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https://github.com/langchain-ai/react-agent-tool-server.git
synced 2026-07-01 18:28:34 -04:00
use prebuilt agent for simplicity
This commit is contained in:
Eugene Yurtsev
@@ -1,38 +1,18 @@
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"""Define a custom Reasoning and Action agent.
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Works with a chat model with tool calling support.
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"""
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"""Create a ReAct agent with access to tools defined in a tool server."""
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from datetime import UTC, datetime
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from typing import Dict, List, Literal, cast
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from langchain_core.messages import AIMessage
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from langchain_core.runnables import RunnableConfig
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from langgraph.graph import StateGraph
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from langgraph.graph.state import CompiledStateGraph
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from langgraph.prebuilt import ToolNode
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from langgraph.prebuilt import create_react_agent
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from react_agent.configuration import APP_STATE
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from react_agent.state import InputState, State
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from react_agent.tools import TOOLBOX
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from react_agent.utils import load_chat_model
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# Define the function that calls the model
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async def call_model(
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state: State, config: RunnableConfig
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) -> Dict[str, List[AIMessage]]:
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"""Call the LLM powering our "agent".
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This function prepares the prompt, initializes the model, and processes the response.
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Args:
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state (State): The current state of the conversation.
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config (RunnableConfig): Configuration for the model run.
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Returns:
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dict: A dictionary containing the model's response message.
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"""
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async def make_graph(config: RunnableConfig) -> CompiledStateGraph:
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"""Create a custom state graph for the Reasoning and Action agent."""
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configuration = APP_STATE.configurable.from_runnable_config(config)
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# Add logic to select tools
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if configuration.selected_tools:
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@@ -52,79 +32,8 @@ async def call_model(
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system_time=datetime.now(tz=UTC).isoformat()
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)
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# Get the model's response
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response = cast(
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AIMessage,
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await model.ainvoke(
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[{"role": "system", "content": system_message}, *state.messages], config
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),
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graph = create_react_agent(
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model, system_message, config_schema=APP_STATE.configurable
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)
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# Handle the case when it's the last step and the model still wants to use a tool
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if state.is_last_step and response.tool_calls:
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return {
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"messages": [
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AIMessage(
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id=response.id,
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content="Sorry, I could not find an answer to your question in the specified number of steps.",
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)
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]
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}
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# Return the model's response as a list to be added to existing messages
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return {"messages": [response]}
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def route_model_output(state: State) -> Literal["__end__", "tools"]:
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"""Determine the next node based on the model's output.
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This function checks if the model's last message contains tool calls.
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Args:
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state (State): The current state of the conversation.
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Returns:
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str: The name of the next node to call ("__end__" or "tools").
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"""
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last_message = state.messages[-1]
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if not isinstance(last_message, AIMessage):
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raise ValueError(
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f"Expected AIMessage in output edges, but got {type(last_message).__name__}"
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)
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# If there is no tool call, then we finish
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if not last_message.tool_calls:
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return "__end__"
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# Otherwise we execute the requested actions
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return "tools"
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async def make_graph(config: RunnableConfig) -> CompiledStateGraph:
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"""Create a custom state graph for the Reasoning and Action agent."""
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tools = TOOLBOX.get_tools()
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builder = StateGraph(State, input=InputState, config_schema=APP_STATE.configurable)
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# Define the two nodes we will cycle between
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builder.add_node(call_model)
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builder.add_node("tools", ToolNode(tools))
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# Set the entrypoint as `call_model`
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# This means that this node is the first one called
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builder.add_edge("__start__", "call_model")
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# Add a conditional edge to determine the next step after `call_model`
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builder.add_conditional_edges(
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"call_model",
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# After call_model finishes running, the next node(s) are scheduled
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# based on the output from route_model_output
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route_model_output,
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)
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# Add a normal edge from `tools` to `call_model`
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# This creates a cycle: after using tools, we always return to the model
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builder.add_edge("tools", "call_model")
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# Compile the builder into an executable graph
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# You can customize this by adding interrupt points for state updates
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graph = builder.compile()
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graph.name = "ReAct Agent" # This customizes the name in LangSmith
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return graph
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@@ -1,60 +0,0 @@
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"""Define the state structures for the agent."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Sequence
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from langchain_core.messages import AnyMessage
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from langgraph.graph import add_messages
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from langgraph.managed import IsLastStep
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from typing_extensions import Annotated
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@dataclass
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class InputState:
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"""Defines the input state for the agent, representing a narrower interface to the outside world.
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This class is used to define the initial state and structure of incoming data.
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"""
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messages: Annotated[Sequence[AnyMessage], add_messages] = field(
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default_factory=list
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)
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"""
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Messages tracking the primary execution state of the agent.
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Typically accumulates a pattern of:
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1. HumanMessage - user input
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2. AIMessage with .tool_calls - agent picking tool(s) to use to collect information
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3. ToolMessage(s) - the responses (or errors) from the executed tools
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4. AIMessage without .tool_calls - agent responding in unstructured format to the user
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5. HumanMessage - user responds with the next conversational turn
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Steps 2-5 may repeat as needed.
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The `add_messages` annotation ensures that new messages are merged with existing ones,
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updating by ID to maintain an "append-only" state unless a message with the same ID is provided.
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"""
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@dataclass
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class State(InputState):
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"""Represents the complete state of the agent, extending InputState with additional attributes.
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This class can be used to store any information needed throughout the agent's lifecycle.
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"""
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is_last_step: IsLastStep = field(default=False)
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"""
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Indicates whether the current step is the last one before the graph raises an error.
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This is a 'managed' variable, controlled by the state machine rather than user code.
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It is set to 'True' when the step count reaches recursion_limit - 1.
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"""
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# Additional attributes can be added here as needed.
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# Common examples include:
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# retrieved_documents: List[Document] = field(default_factory=list)
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# extracted_entities: Dict[str, Any] = field(default_factory=dict)
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# api_connections: Dict[str, Any] = field(default_factory=dict)
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