diff --git a/docs/docs/expression_language/why.ipynb b/docs/docs/expression_language/why.ipynb
index 3c0eaf0e57539..9ee71c4e77108 100644
--- a/docs/docs/expression_language/why.ipynb
+++ b/docs/docs/expression_language/why.ipynb
@@ -1007,7 +1007,7 @@
     "from langchain_openai import OpenAI\n",
     "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough, ConfigurableField\n",
     "\n",
     "os.environ[\"LANGCHAIN_API_KEY\"] = \"...\"\n",
     "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
diff --git a/libs/community/langchain_community/cache.py b/libs/community/langchain_community/cache.py
index 14af98fdfb7fc..0ad635092e8ad 100644
--- a/libs/community/langchain_community/cache.py
+++ b/libs/community/langchain_community/cache.py
@@ -56,7 +56,7 @@
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models.llms import LLM, get_prompts
 from langchain_core.load.dump import dumps
-from langchain_core.load.load import _loads_suppress_warning
+from langchain_core.load.load import loads
 from langchain_core.outputs import ChatGeneration, Generation
 from langchain_core.utils import get_from_env
 
@@ -149,10 +149,7 @@ def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
         RETURN_VAL_TYPE: A list of generations.
     """
     try:
-        generations = [
-            _loads_suppress_warning(_item_str)
-            for _item_str in json.loads(generations_str)
-        ]
+        generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
         return generations
     except (json.JSONDecodeError, TypeError):
         # deferring the (soft) handling to after the legacy-format attempt
@@ -227,7 +224,7 @@ def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
             rows = session.execute(stmt).fetchall()
             if rows:
                 try:
-                    return [_loads_suppress_warning(row[0]) for row in rows]
+                    return [loads(row[0]) for row in rows]
                 except Exception:
                     logger.warning(
                         "Retrieving a cache value that could not be deserialized "
@@ -398,7 +395,7 @@ def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
         if results:
             for _, text in results.items():
                 try:
-                    generations.append(_loads_suppress_warning(text))
+                    generations.append(loads(text))
                 except Exception:
                     logger.warning(
                         "Retrieving a cache value that could not be deserialized "
@@ -538,9 +535,7 @@ def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
         if results:
             for document in results:
                 try:
-                    generations.extend(
-                        _loads_suppress_warning(document.metadata["return_val"])
-                    )
+                    generations.extend(loads(document.metadata["return_val"]))
                 except Exception:
                     logger.warning(
                         "Retrieving a cache value that could not be deserialized "
@@ -1190,7 +1185,7 @@ def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
         """Look up based on prompt and llm_string."""
         rows = self._search_rows(prompt, llm_string)
         if rows:
-            return [_loads_suppress_warning(row[0]) for row in rows]
+            return [loads(row[0]) for row in rows]
         return None
 
     def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
diff --git a/libs/community/langchain_community/chat_loaders/langsmith.py b/libs/community/langchain_community/chat_loaders/langsmith.py
index 03e5e9c75705e..4e769283dd2e4 100644
--- a/libs/community/langchain_community/chat_loaders/langsmith.py
+++ b/libs/community/langchain_community/chat_loaders/langsmith.py
@@ -4,7 +4,7 @@
 from typing import TYPE_CHECKING, Dict, Iterable, Iterator, List, Optional, Union, cast
 
 from langchain_core.chat_sessions import ChatSession
-from langchain_core.load.load import _load_suppress_warning
+from langchain_core.load.load import load
 
 from langchain_community.chat_loaders.base import BaseChatLoader
 
@@ -66,10 +66,8 @@ def _get_messages_from_llm_run(llm_run: "Run") -> ChatSession:
             raise ValueError(f"Run has no 'messages' inputs. Got {llm_run.inputs}")
         if not llm_run.outputs:
             raise ValueError("Cannot convert pending run")
-        messages = _load_suppress_warning(llm_run.inputs)["messages"]
-        message_chunk = _load_suppress_warning(llm_run.outputs)["generations"][0][
-            "message"
-        ]
+        messages = load(llm_run.inputs)["messages"]
+        message_chunk = load(llm_run.outputs)["generations"][0]["message"]
         return ChatSession(messages=messages + [message_chunk])
 
     @staticmethod
diff --git a/libs/community/langchain_community/chat_models/anyscale.py b/libs/community/langchain_community/chat_models/anyscale.py
index 2f734aa9b73a1..40d36aa187dcb 100644
--- a/libs/community/langchain_community/chat_models/anyscale.py
+++ b/libs/community/langchain_community/chat_models/anyscale.py
@@ -4,10 +4,9 @@
 import logging
 import os
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Optional, Set
+from typing import TYPE_CHECKING, Dict, Optional, Set
 
 import requests
-from langchain_core._api.deprecation import suppress_langchain_deprecation_warning
 from langchain_core.messages import BaseMessage
 from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
@@ -73,11 +72,6 @@ def is_lc_serializable(cls) -> bool:
     available_models: Optional[Set[str]] = None
     """Available models from Anyscale API."""
 
-    def __init__(self, *kwargs: Any) -> None:
-        # bypass deprecation warning for ChatOpenAI
-        with suppress_langchain_deprecation_warning():
-            super().__init__(*kwargs)
-
     @staticmethod
     def get_available_models(
         anyscale_api_key: Optional[str] = None,
diff --git a/libs/community/langchain_community/chat_models/everlyai.py b/libs/community/langchain_community/chat_models/everlyai.py
index 5d3999308af0f..dca315d18995d 100644
--- a/libs/community/langchain_community/chat_models/everlyai.py
+++ b/libs/community/langchain_community/chat_models/everlyai.py
@@ -3,9 +3,8 @@
 
 import logging
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Optional, Set
+from typing import TYPE_CHECKING, Dict, Optional, Set
 
-from langchain_core._api.deprecation import suppress_langchain_deprecation_warning
 from langchain_core.messages import BaseMessage
 from langchain_core.pydantic_v1 import Field, root_validator
 from langchain_core.utils import get_from_dict_or_env
@@ -65,11 +64,6 @@ def is_lc_serializable(cls) -> bool:
     available_models: Optional[Set[str]] = None
     """Available models from EverlyAI API."""
 
-    def __init__(self, *kwargs: Any) -> None:
-        # bypass deprecation warning for ChatOpenAI
-        with suppress_langchain_deprecation_warning():
-            super().__init__(*kwargs)
-
     @staticmethod
     def get_available_models() -> Set[str]:
         """Get available models from EverlyAI API."""
diff --git a/libs/community/langchain_community/chat_models/promptlayer_openai.py b/libs/community/langchain_community/chat_models/promptlayer_openai.py
index 0ff2e9b439386..551655e4c7dc6 100644
--- a/libs/community/langchain_community/chat_models/promptlayer_openai.py
+++ b/libs/community/langchain_community/chat_models/promptlayer_openai.py
@@ -2,7 +2,6 @@
 import datetime
 from typing import Any, Dict, List, Optional
 
-from langchain_core._api.deprecation import suppress_langchain_deprecation_warning
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -40,11 +39,6 @@ class PromptLayerChatOpenAI(ChatOpenAI):
     pl_tags: Optional[List[str]]
     return_pl_id: Optional[bool] = False
 
-    def __init__(self, *kwargs: Any) -> None:
-        # bypass deprecation warning for ChatOpenAI
-        with suppress_langchain_deprecation_warning():
-            super().__init__(*kwargs)
-
     @classmethod
     def is_lc_serializable(cls) -> bool:
         return False
diff --git a/libs/community/langchain_community/llms/promptlayer_openai.py b/libs/community/langchain_community/llms/promptlayer_openai.py
index 7f3396c8addff..cb904476e8ba3 100644
--- a/libs/community/langchain_community/llms/promptlayer_openai.py
+++ b/libs/community/langchain_community/llms/promptlayer_openai.py
@@ -1,7 +1,6 @@
 import datetime
 from typing import Any, List, Optional
 
-from langchain_core._api.deprecation import suppress_langchain_deprecation_warning
 from langchain_core.callbacks import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -38,11 +37,6 @@ class PromptLayerOpenAI(OpenAI):
     pl_tags: Optional[List[str]]
     return_pl_id: Optional[bool] = False
 
-    def __init__(self, *kwargs: Any) -> None:
-        # bypass deprecation warning for ChatOpenAI
-        with suppress_langchain_deprecation_warning():
-            super().__init__(*kwargs)
-
     @classmethod
     def is_lc_serializable(cls) -> bool:
         return False
diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock
index e4f801d984cee..3a8956f5d6dfe 100644
--- a/libs/community/poetry.lock
+++ b/libs/community/poetry.lock
@@ -3885,7 +3885,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.1.7"
+version = "0.1.8"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.8.1,<4.0"
@@ -9172,4 +9172,4 @@ extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "as
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8.1,<4.0"
-content-hash = "454e721d2b68b5769f70bf8deb0d43285d270e2ca8ae99dc72c773dfa827835b"
+content-hash = "a3765004062afc80420d49d782e3e023aec1707bf35ae2f808d88a1cac53b694"
diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml
index ec0f6088c3a11..7a2d51147e3de 100644
--- a/libs/community/pyproject.toml
+++ b/libs/community/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-community"
-version = "0.0.9"
+version = "0.0.10"
 description = "Community contributed LangChain integrations."
 authors = []
 license = "MIT"
@@ -9,7 +9,7 @@ repository = "https://github.com/langchain-ai/langchain"
 
 [tool.poetry.dependencies]
 python = ">=3.8.1,<4.0"
-langchain-core = ">=0.1.7,<0.2"
+langchain-core = ">=0.1.8,<0.2"
 SQLAlchemy = ">=1.4,<3"
 requests = "^2"
 PyYAML = ">=5.3"
diff --git a/libs/core/langchain_core/_api/beta_decorator.py b/libs/core/langchain_core/_api/beta_decorator.py
index b82c2ee4a977a..9f4dd5b18191e 100644
--- a/libs/core/langchain_core/_api/beta_decorator.py
+++ b/libs/core/langchain_core/_api/beta_decorator.py
@@ -15,6 +15,8 @@
 import warnings
 from typing import Any, Callable, Generator, Type, TypeVar
 
+from langchain_core._api.internal import is_caller_internal
+
 
 class LangChainBetaWarning(DeprecationWarning):
     """A class for issuing beta warnings for LangChain users."""
@@ -78,6 +80,34 @@ def beta(
         _addendum: str = addendum,
     ) -> T:
         """Implementation of the decorator returned by `beta`."""
+
+        def emit_warning() -> None:
+            """Emit the warning."""
+            warn_beta(
+                message=_message,
+                name=_name,
+                obj_type=_obj_type,
+                addendum=_addendum,
+            )
+
+        warned = False
+
+        def warning_emitting_wrapper(*args: Any, **kwargs: Any) -> Any:
+            """Wrapper for the original wrapped callable that emits a warning.
+
+            Args:
+                *args: The positional arguments to the function.
+                **kwargs: The keyword arguments to the function.
+
+            Returns:
+                The return value of the function being wrapped.
+            """
+            nonlocal warned
+            if not warned and not is_caller_internal():
+                warned = True
+                emit_warning()
+            return wrapped(*args, **kwargs)
+
         if isinstance(obj, type):
             if not _obj_type:
                 _obj_type = "class"
@@ -85,14 +115,25 @@ def beta(
             _name = _name or obj.__name__
             old_doc = obj.__doc__
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
+            def finalize(_: Any, new_doc: str) -> T:
                 """Finalize the annotation of a class."""
                 try:
                     obj.__doc__ = new_doc
                 except AttributeError:  # Can't set on some extension objects.
                     pass
+
+                def warn_if_direct_instance(
+                    self: Any, *args: Any, **kwargs: Any
+                ) -> Any:
+                    """Warn that the class is in beta."""
+                    nonlocal warned
+                    if not warned and type(self) is obj and not is_caller_internal():
+                        warned = True
+                        emit_warning()
+                    return wrapped(self, *args, **kwargs)
+
                 obj.__init__ = functools.wraps(obj.__init__)(  # type: ignore[misc]
-                    wrapper
+                    warn_if_direct_instance
                 )
                 return obj
 
@@ -155,28 +196,6 @@ def finalize(  # type: ignore
                 wrapper.__doc__ = new_doc
                 return wrapper
 
-        def emit_warning() -> None:
-            """Emit the warning."""
-            warn_beta(
-                message=_message,
-                name=_name,
-                obj_type=_obj_type,
-                addendum=_addendum,
-            )
-
-        def warning_emitting_wrapper(*args: Any, **kwargs: Any) -> Any:
-            """Wrapper for the original wrapped callable that emits a warning.
-
-            Args:
-                *args: The positional arguments to the function.
-                **kwargs: The keyword arguments to the function.
-
-            Returns:
-                The return value of the function being wrapped.
-            """
-            emit_warning()
-            return wrapped(*args, **kwargs)
-
         old_doc = inspect.cleandoc(old_doc or "").strip("\n")
 
         if not old_doc:
diff --git a/libs/core/langchain_core/_api/deprecation.py b/libs/core/langchain_core/_api/deprecation.py
index 6919504351df6..eee686a1811cf 100644
--- a/libs/core/langchain_core/_api/deprecation.py
+++ b/libs/core/langchain_core/_api/deprecation.py
@@ -16,6 +16,8 @@
 import warnings
 from typing import Any, Callable, Generator, Type, TypeVar
 
+from langchain_core._api.internal import is_caller_internal
+
 
 class LangChainDeprecationWarning(DeprecationWarning):
     """A class for issuing deprecation warnings for LangChain users."""
@@ -107,6 +109,38 @@ def deprecate(
         _addendum: str = addendum,
     ) -> T:
         """Implementation of the decorator returned by `deprecated`."""
+
+        def emit_warning() -> None:
+            """Emit the warning."""
+            warn_deprecated(
+                since,
+                message=_message,
+                name=_name,
+                alternative=_alternative,
+                pending=_pending,
+                obj_type=_obj_type,
+                addendum=_addendum,
+                removal=removal,
+            )
+
+        warned = False
+
+        def warning_emitting_wrapper(*args: Any, **kwargs: Any) -> Any:
+            """Wrapper for the original wrapped callable that emits a warning.
+
+            Args:
+                *args: The positional arguments to the function.
+                **kwargs: The keyword arguments to the function.
+
+            Returns:
+                The return value of the function being wrapped.
+            """
+            nonlocal warned
+            if not warned and not is_caller_internal():
+                warned = True
+                emit_warning()
+            return wrapped(*args, **kwargs)
+
         if isinstance(obj, type):
             if not _obj_type:
                 _obj_type = "class"
@@ -114,14 +148,25 @@ def deprecate(
             _name = _name or obj.__name__
             old_doc = obj.__doc__
 
-            def finalize(wrapper: Callable[..., Any], new_doc: str) -> T:
+            def finalize(_: Any, new_doc: str) -> T:
                 """Finalize the deprecation of a class."""
                 try:
                     obj.__doc__ = new_doc
                 except AttributeError:  # Can't set on some extension objects.
                     pass
+
+                def warn_if_direct_instance(
+                    self: Any, *args: Any, **kwargs: Any
+                ) -> Any:
+                    """Warn that the class is in beta."""
+                    nonlocal warned
+                    if not warned and type(self) is obj and not is_caller_internal():
+                        warned = True
+                        emit_warning()
+                    return wrapped(self, *args, **kwargs)
+
                 obj.__init__ = functools.wraps(obj.__init__)(  # type: ignore[misc]
-                    wrapper
+                    warn_if_direct_instance
                 )
                 return obj
 
@@ -184,32 +229,6 @@ def finalize(  # type: ignore
                 wrapper.__doc__ = new_doc
                 return wrapper
 
-        def emit_warning() -> None:
-            """Emit the warning."""
-            warn_deprecated(
-                since,
-                message=_message,
-                name=_name,
-                alternative=_alternative,
-                pending=_pending,
-                obj_type=_obj_type,
-                addendum=_addendum,
-                removal=removal,
-            )
-
-        def warning_emitting_wrapper(*args: Any, **kwargs: Any) -> Any:
-            """Wrapper for the original wrapped callable that emits a warning.
-
-            Args:
-                *args: The positional arguments to the function.
-                **kwargs: The keyword arguments to the function.
-
-            Returns:
-                The return value of the function being wrapped.
-            """
-            emit_warning()
-            return wrapped(*args, **kwargs)
-
         old_doc = inspect.cleandoc(old_doc or "").strip("\n")
 
         if not old_doc:
diff --git a/libs/core/langchain_core/_api/internal.py b/libs/core/langchain_core/_api/internal.py
new file mode 100644
index 0000000000000..0dca40ad668a8
--- /dev/null
+++ b/libs/core/langchain_core/_api/internal.py
@@ -0,0 +1,23 @@
+import inspect
+
+
+def is_caller_internal(depth: int = 2) -> bool:
+    """Return whether the caller at `depth` of this function is internal."""
+    try:
+        frame = inspect.currentframe()
+    except AttributeError:
+        return False
+    if frame is None:
+        return False
+    try:
+        for _ in range(depth):
+            frame = frame.f_back
+            if frame is None:
+                return False
+        caller_module = inspect.getmodule(frame)
+        if caller_module is None:
+            return False
+        caller_module_name = caller_module.__name__
+        return caller_module_name.startswith("langchain")
+    finally:
+        del frame
diff --git a/libs/core/langchain_core/load/load.py b/libs/core/langchain_core/load/load.py
index 854032d9666ff..2b8cd4bb161ed 100644
--- a/libs/core/langchain_core/load/load.py
+++ b/libs/core/langchain_core/load/load.py
@@ -3,7 +3,7 @@
 import os
 from typing import Any, Dict, List, Optional
 
-from langchain_core._api import beta, suppress_langchain_beta_warning
+from langchain_core._api import beta
 from langchain_core.load.mapping import (
     OLD_PROMPT_TEMPLATE_FORMATS,
     SERIALIZABLE_MAPPING,
@@ -125,16 +125,6 @@ def loads(
     return json.loads(text, object_hook=Reviver(secrets_map, valid_namespaces))
 
 
-def _loads_suppress_warning(
-    text: str,
-    *,
-    secrets_map: Optional[Dict[str, str]] = None,
-    valid_namespaces: Optional[List[str]] = None,
-) -> Any:
-    with suppress_langchain_beta_warning():
-        return loads(text, secrets_map=secrets_map, valid_namespaces=valid_namespaces)
-
-
 @beta()
 def load(
     obj: Any,
@@ -166,13 +156,3 @@ def _load(obj: Any) -> Any:
         return obj
 
     return _load(obj)
-
-
-def _load_suppress_warning(
-    obj: Any,
-    *,
-    secrets_map: Optional[Dict[str, str]] = None,
-    valid_namespaces: Optional[List[str]] = None,
-) -> Any:
-    with suppress_langchain_beta_warning():
-        return load(obj, secrets_map=secrets_map, valid_namespaces=valid_namespaces)
diff --git a/libs/core/langchain_core/runnables/base.py b/libs/core/langchain_core/runnables/base.py
index 8243b9df2e7eb..0bb5ddb11b953 100644
--- a/libs/core/langchain_core/runnables/base.py
+++ b/libs/core/langchain_core/runnables/base.py
@@ -1277,6 +1277,8 @@ async def _atransform_stream_with_config(
         """Helper method to transform an Async Iterator of Input values into an Async
         Iterator of Output values, with callbacks.
         Use this to implement `astream()` or `atransform()` in Runnable subclasses."""
+        from langchain_core.tracers.log_stream import LogStreamCallbackHandler
+
         # tee the input so we can iterate over it twice
         input_for_tracing, input_for_transform = atee(input, 2)
         # Start the input iterator to ensure the input runnable starts before this one
@@ -1302,6 +1304,16 @@ async def _atransform_stream_with_config(
             context = copy_context()
             context.run(var_child_runnable_config.set, child_config)
             iterator = context.run(transformer, input_for_transform, **kwargs)  # type: ignore[arg-type]
+            if stream_log := next(
+                (
+                    h
+                    for h in run_manager.handlers
+                    if isinstance(h, LogStreamCallbackHandler)
+                ),
+                None,
+            ):
+                # populates streamed_output in astream_log() output if needed
+                iterator = stream_log.tap_output_aiter(run_manager.run_id, iterator)
             try:
                 while True:
                     if accepts_context(asyncio.create_task):
@@ -2733,6 +2745,7 @@ def __init__(
                 ],
             ]
         ] = None,
+        name: Optional[str] = None,
     ) -> None:
         """Create a RunnableLambda from a callable, and async callable or both.
 
@@ -2766,7 +2779,9 @@ def __init__(
             )
 
         try:
-            if func_for_name.__name__ != "<lambda>":
+            if name is not None:
+                self.name = name
+            elif func_for_name.__name__ != "<lambda>":
                 self.name = func_for_name.__name__
         except AttributeError:
             pass
@@ -3046,17 +3061,7 @@ async def f(*args, **kwargs):  # type: ignore[no-untyped-def]
     def _config(
         self, config: Optional[RunnableConfig], callable: Callable[..., Any]
     ) -> RunnableConfig:
-        config = ensure_config(config)
-
-        if config.get("run_name") is None:
-            try:
-                run_name = callable.__name__
-            except AttributeError:
-                run_name = None
-            if run_name is not None:
-                return patch_config(config, run_name=run_name)
-
-        return config
+        return ensure_config(config)
 
     def invoke(
         self,
diff --git a/libs/core/langchain_core/runnables/history.py b/libs/core/langchain_core/runnables/history.py
index 0dd35da8f431d..9f6ceb36735a2 100644
--- a/libs/core/langchain_core/runnables/history.py
+++ b/libs/core/langchain_core/runnables/history.py
@@ -14,7 +14,7 @@
 )
 
 from langchain_core.chat_history import BaseChatMessageHistory
-from langchain_core.load.load import _load_suppress_warning
+from langchain_core.load.load import load
 from langchain_core.pydantic_v1 import BaseModel, create_model
 from langchain_core.runnables.base import Runnable, RunnableBindingBase, RunnableLambda
 from langchain_core.runnables.config import run_in_executor
@@ -337,7 +337,7 @@ def _exit_history(self, run: Run, config: RunnableConfig) -> None:
         hist = config["configurable"]["message_history"]
 
         # Get the input messages
-        inputs = _load_suppress_warning(run.inputs)
+        inputs = load(run.inputs)
         input_val = inputs[self.input_messages_key or "input"]
         input_messages = self._get_input_messages(input_val)
 
@@ -348,7 +348,7 @@ def _exit_history(self, run: Run, config: RunnableConfig) -> None:
             input_messages = input_messages[len(historic_messages) :]
 
         # Get the output messages
-        output_val = _load_suppress_warning(run.outputs)
+        output_val = load(run.outputs)
         output_messages = self._get_output_messages(output_val)
 
         for m in input_messages + output_messages:
diff --git a/libs/core/langchain_core/tracers/log_stream.py b/libs/core/langchain_core/tracers/log_stream.py
index 6606dc428ff4c..9287ce8875ef2 100644
--- a/libs/core/langchain_core/tracers/log_stream.py
+++ b/libs/core/langchain_core/tracers/log_stream.py
@@ -12,6 +12,7 @@
     Optional,
     Sequence,
     TypedDict,
+    TypeVar,
     Union,
 )
 from uuid import UUID
@@ -19,7 +20,7 @@
 import jsonpatch  # type: ignore[import]
 from anyio import create_memory_object_stream
 
-from langchain_core.load.load import _load_suppress_warning
+from langchain_core.load.load import load
 from langchain_core.outputs import ChatGenerationChunk, GenerationChunk
 from langchain_core.tracers.base import BaseTracer
 from langchain_core.tracers.schemas import Run
@@ -128,6 +129,9 @@ def __repr__(self) -> str:
         return f"RunLog({pformat(self.state)})"
 
 
+T = TypeVar("T")
+
+
 class LogStreamCallbackHandler(BaseTracer):
     """A tracer that streams run logs to a stream."""
 
@@ -165,6 +169,28 @@ def __init__(
     def __aiter__(self) -> AsyncIterator[RunLogPatch]:
         return self.receive_stream.__aiter__()
 
+    async def tap_output_aiter(
+        self, run_id: UUID, output: AsyncIterator[T]
+    ) -> AsyncIterator[T]:
+        """Tap an output async iterator to stream its values to the log."""
+        async for chunk in output:
+            # root run is handled in .astream_log()
+            if run_id != self.root_id:
+                # if we can't find the run silently ignore
+                # eg. because this run wasn't included in the log
+                if key := self._key_map_by_run_id.get(run_id):
+                    self.send_stream.send_nowait(
+                        RunLogPatch(
+                            {
+                                "op": "add",
+                                "path": f"/logs/{key}/streamed_output/-",
+                                "value": chunk,
+                            }
+                        )
+                    )
+
+            yield chunk
+
     def include_run(self, run: Run) -> bool:
         if run.id == self.root_id:
             return False
@@ -267,7 +293,7 @@ def _on_run_update(self, run: Run) -> None:
                         "op": "add",
                         "path": f"/logs/{index}/final_output",
                         # to undo the dumpd done by some runnables / tracer / etc
-                        "value": _load_suppress_warning(run.outputs),
+                        "value": load(run.outputs),
                     },
                     {
                         "op": "add",
diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml
index 946e2812de72a..b93f939618c87 100644
--- a/libs/core/pyproject.toml
+++ b/libs/core/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-core"
-version = "0.1.7"
+version = "0.1.8"
 description = "Building applications with LLMs through composability"
 authors = []
 license = "MIT"
diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
index a6d1bdc02a181..8ff1fe98dbb91 100644
--- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
+++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr
@@ -472,7 +472,7 @@
 # ---
 # name: test_combining_sequences.3
   list([
-    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'runnables', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, {'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, 'name': None}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001'), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002'), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003'), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='<lambda>', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'runnables', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000004'), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000005'), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000006'), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='baz, qux')}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000007')], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'runnables', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, {'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, 'name': None}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001'), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002'), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='foo, bar')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003'), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'runnables', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000004'), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000005'), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'chat_model', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000006'), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': AIMessage(content='baz, qux')}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000007')], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
   ])
 # ---
 # name: test_each
@@ -1562,6 +1562,134 @@
     Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}], 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'runnables', 'base', 'RunnableLambda'], 'repr': 'RunnableLambda(afunc=passthrough)'}, 'name': None}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001'), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002'), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='passthrough', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain_core', 'runnables', 'base', 'RunnableLambda'], 'repr': 'RunnableLambda(afunc=passthrough)'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003')], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
   ])
 # ---
+# name: test_prompt_with_llm_parser
+  '''
+  {
+    "lc": 1,
+    "type": "constructor",
+    "id": [
+      "langchain",
+      "schema",
+      "runnable",
+      "RunnableSequence"
+    ],
+    "kwargs": {
+      "first": {
+        "lc": 1,
+        "type": "constructor",
+        "id": [
+          "langchain",
+          "prompts",
+          "chat",
+          "ChatPromptTemplate"
+        ],
+        "kwargs": {
+          "messages": [
+            {
+              "lc": 1,
+              "type": "constructor",
+              "id": [
+                "langchain",
+                "prompts",
+                "chat",
+                "SystemMessagePromptTemplate"
+              ],
+              "kwargs": {
+                "prompt": {
+                  "lc": 1,
+                  "type": "constructor",
+                  "id": [
+                    "langchain",
+                    "prompts",
+                    "prompt",
+                    "PromptTemplate"
+                  ],
+                  "kwargs": {
+                    "input_variables": [],
+                    "template": "You are a nice assistant.",
+                    "template_format": "f-string",
+                    "partial_variables": {}
+                  }
+                }
+              }
+            },
+            {
+              "lc": 1,
+              "type": "constructor",
+              "id": [
+                "langchain",
+                "prompts",
+                "chat",
+                "HumanMessagePromptTemplate"
+              ],
+              "kwargs": {
+                "prompt": {
+                  "lc": 1,
+                  "type": "constructor",
+                  "id": [
+                    "langchain",
+                    "prompts",
+                    "prompt",
+                    "PromptTemplate"
+                  ],
+                  "kwargs": {
+                    "input_variables": [
+                      "question"
+                    ],
+                    "template": "{question}",
+                    "template_format": "f-string",
+                    "partial_variables": {}
+                  }
+                }
+              }
+            }
+          ],
+          "input_variables": [
+            "question"
+          ]
+        }
+      },
+      "middle": [
+        {
+          "lc": 1,
+          "type": "not_implemented",
+          "id": [
+            "tests",
+            "unit_tests",
+            "fake",
+            "llm",
+            "FakeStreamingListLLM"
+          ],
+          "repr": "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])"
+        }
+      ],
+      "last": {
+        "lc": 1,
+        "type": "constructor",
+        "id": [
+          "langchain",
+          "output_parsers",
+          "list",
+          "CommaSeparatedListOutputParser"
+        ],
+        "kwargs": {}
+      },
+      "name": null
+    }
+  }
+  '''
+# ---
+# name: test_prompt_with_llm_parser.1
+  list([
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, 'name': None}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001'), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 1}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002'), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003')], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
+  ])
+# ---
+# name: test_prompt_with_llm_parser.2
+  list([
+    Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'], i=1)"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, 'name': None}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'output': ['tomato', 'lettuce', 'onion']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000001'), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'], i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'tomato, lettuce, onion', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000002'), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'tomato, lettuce, onion'}, outputs={'output': ['tomato', 'lettuce', 'onion']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000.20230101T000000000000Z00000000-0000-4000-8000-000000000003')], trace_id=UUID('00000000-0000-4000-8000-000000000000'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000000'),
+    Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'], i=1)"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, 'name': None}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'question': 'What is your favorite color?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your favorite color?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000004'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000005'), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeStreamingListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={'invocation_params': {'responses': ['bear, dog, cat', 'tomato, lettuce, onion'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}, 'batch_size': 2}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['tests', 'unit_tests', 'fake', 'llm', 'FakeStreamingListLLM'], 'repr': "FakeStreamingListLLM(responses=['bear, dog, cat', 'tomato, lettuce, onion'], i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'bear, dog, cat', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000004'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000006'), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0, tzinfo=datetime.timezone.utc)}], inputs={'input': 'bear, dog, cat'}, outputs={'output': ['bear', 'dog', 'cat']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000004'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[], trace_id=UUID('00000000-0000-4000-8000-000000000004'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004.20230101T000000000000Z00000000-0000-4000-8000-000000000007')], trace_id=UUID('00000000-0000-4000-8000-000000000004'), dotted_order='20230101T000000000000Z00000000-0000-4000-8000-000000000004'),
+  ])
+# ---
 # name: test_router_runnable
   '''
   {
diff --git a/libs/core/tests/unit_tests/runnables/test_runnable.py b/libs/core/tests/unit_tests/runnables/test_runnable.py
index 6fb3eee4a3c68..7a8585e40186f 100644
--- a/libs/core/tests/unit_tests/runnables/test_runnable.py
+++ b/libs/core/tests/unit_tests/runnables/test_runnable.py
@@ -2140,6 +2140,272 @@ async def test_prompt_with_llm(
     assert stream_log == expected
 
 
+@freeze_time("2023-01-01")
+async def test_prompt_with_llm_parser(
+    mocker: MockerFixture, snapshot: SnapshotAssertion
+) -> None:
+    prompt = (
+        SystemMessagePromptTemplate.from_template("You are a nice assistant.")
+        + "{question}"
+    )
+    llm = FakeStreamingListLLM(responses=["bear, dog, cat", "tomato, lettuce, onion"])
+    parser = CommaSeparatedListOutputParser()
+
+    chain: Runnable = prompt | llm | parser
+
+    assert isinstance(chain, RunnableSequence)
+    assert chain.first == prompt
+    assert chain.middle == [llm]
+    assert chain.last == parser
+    assert dumps(chain, pretty=True) == snapshot
+
+    # Test invoke
+    prompt_spy = mocker.spy(prompt.__class__, "ainvoke")
+    llm_spy = mocker.spy(llm.__class__, "ainvoke")
+    parser_spy = mocker.spy(parser.__class__, "ainvoke")
+    tracer = FakeTracer()
+    assert await chain.ainvoke(
+        {"question": "What is your name?"}, dict(callbacks=[tracer])
+    ) == ["bear", "dog", "cat"]
+    assert prompt_spy.call_args.args[1] == {"question": "What is your name?"}
+    assert llm_spy.call_args.args[1] == ChatPromptValue(
+        messages=[
+            SystemMessage(content="You are a nice assistant."),
+            HumanMessage(content="What is your name?"),
+        ]
+    )
+    assert parser_spy.call_args.args[1] == "bear, dog, cat"
+    assert tracer.runs == snapshot
+    mocker.stop(prompt_spy)
+    mocker.stop(llm_spy)
+    mocker.stop(parser_spy)
+
+    # Test batch
+    prompt_spy = mocker.spy(prompt.__class__, "abatch")
+    llm_spy = mocker.spy(llm.__class__, "abatch")
+    parser_spy = mocker.spy(parser.__class__, "abatch")
+    tracer = FakeTracer()
+    assert await chain.abatch(
+        [
+            {"question": "What is your name?"},
+            {"question": "What is your favorite color?"},
+        ],
+        dict(callbacks=[tracer]),
+    ) == [["tomato", "lettuce", "onion"], ["bear", "dog", "cat"]]
+    assert prompt_spy.call_args.args[1] == [
+        {"question": "What is your name?"},
+        {"question": "What is your favorite color?"},
+    ]
+    assert llm_spy.call_args.args[1] == [
+        ChatPromptValue(
+            messages=[
+                SystemMessage(content="You are a nice assistant."),
+                HumanMessage(content="What is your name?"),
+            ]
+        ),
+        ChatPromptValue(
+            messages=[
+                SystemMessage(content="You are a nice assistant."),
+                HumanMessage(content="What is your favorite color?"),
+            ]
+        ),
+    ]
+    assert parser_spy.call_args.args[1] == [
+        "tomato, lettuce, onion",
+        "bear, dog, cat",
+    ]
+    assert tracer.runs == snapshot
+    mocker.stop(prompt_spy)
+    mocker.stop(llm_spy)
+    mocker.stop(parser_spy)
+
+    # Test stream
+    prompt_spy = mocker.spy(prompt.__class__, "ainvoke")
+    llm_spy = mocker.spy(llm.__class__, "astream")
+    tracer = FakeTracer()
+    assert [
+        token
+        async for token in chain.astream(
+            {"question": "What is your name?"}, dict(callbacks=[tracer])
+        )
+    ] == [["tomato"], ["lettuce"], ["onion"]]
+    assert prompt_spy.call_args.args[1] == {"question": "What is your name?"}
+    assert llm_spy.call_args.args[1] == ChatPromptValue(
+        messages=[
+            SystemMessage(content="You are a nice assistant."),
+            HumanMessage(content="What is your name?"),
+        ]
+    )
+
+    prompt_spy.reset_mock()
+    llm_spy.reset_mock()
+    stream_log = [
+        part async for part in chain.astream_log({"question": "What is your name?"})
+    ]
+
+    # remove ids from logs
+    for part in stream_log:
+        for op in part.ops:
+            if (
+                isinstance(op["value"], dict)
+                and "id" in op["value"]
+                and not isinstance(op["value"]["id"], list)  # serialized lc id
+            ):
+                del op["value"]["id"]
+
+    expected = [
+        RunLogPatch(
+            {
+                "op": "replace",
+                "path": "",
+                "value": {
+                    "logs": {},
+                    "final_output": None,
+                    "streamed_output": [],
+                },
+            }
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/ChatPromptTemplate",
+                "value": {
+                    "end_time": None,
+                    "final_output": None,
+                    "metadata": {},
+                    "name": "ChatPromptTemplate",
+                    "start_time": "2023-01-01T00:00:00.000+00:00",
+                    "streamed_output": [],
+                    "streamed_output_str": [],
+                    "tags": ["seq:step:1"],
+                    "type": "prompt",
+                },
+            }
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/ChatPromptTemplate/final_output",
+                "value": ChatPromptValue(
+                    messages=[
+                        SystemMessage(content="You are a nice assistant."),
+                        HumanMessage(content="What is your name?"),
+                    ]
+                ),
+            },
+            {
+                "op": "add",
+                "path": "/logs/ChatPromptTemplate/end_time",
+                "value": "2023-01-01T00:00:00.000+00:00",
+            },
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/FakeStreamingListLLM",
+                "value": {
+                    "end_time": None,
+                    "final_output": None,
+                    "metadata": {},
+                    "name": "FakeStreamingListLLM",
+                    "start_time": "2023-01-01T00:00:00.000+00:00",
+                    "streamed_output": [],
+                    "streamed_output_str": [],
+                    "tags": ["seq:step:2"],
+                    "type": "llm",
+                },
+            }
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/FakeStreamingListLLM/final_output",
+                "value": {
+                    "generations": [
+                        [
+                            {
+                                "generation_info": None,
+                                "text": "bear, dog, cat",
+                                "type": "Generation",
+                            }
+                        ]
+                    ],
+                    "llm_output": None,
+                    "run": None,
+                },
+            },
+            {
+                "op": "add",
+                "path": "/logs/FakeStreamingListLLM/end_time",
+                "value": "2023-01-01T00:00:00.000+00:00",
+            },
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/CommaSeparatedListOutputParser",
+                "value": {
+                    "end_time": None,
+                    "final_output": None,
+                    "metadata": {},
+                    "name": "CommaSeparatedListOutputParser",
+                    "start_time": "2023-01-01T00:00:00.000+00:00",
+                    "streamed_output": [],
+                    "streamed_output_str": [],
+                    "tags": ["seq:step:3"],
+                    "type": "parser",
+                },
+            }
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/CommaSeparatedListOutputParser/streamed_output/-",
+                "value": ["bear"],
+            }
+        ),
+        RunLogPatch(
+            {"op": "add", "path": "/streamed_output/-", "value": ["bear"]},
+            {"op": "replace", "path": "/final_output", "value": ["bear"]},
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/CommaSeparatedListOutputParser/streamed_output/-",
+                "value": ["dog"],
+            }
+        ),
+        RunLogPatch(
+            {"op": "add", "path": "/streamed_output/-", "value": ["dog"]},
+            {"op": "add", "path": "/final_output/1", "value": "dog"},
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/CommaSeparatedListOutputParser/streamed_output/-",
+                "value": ["cat"],
+            }
+        ),
+        RunLogPatch(
+            {"op": "add", "path": "/streamed_output/-", "value": ["cat"]},
+            {"op": "add", "path": "/final_output/2", "value": "cat"},
+        ),
+        RunLogPatch(
+            {
+                "op": "add",
+                "path": "/logs/CommaSeparatedListOutputParser/final_output",
+                "value": {"output": ["bear", "dog", "cat"]},
+            },
+            {
+                "op": "add",
+                "path": "/logs/CommaSeparatedListOutputParser/end_time",
+                "value": "2023-01-01T00:00:00.000+00:00",
+            },
+        ),
+    ]
+    assert stream_log == expected
+
+
 @freeze_time("2023-01-01")
 async def test_stream_log_retriever() -> None:
     prompt = (
@@ -4606,6 +4872,14 @@ async def agen(input: str) -> AsyncIterator[int]:
     assert [r.inputs["input"] for r in tracer.runs[0].child_runs] == ["a", "aa", "aaa"]
     assert [(r.outputs or {})["output"] for r in tracer.runs[0].child_runs] == [1, 2, 3]
 
+    tracer = FakeTracer()
+    assert [p async for p in agen.astream_log("a", {"callbacks": [tracer]})]
+    assert len(tracer.runs) == 1
+    assert tracer.runs[0].outputs == {"output": 6}
+    assert len(tracer.runs[0].child_runs) == 3
+    assert [r.inputs["input"] for r in tracer.runs[0].child_runs] == ["a", "aa", "aaa"]
+    assert [(r.outputs or {})["output"] for r in tracer.runs[0].child_runs] == [1, 2, 3]
+
     tracer = FakeTracer()
     assert await agen.abatch(["a", "a"], {"callbacks": [tracer]}) == [6, 6]
     assert len(tracer.runs) == 2