Audit and Improve Tool Documentation

Overview

The AuditAndImproveTool provides intelligent code auditing and improvement capabilities using LLM-powered analysis. It uses BaseGenAgent summaries as context for comprehensive code understanding, with fallback to raw file content for resilience.

File: tools/audit_and_improve_tool.py Class: AuditAndImproveTool Version: 1.0.0 Status: ✅ Active

Architecture

Design Principles

Resilient Context: Uses BaseGenAgent summary, falls back to raw code

LLM-Powered: Leverages LLM for intelligent code analysis

Persona-Based: Uses AutoMINDX personas for specialized analysis

Comprehensive Logging: Logs all operations for audit trail

Structured Output: Returns structured improvement results

Core Components

class AuditAndImproveTool(BaseTool):
    - memory_agent: MemoryAgent - For workspace and logging
    - base_gen_agent: BaseGenAgent - For codebase summaries
    - automindx_agent: AutoMINDXAgent - For personas
    - llm_handler: LLMHandlerInterface - For code generation

Workflow

Step 1: Generate Context (Primary)

Attempts to generate BaseGenAgent summary for comprehensive context:

Generates markdown summary of codebase

Provides rich context for LLM analysis

Falls back if generation fails

Step 2: Fallback to Raw Code

If BaseGenAgent fails:

Reads raw file content directly

Provides basic context for analysis

Ensures tool remains functional

Step 3: Apply Persona

Retrieves specialized persona from AutoMINDX:

Uses "AUDIT_AND_IMPROVE" persona

Provides specialized analysis perspective

Ensures consistent improvement quality

Step 4: Generate Improvements

Uses LLM to generate improved code:

JSON mode for structured output

Includes summary and limitations

Saves improved code to workspace

Step 5: Log Results

Logs operation for audit trail:

Target path and prompt

Summary and limitations

Context source used

Output path

Usage

Basic Audit and Improve

from tools.audit_and_improve_tool import AuditAndImproveTool
from agents.memory_agent import MemoryAgent
from agents.base_gen_agent import BaseGenAgent
from agents.automindx_agent import AutoMINDXAgent
from llm.llm_interface import LLMHandlerInterface

tool = AuditAndImproveTool(
    memory_agent=memory_agent,
    base_gen_agent=base_gen_agent,
    automindx_agent=automindx_agent,
    llm_handler=llm_handler
)
Audit and improve code
result = await tool.execute(
    target_path="/path/to/file.py",
    prompt="Improve error handling and add type hints"
)

Response Format

Success:

{
    "status": "SUCCESS",
    "status_details": "OK" | "DEGRADED_CONTEXT_FALLBACK",
    "summary": "Improvement summary",
    "limitations": "Known limitations",
    "output_path": "/path/to/improved_file.py",
    "context_used": "BaseGen Summary: ..." | "Raw File Content (Fallback): ..."
}

Error:

{
    "status": "ERROR",
    "message": "Error description"
}

Features

1. Resilient Context Generation

Primary: BaseGenAgent summary (comprehensive)

Fallback: Raw file content (basic)

Status Tracking: Indicates which context was used

2. Persona-Based Analysis

Uses specialized personas for:

Code quality analysis

Best practices enforcement

Architecture improvements

Security enhancements

3. Structured Output

LLM generates JSON with:

updated_code: Improved code

summary: What was improved

limitations: Known limitations

4. Workspace Management

Creates dedicated workspace directory

Saves improved code with naming convention

Maintains audit trail

Limitations

Current Limitations

Single File: Processes one file at a time

No Multi-File Context: Doesn't consider related files

No Testing: Doesn't verify improvements work

No Version Control: Doesn't integrate with Git

No Incremental: Always generates full file

Recommended Improvements

Multi-File Analysis: Consider related files

Incremental Improvements: Apply specific changes only

Test Generation: Generate tests for improvements

Git Integration: Create branches for improvements

Review Process: Human review before applying

Diff Generation: Show what changed

Batch Processing: Process multiple files

Integration

With BaseGenAgent

Uses BaseGenAgent for rich context:

summary_report = self.base_gen_agent.generate_markdown_summary(
    root_path_str=target_path,
    output_file_str=str(workspace_dir / f"analysis_context_{Path(target_path).name}.md")
)

With AutoMINDXAgent

Uses personas for specialized analysis:

persona = self.automindx_agent.get_persona("AUDIT_AND_IMPROVE")

With MemoryAgent

Logs all operations:

await self.memory_agent.log_process(
    "audit_and_improve_tool_execution",
    log_data,
    {"agent_id": "audit_and_improve_tool"}
)

Examples

Improve Error Handling

result = await tool.execute(
    target_path="tools/my_tool.py",
    prompt="Add comprehensive error handling with try-except blocks and proper logging"
)

Add Type Hints

result = await tool.execute(
    target_path="utils/helpers.py",
    prompt="Add type hints to all functions and improve docstrings"
)

Refactor for Performance

result = await tool.execute(
    target_path="core/processor.py",
    prompt="Optimize for performance: use async where appropriate, add caching, reduce complexity"
)

Technical Details

Dependencies

tools.base_gen_agent.BaseGenAgent: Context generation

agents.automindx_agent.AutoMINDXAgent: Persona management

llm.llm_interface.LLMHandlerInterface: Code generation

agents.memory_agent.MemoryAgent: Workspace and logging

core.bdi_agent.BaseTool: Base tool class

LLM Prompt Structure

full_prompt = (
    f"{persona}\n\n"
    f"Improvement Request: {prompt}\n\n"
    f"Context from {context_source}:\n

\n{context_content}\n``" ) <pre><code class="lang-">

JSON Response Format

</code></pre>json { "updated_code": "...", "summary": "...", "limitations": "..." } ``

Future Enhancements

Multi-File Context: Analyze related files together

Incremental Changes: Apply specific improvements only

Test Generation: Auto-generate tests for improvements

Code Review: Human-in-the-loop review process

Diff Visualization: Show changes clearly

Batch Mode: Process multiple files

Custom Personas: User-defined analysis personas

Integration Testing: Verify improvements work

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