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Claude Code Agent Module - Agent implementation driving the Claude Code CLI.
This module provides an agent that:
- Writes a task prompt and a wrapper script to the agent's Docker container
- Runs the `claude` CLI inside the container with a timeout, reading Anthropic
API credentials from the container's environment (injected by run.py)
- Parses Claude Code's stream-json (or json) output into a normalized trajectory,
capturing per-step timing, tool uses, and token usage
"""
import json
import logging
import os
import getpass
import subprocess
import time
from da_agent.envs.da_agent import DA_Agent_Env
from da_agent.agent.base import BaseAgent
logger = logging.getLogger("da_agent")
DEFAULT_TIME_OUT = 3600 # 60 minutes
class PromptAgent(BaseAgent):
# Claude Code drives the `claude` CLI; only model is used from the shared
# config (the timeout uses DEFAULT_TIME_OUT instead of max_steps). No
# cross-task state, so __init__ is inherited from BaseAgent.
def set_env_and_task(self, env: DA_Agent_Env):
self.env = env
self.instruction = self.env.task_config['question']
self.trajectory = []
self.raw_output = ""
self.event_timestamps = []
def _build_task_prompt(self):
task = self.instruction
task += f"\n\nYou are working in the directory: {self.work_dir}."
task += " All required data files are available in this directory."
task += " Complete the task and ensure all output files are saved in this directory."
image_file_names = self._get_image_file_names()
if image_file_names:
task += self._build_plotting_instructions(image_file_names)
return task
def _get_image_file_names(self):
image_file_names = []
for post_process_f in self.env.post_process_func:
def image_post_process(output_file_name):
if output_file_name in self.env.task_config.get('output_file_name', []):
return output_file_name
return None
output_file_name = eval(post_process_f)
if output_file_name:
image_file_names.append(output_file_name)
return image_file_names
def _build_plotting_instructions(self, image_file_names):
return f"""
### Plotting (REQUIRED)
If you create a matplotlib plot, you MUST call:
from image import Plotprocess
Plotprocess.plot_process(fig, "<image_file_name>")
Use ONLY these file names:
{", ".join(image_file_names)}
Rules:
- Call AFTER plotting is complete
- Call BEFORE saving the figure
- Use: fig = plt.gcf()
- Replace <image_file_name> with one from the list above
Example:
```python
from image import Plotprocess
import matplotlib.pyplot as plt
# plotting code ...
fig = plt.gcf()
Plotprocess.plot_process(fig, "{image_file_names[0]}")
```"""
def _write_wrapper_script(self):
wrapper_code = f'''#!/usr/bin/env python3
import subprocess
import sys
import threading
with open("{self.work_dir}/.task_prompt.txt") as f:
prompt = f.read()
proc = subprocess.Popen(
["claude", "-p", prompt, "--output-format", "stream-json", "--verbose",
"--model", "{self.model}",
"--dangerously-skip-permissions"],
stdout=sys.stdout, stderr=sys.stderr
)
def timeout_handler():
proc.terminate()
kill_timer = threading.Timer(300, proc.kill)
kill_timer.daemon = True
kill_timer.start()
timer = threading.Timer({DEFAULT_TIME_OUT}, timeout_handler)
timer.daemon = True
timer.start()
sys.exit(proc.wait())
'''
wrapper_path = os.path.join(self.env.mnt_dir, ".run_claude.py")
with open(wrapper_path, "w") as f:
f.write(wrapper_code)
def run(self):
assert self.env is not None, "Environment is not set."
task_prompt = self._build_task_prompt()
container_name = self.env.container.name
# Write task prompt and wrapper script to mounted directory
task_path = os.path.join(self.env.mnt_dir, ".task_prompt.txt")
with open(task_path, "w") as f:
f.write(task_prompt)
self._write_wrapper_script()
# Execute wrapper script inside the container as the non-root user named
# after the host user.
process = subprocess.Popen(
["docker", "exec", "--user", getpass.getuser(), str(container_name),
"python3", f"{self.work_dir}/.run_claude.py"],
stdout=subprocess.PIPE, stderr=subprocess.STDOUT
)
output_lines = []
self.event_timestamps = []
try:
while True:
line = process.stdout.readline()
if not line and process.poll() is not None:
break
if line:
decoded = line.decode("utf-8", errors="ignore")
output_lines.append(decoded)
self.event_timestamps.append(time.time())
logger.debug("Claude Code: %s", decoded.strip())
except Exception as e:
process.kill()
logger.error("Error running Claude Code: %s", e)
self.raw_output = "".join(output_lines)
self._parse_trajectory()
return False, f"Error: {e}"
self.raw_output = "".join(output_lines)
exit_code = process.returncode
self._parse_trajectory()
if exit_code == 0:
return True, "Task completed"
else:
return False, f"Agent exited with code {exit_code}"
def _parse_trajectory(self):
self.trajectory = []
# Try parsing as a single JSON array (--output-format json)
try:
entries = json.loads(self.raw_output.strip())
if isinstance(entries, list):
for entry in entries:
normalized = self._normalize_entry(entry)
self.trajectory.append(normalized)
return
except json.JSONDecodeError:
pass
# Fall back to JSONL parsing (--output-format stream-json)
lines = self.raw_output.strip().split("\n")
for i, line in enumerate(lines):
line = line.strip()
if not line:
continue
try:
entry = json.loads(line)
normalized = self._normalize_entry(entry)
# Add timing info from recorded timestamps (stream-json only)
if i < len(self.event_timestamps):
ts = self.event_timestamps[i]
prev_ts = self.event_timestamps[i - 1] if i > 0 else ts
normalized["timing"] = {
"start_time": prev_ts,
"end_time": ts,
"duration": ts - prev_ts,
}
self.trajectory.append(normalized)
except json.JSONDecodeError:
step = {"type": "raw", "content": line}
if i < len(self.event_timestamps):
ts = self.event_timestamps[i]
prev_ts = self.event_timestamps[i - 1] if i > 0 else ts
step["timing"] = {
"start_time": prev_ts,
"end_time": ts,
"duration": ts - prev_ts,
}
self.trajectory.append(step)
def _normalize_entry(self, entry):
entry_type = entry.get("type", "unknown")
if entry_type == "system":
subtype = entry.get("subtype", "")
if subtype == "init":
return {
"type": "system_init",
"session_id": entry.get("session_id", ""),
"model": entry.get("model", ""),
"cwd": entry.get("cwd", ""),
}
return {"type": "system", "subtype": subtype}
if entry_type == "assistant":
message = entry.get("message", {})
msg_id = message.get("id", "")
content = message.get("content", [])
text_parts = []
code_action = None
tool_uses = []
for block in content if isinstance(content, list) else []:
if not isinstance(block, dict):
continue
if block.get("type") == "text":
text_parts.append(block.get("text", ""))
elif block.get("type") == "tool_use":
tool_name = block.get("name", "")
tool_input = block.get("input", {})
tool_uses.append({"name": tool_name, "input": tool_input})
if tool_name == "Bash" and "command" in tool_input:
code_action = tool_input["command"]
result = {"type": "assistant", "content": "\n".join(text_parts)}
if msg_id:
result["msg_id"] = msg_id
if tool_uses:
result["tool_uses"] = tool_uses
if code_action:
result["code_action"] = code_action
# Extract per-message usage if available
usage = message.get("usage", {})
if usage:
result["usage"] = usage
return result
elif entry_type == "tool_result":
content = entry.get("content", "")
if isinstance(content, list):
text_parts = []
for block in content:
if isinstance(block, dict) and block.get("type") == "text":
text_parts.append(block.get("text", ""))
content = "\n".join(text_parts)
result = {"type": "tool_result", "observations": f"Execution logs:\n{content}"}
return result
elif entry_type == "result":
result_entry = {
"type": "result",
"subtype": entry.get("subtype", ""),
"is_error": entry.get("is_error", False),
"result": entry.get("result", ""),
"stop_reason": entry.get("stop_reason", ""),
"duration_ms": entry.get("duration_ms", 0),
"num_turns": entry.get("num_turns", 0),
}
# Extract aggregate usage from the result event
usage = entry.get("usage", {})
if usage:
result_entry["usage"] = usage
return result_entry
return entry
def get_trajectory(self):
return {
"task": self.instruction,
"trajectory": self.trajectory
} |