g3fi$"ddlZddlZddlmZmZmZddlmZmZm Z m Z m Z m Z ddl mZmZmZddlmZmZmZmZmZddlmZmZmZmZddlmZddlmZm Z dd l!m"Z"dd l#m$Z$m%Z%dd l&m'Z'm(Z(dd l)m*Z*dd l+m,Z,ddl-m.Z.ddl/m0Z0ddl1m2Z2ddl3m4Z4m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z;ddlZ>m?Z?ddl@mAZAmBZBeCe;zZDeCeEe;zZFe?de9Gdde>ZGe?de9Gdde;ZHej5ejde9d e?d!e9Gd"d#eGZKdddej5ejde9d e?d$e9Gd%d&eHZLdddej5ejde9e d'eGeHz(ZMeEeMZNdddd)ZOeePzeeMgefzeeMefzZQdLd*eMd+ePd,ed-efd.ZRd/eQdzd-efd0ZS dMd1ed2eed3eTd-eUfd4ZVd1ed-efd5ZWd6eed-dfd7ZXe?d8e9ddddddddddd9d:dd; d1ePezeeMe0e7gefzeeMe0e7geefzeeMe0e7geeeffzeeMe0e7geeeeffzd2eeezeCePefzeBzd/eQdzde dzd?eNdzd@eEedzdAe4dzdBe2dzdCeZePdzdDeZePdzdEeUdFe dGdHePdzdIed-e,f"dJZ[e[Z\gdKZ]y#1swYxYw#1swYxYw#1swYNxYw)NN) AwaitableCallableSequence) AnnotatedAnyLiteralTypeVarcastget_type_hints) BaseChatModelLanguageModelInputLanguageModelLike) AIMessage AnyMessage BaseMessage SystemMessage ToolMessage)RunnableRunnableBindingRunnableConfigRunnableSequence)BaseTool)RunnableCallable RunnableLike)MISSING) ErrorCodecreate_error_message)END StateGraph) add_messages)CompiledStateGraph)RemainingSteps)Runtime) BaseStore) CheckpointerSend)ContextT)LangGraphDeprecatedSinceV10) BaseModel) NotRequired TypedDict deprecated)ToolCallWithContextToolNodezxAgentState has been moved to `langchain.agents`. Please update your import to `from langchain.agents import AgentState`.categoryc<eZdZUdZeeeefed<e e ed<y) AgentStateThe state of the agent.messagesremaining_stepsN) __name__ __module__ __qualname____doc__rrrr __annotations__r*r"d/var/www/auto_recruiter/arenv/lib/python3.12/site-packages/langgraph/prebuilt/chat_agent_executor.pyr2r25s' "-|;<< 00r<r2zTAgentStatePydantic has been deprecated in favor of AgentState in `langchain.agents`.c:eZdZUdZeeeefed<dZ e ed<y)AgentStatePydanticr3r4r5N) r6r7r8r9rrrr r:r5r"r;r<r=r?r?As& "-|;<<&(O^(r<r?ignorez1AgentState has been moved to `langchain.agents`.*)r0messagezbAgentStateWithStructuredResponse has been deprecated in favor of AgentState in `langchain.agents`.ceZdZUdZeed<y) AgentStateWithStructuredResponse2The state of the agent with a structured response.structured_responseNr6r7r8r9StructuredResponser:r;r<r=rDrDT A//r<rDzjAgentStateWithStructuredResponsePydantic has been deprecated in favor of AgentState in `langchain.agents`.ceZdZUdZeed<y)(AgentStateWithStructuredResponsePydanticrErFNrGr;r<r=rKrKerIr<rK StateSchema)boundPromptstatekeydefaultreturnc`t|tr|j||St|||SN) isinstancedictgetgetattr)rOrPrQs r=_get_state_valuerYs6 eT "  #wUC )r<promptctdt}|Sttr"t tfdt}|Sttrtfdt}|St j rtdt}|Strtt}|Sttr}|Stdt)Nct|dSNr4rY)rOs r=z&_get_prompt_runnable..s*5*=r<namecontentc$gt|dzSr]r^)rO_system_messages r=r_z&_get_prompt_runnable..s?+.>uj.QQr<c$gt|dzSr]r^)rOrZs r=r_z&_get_prompt_runnable..s6(%5eZ%HHr<z"Got unexpected type for `prompt`: ) rPROMPT_RUNNABLE_NAMErUstrrinspectiscoroutinefunctioncallabler ValueErrortype)rZprompt_runnableres` @r=_get_prompt_runnableros ~* =DX < 7 FC '4V'D* Q% 2 + FM ** H% ( !  $ $V ,*  %   & * %   FH %  =d6l^LMMr<modeltools num_builtinc:t|trtd|jD|}t|tsyd|j vry|j d}t |t ||z k7r&tdt |dt ||z td|D}t}|D]H}|jddk(r |dd }n|jd r|d }n8|j|J||z x}rtd |d y ) Nc3LK|]}t|ttfr|ywrTrUrr .0steps r= z%_should_bind_tools..' d_m$DE "$Trqz`Number of tools in the model.bind_tools() and tools passed to create_react_agent must match Got z tools, expected c34K|]}|jywrTr`)rwtools r=ryz%_should_bind_tools..s14TYY1srmfunctionrazMissing tools 'z' in the model.bind_tools()F) rUrnextstepsrkwargslenrlsetrWadd) rprqrr bound_tools tool_namesbound_tool_names bound_toolbound_tool_name missing_toolss r=_should_bind_toolsrsA%)* !KK     e_ -ell",,w'K 5zS% 33 J<0[1AK1O0P R  1511Ju! . >>& !Z /(4V.rzr{zXExpected `model` to be a ChatModel or RunnableBinding (e.g. model.bind_tools(...)), got ) rUrrrrrMr TypeErrorrm)rps r= _get_modelrsr%)* !KK    %)  e] +fgklqgrfs t   Lr<r4cf|Dcgc]%}t|tr|jD]}|'}}}|Dchc]}t|ts|j!}}|Dcgc] }|d|vr| }}|syt d|dddt j}t|cc}}wcc}wcc}w)zLValidate that all tool calls in AIMessages have a corresponding ToolMessage.idNz{Found AIMessages with tool_calls that do not have a corresponding ToolMessage. Here are the first few of those tool calls: z. Every tool call (LLM requesting to call a tool) in the message history MUST have a corresponding ToolMessage (result of a tool invocation to return to the LLM) - this is required by most LLM providers.)rB error_code) rUr tool_callsr tool_call_idrrINVALID_CHAT_HISTORYrl)r4rB tool_callall_tool_callstool_call_ids_with_resultstool_calls_without_results error_messages r=_validate_chat_historyrs    gy ) ++   N-5"!( 7K8X"" ("  T?"< < "" &(77QRTST7U6VWgg11 M ] ##1 ""s*B#B) B)!B.zcreate_react_agent has been moved to `langchain.agents`. Please update your import to `from langchain.agents import create_agent`.Fv2) rZresponse_formatpre_model_hookpost_model_hook state_schemacontext_schema checkpointerstoreinterrupt_beforeinterrupt_afterdebugversionrarrrrrrrrrrrv1rradeprecated_kwargsc  '()*+,-./|jdtx}turtjdt||}t |dkDrt d| dvrtd d Jd d h}|jd tt}|t|z x}rtd |dtntg}t|tr&t|j j#}|}nm|Dcgc]}t|t$s|}}t|Dcgc]}t|t$r|c}}t|j j#}tt&t(f xr t+--xrt-j.,t |dkD}-stt&r ddlm}t7t8|t;|t |r3t ||zdkDr"t7t8j=||zt?z/nd/|Dchc]}|j@s|jBc}.dtDdtFtHdtJf-/fd *dtDdtFtHdtJf,-/fd (dtDdtLdtNf.fd 'dtDdtDffd )dtDdtFtHdtPdtDf')*,-/fd }dtDdtFtHdtPdtDf'()-/fd }MttRr/tUtVrddl,m-}|dtt\df }nGd!d}|}n}dtDdtFtHdtPdtDf*,fd" }dtDdtFtHdtPdtDf(fd# } |st_|$}!|!jad%tc|||&'|!jad'|!jed'd%d'+nd%+|!jg+$|!jad(|!jed%d(C|!jad)tc|| |!jed(d)n|!jed%d)|!ji|| | | | *SdtDdt&ttjzf fd+ }"t_xst|$}!|!jad%tc|||&|!jad,|'|!jad'|!jed'd%d'+nd%+|!jg+g}#+d,g}$6|!jad(|#jmd(|!jed%d(n|#jmd,B|!jad)tc|| |$jmd)n?|#jmd)n-|$jmtnn|#jmtn5dtDdt&ttjzf+fd- }%|!jqd(|%|$.|!jqd%|"|#.dtDdt&f+.fd/ }&.r|!jqd,|&+tng.n|!jed,+|!ji|| | | | *Scc}wcc}w#t4$r t5dwxYwcc}w)0a()Creates an agent graph that calls tools in a loop until a stopping condition is met. For more details on using `create_react_agent`, visit [Agents](https://langchain-ai.github.io/langgraph/agents/overview/) documentation. Args: model: The language model for the agent. Supports static and dynamic model selection. - **Static model**: A chat model instance (e.g., [`ChatOpenAI`][langchain_openai.ChatOpenAI]) or string identifier (e.g., `"openai:gpt-4"`) - **Dynamic model**: A callable with signature `(state, runtime) -> BaseChatModel` that returns different models based on runtime context If the model has tools bound via `bind_tools` or other configurations, the return type should be a `Runnable[LanguageModelInput, BaseMessage]` Coroutines are also supported, allowing for asynchronous model selection. Dynamic functions receive graph state and runtime, enabling context-dependent model selection. Must return a `BaseChatModel` instance. For tool calling, bind tools using `.bind_tools()`. Bound tools must be a subset of the `tools` parameter. !!! example "Dynamic model" ```python from dataclasses import dataclass @dataclass class ModelContext: model_name: str = "gpt-3.5-turbo" # Instantiate models globally gpt4_model = ChatOpenAI(model="gpt-4") gpt35_model = ChatOpenAI(model="gpt-3.5-turbo") def select_model(state: AgentState, runtime: Runtime[ModelContext]) -> ChatOpenAI: model_name = runtime.context.model_name model = gpt4_model if model_name == "gpt-4" else gpt35_model return model.bind_tools(tools) ``` !!! note "Dynamic Model Requirements" Ensure returned models have appropriate tools bound via `.bind_tools()` and support required functionality. Bound tools must be a subset of those specified in the `tools` parameter. tools: A list of tools or a `ToolNode` instance. If an empty list is provided, the agent will consist of a single LLM node without tool calling. prompt: An optional prompt for the LLM. Can take a few different forms: - `str`: This is converted to a `SystemMessage` and added to the beginning of the list of messages in `state["messages"]`. - `SystemMessage`: this is added to the beginning of the list of messages in `state["messages"]`. - `Callable`: This function should take in full graph state and the output is then passed to the language model. - `Runnable`: This runnable should take in full graph state and the output is then passed to the language model. response_format: An optional schema for the final agent output. If provided, output will be formatted to match the given schema and returned in the 'structured_response' state key. If not provided, `structured_response` will not be present in the output state. Can be passed in as: - An OpenAI function/tool schema, - A JSON Schema, - A TypedDict class, - A Pydantic class. - A tuple `(prompt, schema)`, where schema is one of the above. The prompt will be used together with the model that is being used to generate the structured response. !!! Important `response_format` requires the model to support `.with_structured_output` !!! Note The graph will make a separate call to the LLM to generate the structured response after the agent loop is finished. This is not the only strategy to get structured responses, see more options in [this guide](https://langchain-ai.github.io/langgraph/how-tos/react-agent-structured-output/). pre_model_hook: An optional node to add before the `agent` node (i.e., the node that calls the LLM). Useful for managing long message histories (e.g., message trimming, summarization, etc.). Pre-model hook must be a callable or a runnable that takes in current graph state and returns a state update in the form of ```python # At least one of `messages` or `llm_input_messages` MUST be provided { # If provided, will UPDATE the `messages` in the state "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), ...], # If provided, will be used as the input to the LLM, # and will NOT UPDATE `messages` in the state "llm_input_messages": [...], # Any other state keys that need to be propagated ... } ``` !!! Important At least one of `messages` or `llm_input_messages` MUST be provided and will be used as an input to the `agent` node. The rest of the keys will be added to the graph state. !!! Warning If you are returning `messages` in the pre-model hook, you should OVERWRITE the `messages` key by doing the following: ```python { "messages": [RemoveMessage(id=REMOVE_ALL_MESSAGES), *new_messages] ... } ``` post_model_hook: An optional node to add after the `agent` node (i.e., the node that calls the LLM). Useful for implementing human-in-the-loop, guardrails, validation, or other post-processing. Post-model hook must be a callable or a runnable that takes in current graph state and returns a state update. !!! Note Only available with `version="v2"`. state_schema: An optional state schema that defines graph state. Must have `messages` and `remaining_steps` keys. Defaults to `AgentState` that defines those two keys. !!! Note `remaining_steps` is used to limit the number of steps the react agent can take. Calculated roughly as `recursion_limit` - `total_steps_taken`. If `remaining_steps` is less than 2 and tool calls are present in the response, the react agent will return a final AI Message with the content "Sorry, need more steps to process this request.". No `GraphRecusionError` will be raised in this case. context_schema: An optional schema for runtime context. checkpointer: An optional checkpoint saver object. This is 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. This is used for persisting data across multiple threads (e.g., multiple conversations / users). interrupt_before: An optional list of node names to interrupt before. Should be one of the following: `"agent"`, `"tools"`. This is 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. Should be one of the following: `"agent"`, `"tools"`. This is useful if you want to return directly or run additional processing on an output. debug: A flag indicating whether to enable debug mode. version: Determines the version of the graph to create. Can be one of: - `"v1"`: The tool node processes a single message. All tool calls in the message are executed in parallel within the tool node. - `"v2"`: The tool node processes a tool call. Tool calls are distributed across multiple instances of the tool node using the [Send](https://langchain-ai.github.io/langgraph/concepts/low_level/#send) API. name: An optional name for the `CompiledStateGraph`. This name will be automatically used when adding ReAct agent graph to another graph as a subgraph node - particularly useful for building multi-agent systems. !!! warning "`config_schema` Deprecated" The `config_schema` parameter is deprecated in v0.6.0 and support will be removed in v2.0.0. Please use `context_schema` instead to specify the schema for run-scoped context. Returns: A compiled LangChain `Runnable` that can be used for chat interactions. The "agent" node calls the language model with the messages list (after applying the prompt). If the resulting AIMessage contains `tool_calls`, the graph will then call the ["tools"][langgraph.prebuilt.tool_node.ToolNode]. The "tools" node executes the tools (1 tool per `tool_call`) and adds the responses to the messages list as `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 as a dictionary containing the key `'messages'`. ``` mermaid sequenceDiagram participant U as User participant A as LLM participant T as Tools U->>A: Initial input Note over A: Prompt + LLM loop while tool_calls present A->>T: Execute tools T-->>A: ToolMessage for each tool_calls end A->>U: Return final state ``` Example: ```python from langgraph.prebuilt import create_react_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_react_agent( "anthropic:claude-3-7-sonnet-latest", tools=[check_weather], 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) ``` config_schemazW`config_schema` is deprecated and will be removed. Please use `context_schema` instead.r/Nrz7create_react_agent() got unexpected keyword arguments: rzInvalid version z'. 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Use agent.ainvoke() or agent.astream(), or provide a sync model callable.r4/Sorry, need more steps to process this request.rrc) RuntimeErrorr rinvokerar)rOrrmsg model_input dynamic_modelrrrrrrrars r= call_modelz&create_react_agent..call_models "1  s# #,U3 *5':MI}';';K'PQHI|':':;'OPH !% 2#;; Q XJ''r<cNK|} r:||d{}tt|j||d{}n(tt j||d{} |_||rdt|jdgiSd|giS77b7;w)Nr4rr)r rainvokerar) rOrrrrrrrrrrars r= acall_modelz'create_react_agent..acall_models-U3 #2%"AAMI]-B-B;PV-W'WXHI\-A-A+v-V'VWH !% 2#;; Q XJ''#B'W'Vs1B%B#B%B! 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Ps%A B B ABB BB)rragent) input_schemarrr)rrrrrrac t|d}|d}t|tr |js yytSdk(rydk(r3y|jDcgc]}t dt d|| c}Sycc}w) Nr4rrrrqrtool_call_with_context__typerrO)rYrUrrrr&r-)rOr4 last_messagerrrrs r=should_continuez+create_react_agent..should_continue9s#E:6| , 2,:Q:Q*( ,5 $D".,!- 7 7 +#;&*"'  ! sA:rqc t|d}|Dcgc]}t|ts|j!}}t dt |D}|j Dcgc] }|d|vs |}}|r&|Dcgc]}tdtd||c}St|dtrS ytScc}wcc}wcc}w) awRoute to the next node after post_model_hook. Routes to one of: * "tools": if there are pending tool calls without a corresponding message. * "generate_structured_response": if no pending tool calls exist and response_format is specified. * END: if no pending tool calls exist and no response_format is specified. r4c3BK|]}t|ts|ywrT)rUr)rwms r=ryzEcreate_react_agent..post_model_hook_router..s#Ay1I#srrqrrrr) rYrUrrrreversedrr&r-r) rOr4r tool_messageslast_ai_messagecpending_tool_callsr entrypointrs r=post_model_hook_routerz2create_react_agent..post_model_hook_routers(z:H(0#$Jq+4NM###H-#O+55"4 9U" ""!3 +#;&*"'  HRL+6!! ,5 7 " sB:B:! B?.B?:C)path_mapc tt|dD]*}t|tsn|jvs$t cStt r0|jr$tfd|jDrt SS)Nr4c3,K|] }|dv ywrr;rs r=ryzCcreate_react_agent..route_tool_responses..sQD4<#77Qr) rrYrUrrarrrany)rOrrrs r=route_tool_responsesz0create_react_agent..route_tool_responsessm*5*=> Aa-vv--   a # QALLQQ r<)9poprwarningswarnr(rrrlrrr rDr2rUr.r tools_by_namevaluesrVrhrrkrirjlangchain.chat_modelsr ImportErrorr r r bind_toolsro return_directrarLr#r'rrboolrrmrr)pydanticrrradd_noderadd_edgeset_entry_pointcompiler&appendradd_conditional_edges)0rprqrZrrrrrrrrrrrrarr required_keys schema_keys missing_keysllm_builtin_tools tool_classes tool_nodettool_calling_enabledrrrrrrrrworkflowr agent_pathspost_model_hook_pathsrrrrrrrrrrrs0` ````` `` @@@@@@@@@r=create_react_agentrs\+..HH   e0  !*N !EFWEX Y  l"wi'N O  #%67  &   3 4.67 (3{+;; ;< ;7 ~EUVW W* -  %'%"E//6689  (-E1At1DQEEJAjD6IaJK I33::<= %ec8_==Q(5/-T'2M2Me2T|,q0  eS ! 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( ( -   s #"% +% #T :J% N &&  "* '  ""# K C && )Z4E '  ':.   !)'   i FJ !T .Ms0[,[;[[[[2*[2[/)rcreate_tool_calling_executorr2r?rDrKrT)r)^rircollections.abcrrrtypingrrrr r r langchain_core.language_modelsr r rlangchain_core.messagesrrrrrlangchain_core.runnablesrrrrlangchain_core.toolsrlanggraph._internal._runnablerrlanggraph._internal._typingrlanggraph.errorsrrlanggraph.graphrrlanggraph.graph.messager langgraph.graph.stater!langgraph.managedr"langgraph.runtimer#langgraph.store.baser$langgraph.typesr%r&langgraph.typingr'langgraph.warningsr(r r)typing_extensionsr*r+r,langgraph.prebuilt.tool_noder-r.rVrHrmrr2r?catch_warningsfilterwarningsrDrKrLStateSchemaTypergrhrNrYrointr rrrrrrr__all__r;r<r=r9s 99  *H/<+04,%*.%:@@FI%$y/1 ~ (11 1 Z ()) )X0H,C l,0:0 00"X0H,f t,03E0 00"X(H,-z I ($!*.+/+/'+(,")-(,#'9N    WX./>? @ WX./=1IIJ K gh'((3E{3R*SS   gh'((-{:;< > N  Hx'$sCx.8 9H DN  TMN . C) )*+  N $!4'%N &"D('N ("D()N *I$+N ,%-N . t /N 03i$&1N 2#Y%3N 4 5N 6Z 7N 8 *9N :;N <=N  N d 2 {00"00"((s$.K* .K7'L*K47LL