diff --git a/llama_stack/testing/api_recorder.py b/llama_stack/testing/api_recorder.py
index 9f37b63ff..b1244f19f 100644
--- a/llama_stack/testing/api_recorder.py
+++ b/llama_stack/testing/api_recorder.py
@@ -9,6 +9,7 @@ from __future__ import annotations  # for forward references
 import hashlib
 import json
 import os
+import re
 from collections.abc import Generator
 from contextlib import contextmanager
 from enum import StrEnum
@@ -134,6 +135,194 @@ def _normalize_file_ids(obj: Any) -> Any:
         return obj
 
 
+def _get_current_test_id_mappings() -> dict[str, str]:
+    """Get the current test's ID normalization mappings.
+
+    Returns a dict mapping normalized_id -> original_id (e.g., "file-1" -> "file-abc123...")
+    This is the inverse of what's stored in _id_normalizers.
+    """
+    global _id_normalizers
+
+    test_id = _test_context.get()
+    if not test_id or test_id not in _id_normalizers:
+        return {}
+
+    # Invert the mapping: normalized_id -> original_id
+    result = {}
+    for id_type, mappings in _id_normalizers[test_id].items():
+        for original_id, normalized_id in mappings.items():
+            result[normalized_id] = original_id
+
+    return result
+
+
+def _chunk_text_content(chunk: Any) -> tuple[str | None, bool]:
+    """Return (content, has_structured_fields) for OpenAI chat completion chunks."""
+    choices = getattr(chunk, "choices", None)
+    if not choices:
+        return None, False
+
+    delta = choices[0].delta
+    content = getattr(delta, "content", None)
+    if not content:
+        return None, False
+
+    has_structured = bool(getattr(delta, "tool_calls", None) or getattr(delta, "function_call", None))
+    return content, has_structured
+
+
+def _chunk_with_content(chunk: Any, content: str) -> Any:
+    """Return a copy of the chunk with delta.content replaced by the provided string."""
+    choices = getattr(chunk, "choices", None)
+    if not choices:
+        return chunk
+
+    updated_choices = []
+    for choice in choices:
+        delta = choice.delta
+        if getattr(delta, "content", None) is not None:
+            new_delta = delta.model_copy(update={"content": content})
+            updated_choices.append(choice.model_copy(update={"delta": new_delta}))
+        else:
+            updated_choices.append(choice)
+
+    return chunk.model_copy(update={"choices": updated_choices})
+
+
+def _ends_with_partial_identifier(text: str) -> bool:
+    """Return True if text ends in an incomplete file identifier."""
+    match = re.search(r"(?:<\|)?file-[A-Za-z0-9_-]*$", text)
+    if not match:
+        return False
+
+    token = match.group()
+    enclosed = token.startswith("<|")
+    if enclosed and not token.endswith("|>"):
+        return True
+
+    if enclosed:
+        core = token[2:-2] if token.endswith("|>") else token[2:]
+    else:
+        core = token
+
+    suffix = core[len("file-"):]
+    if len(suffix) < 16:
+        return True
+    if not re.fullmatch(r"[A-Za-z0-9_-]+", suffix):
+        return True
+
+    return False
+
+
+def _has_safe_boundary(text: str) -> bool:
+    if not text:
+        return False
+
+    last_char = text[-1]
+    if last_char.isspace():
+        return True
+
+    return last_char in ".,?!;:)]}>\"'"
+
+
+def _coalesce_streaming_chunks(chunks: list[Any]) -> list[Any]:
+    """Merge adjacent text chunks to avoid breaking identifiers across boundaries."""
+    result: list[Any] = []
+    pending_chunk: Any | None = None
+    pending_content = ""
+
+    for chunk in chunks:
+        content, has_structured = _chunk_text_content(chunk)
+
+        if content is None or has_structured:
+            if pending_chunk is not None:
+                result.append(_chunk_with_content(pending_chunk, pending_content))
+                pending_chunk = None
+                pending_content = ""
+
+            result.append(chunk)
+            continue
+
+        if pending_chunk is None:
+            pending_chunk = chunk
+            pending_content = content
+        else:
+            pending_content += content
+
+        if (not _ends_with_partial_identifier(pending_content)) and _has_safe_boundary(pending_content):
+            result.append(_chunk_with_content(pending_chunk, pending_content))
+            pending_chunk = None
+            pending_content = ""
+
+    if pending_chunk is not None:
+        result.append(_chunk_with_content(pending_chunk, pending_content))
+
+    return result
+
+
+def _denormalize_response(obj: Any, recorded_mapping: dict[str, str], current_mapping: dict[str, str]) -> Any:
+    """Replace recorded IDs with current runtime IDs in a response object.
+
+    Args:
+        obj: The response object to denormalize
+        recorded_mapping: normalized_id -> recorded_original_id (from recording file)
+        current_mapping: normalized_id -> current_original_id (from current runtime)
+
+    Returns:
+        The response with all recorded IDs replaced with current IDs
+    """
+    import re
+
+    # Build reverse mapping: recorded_original_id -> current_original_id
+    # via the normalized intermediary
+    id_translation = {}
+    for normalized_id, recorded_id in recorded_mapping.items():
+        if normalized_id in current_mapping:
+            current_id = current_mapping[normalized_id]
+            id_translation[recorded_id] = current_id
+
+    if isinstance(obj, dict):
+        result = {}
+        for k, v in obj.items():
+            # Translate document_id fields
+            if k == "document_id" and isinstance(v, str):
+                result[k] = id_translation.get(v, v)
+            # Translate vector database/store IDs
+            elif k in ("vector_db_id", "vector_store_id", "bank_id") and isinstance(v, str):
+                # Replace any recorded IDs in the value
+                translated = v
+                for recorded_id, current_id in id_translation.items():
+                    translated = translated.replace(recorded_id, current_id)
+                result[k] = translated
+            else:
+                result[k] = _denormalize_response(v, recorded_mapping, current_mapping)
+        return result
+    elif isinstance(obj, list):
+        return [_denormalize_response(item, recorded_mapping, current_mapping) for item in obj]
+    elif hasattr(obj, "model_dump"):
+        # Handle Pydantic/BaseModel instances by denormalizing their dict form
+        data = obj.model_dump(mode="python")
+        denormalized = _denormalize_response(data, recorded_mapping, current_mapping)
+
+        cls = obj.__class__
+        try:
+            return cls.model_validate(denormalized)
+        except Exception:
+            try:
+                return cls.model_construct(**denormalized)
+            except Exception:
+                return denormalized
+    elif isinstance(obj, str):
+        # Replace file-<uuid> patterns in strings (like in text content and citations)
+        translated = obj
+        for recorded_id, current_id in id_translation.items():
+            # Handle both bare file IDs and citation format <|file-...|>
+            translated = translated.replace(recorded_id, current_id)
+        return translated
+    else:
+        return obj
+
+
 def normalize_inference_request(method: str, url: str, headers: dict[str, Any], body: dict[str, Any]) -> str:
     """Create a normalized hash of the request for consistent matching.
 
@@ -438,6 +627,7 @@ class ResponseStorage:
                     "test_id": _test_context.get(),
                     "request": request,
                     "response": serialized_response,
+                    "id_normalization_mapping": _get_current_test_id_mappings(),
                 },
                 f,
                 indent=2,
@@ -569,7 +759,7 @@ async def _patched_tool_invoke_method(
     original_method, provider_name: str, self, tool_name: str, kwargs: dict[str, Any]
 ):
     """Patched version of tool runtime invoke_tool method for recording/replay."""
-    global _current_mode, _current_storage
+    global _current_mode, _current_storage, _id_normalizers
 
     if _current_mode == APIRecordingMode.LIVE or _current_storage is None:
         # Normal operation
@@ -618,7 +808,7 @@ async def _patched_tool_invoke_method(
 
 
 async def _patched_inference_method(original_method, self, client_type, endpoint, *args, **kwargs):
-    global _current_mode, _current_storage
+    global _current_mode, _current_storage, _id_normalizers
 
     mode = _current_mode
     storage = _current_storage
@@ -671,6 +861,48 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
             if recording:
                 response_body = recording["response"]["body"]
 
+                if recording["response"].get("is_streaming", False) and isinstance(response_body, list):
+                    response_body = _coalesce_streaming_chunks(response_body)
+
+                recording["response"]["body"] = response_body
+
+                # Denormalize the response: replace recorded IDs with current runtime IDs
+                recorded_mapping = recording.get("id_normalization_mapping", {})
+                current_mapping = _get_current_test_id_mappings()
+
+                if recorded_mapping or current_mapping:
+                    import sys
+                    print(f"\n=== DENORM DEBUG ===", file=sys.stderr)
+                    print(f"Recorded mapping: {recorded_mapping}", file=sys.stderr)
+                    print(f"Current mapping: {current_mapping}", file=sys.stderr)
+                    print(f"_id_normalizers before: {_id_normalizers.get(_test_context.get(), {})}", file=sys.stderr)
+
+                    response_body = _denormalize_response(response_body, recorded_mapping, current_mapping)
+
+                    # Update _id_normalizers to register the current IDs with their normalized values
+                    # This ensures that if these IDs appear in future requests, they get the same
+                    # normalized value as they had in the recording
+                    test_id = _test_context.get()
+                    if test_id and recorded_mapping:
+                        for normalized_id, recorded_id in recorded_mapping.items():
+                            if normalized_id in current_mapping:
+                                current_id = current_mapping[normalized_id]
+                                # Extract ID type from normalized_id (e.g., "file-1" -> "file")
+                                id_type = normalized_id.rsplit("-", 1)[0]
+
+                                # Ensure structures exist
+                                if test_id not in _id_normalizers:
+                                    _id_normalizers[test_id] = {}
+                                if id_type not in _id_normalizers[test_id]:
+                                    _id_normalizers[test_id][id_type] = {}
+
+                                # Register: current_id -> normalized_id
+                                _id_normalizers[test_id][id_type][current_id] = normalized_id
+                                print(f"Registered {current_id} -> {normalized_id}", file=sys.stderr)
+
+                    print(f"_id_normalizers after: {_id_normalizers.get(_test_context.get(), {})}", file=sys.stderr)
+                    print(f"=== END DENORM DEBUG ===\n", file=sys.stderr)
+
                 if recording["response"].get("is_streaming", False):
 
                     async def replay_stream():
@@ -714,9 +946,11 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
             if is_streaming:
                 # For streaming responses, we need to collect all chunks immediately before yielding
                 # This ensures the recording is saved even if the generator isn't fully consumed
-                chunks = []
+                raw_chunks: list[Any] = []
                 async for chunk in response:
-                    chunks.append(chunk)
+                    raw_chunks.append(chunk)
+
+                chunks = _coalesce_streaming_chunks(raw_chunks)
 
                 # Store the recording immediately
                 response_data = {"body": chunks, "is_streaming": True}
diff --git a/tests/integration/responses/recordings/0413e144be29fe1ffbd0e6511b9ec1e76840d7147993a26057f07246640a7cea.json b/tests/integration/responses/recordings/0413e144be29fe1ffbd0e6511b9ec1e76840d7147993a26057f07246640a7cea.json
new file mode 100644
index 000000000..b9af12b96
--- /dev/null
+++ b/tests/integration/responses/recordings/0413e144be29fe1ffbd0e6511b9ec1e76840d7147993a26057f07246640a7cea.json
@@ -0,0 +1,1578 @@
+{
+  "test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536]",
+  "request": {
+    "method": "POST",
+    "url": "https://api.openai.com/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "text-embedding-3-small",
+      "input": [
+        "Llama 4 Maverick model architecture details"
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "text-embedding-3-small"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+      "__data__": {
+        "data": [
+          {
+            "embedding": [
+              0.028073093,
+              -0.0048557497,
+              0.011718783,
+              -0.039163698,
+              -0.01782006,
+              0.030441398,
+              -0.012592457,
+              -0.002559648,
+              -0.0072601577,
+              -0.029430537,
+              -0.04049226,
+              -0.026383508,
+              0.0018213574,
+              -0.024015201,
+              -0.049387846,
+              -0.019264149,
+              0.026426831,
+              -0.023220953,
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+              0.03096127,
+              -0.022744404,
+              0.015798334,
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+              0.00615904,
+              0.020376097,
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+              0.024101848,
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+              0.0181522,
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diff --git a/tests/integration/responses/recordings/0f79646fcf8aaf90bd27658c7c038a495eba448dfde29b58d887281ab0ffe972.json b/tests/integration/responses/recordings/0f79646fcf8aaf90bd27658c7c038a495eba448dfde29b58d887281ab0ffe972.json
new file mode 100644
index 000000000..5cf5a828e
--- /dev/null
+++ b/tests/integration/responses/recordings/0f79646fcf8aaf90bd27658c7c038a495eba448dfde29b58d887281ab0ffe972.json
@@ -0,0 +1,629 @@
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new file mode 100644
index 000000000..6cbbb9b2c
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diff --git a/tests/integration/responses/recordings/1b0c005eb4b93c22ec7211904cab5145194ec832d8e436fee11599c1c2d80fd8.json b/tests/integration/responses/recordings/1b0c005eb4b93c22ec7211904cab5145194ec832d8e436fee11599c1c2d80fd8.json
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+              "type": "text",
+              "text": "[1] document_id: file-d7cee10212814cfcb75cc091eee11688, score: 2.5781234969335522, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-d7cee10212814cfcb75cc091eee11688', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-d7cee10212814cfcb75cc091eee11688|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
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diff --git a/tests/integration/responses/recordings/39e480e3e6716f7e2e41be654eae0f0250ac22a51707a110eabec377358f6b8f.json b/tests/integration/responses/recordings/39e480e3e6716f7e2e41be654eae0f0250ac22a51707a110eabec377358f6b8f.json
new file mode 100644
index 000000000..c2b46f420
--- /dev/null
+++ b/tests/integration/responses/recordings/39e480e3e6716f7e2e41be654eae0f0250ac22a51707a110eabec377358f6b8f.json
@@ -0,0 +1,1578 @@
+{
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+  "request": {
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+    "headers": {},
+    "body": {
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+      "input": [
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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "text-embedding-3-small"
+  },
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index 000000000..957ef40aa
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diff --git a/tests/integration/responses/recordings/89162a717e49e05ac08013f01ad908e76817c51903b5c476b2109f7763820499.json b/tests/integration/responses/recordings/89162a717e49e05ac08013f01ad908e76817c51903b5c476b2109f7763820499.json
new file mode 100644
index 000000000..0c003e009
--- /dev/null
+++ b/tests/integration/responses/recordings/89162a717e49e05ac08013f01ad908e76817c51903b5c476b2109f7763820499.json
@@ -0,0 +1,629 @@
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new file mode 100644
index 000000000..419d9ebee
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diff --git a/tests/integration/responses/recordings/b7c02d59e1fa664de53afecc490f0bb6d7889a12817d450089650c31351a05d0.json b/tests/integration/responses/recordings/b7c02d59e1fa664de53afecc490f0bb6d7889a12817d450089650c31351a05d0.json
new file mode 100644
index 000000000..aa550551f
--- /dev/null
+++ b/tests/integration/responses/recordings/b7c02d59e1fa664de53afecc490f0bb6d7889a12817d450089650c31351a05d0.json
@@ -0,0 +1,1301 @@
+{
+  "test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536]",
+  "request": {
+    "method": "POST",
+    "url": "https://api.openai.com/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "gpt-4o",
+      "messages": [
+        {
+          "role": "user",
+          "content": "How many experts does the Llama 4 Maverick model have?"
+        },
+        {
+          "role": "assistant",
+          "content": "",
+          "tool_calls": [
+            {
+              "index": 0,
+              "id": "call_aPe1vS1v5bIwPgl789D5bfmW",
+              "type": "function",
+              "function": {
+                "name": "knowledge_search",
+                "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
+              }
+            }
+          ]
+        },
+        {
+          "role": "tool",
+          "tool_call_id": "call_aPe1vS1v5bIwPgl789D5bfmW",
+          "content": [
+            {
+              "type": "text",
+              "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
+            },
+            {
+              "type": "text",
+              "text": "[1] document_id: file-5efa8a5a4b414ce98726c4e133a18747, score: 2.5781234969335522, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-5efa8a5a4b414ce98726c4e133a18747', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-5efa8a5a4b414ce98726c4e133a18747|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
+            },
+            {
+              "type": "text",
+              "text": "END of knowledge_search tool results.\n"
+            },
+            {
+              "type": "text",
+              "text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
+            }
+          ]
+        }
+      ],
+      "stream": true,
+      "tools": [
+        {
+          "type": "function",
+          "function": {
+            "name": "knowledge_search",
+            "description": "Search for information in a database.",
+            "parameters": {
+              "type": "object",
+              "properties": {
+                "query": {
+                  "type": "string",
+                  "description": "The query to search for. Can be a natural language sentence or keywords."
+                }
+              },
+              "required": [
+                "query"
+              ]
+            }
+          }
+        }
+      ]
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "gpt-4o"
+  },
+  "response": {
+    "body": [
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+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null
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+          ],
+          "created": 0,
+          "model": "gpt-4o-2024-08-06",
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+          "service_tier": "default",
+          "system_fingerprint": "fp_f64f290af2",
+          "usage": null,
+          "obfuscation": "O03slw1rkPQ"
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "rec-b7c02d59e1fa",
+          "choices": [
+            {
+              "delta": {
+                "content": "The",
+                "function_call": null,
+                "refusal": null,
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+              "finish_reason": null,
+              "index": 0,
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+          "created": 0,
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+          "service_tier": "default",
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+        }
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+        "__data__": {
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+          "choices": [
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+        }
+      },
+      {
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+        "__data__": {
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+          "choices": [
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+          "obfuscation": "wRB9tjdWt"
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diff --git a/tests/integration/responses/recordings/da9a93d127e1b0f7cc09445fdb876987dc481bd94343d6f1694b6d9e4f9110d0.json b/tests/integration/responses/recordings/da9a93d127e1b0f7cc09445fdb876987dc481bd94343d6f1694b6d9e4f9110d0.json
new file mode 100644
index 000000000..376ef812e
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new file mode 100644
index 000000000..8eb5c3ae1
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diff --git a/tests/integration/responses/recordings/e7777f594336f5ad72b1c9b12db316808a248f008a07ea177c5c88c746004e04.json b/tests/integration/responses/recordings/e7777f594336f5ad72b1c9b12db316808a248f008a07ea177c5c88c746004e04.json
new file mode 100644
index 000000000..f57b20355
--- /dev/null
+++ b/tests/integration/responses/recordings/e7777f594336f5ad72b1c9b12db316808a248f008a07ea177c5c88c746004e04.json
@@ -0,0 +1,639 @@
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+  "request": {
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+    "headers": {},
+    "body": {
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+          "content": "",
+          "tool_calls": [
+            {
+              "index": 0,
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+              "type": "function",
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+                "name": "knowledge_search",
+                "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
+              }
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+        {
+          "role": "tool",
+          "tool_call_id": "call_aPe1vS1v5bIwPgl789D5bfmW",
+          "content": [
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+              "type": "text",
+              "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
+            },
+            {
+              "type": "text",
+              "text": "[1] document_id: file-5efa8a5a4b414ce98726c4e133a18747, score: 2.5781234969335522, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-5efa8a5a4b414ce98726c4e133a18747', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-5efa8a5a4b414ce98726c4e133a18747|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
+            },
+            {
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+              "text": "END of knowledge_search tool results.\n"
+            },
+            {
+              "type": "text",
+              "text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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