Enterprise AI CodeOps Multi-Agent System

Enterprise-style multi-agent coding pipeline inspired by production engineering workflows.

This project orchestrates specialized agents to turn a feature request into:

• an implementation draft
• a test plan and test skeleton
• a security audit report
• a final reviewer summary

It is designed to demonstrate strong software engineering signals for hiring portfolios:

• deterministic orchestration
• explicit reasoning steps
• quality and security gates
• reusable graph nodes
• testable architecture

Why This Repo Stands Out

• Multi-agent architecture with clear responsibilities (Planner, Coder, Tester, Security, Reviewer)
• Deterministic composition: each step is reproducible and easy to debug
• Memory layer: stores historical runs for traceability
• Production-ready repo hygiene: CI, templates, contribution docs, and security policy

Architecture

flowchart LR
  A[Task Input] --> B[Planner Agent]
  B --> C[Coder Agent]
  C --> D[Tester Agent]
  D --> E[Security Agent]
  E --> F[Reviewer Agent]
  F --> G[Artifacts + Memory]

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Detailed design: docs/ARCHITECTURE.md

Quickstart

1. Clone and enter repo

git clone <your-repo-url>
cd "Enterprise AI CodeOps Multi-Agent System"

2. Create virtual environment

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate

3. Install dependencies

pip install -e .[dev]

4. Run the pipeline

python -m codeops_mas.cli ^
  --task "Build a FastAPI endpoint for ride fare estimation" ^
  --stack "FastAPI,Pydantic,Redis" ^
  --constraints "latency<120ms,include unit tests" ^
  --output "artifacts/demo_run"

macOS/Linux:

python -m codeops_mas.cli \
  --task "Build a FastAPI endpoint for ride fare estimation" \
  --stack "FastAPI,Pydantic,Redis" \
  --constraints "latency<120ms,include unit tests" \
  --output "artifacts/demo_run"

Example Output

Each run generates:

• 00_plan.md
• 01_solution.py
• 02_tests.py
• 03_security_report.md
• 04_review.md

Run history is appended to artifacts/memory/history.jsonl.

Project Structure

Enterprise AI CodeOps Multi-Agent System/
├─ .github/
│  ├─ workflows/ci.yml
│  ├─ ISSUE_TEMPLATE/
│  └─ pull_request_template.md
├─ docs/
├─ src/codeops_mas/
│  ├─ agents/
│  ├─ cli.py
│  ├─ graph.py
│  ├─ memory.py
│  └─ models.py
├─ tests/
├─ CONTRIBUTING.md
├─ SECURITY.md
├─ pyproject.toml
└─ README.md

Demo Script for Videos/LinkedIn

Use docs/DEMO_SCRIPT.md to record a polished 90-second walkthrough.

Roadmap

• Add real LLM backends (OpenAI/Ollama) behind an interface
• Add AST-level security analysis
• Add benchmark tasks and scorecards
• Add GitHub App mode for pull request review

Contributing

Read CONTRIBUTING.md. PRs are welcome.

Security

Read SECURITY.md before reporting vulnerabilities.

License

MIT License. See LICENSE.