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CodingAgent — Autonomous code generation, editing, refactoring (Devin/Genspark style)
"""
import json
import os
import re
from typing import Dict, List
import structlog
from .base_agent import BaseAgent
log = structlog.get_logger()
CODING_SYSTEM = """You are an elite autonomous software engineer — like Devin combined with Genspark.
You write production-quality code that is:
- Clean, readable, well-structured
- Properly typed (TypeScript/Python type hints)
- Error-handled and resilient
- Documented with clear comments
- Following best practices for the language/framework
When generating code:
1. Think about the full architecture first
2. Write complete, runnable code (not snippets)
3. Include proper imports
4. Add error handling
5. Include brief usage examples in comments
Support: Python, TypeScript, JavaScript, Go, Rust, SQL, Shell, YAML, JSON
"""
class CodingAgent(BaseAgent):
def __init__(self, ws_manager=None, ai_router=None):
super().__init__("CodingAgent", ws_manager, ai_router)
async def run(self, task: str, context: Dict = {}, **kwargs) -> str:
session_id = kwargs.get("session_id", "")
task_id = kwargs.get("task_id", "")
await self.emit(task_id, "agent_start", {
"agent": "CodingAgent",
"task": task[:80],
}, session_id)
# Build context-aware messages
prev_results = context.get("previous_results", [])
project_ctx = context.get("project_context", "")
plan = context.get("plan", "")
system_content = CODING_SYSTEM
if project_ctx:
system_content += f"\n\nProject Context:\n{project_ctx[:1000]}"
user_content = f"Task: {task}"
if plan:
user_content += f"\n\nExecution Plan:\n{plan[:500]}"
if prev_results:
user_content += f"\n\nPrevious results:\n" + "\n".join(str(r)[:200] for r in prev_results[-3:])
messages = [
{"role": "system", "content": system_content},
{"role": "user", "content": user_content},
]
await self.emit(task_id, "tool_called", {
"agent": "CodingAgent",
"tool": "code_generation",
"step": task[:60],
}, session_id)
result = await self.llm(
messages,
task_id=task_id,
session_id=session_id,
temperature=0.2,
max_tokens=8192,
)
# Extract code blocks for display
code_blocks = self._extract_code_blocks(result)
await self.emit(task_id, "code_generated", {
"agent": "CodingAgent",
"code_blocks": len(code_blocks),
"total_lines": sum(len(b.split("\n")) for b in code_blocks),
"languages": list(set(self._detect_language(b) for b in code_blocks)),
}, session_id)
return result
async def generate_file(
self,
filename: str,
description: str,
task_id: str = "",
session_id: str = "",
context: Dict = {},
) -> str:
"""Generate a complete file with proper structure."""
messages = [
{"role": "system", "content": CODING_SYSTEM},
{"role": "user", "content": (
f"Generate a complete, production-ready file.\n"
f"Filename: {filename}\n"
f"Description: {description}\n"
f"Context: {json.dumps(context)[:500]}\n\n"
f"Return ONLY the file content, no explanation."
)},
]
content = await self.llm(messages, task_id=task_id, session_id=session_id, temperature=0.1, max_tokens=8192)
# Strip markdown code fences if present
content = self._strip_code_fences(content)
# Write to workspace
workspace = os.environ.get("WORKSPACE_DIR", "/tmp/god_workspace")
filepath = os.path.join(workspace, filename)
os.makedirs(os.path.dirname(filepath), exist_ok=True)
with open(filepath, "w") as f:
f.write(content)
await self.emit(task_id, "file_written", {
"filename": filename,
"size": len(content),
"lines": len(content.split("\n")),
}, session_id)
return content
async def refactor(
self,
code: str,
instructions: str,
task_id: str = "",
session_id: str = "",
) -> str:
"""Refactor existing code based on instructions."""
messages = [
{"role": "system", "content": CODING_SYSTEM},
{"role": "user", "content": (
f"Refactor this code based on these instructions:\n"
f"Instructions: {instructions}\n\n"
f"Original code:\n```\n{code}\n```\n\n"
f"Return ONLY the refactored code."
)},
]
return await self.llm(messages, task_id=task_id, session_id=session_id, temperature=0.1, max_tokens=8192)
async def scan_repository(self, repo_path: str) -> Dict:
"""Scan repository and build project intelligence graph."""
import subprocess
try:
result = subprocess.run(
["find", repo_path, "-type", "f", "-name", "*.py", "-o",
"-name", "*.ts", "-o", "-name", "*.js", "-o", "-name", "*.go"],
capture_output=True, text=True, timeout=10
)
files = result.stdout.strip().split("\n")[:50]
# Read key files
key_files = {}
for f in ["package.json", "requirements.txt", "tsconfig.json", "pyproject.toml", "go.mod"]:
path = os.path.join(repo_path, f)
if os.path.exists(path):
with open(path) as fp:
key_files[f] = fp.read()[:1000]
return {
"files": files,
"key_configs": key_files,
"total_files": len(files),
}
except Exception as e:
return {"error": str(e), "files": [], "key_configs": {}}
def _extract_code_blocks(self, text: str) -> List[str]:
pattern = r"```[\w]*\n(.*?)```"
return re.findall(pattern, text, re.DOTALL)
def _strip_code_fences(self, text: str) -> str:
text = re.sub(r"^```[\w]*\n", "", text.strip())
text = re.sub(r"\n```$", "", text)
return text
def _detect_language(self, code: str) -> str:
if "def " in code and "import " in code:
return "python"
if "function " in code or "const " in code or "interface " in code:
return "typescript"
if "package main" in code:
return "go"
return "unknown"
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