Introducing Coding Crew: Your Local AI Swarm for Better Code

Introduction

When I set out to build Coding Crew, I had a clear idea in mind: what if software development could feel less like a solo sprint and more like a collaborative engineering mission — powered by AI, but running entirely on your machine? What if you could harness the strengths of multiple specialized AI agents working together — without ever talking to a cloud API or sending your code off-site?

That’s exactly what Coding Crew on GitHub delivers — a local, multi-agent coding assistant you can adapt and extend to your workflow.

A Swarm of Specialists — Not Just One Generalist

At the heart of Coding Crew is a simple philosophy: complex coding tasks benefit from multiple perspectives.

Instead of relying on one “do-everything” model, Coding Crew spins up a swarm of four specialized agents, each inspired by real software teams:

• Architect — designs clean, scalable solutions
• Coder — implements the design in code
• Reviewer — audits for bugs, best practices, and style
• Tester — authors and validates a robust test suite

These agents collaborate through CrewAI, orchestrating a workflow that closely mirrors how developers actually operate. When you ask the swarm to “build this feature,” the Architect sketches the solution, the Coder implements it, the Reviewer critiques it, and the Tester validates it.

It’s like having a full development team at your fingertips — with every member optimized for a specific role.

This modularity isn’t just elegant; it’s adaptable. Want a data-science crew? Swap in agents skilled at ML workflows. Want a UX-oriented team? Add a designer agent. You can configure multiple distinct crews for different domains, each with its own skills and tools.

Why Local LLMs Matter

One of the biggest advantages of Coding Crew is that everything runs locally. There’s no reliance on OpenAI or other cloud APIs — no API keys, no usage costs, no server-side inference — just your machine and your models.

🔒 Privacy & Control

Your code never leaves your environment. All interactions happen on your machine, keeping sensitive projects truly private.

💸 Zero API Costs

With local backends like Ollama or vLLM, there’s no per-token pricing or rate limiting. Once the model is downloaded, iteration is essentially free.

🚀 Performance at Scale

With the right hardware — especially a CUDA-enabled GPU — inference can be fast and predictable. And because agents communicate locally, there’s no network latency slowing things down.

🧠 Full Customization

Want different prompt strategies? Different models per agent? Embedded domain knowledge? Coding Crew is designed to be extended and reshaped around your needs.

How It Works — Under the Hood

At a high level, this is what happens when you start Coding Crew:

1. Setup — A script configures your environment and installs dependencies, including your chosen LLM backend.
2. Launch — A lightweight MCP server exposes each agent as a callable tool.
3. Orchestrate — Tools like full_coding_task coordinate design, implementation, review, and testing.
4. Interact — From your editor (e.g., VS Code with the Continue extension), you interact with the crew like a real dev team.

Each agent operates with a clear role and purpose, and the system can switch between workflows — from quick code generation to full architectural tasks.

Real-World Workflows, Reimagined

Whether you’re building a REST API, adding new CLI features, or improving test coverage, Coding Crew adapts to your workflow. Because agents know their responsibilities and collaborate explicitly, the output feels more like the work of a coordinated team — not a single chatbot.

Since the codebase is open and modular, you can:

• Define new agent types (e.g., ML Engineer, Security Analyst)
• Create specialized crews for different projects or stacks
• Integrate the system into your existing tooling and pipelines

It’s both a playground and a foundation for serious AI-augmented development.

What’s Next

There’s a wide horizon ahead:

• More agent specializations (documentation, UX, accessibility)
• Smarter coordination strategies between agents
• Hybrid setups combining local LLMs with secure remote inference

But the core is already in place: a local, extensible, multi-agent system for real engineering work.

Get Started

If you’re curious to explore AI-assisted development beyond single-agent chatbots, take a look at the project on GitHub:

👉 https://github.com/giuseppe-sirigu/coding_crew

The instructions for the agents are currently basic - I plan to release an optimized one in next weeks.

Have ideas or extensions in mind? I’d love to see what kinds of crews you build.