# Software Migration Platform

AI-powered migration platform for transforming legacy software into modern architectures using autonomous agents.

---

## Overview

Software Migration Platform is an intelligent Multi-Agent AI system designed to automate the modernization of legacy software applications.

Instead of generating code directly from prompts, the platform first understands the existing application, extracts business logic, analyzes architecture, plans the migration, generates production-ready source code, validates the implementation, reviews architectural consistency, and prepares deployment-ready artifacts.

The platform combines Large Language Models (LLMs), autonomous AI agents, and the Model Context Protocol (MCP) to create a structured and repeatable migration workflow.

---

# Why This Platform?

Migrating large enterprise applications is one of the most difficult software engineering tasks.

A successful migration requires understanding:

- Business Rules
- Database Design
- Application Architecture
- Relationships
- Security
- Permissions
- Validation Logic
- Transaction Boundaries
- Approval Workflows
- Audit History
- API Contracts

Missing even a single business rule can introduce production defects.

The Software Migration Platform solves this by allowing specialized AI agents to collaborate throughout the migration lifecycle instead of relying on a single prompt.

---

# Core Features

- Multi-Agent Architecture
- Legacy Code Analysis
- Business Rule Extraction
- Automated Migration Planning
- AI Code Generation
- Architectural Review
- Automatic Code Repair
- Validation Pipeline
- Workspace Isolation
- MCP Server Integration
- Command Line Interface
- Structured Prompt Management
- Knowledge Engine
- Context Optimization
- Production-ready Project Generation

---

# Architecture

```
                           CLI / MCP
                               │
                               ▼
                        Orchestrator
                               │
      ┌────────────────────────┼────────────────────────┐
      ▼                        ▼                        ▼
 Knowledge Engine        Workspace Manager         LLM Client
      │
      ▼
                Multi-Agent Execution Pipeline
      │
      ▼
 Generated Source Code + Reports + Validation Results
```

---

# Technology Stack

## AI

- OpenAI API
- Structured Outputs
- Prompt Templates
- Multi-Agent Architecture

Future Providers

- Anthropic Claude
- Google Gemini
- Azure OpenAI
- Local LLMs (Ollama)

---

## Backend

- Python
- Typer
- Rich
- Pydantic
- SQLAlchemy
- Alembic
- FastMCP
- Tenacity

---

# Switching Providers

The platform uses `openai` as the default provider and strictly validates configuration on startup.

You can switch the LLM provider by updating `LLM_PROVIDER` in your `.env` file.

**Example: OpenAI (Default)**
```env
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4o
```

**Example: Gemini**
```env
LLM_PROVIDER=gemini
GOOGLE_API_KEY=AIza...
GEMINI_MODEL=gemini-2.5-pro
```

**Example: OpenRouter**
```env
LLM_PROVIDER=openrouter
OPENAI_API_KEY=sk-or-...
OPENAI_MODEL=anthropic/claude-3.5-sonnet
```

---

- Python
- Typer
- Rich
- Pydantic
- SQLAlchemy
- Alembic
- FastMCP
- Tenacity

---

## Target Framework

Currently optimized for

- Litestar
- SQLAlchemy 2.0
- Msgspec
- PostgreSQL
- JWT Authentication
- RBAC Architecture

The platform itself is framework-agnostic and can be extended for other architectures.

---

# Project Structure

```
Software Migration Platform/

agents/
    implementations/
    base.py
    pipeline.py
    registry.py
    context.py
    state.py

knowledge/
    context_manager.py
    document_index.py
    code_index.py
    search.py

shared/
    llm.py
    parser.py
    prompts.py
    response.py

config/
    settings.py
    logging.py
    models.py

workflows/
    orchestrator.py
    workspace.py

mcp/
    server.py
    session.py
    tools.py

cli/
    app.py
    commands.py

workspace/
prompts/
tests/
scripts/
```

---

# Agent Pipeline

## Legacy Discovery Agent

Discovers and locates the requested software module.

Searches for

- Source Files
- Header Files
- Protocol Definitions
- Database Schemas
- Documentation

Output

```
Module Context
```

---

## Legacy Analyzer Agent

Reads the legacy implementation and understands:

- Architecture
- Design Patterns
- Relationships
- Data Flow
- Service Contracts

Output

```
Legacy Analysis
```

---

## Business Rule Extractor

Extracts application behaviour including:

- Validation Rules
- Business Logic
- Status Transitions
- Approval Processes
- Permission Rules
- Transaction Behaviour
- Audit Requirements

Output

```
Business Rule Set
```

---

## Migration Planner

Creates the migration strategy.

Determines which files need to be generated.

Examples

- Models
- Schemas
- Services
- Routes
- Database Migrations
- Permissions
- Seed Data

Output

```
Migration Plan
```

---

## Code Generation Agents

Dedicated generators create:

### Model Generator

Generates

```
models.py
```

---

### Schema Generator

Generates

```
schemas.py
```

---

### Service Generator

Generates

```
service.py
```

---

### Route Generator

Generates

```
routes.py
```

---

### Migration Generator

Generates

```
Alembic Migration
```

---

### Seed Generator

Updates

- Permissions
- Roles
- Record Series
- Default Configuration

---

# Review Agent

Performs architectural review against existing project conventions.

Checks for

- Missing Fields
- Invalid Relationships
- Foreign Keys
- Cascade Rules
- Nullable Constraints
- Audit Implementation
- DTO Consistency
- Route Style
- Service Style
- Permission Mapping

Produces

```
Review Report
```

---

# Auto Fix Agent

Reads the review report and automatically repairs:

- Imports
- Relationships
- Architecture
- Audit Fields
- Permissions
- Validation Logic

Only minimal safe modifications are applied.

---

# Validation Agent

Performs deterministic validation.

Runs

- Python Compilation
- Ruff
- MyPy
- Alembic Validation
- Unit Tests
- Integration Tests

Ensures generated artifacts are production-ready.

---

# Knowledge Engine

The Knowledge Engine prevents unnecessary token usage by retrieving only the relevant context.

It indexes:

- Documentation
- Python Source
- Legacy Source Code
- Database Schemas
- Protocol Definitions
- Markdown Files

Benefits

- Faster Analysis
- Lower Token Cost
- Better Context Quality
- Reduced Hallucinations

---

# Workspace Isolation

Every migration executes inside an isolated workspace.

```
workspace/

runs/

input/

output/

review/

logs/

artifacts/
```

The original project remains untouched until the migration is approved.

---

# LLM Layer

Features

- Prompt Templates
- Structured Outputs
- Retry Logic
- Exponential Backoff
- Context Management
- Token Optimization

Future support

- Multi-Provider Routing
- Cost Estimation
- Local Models

---

# MCP Integration

The platform exposes its capabilities through the Model Context Protocol.

Available tools include

- discover_module
- analyze_module
- plan_migration
- generate_models
- generate_schemas
- generate_service
- generate_routes
- generate_migration
- review_code
- validate_module
- migrate_module

This enables AI clients such as ChatGPT, Claude Desktop, Cursor, and other MCP-compatible applications to interact with the platform.

---

# Command Line Interface

Analyze a module

```bash
erp analyze Company
```

Migrate a module

```bash
erp migrate Company
```

Review generated code

```bash
erp review Company
```

Validate generated code

```bash
erp validate Company
```

Check migration status

```bash
erp status
```

---

# Current Development Status

## Completed

- Configuration Layer
- LLM Infrastructure
- Knowledge Engine
- Workspace Manager
- Agent Framework
- Agent Registry
- Migration Orchestrator
- CLI
- MCP Server
- Review Pipeline
- Auto Fix Pipeline
- Validation Pipeline

---

# Current Workflow

```
Module Selection
        │
        ▼
Discovery
        │
        ▼
Analysis
        │
        ▼
Business Rule Extraction
        │
        ▼
Migration Planning
        │
        ▼
Code Generation
        │
        ▼
Review
        │
        ▼
Automatic Repair
        │
        ▼
Validation
        │
        ▼
Deployment-ready Artifacts
```

---

# Roadmap

- Multi-LLM Support
- Local Model Integration
- Incremental Migrations
- Git Integration
- Automatic Pull Requests
- Docker Validation
- CI/CD Integration
- Cost Estimation
- Prompt Versioning
- Migration Dashboard
- Visual Architecture Comparison
- Continuous Learning from Approved Migrations

---

# Vision

The long-term goal is to modernize software migration by replacing repetitive manual work with intelligent autonomous agents.

Instead of spending weeks understanding a legacy application before writing a single line of code, developers should be able to initiate a migration with a single command:

```bash
erp migrate <module>
```

The platform will autonomously analyze the legacy implementation, understand its architecture and business rules, generate modern production-ready code, review the implementation, validate the result, and produce deployment-ready artifacts while preserving consistency with the target application architecture.

---

# License

This project is licensed under the MIT License.

---

# Author

**Ajmot Hossain**

AI Software Engineer

Building intelligent systems for autonomous software modernization using Multi-Agent AI, MCP, and Large Language Models.