tjC<dZddlmZddlZddlZddlZddlZddlmZm Z m Z ddl m Z m Z mZmZmZmZmZmZddlmZddlmZmZmZmZddlmZdd lmZdd lm Z m!Z!dd l"m#Z#dd l$m%Z%dd l&m'Z'ddl(m)Z)m*Z*ddl+m,Z,ddl-m.Z.m/Z/m0Z0m1Z1ddl2m3Z3ddl4m5Z5m6Z6m7Z7m8Z8m9Z9m:Z:m;Z;mZ>m?Z?m@Z@mAZAddlBmCZCmDZDmEZEmFZFmGZGmHZHmIZImJZJmKZKddlLmMZMeGddee?ZNe rddlOmPZPmQZQmRZRddlSmTZTmUZUddlVmWZWddl"mXZXddlYmZZZddl[m\Z\ddl]m^Z^ddl(m_Z_ddl4m`Z`eQe;e7eQe;e7ge#>#>H>>>>TTrGr;) AwaitableCallableSequence)RunnableRunnableConfig) BaseCache)CompiledStateGraph)Runtime) BaseStore)TransformerFactory) Checkpointer)ToolCallWrapperz)Error: {error} Please fix your mistakes.a Middleware added tools that the agent doesn't know how to execute. Unknown tools: {unknown_tool_names} Registered tools: {available_tool_names} This happens when middleware modifies `request.tools` in `wrap_model_call` to include tools that weren't passed to `create_agent()`. How to fix this: Option 1: Register tools at agent creation (recommended for most cases) Pass the tools to `create_agent(tools=[...])` or set them on `middleware.tools`. This makes tools available for every agent invocation. Option 2: Handle dynamic tools in middleware (for tools created at runtime) Implement `wrap_tool_call` to execute tools that are added dynamically: class MyMiddleware(AgentMiddleware): def wrap_tool_call(self, request, handler): if request.tool_call["name"] == "dynamic_tool": # Execute the dynamic tool yourself or override with tool instance return handler(request.override(tool=my_dynamic_tool)) return handler(request) inputsdict[str, Any]returnc|}|dd|dttt frfdt D|d<|S)zMRemove `runtime` and `handler` from trace inputs before sending to LangSmith.handlerNrequestc\i|](}|jdk |jt|j)S)runtime)namegetattr).0freqs rH z!_scrub_inputs..s<   -.9ATATAFGC((ATATATrG)copypopget isinstancer)r/r)rUfilteredras @rH _scrub_inputsrhs{{}}H LLD!!! ,,y ! !C# o677     28++    OrG)z(^|[/:.])gpt-4\.1($|[-/:])z(^|[/:.])gpt-4o($|[-/:])z(^|[/:.])gpt-5($|[-/:])z(^|[/:.])gpt-5\.1($|[-/:])z.(^|[/:.])gpt-5\.2(-\d{4}-\d{2}-\d{2})?($|[/:])z'(^|[/:.])gpt-5\.2-(chat|codex)($|[-/:])z(^|[/:.])gpt-5\.3($|[-/:])z.(^|[/:.])gpt-5\.4(-\d{4}-\d{2}-\d{2})?($|[/:])z&(^|[/:.])gpt-5\.4-(mini|nano)($|[-/:])z(^|[/:.])gpt-5\.5($|[-/:])zD(^|[/:.])claude-(fable|mythos)-5(?:-\d{8})?(?:-v\d(?::\d)?)?($|[/:])z=(^|[/:.])claude-haiku-4-5(?:-\d{8})?(?:-v\d(?::\d)?)?($|[/:])zD(^|[/:.])claude-opus-4-(5|6|7|8)(?:-\d{8})?(?:-v\d(?::\d)?)?($|[/:])zB(^|[/:.])claude-sonnet-4-(5|6)(?:-\d{8})?(?:-v\d(?::\d)?)?($|[/:])z(^|[/:.])grok-4($|[-.:/])z(^|[/:.])grok-build($|[-/:])result1ModelResponse | AIMessage | ExtendedModelResponser*ct|trt|gdSt|tr|jS|S)zNormalize middleware return value to ModelResponse. At inner composition boundaries, `ExtendedModelResponse` is unwrapped to its underlying `ModelResponse` so that inner middleware always sees `ModelResponse` from the handler. Nristructured_response)rfrr*r&r<)ris rH_normalize_to_model_responsernsM&)$$HVH$GGGG&/00%$$ MrGr<middleware_commandslist[Command[Any]] | Noner>c(d|ji}|j |j|d<|pgD]J}|jrd}t||jrd}t||jrd}t|Kt |g}||pg|S)a,Build a list of Commands from a model response and middleware commands. The first Command contains the model response state (messages and optional structured_response). Middleware commands are appended as-is. Args: model_response: The model response containing messages and optional structured output. middleware_commands: Commands accumulated from middleware layers during composition (inner-first ordering). Returns: List of `Command` objects ready to be returned from a model node. messagesNrmzCommand goto is not yet supported in wrap_model_call middleware. Use the jump_to state field with before_model/after_model hooks instead.zBCommand resume is not yet supported in wrap_model_call middleware.zACommand graph is not yet supported in wrap_model_call middleware.)update)rirmgotoNotImplementedErrorresumegraphrextend)r<rostatecmdmsgr?s rH_build_commandsr|s$()>?E)5'5'I#$"(b + + 8 +[ &c** * : +VC%c** * 9 +UC%c** * +%,5$9$9$9#:H OO'-2... OrGhandlers%Sequence[_ModelCallHandler[ContextT]]*_ComposedModelCallHandler[ContextT] | Nonec|sdS dddt|d kr|d dfd }|Sdfd }||d|d}t|ddD]}|||}|S)a Compose multiple `wrap_model_call` handlers into single middleware stack. Composes handlers so first in list becomes outermost layer. Each handler receives a handler callback to execute inner layers. Commands from each layer are accumulated into a list (inner-first, then outer) without merging. Args: handlers: List of handlers. First handler wraps all others. Returns: Composed handler returning `_ComposedExtendedModelResponse`, or `None` if handlers empty. NriRModelResponse | AIMessage | ExtendedModelResponse | _ComposedExtendedModelResponseextra_commandsrprWr;cNt|pg}t|tr"||j|j}nMt|t r)|j}|j||jnt|}t||Sz?Normalize any handler result to _ComposedExtendedModelResponse.N)r<r? rErfr;rxr?r<r&commandappendrnrirr?r<s rH_to_composed_resultz7_chain_model_call_handlers.._to_composed_result (,N,@b'A'A f< = = B OOFO , , ,#2NN  5 6 6 B#2N~)///9&AAN-^V^____rGrrZModelRequest[ContextT]rY1Callable[[ModelRequest[ContextT]], ModelResponse]c.||SNrFrZrYrsingle_handlers rHnormalized_singlez5_chain_model_call_handlers..normalized_singles"'&~~gw'G'GHH HrGouterA_ModelCallHandler[ContextT] | _ComposedModelCallHandler[ContextT]inner#_ComposedModelCallHandler[ContextT]cdfd }|S) z-Compose two handlers where outer wraps inner.rZrrYrrWr;cRgdfd }||}|pdS)NrarrWr*cH|}t|tr!|j|jSt|t r(|j|j|jSt|Sr clearrfr;rxr?r<r&rrrnra inner_resultaccumulated_commandsrYrs rH inner_handlerzX_chain_model_call_handlers..compose_two..composed..inner_handler*s$**,,,$uS'22 l,JKK7(// 0EFFF'66l,ABB7#+7,33L4HIII'663LAAArGrrarrWr*rFrZrYr outer_resultrrrrs ` @rHcomposedzA_chain_model_call_handlers..compose_two..composed#su 8:  B B B B B B B B!5-88L&&3;t rGrZrrYrrWr;rFrrrrs`` rH compose_twoz/_chain_model_call_handlers..compose_two4         4rGrrirrrprWr;r)rrrrrWrlenreversedr}rrcomposed_handlerhrrs @@rH_chain_model_call_handlersrs$ t59`````$ 8}}!! I I I I I I I !       F#{8B<">> hssm $ $<<&;q*:;; rG*Sequence[_AsyncModelCallHandler[ContextT]]/_ComposedAsyncModelCallHandler[ContextT] | Nonec|sdS dddt|d kr|d dfd }|Sdfd }||d|d}t|ddD]}|||}|S)aCompose multiple async `wrap_model_call` handlers into single middleware stack. Commands from each layer are accumulated into a list (inner-first, then outer) without merging. Args: handlers: List of async handlers. First handler wraps all others. Returns: Composed async handler returning `_ComposedExtendedModelResponse`, or `None` if handlers empty. NrirrrprWr;cNt|pg}t|tr"||j|j}nMt|t r)|j}|j||jnt|}t||Srrrs rHrz=_chain_async_model_call_handlers.._to_composed_result[rrGrrrZrrYK||d{VSrrFrs rHrz;_chain_async_model_call_handlers..normalized_singleps8'&^^GW-M-M'M'M'M'M'M'MNN NrGrK_AsyncModelCallHandler[ContextT] | _ComposedAsyncModelCallHandler[ContextT]r(_ComposedAsyncModelCallHandler[ContextT]cdfd }|S) z3Compose two async handlers where outer wraps inner.rZrrYrrWr;cbKgdfd }||d{V}|pdS)NrarrWr*cXK|d{V}t|tr!|j|jSt|t r(|j|j|jSt|Srrrs rHrz^_chain_async_model_call_handlers..compose_two..composed..inner_handlers$**,,,%*U3%8%8888888 l,JKK7(// 0EFFF'66l,ABB7#+7,33L4HIII'663LAAArGrrrFrs ` @rHrzG_chain_async_model_call_handlers..compose_two..composed~s 8:  B B B B B B B B"'w !>!>>>>>>>L&&3;t rGrZrrYrrWr;rFrs`` rHrz5_chain_async_model_call_handlers..compose_twoxrrGrrrrr)rrrrrWrrrs @@rH _chain_async_model_call_handlersrGs" t59`````$ 8}}!! O O O O O O O !       F#{8B<">> hssm $ $<<&;q*:;; rGd)maxsizeschematypec$t|dS)z&Return cached type hints for a schema.T)include_extras)rrs rH_get_schema_type_hintsrs & 6 6 66rGschemas list[type]tuple[type, type, type]c~d|D}t|ddt|ddt|ddfS)u/Resolve state, input, and output schemas for the given schemas. Schemas are merged in list order; later entries override earlier ones when the same field is declared by multiple schemas. Duplicates are harmless — a type that appears more than once is processed at its last position. c.i|]}|t|SrF)r)r_rs rHrbz$_resolve_schemas..s#QQQvF26::QQQrG StateSchemaN InputSchemainput OutputSchemaoutput)_resolve_schema)r schema_hintss rH_resolve_schemasrsORQQQQL mT:: mW== nh?? rGrdict[type, dict[str, Any]] schema_namestr omit_flag str | Nonec8i}|D]f}|D]O\}}d}|r?t|}|D]-} t| trt | |durd}n.|s|||<Pgt dt||S)auResolve schema by merging schemas and optionally respecting `OmitFromSchema` annotations. Args: schema_hints: Resolved schema annotations to merge schema_name: Name for the generated `TypedDict` omit_flag: If specified, omit fields with this flag set (`'input'` or `'output'`) Returns: Merged schema as `TypedDict` FTr)valuesitems_extract_metadatarfr+r^r r ) rrrall_annotationshints field_name field_type should_omitmetadatametas rHrrs O$$&& 9 9&+kkmm 9 9 "J K ,Z88$D!$77GD)z._supports_provider_strategy..7sO   Igz//11 2 2      rGF) rfrrr^profilererany&FALLBACK_MODELS_WITH_STRUCTURED_OUTPUT)rr model_profilers @rH_supports_provider_strategyr s7"J% E= ) ) E< . . .ugt,, .uj"--    %!!"566 &  & z3// & 0 0 2 222j&6&6&8&8884       A       rGrcz tjd}n#t$rYdSwxYwt||jS)aCheck if a model inherits from `BaseChatOpenAI`. Used to redundantly set `strict=True` on tools when `response_format` is provided, as older versions of `langchain-openai` do not auto-set it. Covers `ChatOpenAI`, `ChatDeepSeek`, `ChatXAI`, etc. Args: model: The chat model to check. Returns: `True` if the model inherits from `BaseChatOpenAI`, `False` otherwise. z!langchain_openai.chat_models.baseF) importlib import_module ImportErrorrfBaseChatOpenAI)rbase_chat_openais rH_is_openai_compatible_modelr@sR$23VWW uu e-< = ==s  %% exception Exceptionresponse_formatResponseFormat[Any]tuple[bool, str]cxt|tsdS|j}|durdS|dur*dtt fSt|t rd|fSt|t rQt|tr:t|r*dtt fSdSt|trGtfd|Dr*dtt fSdSd|fS)zWHandle structured output error. Returns `(should_retry, retry_tool_message)`. )FrFT)errorc38K|]}t|VdSr)rf)r_exc_typers rHrz2_handle_structured_output_error..ls-MM8z)X..MMMMMMrG) rfr8 handle_errors STRUCTURED_OUTPUT_ERROR_TEMPLATEformatrr issubclassrtupler )rrrs` rH_handle_structured_output_errorr"TsS o| 4 4y#1My5<<3y>><RRRR-%%#]""-&& mY / / WJy-4X4X W9@@s9~~@VVV Vy-'' MMMM}MMM M M W9@@s9~~@VVV Vy y)) ))rGwrappersSequence[ToolCallWrapper]ToolCallWrapper | Nonec|sdSt|dkr|dSd d}|d }t|dd D]}|||}|S) aCompose wrappers into middleware stack (first = outermost). Args: wrappers: Wrappers in middleware order. Returns: Composed wrapper, or `None` if empty. Example: ```python wrapper = _chain_tool_call_wrappers([auth, cache, retry]) # Request flows: auth -> cache -> retry -> tool # Response flows: tool -> retry -> cache -> auth ``` NrrrrTrrWcdfd }|S) z-Compose two wrappers where outer wraps inner.rZr/execute7Callable[[ToolCallRequest], ToolMessage | Command[Any]]rWToolMessage | Command[Any]c,dfd }||S)Nrar/rWr*c|SrrFrar(rs rH call_innerzT_chain_tool_call_wrappers..compose_two..composed..call_innersuS'***rGrar/rWr*rFrZr(r.rrs ` rHrz@_chain_tool_call_wrappers..compose_two..composeds;  + + + + + + +5*-- -rG)rZr/r(r)rWr*rFrrrs`` rHrz._chain_tool_call_wrappers..compose_twos. . . . . . . .rGr)rrTrrTrWrTrr#rriwrappers rH_chain_tool_call_wrappersr4rs$ t 8}}{"b\FHSbSM**..Wf-- MrGSequence[Callable[[ToolCallRequest, Callable[[ToolCallRequest], Awaitable[ToolMessage | Command[Any]]]], Awaitable[ToolMessage | Command[Any]]]]Callable[[ToolCallRequest, Callable[[ToolCallRequest], Awaitable[ToolMessage | Command[Any]]]], Awaitable[ToolMessage | Command[Any]]] | Nonec|sdSt|dkr|dSd d}|d }t|dd D]}|||}|S) zCompose async wrappers into middleware stack (first = outermost). Args: wrappers: Async wrappers in middleware order. Returns: Composed async wrapper, or `None` if empty. NrrrCallable[[ToolCallRequest, Callable[[ToolCallRequest], Awaitable[ToolMessage | Command[Any]]]], Awaitable[ToolMessage | Command[Any]]]rrWcdfd }|S) z3Compose two async wrappers where outer wraps inner.rZr/r(BCallable[[ToolCallRequest], Awaitable[ToolMessage | Command[Any]]]rWr*c<Kdfd }||d{VS)Nrar/rWr*c,K|d{VSrrFr-s rHr.zZ_chain_async_tool_call_wrappers..compose_two..composed..call_inners+"U3000000000rGr/rFr0s ` rHrzF_chain_async_tool_call_wrappers..compose_two..composedsQ  1 1 1 1 1 1 1w 33333333 3rG)rZr/r(r:rWr*rFr1s`` rHrz4_chain_async_tool_call_wrappers..compose_twos. 4 4 4 4 4 4 4rGr)rr8rr8rWr8rr2s rH_chain_async_tool_call_wrappersr=s, t 8}}{:b\FHSbSM**..Wf-- MrGrFF) system_promptrr state_schemacontext_schema checkpointerstoreinterrupt_beforeinterrupt_afterdebugr]cache transformers?Sequence[BaseTool | Callable[..., Any] | dict[str, Any]] | Noner>str | SystemMessage | None.Sequence[AgentMiddleware[StateT_co, ContextT]]Noner?r@type[ContextT] | NonerACheckpointer | NonerBBaseStore | NonerClist[str] | NonerDrEr]rFBaseCache[Any] | NonerG#Sequence[TransformerFactory] | NoneUCompiledStateGraph[AgentState[Any], ContextT, InputAgentState, OutputAgentState[Any]]c dSrrFrrr>rrr?r@rArBrCrDrEr]rFrGs rH create_agentrUs $]`\_rG) r>rr?r@rArBrCrDrEr]rFrG'type[AgentState[dict[str, Any]]] | NonekCompiledStateGraph[AgentState[dict[str, Any]], ContextT, InputAgentState, OutputAgentState[dict[str, Any]]]c dSrrFrTs rHrUrU (3rG2ResponseFormat[ResponseT] | type[ResponseT] | None"type[AgentState[ResponseT]] | NoneaCompiledStateGraph[AgentState[ResponseT], ContextT, InputAgentState, OutputAgentState[ResponseT]]c dSrrFrTs rHrUrUrYrGCResponseFormat[ResponseT] | type[ResponseT] | dict[str, Any] | Nonec F BCDEFGHIJKLMNOttrtdK|(t|tr|Knt|K|g}|dIn5t|tt t fr|Int |IdMtIt rt IjMntItrIMiJMr-MjD]%}tj |}|J|j j <&d|D}d|D}dO|rd|D}t|Od|D}dG|rd|D}t|GdLd |D}d |D}||z}|sOsGrt|OG ndLLr*t!Lj|zHnt!|Ht'd |Dt'|krd }t)|d|D}d|D}d|D}d|D}d|D}d|D}dN|rd|D} t+| NdF|rd|D}!t-|!F||nt.}"gd|D|"}#t1|#\}$}%}&t3|$|%|&|}'dKJfd EdLGIJLMOfd" DdMDE fd$ CdNCHIKNfd* }(dMDE fd+ BdNBFHIKfd, })|'d-t7|(|)d./L|'d0L|D]}*|*jjt<jus|*jjt<jurt|*jjt<jur|*jnd}+|*jjt<jur|*jnd},t7|+|,d./}-|'|*j d1|-|$2|*jj t<j us|*jj!t<j!urt|*jj t<j ur|*j nd}.|*jj!t<j!ur|*j!nd}/t7|.|/d./}0|'|*j d3|0|$2|*jj"t<j"us|*jj#t<j#urt|*jj"t<j"ur|*j"nd}1|*jj#t<j#ur|*j#nd}2t7|1|2d./}3|'|*j d4|3|$2|*jj$t<j$us|*jj%t<j%urt|*jj$t<j$ur|*j$nd}4|*jj%t<j%ur|*j%nd}5t7|4|5d./}6|'|*j d5|6|$2|r|d6j d1}7n|r|d6j d3}7nd-}7|r|d6j d3}8nd-}8|r|d6j d4}9nd-}9|r|d7j d5}:ntL}:|''tP|7L|8g};tSd8LjDsJr|;*|:|'+d0t7tYL|8J|:9d./|;d0|:g}<|s|9d-kr|<*|8|'+|9t7t[|8J|::d./|<nt'Jd6kr8|'+|9t7t]|8|:;d./|8|:gnT|9d-kr|''|9|:n7t_|'|d6j d4|:|8|:ta|d6d<=|rtcj2|D]8\}=}>t_|'|=j d1|>j d1|8|:ta|=d>=9t_|'|d7j d1|8|8|:ta|d7d>=|rtcj2|D]8\}=}>t_|'|=j d3|>j d3|8|:ta|=d?=9t_|'|d7j d3d-|8|:ta|d7d?=|r|''d-|d7j d4tgt'|d@z d6d7D]H}?||?}=||?d@z }>t_|'|=j d4|>j d4|8|:ta|=d<=I|rtgt'|d@z d6d7D]H}?||?}=||?d@z }>t_|'|=j d5|>j d5|8|:ta|=dA=It_|'|d6j d5tL|8|:ta|d6dA=dBdCi}@dDdEi|@dF< r |@dFdG<dH|D}A|'4||| | | | tjtlg|A|pdIJ7|@S)Oa'Creates an agent graph that calls tools in a loop until a stopping condition is met. For more details on using `create_agent`, visit the [Agents](https://docs.langchain.com/oss/python/langchain/agents) docs. Args: model: The language model for the agent. Can be a string identifier (e.g., `"openai:gpt-5.5"`) or a direct chat model instance (e.g., [`ChatOpenAI`][langchain_openai.ChatOpenAI] or other another [LangChain chat model](https://docs.langchain.com/oss/python/integrations/chat)). For a full list of supported model strings, see [`init_chat_model`][langchain.chat_models.init_chat_model(model_provider)]. !!! tip "" See the [Models](https://docs.langchain.com/oss/python/langchain/models) docs for more information. tools: A list of tools, `dict`, or `Callable`. If `None` or an empty list, the agent will consist of a model node without a tool calling loop. !!! tip "" See the [Tools](https://docs.langchain.com/oss/python/langchain/tools) docs for more information. system_prompt: An optional system prompt for the LLM. Can be a `str` (which will be converted to a `SystemMessage`) or a `SystemMessage` instance directly. The system message is added to the beginning of the message list when calling the model. middleware: A sequence of middleware instances to apply to the agent. Middleware can intercept and modify agent behavior at various stages. !!! tip "" See the [Middleware](https://docs.langchain.com/oss/python/langchain/middleware) docs for more information. response_format: An optional configuration for structured responses. Can be a `ToolStrategy`, `ProviderStrategy`, or a Pydantic model class. If provided, the agent will handle structured output during the conversation flow. Raw schemas will be wrapped in an appropriate strategy based on model capabilities. !!! tip "" See the [Structured output](https://docs.langchain.com/oss/python/langchain/structured-output) docs for more information. state_schema: An optional `TypedDict` schema that extends `AgentState`. When provided, this schema is used instead of `AgentState` as the base schema for merging with middleware state schemas. This allows users to add custom state fields without needing to create custom middleware. Generally, it's recommended to use `state_schema` extensions via middleware to keep relevant extensions scoped to corresponding hooks / tools. context_schema: An optional schema for runtime context. checkpointer: An optional checkpoint saver object. Used for persisting the state of the graph (e.g., as chat memory) for a single thread (e.g., a single conversation). store: An optional store object. Used for persisting data across multiple threads (e.g., multiple conversations / users). interrupt_before: An optional list of node names to interrupt before. Useful if you want to add a user confirmation or other interrupt before taking an action. interrupt_after: An optional list of node names to interrupt after. Useful if you want to return directly or run additional processing on an output. debug: Whether to enable verbose logging for graph execution. When enabled, prints detailed information about each node execution, state updates, and transitions during agent runtime. Useful for debugging middleware behavior and understanding agent execution flow. name: An optional name for the `CompiledStateGraph`. This name will be automatically used when adding the agent graph to another graph as a subgraph node - particularly useful for building multi-agent systems. cache: An optional `BaseCache` instance to enable caching of graph execution. transformers: Optional sequence of scope-aware `StreamTransformer` factories to register on the compiled graph in addition to the agent defaults. Each factory is invoked as `factory(scope)` so every invocation receives a fresh instance. The final order on the compiled graph is: `ToolCallTransformer`, then any factories declared by middleware via `AgentMiddleware.transformers`, then any factories supplied here. Returns: A compiled `StateGraph` that can be used for chat interactions. Raises: AssertionError: If duplicate middleware instances are provided. The agent node calls the language model with the messages list (after applying the system prompt). If the resulting [`AIMessage`][langchain.messages.AIMessage] contains `tool_calls`, the graph will then call the tools. The tools node executes the tools and adds the responses to the messages list as [`ToolMessage`][langchain.messages.ToolMessage] objects. The agent node then calls the language model again. The process repeats until no more `tool_calls` are present in the response. The agent then returns the full list of messages. Example: ```python from langchain.agents import create_agent def check_weather(location: str) -> str: '''Return the weather forecast for the specified location.''' return f"It's always sunny in {location}" graph = create_agent( model="anthropic:claude-sonnet-4-5-20250929", tools=[check_weather], system_prompt="You are a helpful assistant", ) inputs = {"messages": [{"role": "user", "content": "what is the weather in sf"}]} for chunk in graph.stream(inputs, stream_mode="updates"): print(chunk) ``` N)contentrc:g|]}t|dgD]}|Srr^r_mts rH z create_agent..s1OOOawq'27N7NOO!OOOOrGcrg|]4}|jjtjus|jjtju2|5SrF)rwrap_tool_callr#awrap_tool_callr_res rHrgz create_agent..sK### ; %_-K K K ; &o.M M M  N M MrGcfg|].}t|jdt|j/S)z.wrap_tool_callr]process_inputs)rr]rhrirks rHrgz create_agent..sU    UIaf555m T T T      rGcrg|]4}|jjtjus|jjtju2|5SrF)rrjr#rirks rHrgz create_agent..sK$$$ ; &o.M M M ; %_-K K K  L K KrGcfg|].}t|jdt|j/S)z.awrap_tool_callrm)rr]rhrjrks rHrgz create_agent..sU    VIaf666} U U U!     rGc<g|]}t|t|SrFrfdictr_rfs rHrgz create_agent..s'>>>A*Q*=*=>a>>>rGc<g|]}t|t|SrFrrrts rHrgz create_agent..s'AAA1Z4-@-@AQAAArG)rrirjch|] }|j SrFr]rks rH zcreate_agent..8s ' ' 'qAF ' ' 'rGz-Please remove duplicate middleware instances.crg|]4}|jjtjus|jjtju2|5SrF)r before_agentr# abefore_agentrks rHrgz create_agent..;K!!! ; #?+G G G ; $O,I I I  J I IrGcrg|]4}|jjtjus|jjtju2|5SrF)r before_modelr# abefore_modelrks rHrgz create_agent..Ar|rGcrg|]4}|jjtjus|jjtju2|5SrF)r after_modelr# aafter_modelrks rHrgz create_agent..GK    ; "/*E E E ; #?+G G G  H G GrGcrg|]4}|jjtjus|jjtju2|5SrF)r after_agentr# aafter_agentrks rHrgz create_agent..MrrGcrg|]4}|jjtjus|jjtju2|5SrF)rwrap_model_callr#awrap_model_callrks rHrgz create_agent..VsK$$$ ; &o.M M M ; '/O O O  P O OrGcrg|]4}|jjtjus|jjtju2|5SrF)rrr#rrks rHrgz create_agent.._sK%%% ; '/O O O ; &o.M M M  N M MrGcfg|].}t|jdt|j/S)z.wrap_model_callrm)rr]rhrrks rHrgz create_agent..isU    VIaf666} U U U!     rGcfg|].}t|jdt|j/S)z.awrap_model_callrm)rr]rhrrks rHrgz create_agent..tsU    WIaf777 V V V"     rGc3$K|] }|jV dSr)r?rks rHrzcreate_agent..s$"F"Fa1>"F"F"F"F"F"FrG)r? input_schema output_schemar@rreffective_response_formatResponseFormat[Any] | NonerWrVc t|tr|jsvtj|j} ||}|g|dS#t$r4}t|jj dd}t|||}||d}~wwxYwd|giSt|trZt|trD|jr<fd|jD}|r&d}t|dkrHd|D} t| |}t||\} | s|fd |D} d|g| iS|d } | d } | | d }|jpd |}|t#|| d| d g|dS#t$r[}t| d ||}t||\} | s||d|t#| d| d gicYd}~Sd}~wwxYwd|giS)aHandle model output including structured responses. 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