mirror of
https://github.com/shareAI-lab/analysis_claude_code.git
synced 2026-03-22 02:15:42 +08:00
c6a27ef1d7
- 11 sessions from basic agent loop to autonomous teams - Python MVP implementations for each session - Mental-model-first docs in en/zh/ja - Interactive web platform with step-through visualizations - Incremental architecture: each session adds one mechanism
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
#!/usr/bin/env python3
|
|
"""
|
|
s01_agent_loop.py - The Agent Loop
|
|
|
|
The entire secret of coding agents 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)
|
|
|
|
That's it. The ENTIRE agent is a while loop that feeds tool
|
|
results back to the model until the model decides to stop.
|
|
"""
|
|
|
|
import os
|
|
import subprocess
|
|
|
|
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."
|
|
|
|
TOOLS = [{
|
|
"name": "bash",
|
|
"description": "Run a shell command.",
|
|
"input_schema": {
|
|
"type": "object",
|
|
"properties": {"command": {"type": "string"}},
|
|
"required": ["command"],
|
|
},
|
|
}]
|
|
|
|
|
|
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)"
|
|
|
|
|
|
# -- 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
|
|
messages.append({"role": "assistant", "content": response.content})
|
|
# 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})
|
|
messages.append({"role": "user", "content": results})
|
|
|
|
|
|
if __name__ == "__main__":
|
|
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)
|
|
response_content = history[-1]["content"]
|
|
if isinstance(response_content, list):
|
|
for block in response_content:
|
|
if hasattr(block, "text"):
|
|
print(block.text)
|
|
print()
|