diff --git a/s01_agent_loop/try.py b/s01_agent_loop/try.py
new file mode 100644
index 000000000..200fd0bba
--- /dev/null
+++ b/s01_agent_loop/try.py
@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+"""
+s01_agent_loop.py - The Agent Loop
+
+The entire secret of an AI coding agent in one pattern:
+
+    while stop_reason == "tool_use":
+        response = LLM(messages, tools)
+        execute tools
+        append results
+
+    +----------+      +-------+      +---------+
+    |   User   | ---> |  LLM  | ---> |  Tool   |
+    |  prompt  |      |       |      | execute |
+    +----------+      +---+---+      +----+----+
+                          ^               |
+                          |   tool_result |
+                          +---------------+
+                          (loop continues)
+
+This is the core loop: feed tool results back to the model
+until the model decides to stop. Production agents layer
+policy, hooks, and lifecycle controls on top.
+
+Usage:
+    pip install anthropic python-dotenv
+    ANTHROPIC_API_KEY=... python s01_agent_loop/code.py
+"""
+
+import os
+import subprocess
+import httpx
+http_client = httpx.Client(verify=False)
+
+
+try:
+    import readline
+    # macOS 的 libedit 在处理中文输入时有退格问题，这四行修复它
+    readline.parse_and_bind('set bind-tty-special-chars off')
+    readline.parse_and_bind('set input-meta on')
+    readline.parse_and_bind('set output-meta on')
+    readline.parse_and_bind('set convert-meta off')
+except ImportError:
+    pass
+
+from anthropic import Anthropic
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+if os.getenv("ANTHROPIC_BASE_URL"):
+    os.environ.pop("ANTHROPIC_AUTH_TOKEN", None)
+
+client = Anthropic(base_url=os.getenv("ANTHROPIC_BASE_URL"))
+MODEL = os.environ["MODEL_ID"]
+
+SYSTEM = f"You are a coding agent at {os.getcwd()}. Use bash to solve tasks. Act, don't explain."
+
+# ── Tool definition: just bash ────────────────────────────
+TOOLS = [{
+    "name": "bash",
+    "description": "Run a shell command.",
+    "input_schema": {
+        "type": "object",
+        "properties": {"command": {"type": "string"}},
+        "required": ["command"],
+    },
+}]
+
+
+# ── Tool execution ────────────────────────────────────────
+def run_bash(command: str) -> str:
+    dangerous = ["rm -rf /", "sudo", "shutdown", "reboot", "> /dev/"]
+    if any(d in command for d in dangerous):
+        return "Error: Dangerous command blocked"
+    try:
+        r = subprocess.run(command, shell=True, cwd=os.getcwd(),
+                           capture_output=True, text=True, timeout=120)
+        out = (r.stdout + r.stderr).strip()
+        return out[:50000] if out else "(no output)"
+    except subprocess.TimeoutExpired:
+        return "Error: Timeout (120s)"
+    except (FileNotFoundError, OSError) as e:
+        return f"Error: {e}"
+
+
+# ── The core pattern: a while loop that calls tools until the model stops ──
+def agent_loop(messages: list):
+    while True:
+        response = client.messages.create(
+            model=MODEL, system=SYSTEM, messages=messages,
+            tools=TOOLS, max_tokens=8000,
+        )
+        
+        # Append assistant turn
+        #每次调用大模型后，都会把模型的回复加入messages(即histoty)
+        messages.append({"role": "assistant", "content": response.content})
+        # ✅ 新增：打印大模型每次的回复内容（文本 + 思考过程）
+        for block in response.content:
+            print(f"\033[32m [AI 原始输出]\033[0m {block.model_dump()}")
+            if block.type == "text":
+                print(f"\033[32m🤖 [AI 回复]\033[0m {block.text}")
+            elif block.type == "thinking":
+                # thinking 是 Claude 3.5/4 等新模型的思维链字段
+                print(f"\033[90m🧠 [AI 思考]\033[0m {getattr(block, 'thinking', '')}")
+        
+        print("stop_reason:", response.stop_reason)
+        # If the model didn't call a tool, we're done
+        if response.stop_reason != "tool_use":
+            return
+
+        # Execute each tool call, collect results
+        results = []
+        for block in response.content:
+            if block.type == "tool_use":
+                print(f"\033[33m$ {block.input['command']}\033[0m")
+                output = run_bash(block.input["command"])
+                print(output[:200])
+                results.append({
+                    "type": "tool_result",
+                    "tool_use_id": block.id,
+                    "content": output,
+                })
+
+        # Feed tool results back, loop continues
+        messages.append({"role": "user", "content": results})
+
+
+# ── Entry point ──────────────────────────────────────────
+if __name__ == "__main__":
+    print("s01: Agent Loop")
+    print("输入问题，回车发送。输入 q 退出。\n")
+
+    history = []
+    while True:
+        try:
+            query = input("\033[36ms01 >> \033[0m")
+        except (EOFError, KeyboardInterrupt):
+            break
+        if query.strip().lower() in ("q", "exit", ""):
+            break
+        history.append({"role": "user", "content": query})
+        agent_loop(history)
+        # Print the model's final text response
+        response_content = history[-1]["content"]
+        if isinstance(response_content, list):
+            for block in response_content:
+                if getattr(block, "type", None) == "text":
+                    print(block.text)
+        print()
diff --git a/s09_memory/code.py b/s09_memory/code.py
index 7dd39c813..c7846fe18 100644
--- a/s09_memory/code.py
+++ b/s09_memory/code.py
@@ -259,7 +259,10 @@ def extract_memories(messages: list):
         response = client.messages.create(
             model=MODEL, messages=[{"role": "user", "content": prompt}], max_tokens=800
         )
-        text = response.content[0].text.strip()
+        for block in response.content:
+            if block.type == 'text':
+            text = block.text
+        
         # Extract JSON array from response
         match = re.search(r'\[.*\]', text, re.DOTALL)
         if not match: