Roger Gnanih
For some time now, an open-source project has been drawing strong attention in the AI ecosystem: Ruflo (formerly Claude Flow). With more than 30,000 stars on GitHub, it is no longer just an experimental tool, but a genuine emerging reference in the orchestration of intelligent agents.
But beyond the buzz, a simple question arises: what does Ruflo actually allow you to do, concretely?
To understand properly, we need to change perspective. Here, we are not talking about a chatbot or a model that answers questions. Ruflo is not an LLM and does not directly generate content. Its role lies elsewhere.
Ruflo is a multi-agent orchestration platform. In other words, it acts as a conductor capable of coordinating several specialized agents in order to execute complex tasks autonomously.
In practice, you can have one agent that analyzes, another that plans, another that writes code, and then another that tests or documents. Each agent has a specific role, and Ruflo ensures that everything runs in the right sequence. It is this collaborative logic that completely changes the way AI is used.
It is also important to clarify what Ruflo is not. Ruflo does not provide an AI model and does not give free access to Claude, GPT, or other models. In reality, it relies on an external model to work, and that is precisely where all its power lies.
Rather than depending on costly APIs, Ruflo can be connected to Ollama to run open-source models locally. This choice completely changes the game. You get zero API costs, full control over your data, and above all complete independence from external platforms. In other words, you build your own AI infrastructure.
The overall workflow remains simple: your request goes through Ruflo, which orchestrates specialized agents, which then interact with an AI model, local or remote. Ruflo does not replace the model, it organizes the work around it. And it is this organization that makes it possible to automate complex tasks.
On the features side, Ruflo goes far beyond a simple orchestrator. It offers more than 60 specialized agents, a flexible swarm or mesh architecture, more than 300 built-in MCP tools for CI/CD, security or deployment, as well as a very fast vector memory system for RAG.
Added to this are native security mechanisms against attacks such as prompt injection or path traversal, as well as the ability to connect multiple instances to each other. We are getting closer here to a truly distributed autonomous system.
Now let's move on to the setup.
With a VPS with 8 GB of RAM, it is already possible to run Ruflo properly.
Start by installing Ollama:
curl -fsSL https://ollama.com/install.sh | sh
Then download a suitable model:
ollama pull qwen2.5:7b
Start the service:
ollama serve &
Install Ruflo:
npm install -g ruflo@latest
Configure the connection to Ollama:
export OLLAMA_BASE_URL=http://localhost:11434
Initialize the project:
npx ruflo@latest init --model ollama/qwen2.5:7b
Finally, launch your first swarm:
ruflo swarm "Analyse les vulnérabilités de ce fichier PHP" --agents 5
In just a few minutes, you get a working multi-agent system.
Regarding the choice of models with 8 GB of RAM, several options are interesting. Qwen 2.5 7B offers an excellent balance between performance and versatility. Mistral 7B is fast and efficient for general use. DeepSeek-Coder is particularly suited to development and security, while Phi-3 Mini is lighter but still very performant.
Without a GPU, generation speed generally runs between 3 and 8 tokens per second. This is not instant, but largely sufficient for automated workflows.
It is precisely on use cases that Ruflo becomes really interesting. You can automate a code audit, analyze an entire repository for vulnerabilities, automatically generate documentation, or integrate Ruflo into your CI/CD pipelines.
In practice, you no longer work alone. You build a true team of AI agents capable of collaborating with each other.
And that is where the change runs deep. We are no longer talking about using AI occasionally, but about designing autonomous systems capable of organizing themselves and producing complex results, with the added bonus of total control over your costs, your data and your infrastructure.
So Ruflo does not simply improve how AI is used. It offers a new way of integrating it into real systems.
If you are a developer, an engineer or simply curious about exploring more advanced AI architectures without depending on paid APIs, it is clearly a tool to keep an eye on.
Open-source project:
https://github.com/ruvnet/ruflo
