diff --git a/08_streaming.py b/08_streaming.py
new file mode 100644
index 0000000..7dd0a29
--- /dev/null
+++ b/08_streaming.py
@@ -0,0 +1,254 @@
+"""LangGraph Streaming 示例：节点更新 + 模型 Token 流。
+
+图结构：
+
+    START -> model
+               | 有 tool_calls
+               v
+             tools
+               |
+               +---------> model
+
+             model -- 无 tool_calls --> END
+
+本课重点不是工具本身，而是三种 stream_mode：
+
+1. updates  —— 每个节点执行完后，推送该节点的状态增量
+2. messages —— 模型生成时，逐 token 推送 AIMessageChunk
+3. 同时开两者 —— 一边打字机输出，一边看到节点何时完成
+
+运行：
+    uv run --with langgraph --with langchain-openai --with python-dotenv \
+        python 08_streaming.py
+"""
+
+import os
+from typing import Literal
+
+from dotenv import load_dotenv
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, MessagesState, StateGraph
+from langgraph.prebuilt import ToolNode
+
+load_dotenv()
+
+
+# -----------------------------------------------------------------------------
+# 1. 工具与模型（沿用上一课的天气查询，方便观察节点切换）
+# -----------------------------------------------------------------------------
+
+
+@tool
+def get_weather(city: str) -> str:
+    """查询指定城市当前的天气。城市名称使用中文，例如北京、上海。"""
+    weather_data = {
+        "北京": "晴，25°C，北风 2 级",
+        "上海": "多云，28°C，东南风 3 级",
+        "广州": "雷阵雨，30°C，南风 2 级",
+        "深圳": "阴，29°C，东风 2 级",
+    }
+    return weather_data.get(city, f"暂时没有 {city} 的天气数据")
+
+
+tools = [get_weather]
+model = ChatOpenAI(
+    model=os.getenv("OPENAI_MODEL", "openai/gpt-5.4"),
+    api_key=os.getenv("OPENAI_API_KEY"),
+    base_url=os.getenv("OPENAI_BASE_URL", "https://api.qnaigc.com/v1"),
+    temperature=0,
+    max_retries=2,
+)
+model_with_tools = model.bind_tools(tools)
+
+
+def call_model(state: MessagesState) -> dict:
+    # 即使这里写的是 .invoke，stream_mode="messages" 仍能拿到 token 流。
+    # LangGraph 会自动把内部 LLM 调用的流式事件透传出来。
+    response = model_with_tools.invoke(state["messages"])
+    return {"messages": [response]}
+
+
+def route_after_model(state: MessagesState) -> Literal["tools", "__end__"]:
+    last_message = state["messages"][-1]
+    if last_message.tool_calls:
+        return "tools"
+    return END
+
+
+tool_node = ToolNode(tools)
+
+builder = StateGraph(MessagesState)
+builder.add_node("model", call_model)
+builder.add_node("tools", tool_node)
+builder.add_edge(START, "model")
+builder.add_conditional_edges(
+    "model",
+    route_after_model,
+    {"tools": "tools", END: END},
+)
+builder.add_edge("tools", "model")
+graph = builder.compile()
+
+
+# -----------------------------------------------------------------------------
+# 2. 演示 A：stream_mode="updates" —— 看节点级增量
+# -----------------------------------------------------------------------------
+
+
+def demo_updates(question: str) -> None:
+    """每个节点跑完才出一包数据：{节点名: 该节点返回的状态增量}。"""
+    print("\n--- stream_mode='updates' ---")
+
+    for event in graph.stream(
+        {"messages": [{"role": "user", "content": question}]},
+        stream_mode="updates",
+    ):
+        # event 形如：{"model": {"messages": [AIMessage(...)]}}
+        #          或：{"tools": {"messages": [ToolMessage(...)]}}
+        for node_name, update in event.items():
+            print(f"\n[{node_name}] 节点完成，增量：")
+            for message in update.get("messages", []):
+                message.pretty_print()
+
+
+# -----------------------------------------------------------------------------
+# 3. 演示 B：stream_mode="messages" —— 看模型 Token 流
+# -----------------------------------------------------------------------------
+
+
+def demo_messages(question: str) -> None:
+    """模型一边生成，一边吐 AIMessageChunk；适合做打字机效果。"""
+    print("\n--- stream_mode='messages' ---")
+    print("助手：", end="", flush=True)
+
+    for chunk in graph.stream(
+        {"messages": [{"role": "user", "content": question}]},
+        stream_mode="messages",
+        version="v2",
+    ):
+        # v2 统一格式：{"type": "messages", "data": (message_chunk, metadata)}
+        if chunk["type"] != "messages":
+            continue
+
+        message_chunk, metadata = chunk["data"]
+
+        # 只打印 model 节点吐出的文本 token；工具调用阶段 content 常为空。
+        if metadata.get("langgraph_node") != "model":
+            continue
+
+        content = message_chunk.content
+        if content:
+            # content 可能是 str，也可能是 content blocks 列表，这里只处理文本。
+            if isinstance(content, str):
+                print(content, end="", flush=True)
+            elif isinstance(content, list):
+                for block in content:
+                    if isinstance(block, dict) and block.get("type") == "text":
+                        print(block.get("text", ""), end="", flush=True)
+                    elif isinstance(block, str):
+                        print(block, end="", flush=True)
+
+    print()  # 换行
+
+
+# -----------------------------------------------------------------------------
+# 4. 演示 C：同时开 updates + messages —— 最实用的组合
+# -----------------------------------------------------------------------------
+
+
+def demo_both(question: str) -> None:
+    """一边流式打印 token，一边在节点结束时打印节点名。"""
+    print("\n--- stream_mode=['updates', 'messages'] ---")
+    print("助手：", end="", flush=True)
+    printed_tokens = False
+
+    for chunk in graph.stream(
+        {"messages": [{"role": "user", "content": question}]},
+        stream_mode=["updates", "messages"],
+        version="v2",
+    ):
+        if chunk["type"] == "messages":
+            message_chunk, metadata = chunk["data"]
+            if metadata.get("langgraph_node") != "model":
+                continue
+
+            content = message_chunk.content
+            if isinstance(content, str) and content:
+                print(content, end="", flush=True)
+                printed_tokens = True
+            elif isinstance(content, list):
+                for block in content:
+                    text = ""
+                    if isinstance(block, dict) and block.get("type") == "text":
+                        text = block.get("text", "")
+                    elif isinstance(block, str):
+                        text = block
+                    if text:
+                        print(text, end="", flush=True)
+                        printed_tokens = True
+
+        elif chunk["type"] == "updates":
+            # 节点结束后再换行并标注，避免和 token 混在同一行。
+            for node_name in chunk["data"]:
+                if printed_tokens:
+                    print()
+                    printed_tokens = False
+                print(f"  ✓ 节点 [{node_name}] 完成")
+                if node_name == "model":
+                    print("助手：", end="", flush=True)
+
+    if printed_tokens:
+        print()
+
+
+# -----------------------------------------------------------------------------
+# 5. 入口：依次跑三种模式，对比输出差异
+# -----------------------------------------------------------------------------
+
+
+def main() -> None:
+    print("=== LangGraph Streaming：节点更新 + Token 流 ===")
+    print("建议先问：北京天气怎么样？  （会触发 tools -> model 两跳）")
+    print("或问：用一句话介绍 LangGraph。 （只走 model）")
+    print("输入 quit 退出。\n")
+    print("命令：")
+    print("  /u  只看 updates")
+    print("  /m  只看 messages（token）")
+    print("  /b  同时看两者（默认）")
+
+    mode = "both"
+
+    while True:
+        question = input(f"\n用户[{mode}]：").strip()
+        if question.lower() in {"quit", "exit", "q"}:
+            break
+        if not question:
+            continue
+
+        if question == "/u":
+            mode = "updates"
+            print("已切换到 updates")
+            continue
+        if question == "/m":
+            mode = "messages"
+            print("已切换到 messages")
+            continue
+        if question == "/b":
+            mode = "both"
+            print("已切换到 both")
+            continue
+
+        try:
+            if mode == "updates":
+                demo_updates(question)
+            elif mode == "messages":
+                demo_messages(question)
+            else:
+                demo_both(question)
+        except Exception as exc:
+            print(f"\n执行失败：{type(exc).__name__}: {exc}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/09_retry_and_errors.py b/09_retry_and_errors.py
new file mode 100644
index 0000000..6e7a75e
--- /dev/null
+++ b/09_retry_and_errors.py
@@ -0,0 +1,185 @@
+"""LangGraph 容错示例：重试、错误恢复与循环保护。
+
+本文件不调用真实 LLM/API，可以直接运行并稳定复现实验：
+
+1. transient：前两次 ConnectionError，第三次成功
+2. permanent：ConnectionError 重试耗尽后进入 error_handler
+3. invalid：ValueError 不重试，直接进入 error_handler
+4. loop：故意无限循环，由 recursion_limit 终止
+
+需要 langgraph >= 1.2（节点级 error_handler / NodeError 是 1.2+ 能力）。
+
+运行：
+    python 09_retry_and_errors.py
+"""
+
+from typing import Literal, TypedDict
+
+from langgraph.errors import GraphRecursionError, NodeError
+from langgraph.graph import END, START, StateGraph
+from langgraph.runtime import Runtime
+from langgraph.types import Command, RetryPolicy
+
+
+# -----------------------------------------------------------------------------
+# 1. 节点重试 + 重试耗尽后的错误恢复
+# -----------------------------------------------------------------------------
+
+
+class ApiState(TypedDict):
+    mode: Literal["transient", "permanent", "invalid"]
+    result: str
+    status: str
+    error: str
+
+
+def call_unstable_api(
+    state: ApiState,
+    runtime: Runtime,
+) -> Command[Literal["finalize"]]:
+    """模拟一个不稳定的外部 API。
+
+    注意：失败尝试产生的 State 更新不会提交，因此不要用 State 字段记录
+    当前重试次数。LangGraph 已通过 execution_info 提供 node_attempt。
+    """
+    attempt = runtime.execution_info.node_attempt
+    mode = state["mode"]
+    print(f"  call_unstable_api：第 {attempt} 次尝试，mode={mode}")
+
+    if mode == "transient" and attempt < 3:
+        raise ConnectionError("模拟网络抖动")
+
+    if mode == "permanent":
+        raise ConnectionError("模拟上游服务持续不可用")
+
+    if mode == "invalid":
+        # ValueError 通常代表参数/业务错误，重复请求不会自行恢复。
+        raise ValueError("模拟无效请求参数")
+
+    return Command(
+        update={
+            "result": "API 调用成功",
+            "status": "success",
+            "error": "",
+        },
+        goto="finalize",
+    )
+
+
+def api_error_handler(
+    state: ApiState,
+    error: NodeError,
+) -> Command[Literal["finalize"]]:
+    """在不重试或重试耗尽后，将异常转成可控的业务结果。"""
+    print(
+        f"  error_handler：节点 {error.node} 最终失败："
+        f"{type(error.error).__name__}: {error.error}"
+    )
+    return Command(
+        update={
+            "result": "已启用降级结果，请稍后重试",
+            "status": "recovered",
+            "error": f"{type(error.error).__name__}: {error.error}",
+        },
+        goto="finalize",
+    )
+
+
+def finalize(state: ApiState) -> dict:
+    """统一收尾节点；成功和降级路径都会到达这里。"""
+    print(f"  finalize：status={state['status']}, result={state['result']}")
+    return {}
+
+
+api_builder = StateGraph(ApiState)
+api_builder.add_node(
+    "call_api",
+    call_unstable_api,
+    retry_policy=RetryPolicy(
+        max_attempts=3,          # 包含第一次，共最多执行 3 次
+        initial_interval=0.1,   # 第一次重试前等待 0.1 秒
+        backoff_factor=2.0,     # 后续等待时间指数增长
+        max_interval=1.0,
+        jitter=False,           # 教学输出保持可预测
+        retry_on=ConnectionError,
+    ),
+    error_handler=api_error_handler,
+)
+api_builder.add_node("finalize", finalize)
+api_builder.add_edge(START, "call_api")
+api_builder.add_edge("finalize", END)
+api_graph = api_builder.compile()
+
+
+def run_api_case(mode: Literal["transient", "permanent", "invalid"]) -> None:
+    """运行一个重试/错误恢复场景，并打印最终状态。"""
+    print(f"\n=== 场景：{mode} ===")
+    result = api_graph.invoke(
+        {
+            "mode": mode,
+            "result": "",
+            "status": "pending",
+            "error": "",
+        }
+    )
+    print("  最终状态：", result)
+
+
+# -----------------------------------------------------------------------------
+# 2. 循环保护：recursion_limit
+# -----------------------------------------------------------------------------
+
+
+class LoopState(TypedDict):
+    count: int
+
+
+def loop_forever(state: LoopState) -> dict:
+    """故意不提供终止条件，用于演示 GraphRecursionError。"""
+    next_count = state["count"] + 1
+    print(f"  loop：count={next_count}")
+    return {"count": next_count}
+
+
+loop_builder = StateGraph(LoopState)
+loop_builder.add_node("loop", loop_forever)
+loop_builder.add_edge(START, "loop")
+loop_builder.add_edge("loop", "loop")
+loop_graph = loop_builder.compile()
+
+
+def run_loop_case() -> None:
+    """用较小的递归限制安全终止错误设计的循环图。"""
+    print("\n=== 场景：loop / recursion_limit ===")
+    try:
+        loop_graph.invoke(
+            {"count": 0},
+            config={"recursion_limit": 4},
+        )
+    except GraphRecursionError as exc:
+        print(f"  已捕获 {type(exc).__name__}")
+        print("  原因：图在限制步数内没有到达 END。")
+        print("  正确做法：为循环增加业务终止条件；recursion_limit 只是最后防线。")
+
+
+# -----------------------------------------------------------------------------
+# 3. 入口
+# -----------------------------------------------------------------------------
+
+
+def main() -> None:
+    print("=== LangGraph Retry & Errors ===")
+    run_api_case("transient")
+    run_api_case("permanent")
+    run_api_case("invalid")
+    run_loop_case()
+
+    print("\n=== 观察结论 ===")
+    print("1. ConnectionError 会按照 RetryPolicy 自动重试。")
+    print("2. ValueError 不符合 retry_on，第一次失败后立即交给 error_handler。")
+    print("3. 重试耗尽后，error_handler 可用 Command 更新状态并走降级路径。")
+    print("4. recursion_limit 能阻止失控循环，但不能替代正确的终止条件。")
+
+
+if __name__ == "__main__":
+    main()