langgraph basic use in tool inject

examples/qwen_langgraph_stream.py

@@ -0,0 +1,56 @@
+"""
+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()