4d2786935a
Co-authored-by: Eric Gustin <eric@arcade-ai.com> Co-authored-by: Nate Barbettini <nathanaelb@gmail.com> Co-authored-by: Nate Barbettini <nate@arcade-ai.com>
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import os
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# Import necessary modules and classes
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from langchain_arcade import ArcadeToolManager
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from langchain_core.messages import HumanMessage
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from langchain_openai import ChatOpenAI
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from langgraph.prebuilt import create_react_agent
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arcade_api_key = os.environ["ARCADE_API_KEY"]
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openai_api_key = os.environ["OPENAI_API_KEY"]
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# Initialize the tool manager that fetches
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# tools from arcade and wraps them as langgraph tools
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tool_manager = ArcadeToolManager(api_key=arcade_api_key)
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tools = tool_manager.get_tools(langgraph=True)
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# Create an instance of the AI language model
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model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key)
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# Init a prebuilt agent that can use tools
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# in a REACT style langgraph
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graph = create_react_agent(model, tools=tools)
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# Define the initial input message from the user
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inputs = {
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"messages": [HumanMessage(content="Star arcadeai/arcade-ai on GitHub!")],
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}
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# Configuration parameters for the agent and tools
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config = {
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"configurable": {
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"thread_id": "2",
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"user_id": "user@example.com",
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}
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}
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# Stream the assistant's responses by executing the graph
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for chunk in graph.stream(inputs, stream_mode="values", config=config):
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# Access the latest message from the conversation
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last_message = chunk["messages"][-1]
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# Print the assistant's message content
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print(last_message.content)
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