140f4eca17
- **New Class Structure**: Introduced `ToolManager` and `AsyncToolManager` classes (`ArcadeToolManager` is deprecated) - **Async Support**: Full async implementation for modern LangChain applications - **Better Tool Management**: New methods for adding individual tools and toolkits - **CI/CD**: for langchain_arcade ## Upgrade Changes ```python # Old pattern manager = ArcadeToolManager(api_key="...") tools = manager.get_tools(toolkits=["Google"]) # New pattern manager = ToolManager(api_key="...") manager.init_tools(toolkits=["Google"]) tools = manager.to_langchain() ``` Now supports underscores vs dots in tool names for better model compatibility.
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
import os
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from langchain_arcade import ToolManager
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from langchain_openai import ChatOpenAI
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from langgraph.checkpoint.memory import MemorySaver
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from langgraph.prebuilt import create_react_agent
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# 1) Set API keys (place your real keys in env variables or directly below)
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arcade_api_key = os.environ.get("ARCADE_API_KEY", "YOUR_ARCADE_API_KEY")
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openai_api_key = os.environ.get("OPENAI_API_KEY", "YOUR_OPENAI_API_KEY")
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# 2) Create an ToolManager and fetch/add tools/toolkits
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manager = ToolManager(api_key=arcade_api_key)
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# Tool names follow the format "ToolkitName.ToolName"
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tools = manager.init_tools(tools=["Web.ScrapeUrl"])
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print(manager.tools)
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# Get all tools from a toolkit
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tools = manager.init_tools(toolkits=["Google"])
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print(manager.tools)
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# add a tool
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manager.add_tool("Search.SearchGoogle")
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print(manager.tools)
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# add a toolkit
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manager.add_toolkit("Search")
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print(manager.tools)
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# 3) Get StructuredTool objects for langchain
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lc_tools = manager.to_langchain()
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# 4) Create a ChatOpenAI model and bind the Arcade tools.
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model = ChatOpenAI(model="gpt-4o", api_key=openai_api_key)
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bound_model = model.bind_tools(lc_tools)
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# 5) Use MemorySaver for checkpointing.
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memory = MemorySaver()
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# 5) Create a ReAct-style agent from the prebuilt function.
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graph = create_react_agent(model=bound_model, tools=lc_tools, checkpointer=memory)
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# 6) Provide basic config and a user query.
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# Note: user_id is required for the tool to be authorized
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config = {"configurable": {"thread_id": "1", "user_id": "userrerr@example.coom"}}
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user_input = {"messages": [("user", "List any new and important emails in my inbox.")]}
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# 7) Stream the agent's output. If the tool is unauthorized, it may trigger interrupts
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for chunk in graph.stream(user_input, config, stream_mode="values", debug=True):
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chunk["messages"][-1].pretty_print()
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# if we were interrupted, we can check for interrupts in state
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current_state = graph.get_state(config)
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if current_state.tasks:
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for task in current_state.tasks:
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if hasattr(task, "interrupts"):
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for interrupt in task.interrupts:
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print(interrupt.value)
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# Once you login using the printed link, you can resume the agent
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