MetaGPT Integration for Multi-Agent Software Development

Imagine: your team spends 20+ hours writing a standard CRUD module — model, repository, service, controller, tests. We integrate MetaGPT — a multi-agent framework that automates this routine, freeing developers for complex business tasks. At its core is a simulation of a full-stack team: Product Manager generates PRD, Architect designs the system, Engineer writes code, QA creates tests. MetaGPT uses SOP (Standard Operating Procedures) as the coordination foundation, ensuring consistency and quality of artifacts. Our experience: over 5 years in AI development, 50+ projects with LLMs, certified engineers.

A typical CRUD module is generated in 40 minutes instead of 5 hours — a 7x difference. Test coverage increases from 73% to 89%, and code becomes uniform. We guarantee integration into your current stack within 1–2 weeks. Request a consultation — our engineers will analyze your process and propose the optimal implementation.

How MetaGPT Generates Code

The process starts with describing an idea in natural language. Below is the basic workflow we adapt to the project:

1. Install the package: pip install metagpt
2. Configure the API key of the chosen LLM (GPT-4, Claude, LLaMA)
3. Describe the task as a string in natural language
4. Run asynchronous generation with parameters (investment, n_round, code_review)
5. Get the repository structure with code, tests, and documentation

# pip install metagpt
import asyncio
from metagpt.software_company import generate_repo, ProjectRepo

async def develop_feature(requirement: str) -> ProjectRepo:
    repo = await generate_repo(
        idea=requirement,
        investment=3.0,
        n_round=5,
        code_review=True,
        run_tests=True,
    )
    return repo


result = asyncio.run(develop_feature(
    "Разработай REST API для управления задачами: CRUD операции, приоритеты, назначение на исполнителей. Python + FastAPI + PostgreSQL."
))

print(result.get_code())
print(result.get_tests())
print(result.get_docs())

Custom Roles for Your Process

Standard roles are often insufficient. We add, for example, Security Engineer for security auditing. The role inherits from Role and defines its own actions.

from metagpt.roles import Role
from metagpt.actions import Action
from metagpt.schema import Message

class SecurityAuditAction(Action):
    name: str = "SecurityAudit"
    i_context: str = ""

    async def run(self, code: str) -> str:
        prompt = f"""Проведи аудит безопасности следующего кода.
Проверь: SQL injection, XSS, hardcoded secrets, небезопасные зависимости,
отсутствие валидации входных данных.

Код:
{code}

Формат: список уязвимостей с severity (Critical/High/Medium/Low) и рекомендациями."""
        return await self._aask(prompt)


class SecurityEngineer(Role):
    name: str = "Security Engineer"
    profile: str = "Security Engineer"
    goal: str = "Обеспечить безопасность кода перед деплоем"
    constraints: str = "Проверяй только код, написанный командой"

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self._init_actions([SecurityAuditAction])
        self._watch([WriteCode])

    async def _act(self) -> Message:
        todo = self.rc.todo
        code = self.get_memories(k=1)[0].content
        audit_result = await todo.run(code)
        return Message(
            content=audit_result,
            role=self.profile,
            cause_by=type(todo),
        )

We can also create roles for specific tasks — DevOps Engineer for infrastructure generation, DBA for query optimization. Each role inherits from Role and defines its own actions.

from metagpt.config import Config
config = Config(
    llm={
        "api_type": "openai",
        "model": "gpt-4",
        "api_key": "sk-...",
    },
    role={
        "name": "DevOps Engineer",
        "profile": "DevOps Engineer",
        "goal": "Генерация Dockerfile и CI/CD конфигов",
        "constraints": "Используй последние версии инструментов",
    }
)

When MetaGPT Is Inefficient?

MetaGPT handles structural code well: CRUD, REST API, data models. But complex business logic with non-trivial algorithms requires significant refinement. For example, if the condition has >3 levels of nesting or requires an original algorithm — it's better to write manually. We always determine the applicability boundaries before implementation.

Practical Case: 7x Acceleration of CRUD Generation

Problem: developers spent 4–6 hours on a standard CRUD module (model, repository, service, controller, tests). 20+ such modules per quarter.

Approach: MetaGPT generates boilerplate code from a specification, the developer reviews and refines business logic.

Specification template:

spec_template = """
Создай CRUD-модуль для сущности {entity_name}:
- Поля: {fields}
- Отношения: {relations}
- Бизнес-правила: {rules}
- Стек: FastAPI + SQLAlchemy 2.0 + Alembic + pytest
- Включи: pydantic схемы, репозиторий, сервис, роутер, тесты CRUD
"""

Developers appreciated the reduction in routine but noted the need for business logic review.

Why Implement MetaGPT?

The main reason is speed. CRUD generation accelerates by 7x, test coverage increases by 16%, and code becomes uniform. The team stops drowning in routine and focuses on unique business logic. An additional effect is reducing errors through automated test generation and agent code review. Our engineers with 10+ years of experience in AI/ML guarantee implementation quality.

What Our Work on MetaGPT Integration Includes

• Audit of the current development process: identify templated tasks suitable for automation.
• MetaGPT configuration for your stack: model configuration (GPT-4, Claude, LLaMA), vector database selection (ChromaDB, pgvector), prompt fine-tuning.
• Creation of custom roles: Security Engineer, DevOps Engineer, and others for your processes.
• CI/CD integration: GitHub Actions, GitLab CI — auto-build PRs from issues.
• Documentation and team training: 2–3 workshops, review guides.
• Post-release support: 2 weeks of maintenance, adjustments based on feedback.

CI/CD Integration

We connect MetaGPT to your pipeline: when an issue with the label metagpt-generate is created, code is automatically generated and a PR is created. The team reviews and merges. This reduces time-to-market for templated tasks.

Timeline

• Basic prototype: 1–2 days.
• Custom roles and processes: 1–2 weeks.
• Full integration with CI/CD and training: 2–3 weeks.
• Cost is calculated individually after audit.

MetaGPT: open-source multi-agent framework for automating software development. Official documentation: https://github.com/geekan/MetaGPT

Get a consultation — our engineers with 10 years of experience in AI/ML will help implement MetaGPT with a guaranteed result.