langgraph basic use in tool inject

examples/qwen_langgraph_custom_model.py

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+"""
+Use the custom BaseChatModel-like class ChatQwenOpenAICompat with LangGraph.
+
+Prereqs:
+  pip install -U langgraph langchain httpx
+
+Env:
+  export QWEN_API_KEY=sk-...
+  export QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1
+  export QWEN_MODEL=qwen3-coder-flash
+"""
+
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages import HumanMessage
+
+try:
+    from langchain.tools import tool
+except Exception:
+    from langchain_core.tools import tool  # type: ignore
+
+from langgraph_qwen import ChatQwenOpenAICompat
+
+
+@tool
+def multiply(x_and_y: str) -> str:
+    """Multiply two integers given as 'x y'."""
+    try:
+        a, b = [int(s) for s in x_and_y.strip().split()]
+    except Exception:
+        return "Please provide two integers: 'x y'"
+    return str(a * b)
+
+
+def main():
+    base = ChatQwenOpenAICompat(temperature=0)
+    model = base.bind_tools([multiply]).bind(tool_choice="auto")
+
+    agent = create_react_agent(model, [multiply])
+    res = agent.invoke({"messages": [HumanMessage(content="计算 6 和 7 的乘积，然后解释你的步骤。")]} )
+    print("\n=== Final ===\n")
+    print(res["messages"][-1].content)
+
+
+if __name__ == "__main__":
+    main()
+

examples/qwen_langgraph_react.py

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+"""
+Minimal LangGraph ReAct agent using Qwen3-Coder(-Flash) via OpenAI-compatible API.
+
+Prereqs:
+  pip install -U langgraph langchain langchain-openai
+
+Env:
+  export QWEN_API_KEY=sk-...
+  export QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1  # or your gateway
+  export QWEN_MODEL=qwen3-coder-flash  # use console's actual model name
+"""
+import sys,os
+
+
+from typing import Optional
+
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages import HumanMessage
+
+try:
+    from langchain.tools import tool
+except Exception:  # older versions
+    from langchain_core.tools import tool  # type: ignore
+
+from langgraph_qwen import ChatQwenOpenAICompat, bind_qwen_tools
+
+
+@tool
+def get_time(_: str = "") -> str:
+    """Get current local time in ISO format."""
+    import datetime as _dt
+    return _dt.datetime.now().isoformat()
+
+
+@tool
+def add(x_and_y: str) -> str:
+    """Add two integers given as 'x y'."""
+    xs = [int(s) for s in x_and_y.strip().split()]
+    if len(xs) != 2:
+        return "Please provide two integers: 'x y'"
+    return str(xs[0] + xs[1])
+
+
+def main():
+    # Prefer the custom adapter to avoid extra deps (langchain_openai)
+    base = ChatQwenOpenAICompat(temperature=0)
+    model = bind_qwen_tools(base, [get_time, add], tool_choice="auto")
+
+    agent = create_react_agent(model, [get_time, add])
+
+    # A prompt that can trigger multiple tool calls
+    question = (
+        "现在是几点？然后把 7 和 35 相加，并把两者结果合并成一句简洁中文回答。"
+    )
+
+    result = agent.invoke({"messages": [HumanMessage(content=question)]})
+
+    # Unpack final message text
+    last = result["messages"][-1]
+    print("\n=== Agent Final Answer ===\n")
+    print(getattr(last, "content", last))
+
+
+if __name__ == "__main__":
+    main()

examples/qwen_langgraph_stream.py

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+"""
+Streaming example with LangGraph + Qwen. Prints incremental model tokens and
+tool call events.
+
+Prereqs:
+  pip install -U langgraph langchain langchain-openai
+Env:
+  export QWEN_API_KEY=sk-...
+  export QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1
+  export QWEN_MODEL=qwen3-coder-flash
+"""
+
+from langgraph.prebuilt import create_react_agent
+from langchain_core.messages import HumanMessage
+
+try:
+    from langchain.tools import tool
+except Exception:  # older versions
+    from langchain_core.tools import tool  # type: ignore
+
+from langgraph_qwen import ChatQwenOpenAICompat, bind_qwen_tools
+
+
+@tool
+def calc(expr: str) -> str:
+    """Safely evaluate a simple arithmetic expression."""
+    import math
+    allowed = {k: getattr(math, k) for k in ["sqrt", "sin", "cos"]}
+    try:
+        return str(eval(expr, {"__builtins__": {}}, allowed))
+    except Exception as e:
+        return f"error: {e}"
+
+
+def main():
+    base = ChatQwenOpenAICompat(temperature=0)
+    model = bind_qwen_tools(base, [calc], tool_choice="auto")
+    agent = create_react_agent(model, [calc])
+
+    inputs = {"messages": [HumanMessage(content="先说一句你好，然后计算 sqrt(144)。")]} 
+
+    print("Streaming events (tokens and tool calls):\n")
+    for ev in agent.stream(inputs, stream_mode="values"):
+        # ev is the partial state (dict with messages)
+        msg = ev["messages"][-1]
+        role = getattr(msg, "type", getattr(msg, "role", ""))
+        content = getattr(msg, "content", "")
+        # Filter to only incremental assistant outputs
+        if role in ("ai", "assistant"):
+            print(content, end="", flush=True)
+
+    print("\n\nDone.")
+
+
+if __name__ == "__main__":
+    main()

examples/qwen_langgraph_sugar.py

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+"""
+Example using the sugar: create_qwen_react_agent(...)
+
+Prereqs:
+  uv pip install -e '.[openai,custom]'
+
+Env:
+  export QWEN_API_KEY=sk-...
+  export QWEN_BASE_URL=https://dashscope-intl.aliyuncs.com/compatible-mode/v1
+  export QWEN_MODEL=qwen3-coder-flash
+"""
+
+from langchain_core.messages import HumanMessage
+
+try:
+    from langchain.tools import tool
+except Exception:
+    from langchain_core.tools import tool  # type: ignore
+
+from langgraph_qwen import create_qwen_react_agent
+
+
+@tool
+def echo(text: str) -> str:
+    """Echo back the given text."""
+    return text
+
+
+def main():
+    agent = create_qwen_react_agent(
+        [echo],
+        prefer="custom",  # use our custom BaseChatModel by default
+        model_kwargs={"temperature": 0},
+    )
+
+    res = agent.invoke({"messages": [HumanMessage(content="调用 echo 工具返回：你好，LangGraph！")]})
+    print("\n=== Final ===\n")
+    print(res["messages"][-1].content)
+
+
+if __name__ == "__main__":
+    main()
+