mirror of
https://github.com/shareAI-lab/analysis_claude_code.git
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138 lines
4.6 KiB
Python
138 lines
4.6 KiB
Python
#!/usr/bin/env python3
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"""
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s01_agent_loop.py - The Agent Loop
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The entire secret of an AI coding agent in one pattern:
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while stop_reason == "tool_use":
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response = LLM(messages, tools)
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execute tools
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append results
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+----------+ +-------+ +---------+
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| User | ---> | LLM | ---> | Tool |
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| prompt | | | | execute |
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+----------+ +---+---+ +----+----+
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^ |
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| tool_result |
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+---------------+
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(loop continues)
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This is the core loop: feed tool results back to the model
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until the model decides to stop. Production agents layer
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policy, hooks, and lifecycle controls on top.
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Usage:
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pip install anthropic python-dotenv
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ANTHROPIC_API_KEY=... python s01_agent_loop/code.py
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"""
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import os
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import subprocess
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try:
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import readline
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# macOS 的 libedit 在处理中文输入时有退格问题，这四行修复它
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readline.parse_and_bind('set bind-tty-special-chars off')
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readline.parse_and_bind('set input-meta on')
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readline.parse_and_bind('set output-meta on')
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readline.parse_and_bind('set convert-meta off')
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except ImportError:
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pass
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from anthropic import Anthropic
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from dotenv import load_dotenv
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load_dotenv(override=True)
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if os.getenv("ANTHROPIC_BASE_URL"):
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os.environ.pop("ANTHROPIC_AUTH_TOKEN", None)
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client = Anthropic(base_url=os.getenv("ANTHROPIC_BASE_URL"))
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MODEL = os.environ["MODEL_ID"]
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SYSTEM = f"You are a coding agent at {os.getcwd()}. Use bash to solve tasks. Act, don't explain."
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# ── Tool definition: just bash ────────────────────────────
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TOOLS = [{
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"name": "bash",
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"description": "Run a shell command.",
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"input_schema": {
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"type": "object",
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"properties": {"command": {"type": "string"}},
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"required": ["command"],
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},
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}]
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# ── Tool execution ────────────────────────────────────────
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def run_bash(command: str) -> str:
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dangerous = ["rm -rf /", "sudo", "shutdown", "reboot", "> /dev/"]
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if any(d in command for d in dangerous):
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return "Error: Dangerous command blocked"
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try:
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r = subprocess.run(command, shell=True, cwd=os.getcwd(),
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capture_output=True, text=True, timeout=120)
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out = (r.stdout + r.stderr).strip()
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return out[:50000] if out else "(no output)"
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except subprocess.TimeoutExpired:
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return "Error: Timeout (120s)"
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except (FileNotFoundError, OSError) as e:
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return f"Error: {e}"
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# ── The core pattern: a while loop that calls tools until the model stops ──
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def agent_loop(messages: list):
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while True:
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response = client.messages.create(
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model=MODEL, system=SYSTEM, messages=messages,
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tools=TOOLS, max_tokens=8000,
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)
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# Append assistant turn
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messages.append({"role": "assistant", "content": response.content})
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# If the model didn't call a tool, we're done
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if response.stop_reason != "tool_use":
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return
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# Execute each tool call, collect results
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results = []
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for block in response.content:
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if block.type == "tool_use":
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print(f"\033[33m$ {block.input['command']}\033[0m")
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output = run_bash(block.input["command"])
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print(output[:200])
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results.append({
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"type": "tool_result",
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"tool_use_id": block.id,
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"content": output,
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})
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# Feed tool results back, loop continues
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messages.append({"role": "user", "content": results})
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# ── Entry point ──────────────────────────────────────────
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if __name__ == "__main__":
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print("s01: Agent Loop")
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print("输入问题，回车发送。输入 q 退出。\n")
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history = []
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while True:
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try:
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query = input("\033[36ms01 >> \033[0m")
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except (EOFError, KeyboardInterrupt):
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break
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if query.strip().lower() in ("q", "exit", ""):
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break
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history.append({"role": "user", "content": query})
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agent_loop(history)
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# Print the model's final text response
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response_content = history[-1]["content"]
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if isinstance(response_content, list):
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for block in response_content:
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if getattr(block, "type", None) == "text":
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print(block.text)
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print()
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