_call_once(
-                namespace=namespace,
-                subagent=subagent,
-                name=tool_name,
-                args=args,
-                tool_call_id=tool_call_id,
-            )
+        self._pending_invalid_tool_calls.pop(tool_call_id, None)
+        self._pending_tool_calls[
+            (self._tool_scope(namespace, subagent), tool_call_id)
+        ] = (
+            tool_name,
+            args,
         )
         return events
 
@@ -465,6 +532,20 @@ class _V3EventProcessor:
             ]
         return [self.emitter.tool_call(name, args, tool_call_id).data]
 
+    def _pending_call_id(
+        self,
+        *,
+        scope: tuple[str, ...],
+        name: str,
+        args: dict[str, Any],
+    ) -> str:
+        matches = [
+            call_id
+            for (candidate_scope, call_id), candidate in self._pending_tool_calls.items()
+            if candidate_scope == scope and candidate == (name, args)
+        ]
+        return matches[0] if len(matches) == 1 else ""
+
     def _process_whole_message(
         self,
         msg: AIMessage | AIMessageChunk,
@@ -472,6 +553,16 @@ class _V3EventProcessor:
         namespace: tuple[str, ...],
     ) -> list[dict[str, Any]]:
         events: list[dict[str, Any]] = []
+        for invalid in getattr(msg, "invalid_tool_calls", ()) or ():
+            invalid_map = _as_raw_map(invalid)
+            if invalid_map is None:
+                continue
+            call_id = str(
+                invalid_map.get("id") or invalid_map.get("tool_call_id") or ""
+            )
+            name = str(invalid_map.get("name") or invalid_map.get("tool_name") or "")
+            key = call_id or f"chunk_{len(self._pending_invalid_tool_calls)}"
+            self._pending_invalid_tool_calls[key] = (call_id, name)
         additional = msg.additional_kwargs
         reasoning = additional.get("reasoning_content")
         emitted_reasoning = False
@@ -494,14 +585,12 @@ class _V3EventProcessor:
             if tool_call is None:
                 continue
             tool_name, args, tool_call_id = tool_call
-            events.extend(
-                self._emit_tool_call_once(
-                    namespace=namespace,
-                    subagent=subagent,
-                    name=tool_name,
-                    args=args,
-                    tool_call_id=tool_call_id,
-                )
+            self._pending_invalid_tool_calls.pop(tool_call_id, None)
+            self._pending_tool_calls[
+                (self._tool_scope(namespace, subagent), tool_call_id)
+            ] = (
+                tool_name,
+                args,
             )
 
         if subagent is None:
@@ -534,6 +623,9 @@ class _V3EventProcessor:
                 name,
                 args,
             )
+            self._pending_tool_calls.pop(
+                (self._tool_scope(namespace, subagent), tool_call_id), None
+            )
             events.extend(
                 self._emit_tool_call_once(
                     namespace=namespace,
@@ -580,6 +672,7 @@ class _V3EventProcessor:
                     content += "\n... (truncated)"
                 success = is_success(content)
 
+            lifecycle_key = (self._tool_scope(namespace, subagent), tool_call_id)
             if subagent is not None:
                 events.append(
                     self.emitter.subagent_tool_result(
@@ -591,22 +684,78 @@ class _V3EventProcessor:
                         instance_id=subagent.instance_id,
                     ).data
                 )
-                return events
-            events.append(
-                self.emitter.tool_result(
-                    name, content, success, tool_call_id=tool_call_id
-                ).data
-            )
+            else:
+                events.append(
+                    self.emitter.tool_result(
+                        name, content, success, tool_call_id=tool_call_id
+                    ).data
+                )
+            self._emitted_tool_calls.discard(lifecycle_key)
+            self._pending_tool_calls.pop(lifecycle_key, None)
             return events
 
         return []
 
-    def _process_update_event(self, data: object) -> list[dict[str, Any]]:
+    @staticmethod
+    def _normalize_task_items(value: object) -> list[dict[str, str]] | None:
+        if not isinstance(value, list):
+            return None
+        aliases = {
+            "todo": "pending",
+            "pending": "pending",
+            "active": "in_progress",
+            "in-progress": "in_progress",
+            "in_progress": "in_progress",
+            "done": "completed",
+            "completed": "completed",
+        }
+        items: list[dict[str, str]] = []
+        for raw in value:
+            raw_map = _as_raw_map(raw)
+            if raw_map is None:
+                continue
+            content = str(raw_map.get("content") or raw_map.get("task") or "").strip()
+            if not content:
+                continue
+            status = aliases.get(str(raw_map.get("status") or "pending").lower())
+            if status is None:
+                continue
+            items.append({"content": content, "status": status})
+        return items
+
+    @classmethod
+    def _find_task_items(cls, data: object) -> list[dict[str, str]] | None:
+        data_map = _as_raw_map(data)
+        if data_map is None:
+            return None
+        if "todos" in data_map:
+            return cls._normalize_task_items(data_map["todos"])
+        for value in data_map.values():
+            nested = _as_raw_map(value)
+            if nested is not None and "todos" in nested:
+                return cls._normalize_task_items(nested["todos"])
+        return None
+
+    def _process_update_event(
+        self,
+        data: object,
+        *,
+        namespace: tuple[str, ...] = (),
+        source: str = "update",
+    ) -> list[dict[str, Any]]:
         events: list[dict[str, Any]] = []
         data_map = _as_raw_map(data)
         if data_map is not None and "__interrupt__" in data_map:
             events.extend(self._process_interrupts(data_map["__interrupt__"]))
 
+        if not namespace:
+            items = self._find_task_items(data)
+            if items is not None:
+                signature = tuple((item["content"], item["status"]) for item in items)
+                if signature != self._last_task_snapshot:
+                    self._last_task_snapshot = signature
+                    events.append(self.emitter.task_snapshot(source, items).data)
+
         summarization_event = _find_summarization_event_payload(data)
         if summarization_event and not self._summarization_in_progress:
             signature = _summarization_event_signature(summarization_event)
@@ -622,6 +771,10 @@ class _V3EventProcessor:
             events.extend(self._emit_summarization_text(summary_text))
         return events
 
+    def _flush_invalid_tool_calls(self) -> list[dict[str, Any]]:
+        self._pending_invalid_tool_calls.clear()
+        return []
+
     def _process_interrupts(self, interrupts: object) -> list[dict[str, Any]]:
         events: list[dict[str, Any]] = []
         if not isinstance(interrupts, list | tuple):
@@ -660,26 +813,74 @@ class _V3EventProcessor:
             raw_questions = interrupt_map.get("questions")
             questions = raw_questions if isinstance(raw_questions, list) else []
             tc_id = str(interrupt_map.get("tool_call_id", ""))
-            return self._dedupe_interrupt_event(
-                self.emitter.ask_user_interrupt(
-                    interrupt_id,
-                    questions,
-                    tc_id,
-                ).data
+            events: list[dict[str, Any]] = []
+            candidate = self._pending_tool_calls.get(((), tc_id)) if tc_id else None
+            if candidate is not None:
+                events.extend(
+                    self._emit_tool_call_once(
+                        namespace=(),
+                        subagent=None,
+                        name=candidate[0],
+                        args=candidate[1],
+                        tool_call_id=tc_id,
+                    )
+                )
+            events.extend(
+                self._dedupe_interrupt_event(
+                    self.emitter.ask_user_interrupt(
+                        interrupt_id,
+                        questions,
+                        tc_id,
+                    ).data
+                )
             )
+            return events
 
         raw_action_reqs = interrupt_map.get("action_requests")
         action_reqs = raw_action_reqs if isinstance(raw_action_reqs, list) else []
         raw_review_cfgs = interrupt_map.get("review_configs")
         review_cfgs = raw_review_cfgs if isinstance(raw_review_cfgs, list) else None
         if action_reqs:
-            return self._dedupe_interrupt_event(
-                self.emitter.interrupt(
-                    interrupt_id,
-                    action_reqs,
-                    review_cfgs,
-                ).data
+            events: list[dict[str, Any]] = []
+            for raw_request in action_reqs:
+                request_map = _as_raw_map(raw_request)
+                if request_map is None:
+                    continue
+                call_id = str(
+                    request_map.get("id") or request_map.get("tool_call_id") or ""
+                )
+                name = str(request_map.get("name") or request_map.get("tool_name") or "")
+                args_map = _as_raw_map(
+                    request_map.get("args")
+                    if "args" in request_map
+                    else request_map.get("input")
+                )
+                if not call_id and name and args_map is not None:
+                    call_id = self._pending_call_id(
+                        scope=(),
+                        name=name,
+                        args=dict(args_map),
+                    )
+                if call_id and name and args_map is not None:
+                    events.extend(
+                        self._emit_tool_call_once(
+                            namespace=(),
+                            subagent=None,
+                            name=name,
+                            args=dict(args_map),
+                            tool_call_id=call_id,
+                        )
+                    )
+            events.extend(
+                self._dedupe_interrupt_event(
+                    self.emitter.interrupt(
+                        interrupt_id,
+                        action_reqs,
+                        review_cfgs,
+                    ).data
+                )
             )
+            return events
         return []
 
     def _dedupe_interrupt_event(self, event: dict[str, Any]) -> list[dict[str, Any]]:
@@ -805,6 +1006,8 @@ async def stream_agent_events(
     thread_id: str,
     metadata: dict[str, Any] | None = None,
     media: list[str] | None = None,
+    callbacks: list[Any] | None = None,
+    error_mode: str = "emit",
 ) -> AsyncGenerator[dict[str, Any], None]:
     """Stream events from a DeepAgents/LangGraph v3 run.
 
@@ -820,6 +1023,9 @@ async def stream_agent_events(
         metadata: Optional metadata dict merged into the LangGraph config
             (e.g. agent_name, updated_at for checkpoint persistence).
         media: Optional list of local file paths for attachments.
+        callbacks: Optional Runnable callbacks propagated to all nested model calls.
+        error_mode: ``emit`` preserves the generic error event; ``raise`` lets an
+            embedding host produce the single terminal error envelope.
 
     Yields:
         Event dicts: thinking, text, tool_call, tool_result,
@@ -829,6 +1035,8 @@ async def stream_agent_events(
     config: dict[str, Any] = {"configurable": {"thread_id": thread_id}}
     if metadata:
         config["metadata"] = metadata
+    if callbacks:
+        config["callbacks"] = callbacks
     emitter = StreamEventEmitter()
     existing_summarization_event: Mapping[str, object] | None = None
     try:
@@ -957,7 +1165,30 @@ async def stream_agent_events(
             yield item
     except Exception as e:
         _run_raised = True
-        yield emitter.error(str(e)).data
+        if error_mode == "emit":
+            payload = e.model_dump() if hasattr(e, "model_dump") else {}
+            if not isinstance(payload, Mapping):
+                payload = {}
+            code = str(payload.get("code") or "") or None
+            details = {
+                key: payload[key]
+                for key in (
+                    "reason",
+                    "provider",
+                    "model",
+                    "route_key",
+                    "config_generation",
+                    "api_mode",
+                    "call_id",
+                )
+                if payload.get(key) is not None
+            }
+            yield emitter.error(
+                str(payload.get("message") or e),
+                code=code,
+                recoverable=bool(payload.get("recoverable", True)) if code else None,
+                details=details or None,
+            ).data
         raise
     finally:
         if stream is not None:

tests/test_agent_factory_extensions.py

@@ -69,3 +69,69 @@ def test_create_cli_agent_accepts_host_backend_and_memory_options(
     assert calls["middleware_kwargs"]["memory_max_inline_profile_chars"] == 1000
     assert calls["middleware_kwargs"]["enable_background_execution"] is False
     assert calls["agent_config"] == {"recursion_limit": 321}
+
+
+def test_create_cli_agent_installs_route_middleware_in_fixed_slot(monkeypatch, tmp_path):
+    import EvoScientist.EvoScientist as agent_module
+    from EvoScientist.config.settings import EvoScientistConfig
+
+    calls = {}
+
+    class _Middleware:
+        def __init__(self, name):
+            self.name = name
+
+    class _Backend:
+        def __init__(self, **_kwargs):
+            pass
+
+    class _CompositeBackend:
+        def __init__(self, **_kwargs):
+            pass
+
+    class _Agent:
+        def with_config(self, _config):
+            return self
+
+    default_chain = [
+        _Middleware("error_normalization"),
+        _Middleware("configurable_model"),
+        _Middleware("context_editing"),
+        _Middleware("tool_protocol_guard"),
+    ]
+    route = _Middleware("gateway_route_fallback")
+
+    monkeypatch.setattr("deepagents.backends.CompositeBackend", _CompositeBackend)
+    monkeypatch.setattr("deepagents.create_deep_agent", lambda **_kwargs: _Agent())
+    monkeypatch.setattr("EvoScientist.backends.MemoryFilesystemBackend", _Backend)
+    monkeypatch.setattr("EvoScientist.backends.MergedSkillsBackend", _Backend)
+    monkeypatch.setattr(agent_module, "set_active_workspace", lambda _path: None)
+    def fake_default_middleware(**kwargs):
+        calls["middleware_kwargs"] = kwargs
+        return list(default_chain)
+
+    monkeypatch.setattr(agent_module, "_get_default_middleware", fake_default_middleware)
+
+    def fake_load(_backend, middleware, **_kwargs):
+        calls["middleware"] = middleware
+        return {"subagents": []}
+
+    monkeypatch.setattr(agent_module, "load_mcp_and_build_kwargs", fake_load)
+
+    agent_module.create_cli_agent(
+        workspace_dir=str(tmp_path),
+        checkpointer=object(),
+        config=EvoScientistConfig(auto_approve=True),
+        chat_model=object(),
+        workspace_backend=object(),
+        main_agent_route_middleware=route,
+    )
+
+    assert calls["middleware_kwargs"]["enable_legacy_model_fallback"] is False
+    assert [middleware.name for middleware in calls["middleware"][:5]] == [
+        "error_normalization",
+        "configurable_model",
+        "gateway_route_fallback",
+        "context_editing",
+        "tool_protocol_guard",
+    ]

tests/test_config.py

@@ -153,6 +153,8 @@ class TestEvoScientistConfig:
         assert config.channel_debug_tracing is False
         assert config.imessage_enabled is False
         assert config.imessage_allowed_senders == ""
+        assert config.repetitive_tool_call_threshold == 2
+        assert config.max_consecutive_tool_errors == 3
 
     def test_auth_mode_default(self):
         """Test that anthropic_auth_mode defaults to api_key."""
@@ -200,6 +202,18 @@ class TestEvoScientistConfig:
         assert config.dangerous_mode is True
         assert config.auto_approve is True
 
+    @pytest.mark.parametrize(
+        "kwargs",
+        [
+            {"repetitive_tool_call_threshold": -1},
+            {"max_consecutive_tool_errors": -1},
+            {"max_consecutive_tool_errors": True},
+        ],
+    )
+    def test_tool_guard_thresholds_must_be_non_negative_integers(self, kwargs):
+        with pytest.raises(ValueError, match="non-negative integer"):
+            EvoScientistConfig(**kwargs)
+
 
 # =============================================================================
 # Test config path functions

tests/test_error_normalization_middleware.py

@@ -15,7 +15,11 @@ from types import SimpleNamespace
 
 import pytest
 
-from EvoScientist.llm.errors import ProviderStreamError
+from EvoScientist.llm.errors import (
+    AgentControlError,
+    ModelToolProtocolError,
+    ProviderStreamError,
+)
 from EvoScientist.middleware.error_normalization import (
     ErrorNormalizationMiddleware,
     _normalize,
@@ -148,6 +152,23 @@ class TestNormalize:
         )
         assert _normalize(req, pre_wrapped) is None
 
+    @pytest.mark.parametrize(
+        "error",
+        [
+            AgentControlError("MODEL_TOOL_LOOP_DETECTED", "loop stopped"),
+            ModelToolProtocolError(
+                "missing_name",
+                provider="openai",
+                model="gpt-example",
+                route_key="route-1",
+            ),
+        ],
+    )
+    def test_platform_control_error_passes_through(self, error):
+        req = _request(_openai_model())
+
+        assert _normalize(req, error) is None
+
 
 # ---------------------------------------------------------------------------
 # _is_provider_error — used by tool selector to distinguish provider
@@ -355,6 +376,25 @@ class TestMiddleware:
             self._run_awrap(mw, req, handler)
         assert excinfo.value is raised
 
+    def test_awrap_preserves_model_tool_protocol_error_identity(self):
+        raised = ModelToolProtocolError(
+            "missing_name",
+            provider="openai",
+            model="gpt-example",
+            route_key="route-1",
+        )
+
+        async def handler(_req):
+            raise raised
+
+        req = _request(_openai_model())
+        with pytest.raises(ModelToolProtocolError) as excinfo:
+            self._run_awrap(ErrorNormalizationMiddleware(), req, handler)
+
+        assert excinfo.value is raised
+        assert excinfo.value.code == "MODEL_TOOL_PROTOCOL_INVALID"
+        assert excinfo.value.fallbackable is True
+
     def test_awrap_wraps_any_exception_from_recognized_model(self):
         """Any exception raised inside a call to a provider-recognized
         model gets wrapped — including builtins like ``RuntimeError``.

tests/test_graph_gateway.py

@@ -924,6 +924,12 @@ async def test_langgraph_server_gateway_emits_state_interrupt_before_done():
     events = await _collect()
 
     assert events == [
+        {
+            "type": "tool_call",
+            "name": "execute",
+            "args": {"command": "echo hello"},
+            "id": "tool-1",
+        },
         {
             "type": "interrupt",
             "interrupt_id": "interrupt-1",

tests/test_host_metering_extensions.py

@@ -0,0 +1,13 @@
+from EvoScientist.llm.errors import AgentControlError
+from EvoScientist.middleware.model_fallback import _is_non_fallbackable
+
+
+def test_agent_control_error_is_non_fallbackable():
+    error = AgentControlError(
+        "INSUFFICIENT_BALANCE",
+        "balance unavailable",
+        status_code=403,
+    )
+
+    assert "platform control error" in (_is_non_fallbackable(error) or "")
+    assert error.model_dump()["code"] == "INSUFFICIENT_BALANCE"

tests/test_llm.py

@@ -1293,6 +1293,107 @@ class TestPatchOpenAICompatContent:
         assert len(set(call_ids)) == 2
         assert [message.tool_call_id for message in first[1:]] == call_ids
 
+    def test_content_tool_block_is_normalized_to_parsed_call(self):
+        from langchain_core.messages import AIMessage, ToolMessage
+
+        from EvoScientist.llm.patches import _ensure_openai_tool_call_ids
+
+        normalized = _ensure_openai_tool_call_ids(
+            [
+                AIMessage(
+                    content=[
+                        {
+                            "type": "tool_call",
+                            "id": "wrong-id",
+                            "name": "wrong-name",
+                            "args": {},
+                        }
+                    ],
+                    tool_calls=[{"id": "call-1", "name": "execute", "args": {}}],
+                ),
+                ToolMessage(content="ok", tool_call_id="call-1"),
+            ]
+        )
+
+        assert normalized[0].content[0]["id"] == "call-1"
+        assert normalized[0].content[0]["name"] == "execute"
+
+    def test_invalid_tool_call_is_not_replayed_to_responses_api(self):
+        from langchain_core.messages import AIMessage, HumanMessage
+        from langchain_openai.chat_models.base import _construct_responses_api_input
+
+        from EvoScientist.llm.patches import _sanitize_messages
+
+        invalid = AIMessage(
+            content=[
+                {"type": "reasoning", "reasoning": "partial"},
+                {
+                    "type": "tool_call",
+                    "id": None,
+                    "name": "execute",
+                    "args": '{"command":',
+                },
+            ],
+            invalid_tool_calls=[
+                {
+                    "type": "invalid_tool_call",
+                    "id": None,
+                    "name": "execute",
+                    "args": '{"command":',
+                    "error": "Failed to parse tool call arguments as JSON",
+                }
+            ],
+        )
+
+        normalized = _sanitize_messages([invalid, HumanMessage(content="retry")])
+        payload = _construct_responses_api_input(normalized)
+
+        assert all(item.get("type") != "function_call" for item in payload)
+        assert [message.type for message in normalized] == ["human"]
+
+    def test_invalid_tool_call_preserves_replayable_assistant_text(self):
+        from langchain_core.messages import AIMessage
+
+        from EvoScientist.llm.patches import _sanitize_messages
+
+        invalid = AIMessage(
+            content="I could not finish the tool request.",
+            invalid_tool_calls=[
+                {
+                    "type": "invalid_tool_call",
+                    "id": None,
+                    "name": "execute",
+                    "args": "{",
+                    "error": "bad json",
+                }
+            ],
+        )
+
+        normalized = _sanitize_messages([invalid])
+
+        assert len(normalized) == 1
+        assert normalized[0].content == "I could not finish the tool request."
+        assert normalized[0].invalid_tool_calls == []
+
+    def test_orphan_tool_results_and_unanswered_calls_are_removed(self):
+        from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+
+        from EvoScientist.llm.patches import _sanitize_messages
+
+        messages = [
+            ToolMessage(content="orphan", tool_call_id="missing"),
+            AIMessage(
+                content="waiting",
+                tool_calls=[{"id": "call_unanswered", "name": "execute", "args": {}}],
+            ),
+            HumanMessage(content="continue"),
+        ]
+
+        normalized = _sanitize_messages(messages)
+
+        assert [message.type for message in normalized] == ["ai", "human"]
+        assert normalized[0].tool_calls == []
+
     def test_generate_flattened(self):
         from langchain_core.messages import HumanMessage
 

tests/test_model_fallback.py

@@ -84,6 +84,7 @@ class TestIsNonFallbackable:
             "Error 400: invalid_request_error",
             "400 Bad Request: invalid request body",
             "400: malformed JSON in request",
+            "<400> InvalidParameter: Repetitive tool calls detected in history",
         ],
     )
     def test_malformed_request_400_patterns(self, msg):

tests/test_repetitive_tool_guard.py

@@ -0,0 +1,177 @@
+"""Deterministic tool-loop guard and provider projection tests."""
+
+from dataclasses import dataclass, replace
+from typing import Any
+
+import pytest
+from langchain.agents.middleware.types import ModelResponse
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
+
+from EvoScientist.llm.errors import AgentControlError
+from EvoScientist.middleware.repetitive_tool_guard import (
+    RepetitiveToolCallGuardMiddleware,
+    collapse_repetitive_tool_rounds,
+)
+
+
+def _round(
+    call_id: str,
+    *,
+    name: str = "execute",
+    command: str = "pwd",
+    content: str = "Error: invalid argument: command rejected by schema",
+    status: str = "error",
+) -> list[Any]:
+    return [
+        AIMessage(
+            content="",
+            tool_calls=[{"id": call_id, "name": name, "args": {"command": command}}],
+        ),
+        ToolMessage(
+            content=content,
+            tool_call_id=call_id,
+            name=name,
+            status=status,
+        ),
+    ]
+
+
+@dataclass(frozen=True)
+class _Request:
+    messages: list[Any]
+    tools: list[Any]
+
+    def override(self, **updates: Any):
+        return replace(self, **updates)
+
+
+def test_provider_projection_keeps_first_and_last_deterministic_error_rounds():
+    messages = [HumanMessage(content="inspect")]
+    for index in range(4):
+        messages.extend(_round(f"call-{index}"))
+    messages.append(HumanMessage(content="continue"))
+
+    repair = collapse_repetitive_tool_rounds(messages, threshold=2)
+
+    assert repair.removed_rounds == 2
+    assert [m.type for m in repair.messages] == [
+        "human",
+        "ai",
+        "tool",
+        "ai",
+        "tool",
+        "human",
+    ]
+    assert repair.messages[1].tool_calls[0]["id"] == "call-0"
+    assert repair.messages[3].tool_calls[0]["id"] == "call-3"
+
+
+def test_successful_repeated_calls_are_never_projected_away():
+    messages = [
+        *_round("call-1", content="ok", status="success"),
+        *_round("call-2", content="ok", status="success"),
+        *_round("call-3", content="ok", status="success"),
+    ]
+    repair = collapse_repetitive_tool_rounds(messages)
+    assert repair.messages == messages
+    assert repair.removed_rounds == 0
+    assert repair.tail_repetitions == 0
+
+
+def test_transient_and_unknown_errors_do_not_count_as_semantic_loop():
+    transient = [
+        *_round("call-1", content="Error: connection timeout"),
+        *_round("call-2", content="Error: connection timeout"),
+    ]
+    unknown = [
+        *_round("call-3", content="Error: something unusual"),
+        *_round("call-4", content="Error: something unusual"),
+    ]
+    assert collapse_repetitive_tool_rounds(transient).tail_repetitions == 0
+    assert collapse_repetitive_tool_rounds(unknown).tail_consecutive_errors == 0
+
+
+def test_generic_raw_execution_error_code_remains_unknown():
+    messages = _round("call-1", content="Error: something unusual")
+    messages[1].additional_kwargs["error_code"] = "TOOL_EXECUTION_FAILED"
+
+    repair = collapse_repetitive_tool_rounds(messages)
+
+    assert repair.tail_consecutive_errors == 0
+
+
+def test_identical_tail_loop_stops_before_next_model_call():
+    request = _Request(
+        messages=[*_round("call-1"), *_round("call-2")],
+        tools=[{"name": "execute"}],
+    )
+    called = False
+
+    def handler(_request):
+        nonlocal called
+        called = True
+        return ModelResponse(result=[AIMessage(content="should not run")])
+
+    with pytest.raises(AgentControlError) as caught:
+        RepetitiveToolCallGuardMiddleware(threshold=2).wrap_model_call(request, handler)
+
+    assert caught.value.code == "MODEL_TOOL_LOOP_DETECTED"
+    assert called is False
+
+
+def test_different_deterministic_errors_hit_consecutive_limit():
+    request = _Request(
+        messages=[
+            *_round("one", name="execute"),
+            *_round("two", name="read_file"),
+            *_round("three", name="search"),
+        ],
+        tools=[],
+    )
+
+    with pytest.raises(AgentControlError) as caught:
+        RepetitiveToolCallGuardMiddleware(
+            threshold=0, max_consecutive_errors=3
+        ).wrap_model_call(request, lambda _request: None)
+
+    assert caught.value.code == "MODEL_TOOL_ERROR_LIMIT"
+
+
+def test_user_message_breaks_tail_loop_but_historical_projection_is_temporary():
+    original = [
+        *_round("call-1"),
+        *_round("call-2"),
+        *_round("call-3"),
+        HumanMessage(content="try a new approach"),
+    ]
+    request = _Request(messages=original, tools=[])
+    captured = []
+
+    def handler(prepared):
+        captured.append(prepared)
+        return ModelResponse(result=[AIMessage(content="continued")])
+
+    RepetitiveToolCallGuardMiddleware().wrap_model_call(request, handler)
+    assert len(captured[0].messages) == 5
+    assert len(original) == 7
+
+
+def test_zero_thresholds_disable_only_semantic_loop_guards():
+    request = _Request(messages=[*_round("one"), *_round("two")], tools=[])
+    captured = []
+    middleware = RepetitiveToolCallGuardMiddleware(
+        threshold=0, max_consecutive_errors=0
+    )
+    middleware.wrap_model_call(
+        request,
+        lambda prepared: (
+            captured.append(prepared) or ModelResponse(result=[AIMessage(content="ok")])
+        ),
+    )
+    assert captured == [request]
+
+
+@pytest.mark.parametrize("kwargs", [{"threshold": -1}, {"max_consecutive_errors": -1}])
+def test_negative_threshold_is_rejected(kwargs):
+    with pytest.raises(ValueError, match="non-negative"):
+        RepetitiveToolCallGuardMiddleware(**kwargs)

tests/test_stream_events.py

@@ -11,6 +11,7 @@ from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.types import Command, Interrupt
 
 from EvoScientist.middleware.ask_user import AskUserMiddleware
+from EvoScientist.stream.emitter import STREAM_PROTOCOL_CAPABILITIES
 from EvoScientist.stream.events import stream_agent_events
 from EvoScientist.stream.summarization import (
     _extract_summary_message_text,
@@ -1121,6 +1122,74 @@ class TestUsageStatsExtraction:
         assert len(usage_events) == 0
 
 
+class TestCanonicalSourceCapabilities:
+    async def test_root_update_emits_full_task_snapshot_and_empty_clear(self):
+        agent = FakeV3Agent(
+            [
+                protocol_event(
+                    "updates",
+                    {"model": {"todos": [{"content": "Inspect", "status": "active"}]}},
+                ),
+                protocol_event("updates", {"model": {"todos": []}}),
+            ]
+        )
+        events = await collect_events(agent)
+        snapshots = [event for event in events if event.get("type") == "task_snapshot"]
+        assert snapshots == [
+            {
+                "type": "task_snapshot",
+                "source": "update",
+                "items": [{"content": "Inspect", "status": "in_progress"}],
+            },
+            {"type": "task_snapshot", "source": "update", "items": []},
+        ]
+
+    async def test_subagent_todos_do_not_replace_root_snapshot(self):
+        agent = FakeV3Agent(
+            [
+                protocol_event(
+                    "updates",
+                    {"todos": [{"content": "Nested", "status": "pending"}]},
+                    namespace=("subagent",),
+                )
+            ]
+        )
+        events = await collect_events(agent)
+        assert not any(event.get("type") == "task_snapshot" for event in events)
+
+    async def test_invalid_tool_call_candidate_is_not_committed_by_stream_processor(self):
+        invalid = AIMessage(
+            content="",
+            invalid_tool_calls=[
+                {
+                    "name": "write_todos",
+                    "args": "{bad",
+                    "id": "call-invalid",
+                    "error": "invalid json",
+                    "type": "invalid_tool_call",
+                }
+            ],
+        )
+        agent = FakeV3Agent(
+            [
+                protocol_event("messages", (invalid, {})),
+                message_finish(),
+            ]
+        )
+        events = await collect_events(agent)
+        assert not any(event.get("type") in {"tool_call", "error"} for event in events)
+
+    def test_stream_capabilities_are_explicit(self):
+        assert STREAM_PROTOCOL_CAPABILITIES == frozenset(
+            {
+                "task_snapshot_v1",
+                "complete_tool_call_v1",
+                "correlated_tool_call_id_v1",
+                "final_invalid_tool_call_v1",
+            }
+        )
+
+
 class TestSummarizationHelpers:
     """Summarization extraction helpers."""
 

tests/test_stream_recovery.py

@@ -9,10 +9,12 @@ so they actually verify the two claims the recovery rests on:
    left intact, so a pending question is never silently discarded.
 """
 
-from typing import TypedDict
+from typing import Annotated, TypedDict
 
+from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
 from langgraph.types import interrupt
 
 from EvoScientist.stream.events import _clear_interrupted_graph_state
@@ -22,6 +24,10 @@ class _S(TypedDict):
     x: int
 
 
+class _MessageState(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
 def _crashing_app():
     # Node 'b' crashes once, then succeeds — so a post-recovery run can complete
     # and prove the graph is genuinely unstuck (not replaying the dead step).
@@ -57,6 +63,75 @@ def _interrupting_app():
     return g.compile(checkpointer=InMemorySaver())
 
 
+def _invalid_tool_call_app():
+    def write_invalid_call(state):
+        return {
+            "messages": [
+                AIMessage(
+                    content="",
+                    invalid_tool_calls=[
+                        {
+                            "type": "invalid_tool_call",
+                            "id": None,
+                            "name": "execute",
+                            "args": '{"command":',
+                            "error": "bad json",
+                        }
+                    ],
+                )
+            ]
+        }
+
+    def crash(state):
+        raise RuntimeError("provider stream failed")
+
+    g = StateGraph(_MessageState)
+    g.add_node("write_invalid_call", write_invalid_call)
+    g.add_node("crash", crash)
+    g.add_edge(START, "write_invalid_call")
+    g.add_edge("write_invalid_call", "crash")
+    g.add_edge("crash", END)
+    return g.compile(checkpointer=InMemorySaver())
+
+
+def _repetitive_tool_call_app():
+    messages = [HumanMessage(content="inspect")]
+    for call_id in ("call-1", "call-2"):
+        messages.extend(
+            [
+                AIMessage(
+                    content="",
+                    tool_calls=[
+                        {
+                            "id": call_id,
+                            "name": "execute",
+                            "args": {"command": "pwd"},
+                        }
+                    ],
+                ),
+                ToolMessage(
+                    content="/workspace",
+                    tool_call_id=call_id,
+                    name="execute",
+                ),
+            ]
+        )
+
+    def write_repetitive_history(state):
+        return {"messages": messages}
+
+    def crash(state):
+        raise RuntimeError("provider rejected repetitive tool history")
+
+    g = StateGraph(_MessageState)
+    g.add_node("write_repetitive_history", write_repetitive_history)
+    g.add_node("crash", crash)
+    g.add_edge(START, "write_repetitive_history")
+    g.add_edge("write_repetitive_history", "crash")
+    g.add_edge("crash", END)
+    return g.compile(checkpointer=InMemorySaver())
+
+
 async def test_recovery_clears_stuck_state_after_crash():
     app = _crashing_app()
     cfg = {"configurable": {"thread_id": "t1"}}
@@ -91,3 +166,52 @@ async def test_recovery_preserves_pending_hitl_interrupt():
     after = app.get_state(cfg)
     assert after.next == ("ask",)  # interrupt left intact, still resumable
     assert after.interrupts
+
+
+async def test_recovery_removes_invalid_tool_call_from_checkpoint():
+    app = _invalid_tool_call_app()
+    cfg = {"configurable": {"thread_id": "tool-history"}}
+    try:
+        await app.ainvoke(
+            {"messages": [HumanMessage(content="run the command")]},
+            cfg,
+        )
+    except RuntimeError:
+        pass
+
+    before = await app.aget_state(cfg)
+    assert before.next == ("crash",)
+    assert any(
+        isinstance(message, AIMessage) and message.invalid_tool_calls
+        for message in before.values["messages"]
+    )
+
+    await _clear_interrupted_graph_state(app, cfg)
+
+    after = await app.aget_state(cfg)
+    assert after.next == ()
+    assert [message.type for message in after.values["messages"]] == ["human"]
+
+
+async def test_recovery_preserves_complete_repetitive_tool_rounds_in_checkpoint():
+    app = _repetitive_tool_call_app()
+    cfg = {"configurable": {"thread_id": "tool-loop-history"}}
+    try:
+        await app.ainvoke({"messages": []}, cfg)
+    except RuntimeError:
+        pass
+
+    before = await app.aget_state(cfg)
+    assert before.next == ("crash",)
+    assert len(before.values["messages"]) == 5
+
+    await _clear_interrupted_graph_state(app, cfg)
+
+    after = await app.aget_state(cfg)
+    messages = after.values["messages"]
+    assert after.next == ()
+    assert [message.type for message in messages] == ["human", "ai", "tool", "ai", "tool"]
+    assert [messages[1].tool_calls[0]["id"], messages[3].tool_calls[0]["id"]] == [
+        "call-1",
+        "call-2",
+    ]

tests/test_tool_protocol_guard.py

@@ -0,0 +1,246 @@
+"""Final model tool protocol validation tests."""
+
+from dataclasses import dataclass, field, replace
+from types import SimpleNamespace
+from typing import Any
+
+import pytest
+from langchain.agents.middleware.types import ExtendedModelResponse, ModelResponse
+from langchain_core.messages import AIMessage
+
+from EvoScientist.llm.errors import ModelToolProtocolError
+from EvoScientist.middleware.tool_protocol_guard import ToolProtocolGuardMiddleware
+
+
+@dataclass(frozen=True)
+class _Request:
+    tools: list[Any]
+    model: Any = field(default_factory=lambda: SimpleNamespace(metadata={}))
+
+    def override(self, **updates: Any):
+        return replace(self, **updates)
+
+
+def _response(*calls: dict[str, Any], content: Any = "") -> ModelResponse:
+    return ModelResponse(result=[AIMessage(content=content, tool_calls=list(calls))])
+
+
+def _call(call_id: str = "call-1", name: str = "search", args: Any = None):
+    return {"id": call_id, "name": name, "args": {} if args is None else args}
+
+
+@pytest.mark.parametrize(
+    ("call", "reason"),
+    [
+        (_call(name=""), "missing_name"),
+        (_call(name="  "), "missing_name"),
+        (_call(name="missing"), "unknown_name"),
+        (_call(call_id=""), "missing_id"),
+    ],
+)
+def test_invalid_final_tool_call_fails_closed(call, reason):
+    middleware = ToolProtocolGuardMiddleware()
+    request = _Request(tools=[{"name": "search"}])
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        middleware.wrap_model_call(request, lambda _request: _response(call))
+
+    assert caught.value.reason == reason
+    assert caught.value.retryable is False
+    assert caught.value.fallbackable is True
+
+
+def test_non_mapping_args_are_rejected_if_adapter_bypasses_message_validation():
+    message = AIMessage(content="", tool_calls=[_call()])
+    message.tool_calls[0]["args"] = "{}"
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            _Request(tools=[{"name": "search"}]),
+            lambda _request: ModelResponse(result=[message]),
+        )
+
+    assert caught.value.reason == "invalid_args"
+
+
+def test_duplicate_parallel_call_id_rejects_whole_response():
+    request = _Request(tools=[{"name": "search"}, {"name": "read_file"}])
+    response = _response(_call(name="search"), _call(name="read_file"))
+
+    with pytest.raises(
+        ModelToolProtocolError, match="invalid structured tool call"
+    ) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            request, lambda _request: response
+        )
+
+    assert caught.value.reason == "duplicate_id"
+
+
+def test_one_invalid_parallel_call_rejects_atomically():
+    request = _Request(tools=[{"name": "search"}])
+    response = _response(_call(call_id="one"), _call(call_id="two", name="missing"))
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            request, lambda _request: response
+        )
+
+    assert caught.value.reason == "unknown_name"
+
+
+def test_final_invalid_tool_calls_are_rejected():
+    message = AIMessage(
+        content="",
+        invalid_tool_calls=[
+            {"id": "bad", "name": "search", "args": "{", "error": "bad json"}
+        ],
+    )
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            _Request(tools=[{"name": "search"}]),
+            lambda _request: ModelResponse(result=[message]),
+        )
+
+    assert caught.value.reason == "invalid_final_call"
+    assert caught.value.call_id == "bad"
+
+
+def test_content_block_must_match_parsed_call():
+    response = _response(
+        _call(),
+        content=[
+            {"type": "tool_call", "id": "call-1", "name": "read_file", "args": {}}
+        ],
+    )
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            _Request(tools=[{"name": "search"}, {"name": "read_file"}]),
+            lambda _request: response,
+        )
+
+    assert caught.value.reason == "inconsistent_block"
+
+
+def test_parsed_only_valid_call_and_extended_response_pass():
+    response = ExtendedModelResponse(model_response=_response(_call()))
+    result = ToolProtocolGuardMiddleware().wrap_model_call(
+        _Request(tools=[{"type": "function", "function": {"name": "search"}}]),
+        lambda _request: response,
+    )
+    assert result is response
+
+
+async def test_async_direct_ai_message_shape_passes():
+    response = AIMessage(content="", tool_calls=[_call()])
+
+    async def handler(_request):
+        return response
+
+    result = await ToolProtocolGuardMiddleware().awrap_model_call(
+        _Request(tools=[{"name": "search"}]), handler
+    )
+    assert result is response
+
+
+def test_error_carries_safe_route_metadata():
+    model = SimpleNamespace(
+        metadata={
+            "route_provider": "openai",
+            "route_model": "gpt-example",
+            "route_key": "route-safe",
+            "route_config_generation": 12,
+            "route_api_mode": "chat_completions",
+            "route_endpoint": "primary",
+            "route_tool_call_transport": "streaming",
+        }
+    )
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            _Request(tools=[{"name": "search"}], model=model),
+            lambda _request: _response(_call(name="")),
+        )
+
+    payload = caught.value.model_dump()
+    assert payload["route_key"] == "route-safe"
+    assert payload["config_generation"] == 12
+    assert payload["endpoint"] == "primary"
+    assert payload["tool_call_transport"] == "streaming"
+    assert "args" not in payload
+
+
+def test_missing_id_carries_redacted_call_diagnostic_only_for_internal_logging():
+    call = _call(call_id="", name="search", args={"query": "private search text"})
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            _Request(tools=[{"name": "search"}]),
+            lambda _request: _response(call),
+        )
+
+    diagnostic = caught.value.call_diagnostic
+    assert diagnostic == {
+        "source": "parsed_tool_calls",
+        "call_index": 0,
+        "call_count": 1,
+        "call_type": "object",
+        "name": "search",
+        "id_present": False,
+        "args_present": True,
+        "args_type": "object",
+        "args_key_count": 1,
+        "args_keys": ["query"],
+        "args_keys_truncated": False,
+        "args_digest": diagnostic["args_digest"],
+        "raw_openai_call_available": False,
+    }
+    assert diagnostic["args_digest"].startswith("sha256:")
+    assert "private search text" not in str(diagnostic)
+    assert "call_diagnostic" not in caught.value.model_dump()
+
+
+def test_diagnostic_compares_parsed_and_preserved_raw_openai_call_shapes():
+    parsed = _call(call_id="", name="search", args={"query": "secret"})
+    raw = {
+        "id": "provider-call-id",
+        "type": "function",
+        "function": {"name": "search", "arguments": '{"query":"secret"}'},
+    }
+    message = AIMessage(
+        content="",
+        tool_calls=[parsed],
+        additional_kwargs={"tool_calls": [raw]},
+    )
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            _Request(tools=[{"name": "search"}]),
+            lambda _request: ModelResponse(result=[message]),
+        )
+
+    diagnostic = caught.value.call_diagnostic
+    assert diagnostic["id_present"] is False
+    assert diagnostic["raw_openai_call_available"] is True
+    assert diagnostic["raw_openai_call"]["id_present"] is True
+    assert diagnostic["raw_openai_call"]["name"] == "search"
+    assert "provider-call-id" not in str(diagnostic)
+    assert "secret" not in str(diagnostic)
+
+
+def test_diagnostic_failure_cannot_mask_the_protocol_error():
+    circular: dict[str, Any] = {}
+    circular["self"] = circular
+    message = AIMessage(content="", tool_calls=[_call(call_id="", args={})])
+    message.tool_calls[0]["args"] = circular
+
+    with pytest.raises(ModelToolProtocolError) as caught:
+        ToolProtocolGuardMiddleware().wrap_model_call(
+            _Request(tools=[{"name": "search"}]),
+            lambda _request: ModelResponse(result=[message]),
+        )
+
+    assert caught.value.reason == "missing_id"
+    assert caught.value.call_diagnostic["args_digest"].startswith("sha256:")