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code_implementation_workflow_index.py
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997 lines (822 loc) · 41.2 KB
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"""
Paper Code Implementation Workflow - MCP-compliant Iterative Development
Features:
1. File Tree Creation
2. Code Implementation - Based on aisi-basic-agent iterative development
MCP Architecture:
- MCP Server: tools/code_implementation_server.py
- MCP Client: Called through mcp_agent framework
- Configuration: mcp_agent.config.yaml
"""
import asyncio
import json
import logging
import os
import sys
import time
import yaml
from pathlib import Path
from typing import Dict, Any, Optional, List
# MCP Agent imports
from mcp_agent.agents.agent import Agent
# Local imports
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from prompts.code_prompts import STRUCTURE_GENERATOR_PROMPT
from prompts.code_prompts import (
PURE_CODE_IMPLEMENTATION_SYSTEM_PROMPT_INDEX,
)
from workflows.agents import CodeImplementationAgent
from workflows.agents.memory_agent_concise import ConciseMemoryAgent
from config.mcp_tool_definitions_index import get_mcp_tools
from utils.llm_utils import get_preferred_llm_class, get_default_models
# DialogueLogger removed - no longer needed
class CodeImplementationWorkflowWithIndex:
"""
Paper Code Implementation Workflow Manager with Code Reference Indexer
Uses standard MCP architecture with enhanced indexing capabilities:
1. Connect to code-implementation server via MCP client
2. Use MCP protocol for tool calls
3. Support workspace management and operation history tracking
4. Integrated code reference indexer for enhanced code understanding
"""
# ==================== 1. Class Initialization and Configuration (Infrastructure Layer) ====================
def __init__(self, config_path: str = "mcp_agent.secrets.yaml"):
"""Initialize workflow with configuration"""
self.config_path = config_path
self.api_config = self._load_api_config()
self.default_models = get_default_models("mcp_agent.config.yaml")
self.logger = self._create_logger()
self.mcp_agent = None
self.enable_read_tools = (
True # Default value, will be overridden by run_workflow parameter
)
def _load_api_config(self) -> Dict[str, Any]:
"""Load API configuration from YAML file"""
try:
with open(self.config_path, "r", encoding="utf-8") as f:
return yaml.safe_load(f)
except Exception as e:
raise Exception(f"Failed to load API config: {e}")
def _create_logger(self) -> logging.Logger:
"""Create and configure logger"""
logger = logging.getLogger(__name__)
# Don't add handlers to child loggers - let them propagate to root
logger.setLevel(logging.INFO)
return logger
def _read_plan_file(self, plan_file_path: str) -> str:
"""Read implementation plan file"""
plan_path = Path(plan_file_path)
if not plan_path.exists():
raise FileNotFoundError(
f"Implementation plan file not found: {plan_file_path}"
)
with open(plan_path, "r", encoding="utf-8") as f:
return f.read()
def _check_file_tree_exists(self, target_directory: str) -> bool:
"""Check if file tree structure already exists"""
code_directory = os.path.join(target_directory, "generate_code")
return os.path.exists(code_directory) and len(os.listdir(code_directory)) > 0
# ==================== 2. Public Interface Methods (External API Layer) ====================
async def run_workflow(
self,
plan_file_path: str,
target_directory: Optional[str] = None,
pure_code_mode: bool = False,
enable_read_tools: bool = True,
):
"""Run complete workflow - Main public interface"""
# Set the read tools configuration
self.enable_read_tools = enable_read_tools
try:
plan_content = self._read_plan_file(plan_file_path)
if target_directory is None:
target_directory = str(Path(plan_file_path).parent)
# Calculate code directory for workspace alignment
code_directory = os.path.join(target_directory, "generate_code")
self.logger.info("=" * 80)
self.logger.info("🚀 STARTING CODE IMPLEMENTATION WORKFLOW")
self.logger.info("=" * 80)
self.logger.info(f"📄 Plan file: {plan_file_path}")
self.logger.info(f"📂 Plan file parent: {target_directory}")
self.logger.info(f"🎯 Code directory (MCP workspace): {code_directory}")
self.logger.info(
f"⚙️ Read tools: {'ENABLED' if self.enable_read_tools else 'DISABLED'}"
)
self.logger.info("=" * 80)
results = {}
# Check if file tree exists
if self._check_file_tree_exists(target_directory):
self.logger.info("File tree exists, skipping creation")
results["file_tree"] = "Already exists, skipped creation"
else:
self.logger.info("Creating file tree...")
results["file_tree"] = await self.create_file_structure(
plan_content, target_directory
)
# Code implementation
if pure_code_mode:
self.logger.info("Starting pure code implementation...")
results["code_implementation"] = await self.implement_code_pure(
plan_content, target_directory, code_directory
)
else:
pass
self.logger.info("Workflow execution successful")
return {
"status": "success",
"plan_file": plan_file_path,
"target_directory": target_directory,
"code_directory": os.path.join(target_directory, "generate_code"),
"results": results,
"mcp_architecture": "standard",
}
except Exception as e:
self.logger.error(f"Workflow execution failed: {e}")
return {"status": "error", "message": str(e), "plan_file": plan_file_path}
finally:
await self._cleanup_mcp_agent()
async def create_file_structure(
self, plan_content: str, target_directory: str
) -> str:
"""Create file tree structure based on implementation plan"""
self.logger.info("Starting file tree creation...")
structure_agent = Agent(
name="StructureGeneratorAgent",
instruction=STRUCTURE_GENERATOR_PROMPT,
server_names=["command-executor"],
)
async with structure_agent:
creator = await structure_agent.attach_llm(
get_preferred_llm_class(self.config_path)
)
message = f"""Analyze the following implementation plan and generate shell commands to create the file tree structure.
Target Directory: {target_directory}/generate_code
Implementation Plan:
{plan_content}
Tasks:
1. Find the file tree structure in the implementation plan
2. Generate shell commands (mkdir -p, touch) to create that structure
3. Use the execute_commands tool to run the commands and create the file structure
Requirements:
- Use mkdir -p to create directories
- Use touch to create files
- Include __init__.py file for Python packages
- Use relative paths to the target directory
- Execute commands to actually create the file structure"""
result = await creator.generate_str(message=message)
self.logger.info("File tree structure creation completed")
return result
async def implement_code_pure(
self, plan_content: str, target_directory: str, code_directory: str = None
) -> str:
"""Pure code implementation - focus on code writing without testing"""
self.logger.info("Starting pure code implementation (no testing)...")
# Use provided code_directory or calculate it (for backwards compatibility)
if code_directory is None:
code_directory = os.path.join(target_directory, "generate_code")
self.logger.info(f"🎯 Using code directory (MCP workspace): {code_directory}")
if not os.path.exists(code_directory):
raise FileNotFoundError(
"File tree structure not found, please run file tree creation first"
)
try:
client, client_type = await self._initialize_llm_client()
await self._initialize_mcp_agent(code_directory)
tools = self._prepare_mcp_tool_definitions()
system_message = PURE_CODE_IMPLEMENTATION_SYSTEM_PROMPT_INDEX
messages = []
# implementation_message = f"""**TASK: Implement Research Paper Reproduction Code**
# You are implementing a complete, working codebase that reproduces the core algorithms, experiments, and methods described in a research paper. Your goal is to create functional code that can replicate the paper's key results and contributions.
# **What you need to do:**
# - Analyze the paper content and reproduction plan to understand requirements
# - Implement all core algorithms mentioned in the main body of the paper
# - Create the necessary components following the planned architecture
# - Test each component to ensure functionality
# - Integrate components into a cohesive, executable system
# - Focus on reproducing main contributions rather than appendix-only experiments
# **RESOURCES:**
# - **Paper & Reproduction Plan**: `{target_directory}/` (contains .md paper files and initial_plan.txt with detailed implementation guidance)
# - **Reference Code Indexes**: `{target_directory}/indexes/` (JSON files with implementation patterns from related codebases)
# - **Implementation Directory**: `{code_directory}/` (your working directory for all code files)
# **CURRENT OBJECTIVE:**
# Start by reading the reproduction plan (`{target_directory}/initial_plan.txt`) to understand the implementation strategy, then examine the paper content to identify the first priority component to implement. Use the search_code tool to find relevant reference implementations from the indexes directory (`{target_directory}/indexes/*.json`) before coding.
# ---
# **START:** Review the plan above and begin implementation."""
implementation_message = f"""**Task: Implement code based on the following reproduction plan**
**Code Reproduction Plan:**
{plan_content}
**Working Directory:** {code_directory}
**Current Objective:** Begin implementation by analyzing the plan structure, examining the current project layout, and implementing the first foundation file according to the plan's priority order."""
messages.append({"role": "user", "content": implementation_message})
result = await self._pure_code_implementation_loop(
client,
client_type,
system_message,
messages,
tools,
plan_content,
target_directory,
)
return result
finally:
await self._cleanup_mcp_agent()
# ==================== 3. Core Business Logic (Implementation Layer) ====================
async def _pure_code_implementation_loop(
self,
client,
client_type,
system_message,
messages,
tools,
plan_content,
target_directory,
):
"""Pure code implementation loop with memory optimization and phase consistency"""
max_iterations = 500
iteration = 0
start_time = time.time()
max_time = 2400 # 40 minutes
# Initialize specialized agents
code_agent = CodeImplementationAgent(
self.mcp_agent, self.logger, self.enable_read_tools
)
memory_agent = ConciseMemoryAgent(plan_content, self.logger, target_directory)
# Log read tools configuration
read_tools_status = "ENABLED" if self.enable_read_tools else "DISABLED"
self.logger.info(
f"🔧 Read tools (read_file, read_code_mem): {read_tools_status}"
)
if not self.enable_read_tools:
self.logger.info(
"🚫 No read mode: read_file and read_code_mem tools will be skipped"
)
# Connect code agent with memory agent for summary generation
# Note: Concise memory agent doesn't need LLM client for summary generation
code_agent.set_memory_agent(memory_agent, client, client_type)
# Initialize memory agent with iteration 0
memory_agent.start_new_round(iteration=0)
while iteration < max_iterations:
iteration += 1
elapsed_time = time.time() - start_time
if elapsed_time > max_time:
self.logger.warning(f"Time limit reached: {elapsed_time:.2f}s")
break
# # Test simplified memory approach if we have files implemented
# if iteration == 5 and code_agent.get_files_implemented_count() > 0:
# self.logger.info("🧪 Testing simplified memory approach...")
# test_results = await memory_agent.test_simplified_memory_approach()
# self.logger.info(f"Memory test results: {test_results}")
# self.logger.info(f"Pure code implementation iteration {iteration}: generating code")
messages = self._validate_messages(messages)
current_system_message = code_agent.get_system_prompt()
# Round logging removed
# Call LLM
response = await self._call_llm_with_tools(
client, client_type, current_system_message, messages, tools
)
response_content = response.get("content", "").strip()
if not response_content:
response_content = "Continue implementing code files..."
messages.append({"role": "assistant", "content": response_content})
# Handle tool calls
if response.get("tool_calls"):
tool_results = await code_agent.execute_tool_calls(
response["tool_calls"]
)
# Record essential tool results in concise memory agent
for tool_call, tool_result in zip(response["tool_calls"], tool_results):
memory_agent.record_tool_result(
tool_name=tool_call["name"],
tool_input=tool_call["input"],
tool_result=tool_result.get("result"),
)
# NEW LOGIC: Check if write_file was called and trigger memory optimization immediately
# Determine guidance based on results
has_error = self._check_tool_results_for_errors(tool_results)
files_count = code_agent.get_files_implemented_count()
if has_error:
guidance = self._generate_error_guidance()
else:
guidance = self._generate_success_guidance(files_count)
compiled_response = self._compile_user_response(tool_results, guidance)
messages.append({"role": "user", "content": compiled_response})
# NEW LOGIC: Apply memory optimization immediately after write_file detection
if memory_agent.should_trigger_memory_optimization(
messages, code_agent.get_files_implemented_count()
):
# Memory optimization triggered
# Apply concise memory optimization
files_implemented_count = code_agent.get_files_implemented_count()
current_system_message = code_agent.get_system_prompt()
messages = memory_agent.apply_memory_optimization(
current_system_message, messages, files_implemented_count
)
# Memory optimization completed
else:
files_count = code_agent.get_files_implemented_count()
no_tools_guidance = self._generate_no_tools_guidance(files_count)
messages.append({"role": "user", "content": no_tools_guidance})
# Check for analysis loop and provide corrective guidance
if code_agent.is_in_analysis_loop():
analysis_loop_guidance = code_agent.get_analysis_loop_guidance()
messages.append({"role": "user", "content": analysis_loop_guidance})
self.logger.warning(
"Analysis loop detected and corrective guidance provided"
)
# Record file implementations in memory agent (for the current round)
for file_info in code_agent.get_implementation_summary()["completed_files"]:
memory_agent.record_file_implementation(file_info["file"])
# REMOVED: Old memory optimization logic - now happens immediately after write_file
# Memory optimization is now triggered immediately after write_file detection
# Start new round for next iteration, sync with workflow iteration
memory_agent.start_new_round(iteration=iteration)
# Check completion
if any(
keyword in response_content.lower()
for keyword in [
"all files implemented",
"all phases completed",
"reproduction plan fully implemented",
"all code of repo implementation complete",
]
):
self.logger.info("Code implementation declared complete")
break
# Emergency trim if too long
if len(messages) > 50:
self.logger.warning(
"Emergency message trim - applying concise memory optimization"
)
current_system_message = code_agent.get_system_prompt()
files_implemented_count = code_agent.get_files_implemented_count()
messages = memory_agent.apply_memory_optimization(
current_system_message, messages, files_implemented_count
)
return await self._generate_pure_code_final_report_with_concise_agents(
iteration, time.time() - start_time, code_agent, memory_agent
)
# ==================== 4. MCP Agent and LLM Communication Management (Communication Layer) ====================
async def _initialize_mcp_agent(self, code_directory: str):
"""Initialize MCP agent and connect to code-implementation server"""
try:
self.mcp_agent = Agent(
name="CodeImplementationAgent",
instruction="You are a code implementation assistant, using MCP tools to implement paper code replication.",
server_names=["code-implementation", "code-reference-indexer"],
)
await self.mcp_agent.__aenter__()
llm = await self.mcp_agent.attach_llm(
get_preferred_llm_class(self.config_path)
)
# Set workspace to the target code directory
workspace_result = await self.mcp_agent.call_tool(
"set_workspace", {"workspace_path": code_directory}
)
self.logger.info(f"Workspace setup result: {workspace_result}")
return llm
except Exception as e:
self.logger.error(f"Failed to initialize MCP agent: {e}")
if self.mcp_agent:
try:
await self.mcp_agent.__aexit__(None, None, None)
except Exception:
pass
self.mcp_agent = None
raise
async def _cleanup_mcp_agent(self):
"""Clean up MCP agent resources"""
if self.mcp_agent:
try:
await self.mcp_agent.__aexit__(None, None, None)
self.logger.info("MCP agent connection closed")
except Exception as e:
self.logger.warning(f"Error closing MCP agent: {e}")
finally:
self.mcp_agent = None
async def _initialize_llm_client(self):
"""Initialize LLM client (Anthropic or OpenAI) based on API key availability"""
# Check which API has available key and try that first
anthropic_key = self.api_config.get("anthropic", {}).get("api_key", "")
openai_key = self.api_config.get("openai", {}).get("api_key", "")
# Try Anthropic API first if key is available
if anthropic_key and anthropic_key.strip():
try:
from anthropic import AsyncAnthropic
client = AsyncAnthropic(api_key=anthropic_key)
# Test connection with default model from config
await client.messages.create(
model=self.default_models["anthropic"],
max_tokens=20,
messages=[{"role": "user", "content": "test"}],
)
self.logger.info(
f"Using Anthropic API with model: {self.default_models['anthropic']}"
)
return client, "anthropic"
except Exception as e:
self.logger.warning(f"Anthropic API unavailable: {e}")
# Try OpenAI API if Anthropic failed or key not available
if openai_key and openai_key.strip():
try:
from openai import AsyncOpenAI
# Handle custom base_url if specified
openai_config = self.api_config.get("openai", {})
base_url = openai_config.get("base_url")
if base_url:
client = AsyncOpenAI(api_key=openai_key, base_url=base_url)
else:
client = AsyncOpenAI(api_key=openai_key)
# Test connection with default model from config
# Try max_tokens first, fallback to max_completion_tokens if unsupported
try:
await client.chat.completions.create(
model=self.default_models["openai"],
max_tokens=20,
messages=[{"role": "user", "content": "test"}],
)
except Exception as e:
if "max_tokens" in str(e) and "max_completion_tokens" in str(e):
# Retry with max_completion_tokens for models that require it
await client.chat.completions.create(
model=self.default_models["openai"],
max_completion_tokens=20,
messages=[{"role": "user", "content": "test"}],
)
else:
raise
self.logger.info(
f"Using OpenAI API with model: {self.default_models['openai']}"
)
if base_url:
self.logger.info(f"Using custom base URL: {base_url}")
return client, "openai"
except Exception as e:
self.logger.warning(f"OpenAI API unavailable: {e}")
raise ValueError(
"No available LLM API - please check your API keys in configuration"
)
async def _call_llm_with_tools(
self, client, client_type, system_message, messages, tools, max_tokens=8192
):
"""Call LLM with tools"""
try:
if client_type == "anthropic":
return await self._call_anthropic_with_tools(
client, system_message, messages, tools, max_tokens
)
elif client_type == "openai":
return await self._call_openai_with_tools(
client, system_message, messages, tools, max_tokens
)
else:
raise ValueError(f"Unsupported client type: {client_type}")
except Exception as e:
self.logger.error(f"LLM call failed: {e}")
raise
async def _call_anthropic_with_tools(
self, client, system_message, messages, tools, max_tokens
):
"""Call Anthropic API"""
validated_messages = self._validate_messages(messages)
if not validated_messages:
validated_messages = [
{"role": "user", "content": "Please continue implementing code"}
]
try:
response = await client.messages.create(
model=self.default_models["anthropic"],
system=system_message,
messages=validated_messages,
tools=tools,
max_tokens=max_tokens,
temperature=0.2,
)
except Exception as e:
self.logger.error(f"Anthropic API call failed: {e}")
raise
content = ""
tool_calls = []
for block in response.content:
if block.type == "text":
content += block.text
elif block.type == "tool_use":
tool_calls.append(
{"id": block.id, "name": block.name, "input": block.input}
)
return {"content": content, "tool_calls": tool_calls}
async def _call_openai_with_tools(
self, client, system_message, messages, tools, max_tokens
):
"""Call OpenAI API"""
openai_tools = []
for tool in tools:
openai_tools.append(
{
"type": "function",
"function": {
"name": tool["name"],
"description": tool["description"],
"parameters": tool["input_schema"],
},
}
)
openai_messages = [{"role": "system", "content": system_message}]
openai_messages.extend(messages)
# Try max_tokens first, fallback to max_completion_tokens if unsupported
try:
response = await client.chat.completions.create(
model=self.default_models["openai"],
messages=openai_messages,
tools=openai_tools if openai_tools else None,
max_tokens=max_tokens,
temperature=0.2,
)
except Exception as e:
if "max_tokens" in str(e) and "max_completion_tokens" in str(e):
# Retry with max_completion_tokens for models that require it
response = await client.chat.completions.create(
model=self.default_models["openai"],
messages=openai_messages,
tools=openai_tools if openai_tools else None,
max_completion_tokens=max_tokens,
)
else:
raise
message = response.choices[0].message
content = message.content or ""
tool_calls = []
if message.tool_calls:
for tool_call in message.tool_calls:
tool_calls.append(
{
"id": tool_call.id,
"name": tool_call.function.name,
"input": json.loads(tool_call.function.arguments),
}
)
return {"content": content, "tool_calls": tool_calls}
# ==================== 5. Tools and Utility Methods (Utility Layer) ====================
def _validate_messages(self, messages: List[Dict]) -> List[Dict]:
"""Validate and clean message list"""
valid_messages = []
for msg in messages:
content = msg.get("content", "").strip()
if content:
valid_messages.append(
{"role": msg.get("role", "user"), "content": content}
)
else:
self.logger.warning(f"Skipping empty message: {msg}")
return valid_messages
def _prepare_mcp_tool_definitions(self) -> List[Dict[str, Any]]:
"""Prepare tool definitions in Anthropic API standard format"""
return get_mcp_tools("code_implementation")
def _check_tool_results_for_errors(self, tool_results: List[Dict]) -> bool:
"""Check tool results for errors"""
for result in tool_results:
try:
if hasattr(result["result"], "content") and result["result"].content:
content_text = result["result"].content[0].text
parsed_result = json.loads(content_text)
if parsed_result.get("status") == "error":
return True
elif isinstance(result["result"], str):
if "error" in result["result"].lower():
return True
except (json.JSONDecodeError, AttributeError, IndexError):
result_str = str(result["result"])
if "error" in result_str.lower():
return True
return False
# ==================== 6. User Interaction and Feedback (Interaction Layer) ====================
def _generate_success_guidance(self, files_count: int) -> str:
"""Generate concise success guidance for continuing implementation"""
return f"""✅ File implementation completed successfully!
📊 **Progress Status:** {files_count} files implemented
🎯 **Next Action:** Check if ALL files from the reproduction plan are implemented.
⚡ **Decision Process:**
1. **If ALL files are implemented:** Use `execute_python` or `execute_bash` to test the complete implementation, then respond "**implementation complete**" to end the conversation
2. **If MORE files need implementation:** Continue with dependency-aware workflow:
- **Start with `read_code_mem`** to understand existing implementations and dependencies
- **Optionally use `search_code_references`** for reference patterns (OPTIONAL - use for inspiration only, original paper specs take priority)
- **Then `write_file`** to implement the new component
- **Finally: Test** if needed
💡 **Key Point:** Always verify completion status before continuing with new file creation."""
def _generate_error_guidance(self) -> str:
"""Generate error guidance for handling issues"""
return """❌ Error detected during file implementation.
🔧 **Action Required:**
1. Review the error details above
2. Fix the identified issue
3. **Check if ALL files from the reproduction plan are implemented:**
- **If YES:** Use `execute_python` or `execute_bash` to test the complete implementation, then respond "**implementation complete**" to end the conversation
- **If NO:** Continue with proper development cycle for next file:
- **Start with `read_code_mem`** to understand existing implementations
- **Optionally use `search_code_references`** for reference patterns (OPTIONAL - for inspiration only)
- **Then `write_file`** to implement properly
- **Test** if needed
4. Ensure proper error handling in future implementations
💡 **Remember:** Always verify if all planned files are implemented before continuing with new file creation."""
def _generate_no_tools_guidance(self, files_count: int) -> str:
"""Generate concise guidance when no tools are called"""
return f"""⚠️ No tool calls detected in your response.
📊 **Current Progress:** {files_count} files implemented
🚨 **Action Required:** You must use tools. **FIRST check if ALL files from the reproduction plan are implemented:**
⚡ **Decision Process:**
1. **If ALL files are implemented:** Use `execute_python` or `execute_bash` to test the complete implementation, then respond "**implementation complete**" to end the conversation
2. **If MORE files need implementation:** Follow the development cycle:
- **Start with `read_code_mem`** to understand existing implementations
- **Optionally use `search_code_references`** for reference patterns (OPTIONAL - for inspiration only)
- **Then `write_file`** to implement the new component
- **Finally: Test** if needed
🚨 **Critical:** Always verify completion status first, then use appropriate tools - not just explanations!"""
def _compile_user_response(self, tool_results: List[Dict], guidance: str) -> str:
"""Compile tool results and guidance into a single user response"""
response_parts = []
if tool_results:
response_parts.append("🔧 **Tool Execution Results:**")
for tool_result in tool_results:
tool_name = tool_result["tool_name"]
result_content = tool_result["result"]
response_parts.append(
f"```\nTool: {tool_name}\nResult: {result_content}\n```"
)
if guidance:
response_parts.append("\n" + guidance)
return "\n\n".join(response_parts)
# ==================== 7. Reporting and Output (Output Layer) ====================
async def _generate_pure_code_final_report_with_concise_agents(
self,
iterations: int,
elapsed_time: float,
code_agent: CodeImplementationAgent,
memory_agent: ConciseMemoryAgent,
):
"""Generate final report using concise agent statistics"""
try:
code_stats = code_agent.get_implementation_statistics()
memory_stats = memory_agent.get_memory_statistics(
code_stats["files_implemented_count"]
)
if self.mcp_agent:
history_result = await self.mcp_agent.call_tool(
"get_operation_history", {"last_n": 30}
)
history_data = (
json.loads(history_result)
if isinstance(history_result, str)
else history_result
)
else:
history_data = {"total_operations": 0, "history": []}
write_operations = 0
files_created = []
if "history" in history_data:
for item in history_data["history"]:
if item.get("action") == "write_file":
write_operations += 1
file_path = item.get("details", {}).get("file_path", "unknown")
files_created.append(file_path)
report = f"""
# Pure Code Implementation Completion Report (Write-File-Based Memory Mode)
## Execution Summary
- Implementation iterations: {iterations}
- Total elapsed time: {elapsed_time:.2f} seconds
- Files implemented: {code_stats['total_files_implemented']}
- File write operations: {write_operations}
- Total MCP operations: {history_data.get('total_operations', 0)}
## Read Tools Configuration
- Read tools enabled: {code_stats['read_tools_status']['read_tools_enabled']}
- Status: {code_stats['read_tools_status']['status']}
- Tools affected: {', '.join(code_stats['read_tools_status']['tools_affected'])}
## Agent Performance
### Code Implementation Agent
- Files tracked: {code_stats['files_implemented_count']}
- Technical decisions: {code_stats['technical_decisions_count']}
- Constraints tracked: {code_stats['constraints_count']}
- Architecture notes: {code_stats['architecture_notes_count']}
- Dependency analysis performed: {code_stats['dependency_analysis_count']}
- Files read for dependencies: {code_stats['files_read_for_dependencies']}
- Last summary triggered at file count: {code_stats['last_summary_file_count']}
### Concise Memory Agent (Write-File-Based)
- Last write_file detected: {memory_stats['last_write_file_detected']}
- Should clear memory next: {memory_stats['should_clear_memory_next']}
- Files implemented count: {memory_stats['implemented_files_tracked']}
- Current round: {memory_stats['current_round']}
- Concise mode active: {memory_stats['concise_mode_active']}
- Current round tool results: {memory_stats['current_round_tool_results']}
- Essential tools recorded: {memory_stats['essential_tools_recorded']}
## Files Created
"""
for file_path in files_created[-20:]:
report += f"- {file_path}\n"
if len(files_created) > 20:
report += f"... and {len(files_created) - 20} more files\n"
report += """
## Architecture Features
✅ WRITE-FILE-BASED Memory Agent - Clear after each file generation
✅ After write_file: Clear history → Keep system prompt + initial plan + tool results
✅ Tool accumulation: read_code_mem, read_file, search_reference_code until next write_file
✅ Clean memory cycle: write_file → clear → accumulate → write_file → clear
✅ Essential tool recording with write_file detection
✅ Specialized agent separation for clean code organization
✅ MCP-compliant tool execution
✅ Production-grade code with comprehensive type hints
✅ Intelligent dependency analysis and file reading
✅ Automated read_file usage for implementation context
✅ Eliminates conversation clutter between file generations
✅ Focused memory for efficient next file generation
"""
return report
except Exception as e:
self.logger.error(f"Failed to generate final report: {e}")
return f"Failed to generate final report: {str(e)}"
async def main():
"""Main function for running the workflow"""
# Configure root logger carefully to avoid duplicates
root_logger = logging.getLogger()
if not root_logger.handlers:
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s:%(name)s:%(message)s")
handler.setFormatter(formatter)
root_logger.addHandler(handler)
root_logger.setLevel(logging.INFO)
workflow = CodeImplementationWorkflowWithIndex()
print("=" * 60)
print("Code Implementation Workflow with UNIFIED Reference Indexer")
print("=" * 60)
print("Select mode:")
print("1. Test Code Reference Indexer Integration")
print("2. Run Full Implementation Workflow")
print("3. Run Implementation with Pure Code Mode")
print("4. Test Read Tools Configuration")
# mode_choice = input("Enter choice (1-4, default: 3): ").strip()
# For testing purposes, we'll run the test first
# if mode_choice == "4":
# print("Testing Read Tools Configuration...")
# # Create a test workflow normally
# test_workflow = CodeImplementationWorkflow()
# # Create a mock code agent for testing
# print("\n🧪 Testing with read tools DISABLED:")
# test_agent_disabled = CodeImplementationAgent(None, enable_read_tools=False)
# await test_agent_disabled.test_read_tools_configuration()
# print("\n🧪 Testing with read tools ENABLED:")
# test_agent_enabled = CodeImplementationAgent(None, enable_read_tools=True)
# await test_agent_enabled.test_read_tools_configuration()
# print("✅ Read tools configuration testing completed!")
# return
# print("Running Code Reference Indexer Integration Test...")
test_success = True
if test_success:
print("\n" + "=" * 60)
print("🎉 UNIFIED Code Reference Indexer Integration Test PASSED!")
print("🔧 Three-step process successfully merged into ONE tool")
print("=" * 60)
# Ask if user wants to continue with actual workflow
print("\nContinuing with workflow execution...")
plan_file = "/Users/lizongwei/Reasearch/DeepCode_Base/DeepCode/deepcode_lab/papers/1/initial_plan.txt"
# plan_file = "/data2/bjdwhzzh/project-hku/Code-Agent2.0/Code-Agent/deepcode-mcp/agent_folders/papers/1/initial_plan.txt"
target_directory = (
"/Users/lizongwei/Reasearch/DeepCode_Base/DeepCode/deepcode_lab/papers/1/"
)
print("Implementation Mode Selection:")
print("1. Pure Code Implementation Mode (Recommended)")
print("2. Iterative Implementation Mode")
pure_code_mode = True
mode_name = "Pure Code Implementation Mode with Memory Agent Architecture + Code Reference Indexer"
print(f"Using: {mode_name}")
# Configure read tools - modify this parameter to enable/disable read tools
enable_read_tools = (
True # Set to False to disable read_file and read_code_mem tools
)
read_tools_status = "ENABLED" if enable_read_tools else "DISABLED"
print(f"🔧 Read tools (read_file, read_code_mem): {read_tools_status}")
# NOTE: To test without read tools, change the line above to:
# enable_read_tools = False
result = await workflow.run_workflow(
plan_file,
target_directory=target_directory,
pure_code_mode=pure_code_mode,
enable_read_tools=enable_read_tools,
)
print("=" * 60)
print("Workflow Execution Results:")
print(f"Status: {result['status']}")
print(f"Mode: {mode_name}")
if result["status"] == "success":
print(f"Code Directory: {result['code_directory']}")
print(f"MCP Architecture: {result.get('mcp_architecture', 'unknown')}")
print("Execution completed!")
else:
print(f"Error Message: {result['message']}")
print("=" * 60)
print(
"✅ Using Standard MCP Architecture with Memory Agent + Code Reference Indexer"
)
else:
print("\n" + "=" * 60)
print("❌ Code Reference Indexer Integration Test FAILED!")
print("Please check the configuration and try again.")
print("=" * 60)
if __name__ == "__main__":
asyncio.run(main())