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import asyncio
import re
from typing import Any, Callable
from core.llm_engine import get_llm
from core.config import AgentConfig
from schemas.schema import CodingAgentState, CodingSubtask
from langchain_core.messages import HumanMessage, SystemMessage
from utils.json_helpers import (
sanitize_fenced_json,
extract_first_json_object,
load_json_object,
clamp_score,
normalize_text,
normalize_errors,
normalize_serious_mistakes,
)
from utils.graph_nodes import get_coding_node_coords
# βββ Node Coordinates (for frontend graph) ββββββββββββββββββββββββββββββββββββ
NODE_COORDS = get_coding_node_coords()
def get_node_coords() -> dict:
"""Return the node coordinate map for frontend graph rendering."""
return NODE_COORDS
# βββ Code Planner Node βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
async def code_planner_node(state: CodingAgentState) -> dict:
"""Decomposes the task into coding subtasks with shared interface."""
llm = get_llm(temperature=AgentConfig.CodingAgent.PLANNER_TEMPERATURE, instant=True)
task = state["original_task"]
prompt = f"""You are a senior software architect. Given the following coding task, break it into multiple independent subtasks that can be implemented in parallel.
Task: {task}
INSTRUCTIONS:
- Analyze what the task requires (could be any language: Python, HTML/CSS/JS, Java, etc.)
- Break it into Multiple logical, independent subtasks
- Each subtask should have a clear description of what it implements
- Include file names if the task naturally produces multiple files.
- The shared_contract should describe the overall structure/interfaces that all agents must respect
Respond in EXACTLY this JSON format (no other text):
{{
"subtasks": [
{{
"id": 1,
"description": "Clear description of what this subtask implements",
"signatures": ["Describe the key elements/functions this subtask should produce"]
}},
{{
"id": 2,
"description": "Clear description of what this subtask implements",
"signatures": ["Describe the key elements/functions this subtask should produce"]
}},
{{
"id": 3,
"description": "Clear description of what this subtask implements",
"signatures": ["Describe the key elements/functions this subtask should produce"]
}}
],
"shared_contract": "Overall structure and interfaces that all agents must respect"
}}
Each subtask should be independently implementable. The shared_contract must describe the overall structure so each coder knows how their work fits with others."""
response = await llm.ainvoke([
SystemMessage(content="You are a software architect. Output only valid JSON."),
HumanMessage(content=prompt),
])
try:
content = response.content.strip()
if "```json" in content:
content = content.split("```json")[1].split("```")[0].strip()
elif "```" in content:
content = content.split("```")[1].split("```")[0].strip()
parsed = json.loads(content)
subtasks = parsed.get("subtasks", [])
shared_contract = parsed.get("shared_contract", "")
except Exception:
subtasks = [
{"id": 1, "description": f"Implement core data structures for: {task}", "signatures": ["Core structures and data types"]},
{"id": 2, "description": f"Implement main algorithm/logic for: {task}", "signatures": ["Main logic and algorithms"]},
{"id": 3, "description": f"Implement helper functions and utilities for: {task}", "signatures": ["Helper functions and utilities"]},
]
shared_contract = "All agents should implement their assigned parts with clear interfaces."
while len(subtasks) < 3:
subtasks.append({"id": len(subtasks) + 1, "description": f"Additional implementation for: {task}", "signatures": []})
coding_subtasks: list[CodingSubtask] = []
for st in subtasks[:3]:
coding_subtasks.append({
"id": st.get("id", len(coding_subtasks) + 1),
"description": st.get("description", ""),
"signatures": st.get("signatures", []),
"result": None,
})
return {
"subtasks": coding_subtasks,
"shared_contract": shared_contract,
}
# βββ Parallel Coders Node ββββββββββββββββββββββββββββββββββββββββββββββββββββ
async def parallel_coders_node(state: CodingAgentState) -> dict:
"""Runs 3 coding agents in parallel, each implementing its subtask."""
subtasks = state["subtasks"]
shared_contract = state["shared_contract"]
original_task = state["original_task"]
async def run_coder(subtask: CodingSubtask, coder_idx: int) -> str:
llm = get_llm(temperature=AgentConfig.CodingAgent.CODER_TEMPERATURE, change=False)
signatures_text = "\n".join(subtask.get("signatures", []))
prompt = f"""You are Coding Agent {coder_idx + 1}. You are part of a team implementing: {original_task}
YOUR SPECIFIC TASK:
{subtask['description']}
YOUR KEY ELEMENTS TO IMPLEMENT:
{signatures_text}
SHARED CONTRACT (overall structure all agents must respect):
{shared_contract}
IMPORTANT RULES:
1. Only implement the parts assigned to you
2. Follow the shared contract for naming, structure, and interfaces
3. Do NOT implement parts assigned to other agents β just reference them if needed
4. Write clean, well-documented code in whatever language best fits the task
5. Include comments explaining complex logic
6. After your code os written, provide a Detailed explanation separated by "---"
OUTPUT FORMAT:
Write your code first, then after a line with "---", write a brief explanation of what your code does and how it works.
Write your implementation:"""
response = await llm.ainvoke([
SystemMessage(content="You are a coding agent. Write clean, well-documented code in whatever language best fits the task. After your code, provide a brief explanation."),
HumanMessage(content=prompt),
])
return response.content
tasks = [run_coder(subtasks[i], i) for i in range(min(3, len(subtasks)))]
results = await asyncio.gather(*tasks)
cleaned_results = []
for result in results:
cleaned = re.sub(r'```\w*\n', '', result)
cleaned = cleaned.replace('```', '').strip()
cleaned_results.append(cleaned)
results = cleaned_results
while len(results) < 3:
results.append("# No implementation provided")
return {"coder_results": list(results)}
# βββ Code Aggregator Node βββββββββββββββββββββββββββββββββββββββββββββββββββββ
async def code_aggregator_node(state: CodingAgentState) -> dict:
"""Merges 3 coder outputs into one coherent codebase."""
llm = get_llm(temperature=AgentConfig.CodingAgent.AGGREGATOR_TEMPERATURE, change=True)
coder_results = state["coder_results"]
shared_contract = state["shared_contract"]
original_task = state["original_task"]
review_errors = state.get("review_errors", [])
coder_sections = "\n\n".join(
f"--- Coder {i+1} Output ---\n{result}" for i, result in enumerate(coder_results)
)
error_section = ""
if review_errors:
error_section = f"""
CRITICAL: The previous version had these errors that MUST be fixed:
{chr(10).join(f'- {e}' for e in review_errors)}
You MUST address all of these errors in your merged output.
"""
prompt = f"""You are a senior code aggregator. Your job is to merge 3 coding agent outputs into a working, production-ready codebase.
Original Task: {original_task}
Shared Contract (overall structure all agents agreed on):
{shared_contract}
Coder Outputs (each coder includes code + explanation separated by "---"):
{coder_sections}
{error_section}
CRITICAL INSTRUCTIONS:
1. Output MULTIPLE files if the task naturally requires it
2. Use this EXACT format to separate files β NO markdown fences, ONLY use the file separator:
# === FILE: filename ===
[raw code content here, NO fences]
# === FILE: another_file ===
[raw code content here, NO fences]
3. Preserve the explanations from each coder. Write them in response in such manner that it preserve the Explantions as well as the Flow.
4. Do NOT add new functionality β only merge, fix references, and ensure interoperability
5. ABSOLUTELY DO NOT use markdown code fences (```) β output raw code only.
Merged code:"""
response = await llm.ainvoke([HumanMessage(content=prompt)])
return {"merged_code": response.content}
# βββ Code Reviewer Node βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
async def code_reviewer_node(state: CodingAgentState) -> dict:
"""Reviews merged code for correctness and returns scores + errors."""
llm = get_llm(temperature=AgentConfig.CodingAgent.REVIEWER_TEMPERATURE, change=True)
merged_code = state["merged_code"]
original_task = state["original_task"]
retry_count = state.get("retry_count", 0)
prompt = f"""You are a strict code reviewer and quality gate.
Evaluate the merged code against the original task and report only concrete, high-value findings.
SCORING RUBRIC (integer 0-100):
- confidence: Correctness and production readiness of the implementation.
0-39 = fundamentally broken/incomplete; 40-69 = partially correct with major gaps;
70-89 = mostly correct with manageable issues; 90-100 = robust, correct, and production-ready.
- consistency: Internal coherence and contract alignment across files/interfaces.
0-39 = contradictory/incompatible; 40-69 = mixed integration quality;
70-89 = coherent with minor integration issues; 90-100 = cleanly integrated and consistent.
ISSUE CRITERIA:
- "errors": blocker defects that must be fixed before acceptance.
- "serious_mistakes": security vulnerabilities, requirement misses, data-loss risks,
broken interfaces, or logic errors with high user impact.
Every issue must be specific and actionable.
Original Task: {original_task}
Code to Review:
{merged_code}
Output contract (STRICT JSON ONLY; no markdown/backticks/preamble):
{{
"confidence": 0,
"consistency": 0,
"friendly_feedback": "2-4 sentence summary with concrete next steps.",
"errors": ["Actionable blocker 1", "Actionable blocker 2"],
"serious_mistakes": [
{{
"severity": "high|critical",
"description": "What is wrong and where it appears.",
"action": "Specific fix to apply."
}}
]
}}
If there are no blockers, return "errors": [].
If there are no serious mistakes, return "serious_mistakes": []."""
response = await llm.ainvoke([
SystemMessage(content="You are a data-formatting agent. Output raw JSON only."),
HumanMessage(content=prompt)
])
parse_error = ""
parsed: dict[str, Any] = {}
try:
parsed = load_json_object(response.content if isinstance(response.content, str) else str(response.content))
except ValueError as exc:
parse_error = str(exc)
confidence_default = 25 if parse_error else 70
consistency_default = 25 if parse_error else 70
confidence = clamp_score(parsed.get("confidence"), default=confidence_default)
consistency = clamp_score(
parsed.get("consistency", parsed.get("logical_consistency", parsed.get("consistency_score"))),
default=consistency_default,
)
errors = normalize_errors(parsed.get("errors", parsed.get("review_errors", [])))
if parse_error and not errors:
errors = [
"Reviewer output was not valid JSON. Re-run merge/review and verify every requirement explicitly."
]
feedback = normalize_text(parsed.get("friendly_feedback", parsed.get("critic_feedback")))
if not feedback:
feedback = (
"Reviewer output failed strict JSON validation; a conservative failure response was applied."
if parse_error
else "Review complete. Address blockers and rerun the reviewer."
)
serious_mistakes = normalize_serious_mistakes(parsed.get("serious_mistakes", []))
if parse_error and not serious_mistakes:
serious_mistakes = [
{
"severity": "high",
"description": "Reviewer response was not valid JSON, reducing trust in the quality gate.",
"action": "Regenerate reviewer output with strict JSON-only compliance.",
}
]
return {
"confidence_score": confidence,
"logical_consistency": consistency,
"review_errors": errors,
"critic_feedback": feedback,
"serious_mistakes": serious_mistakes,
"retry_count": retry_count + 1,
}
# βββ Retry Logic ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def should_retry(state: CodingAgentState) -> str:
"""Decide whether to retry aggregation or format final output."""
if state.get("review_errors") and state["retry_count"] < 2:
return "code_aggregator"
return "format_output"
# βββ Format Output Node βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def detect_language(content: str) -> str:
"""Detect the programming language from code content."""
content_lower = content.lower().strip()
if "<!doctype html>" in content_lower or "<html" in content_lower or "<form" in content_lower or "<div" in content_lower:
return "html"
if content.strip().startswith(("body {", ".class", "#id", "@keyframes", "@media")) or "{ color:" in content or "{ background:" in content:
return "css"
if "function " in content or "const " in content or "let " in content or "var " in content or "document." in content or "addEventListener" in content:
return "javascript"
if "public class " in content or "public static void main" in content or "import java." in content:
return "java"
if "def " in content or "import " in content or "class " in content or "print(" in content:
return "python"
return "python"
async def format_output_node(state: CodingAgentState) -> dict:
"""Parses merged code into a structured list of {filename, content, language} dicts."""
merged_code = state["merged_code"]
# Strip any stray markdown fences first
if "```" in merged_code:
fenced_blocks = re.findall(r'```(\w*)\n(.*?)```', merged_code, re.DOTALL)
if fenced_blocks:
merged_code = "\n\n".join(block_content.strip() for _, block_content in fenced_blocks)
else:
merged_code = re.sub(r'```\w*\n?', '', merged_code).replace('```', '').strip()
parsed_files = []
file_separator = "# === FILE:"
if file_separator in merged_code:
file_blocks = merged_code.split(file_separator)
for file_block in file_blocks:
file_block = file_block.strip()
if not file_block:
continue
lines = file_block.split("\n")
filename = lines[0].strip().replace("===", "").strip()
if not filename:
continue
full_content = "\n".join(lines[1:])
# Strip explanation after "---"
file_content = full_content
if "\n---\n" in full_content:
file_content = re.split(r'\n---\n', full_content, maxsplit=1)[0].strip()
if file_content:
parsed_files.append({
"filename": filename,
"content": file_content,
"language": detect_language(file_content),
})
else:
all_blocks = re.findall(r'```(\w*)\n(.*?)```', merged_code, re.DOTALL)
ext_map = {"html": "index.html", "css": "styles.css", "javascript": "script.js", "python": "main.py", "java": "Main.java"}
if len(all_blocks) > 1:
for idx, (lang_hint, block_content) in enumerate(all_blocks):
block_content = block_content.strip()
if not block_content:
continue
lang = lang_hint if lang_hint else detect_language(block_content)
filename = ext_map.get(lang, f"file_{idx + 1}.{lang or 'txt'}")
parsed_files.append({"filename": filename, "content": block_content, "language": lang})
else:
# Single file fallback
code_content = merged_code.split("\n---\n", 1)[0].strip() if "\n---\n" in merged_code else merged_code
lang = detect_language(code_content)
parsed_files.append({
"filename": ext_map.get(lang, "output.txt"),
"content": code_content,
"language": lang,
})
return {"parsed_files": parsed_files}
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