The use of code agents in SWARM mode (SAVIM) is developing rapidly in developers. More precise code, time saving … The advantages are numerous.
We already told you about it last March with the advanced Crewai orchestration framework. Specialized and parallelized agents are now attacking the code. Hosted by the desire to optimize their process, developers are starting to use swarm agentic frameworks. The principle is simple: use agents specialized in a given task and orchestrate everything to advance even faster.
Many Agentic Swarm projects have been formed around Claude Code, the anthropic code agent, always much appreciated by the developer community. One of the most popular, Claude-Flow (8,200 stars on GitHub), makes it possible to deploy several specialized code agents (of Claude Code instances therefore) at the same time.
How does Claude-Flow work?
Claude-Flow offers two separate operating modes: “Swarm” mode for fast tasks, and “hive-mind” mode for complex projects requiring persistent memory. At the heart of the system, 64 specialized agents distribute tasks according to their field of expertise: Fronend development, backend, code analysis, tests, documentation, or research. Agents can use tools using MCP protocol. Claude-Flow supports 87 different tools (GitHub, SQL database, analysis tools) for the time being.
Claude-Flow’s architecture is based on the concept of “Hive-Mind Intelligence”, inspired by the functioning of the hives. A queen coordinates the activity of specialized workers agents, based on a planning system. Precisely, the framework uses the so-called Goap (Goal-Oriented Action Planning) method coupled with an algorithm (in this case a*) to determine the most effective path to reach the final goal through the previously generated steps. Finally, Claude-Flow also uses neural networks to optimize the reasoning of agents, recognize recurring patterns and gradually improve coordination.
In swarm mode, the agents are automatically created for the current task, work in RAM without prior configuration, then dissolve once the objective is reached. It is the perfect use to quickly make changes on a code base or create a new feature. Conversely, the “Hive-Mind” mode keeps the context in persistence thanks to a local SQLite database. It is also possible to manually choose specialized agents to use in the project. This last mode is thus recommended for complex projects.
Install and start with Claude-Flow
To use Claude-Flow you must have node.js (at least version 18), NPM 9 and of course Claude Code. To install Claude-Flow, only one order is necessary:
npx claude-flow@alpha init --force
Once Claude-Flow has been installed, it will be necessary to connect it to Claude Code using the MCP:
claude mcp add claude-flow npx claude-flow@alpha mcp start claude mcp add ruv-swarm npx ruv-swarm@latest mcp start claude mcp add flow-nexus npx flow-nexus@latest mcp start claude mcp add sublinear-solver npx sublinear-time-solver@latest mcp start
To launch the generation or code editing, it is enough to launch the command:
NPX Claude-Flow@alpha swarm “prompt”–class By simply replacing prompt with your request in natural language. Claude-Flow will use the most automated Swarm mode here, valid for 90% of the tasks.
For example, we ask the AI to generate a weather dashboard based on the API Meteo France of Open Meteo. Agent’s swarm is then launched instantly by Claude-Flow. For this project, the framework decides to launch 5 agents: a swarm coordinator, a dependencies analyst, a Fronend developer, an API Backend integrator and finally an QA tester. All are launched in parallel and work using tool calls.
The agents will work for about 11 minutes and produce more than 3,000 lines of code. The tester agent goes so far as to produce 53 different unit tests. At the end, the project is delivered as it is in production. With Claude Code in classic mode, the generation of such a project could have taken up to 30 minutes at an hour. The only downside? We used in a prompt almost all of our daily quota of Claude Code (Pro Subscription).
Precision and speed gains
Thanks to its optimization, Claude-Flow obtains very high scores in the benchmarks. On the Swe-Bench reference benchmark, he obtained 84.8%, a score that no raw model still manages to reach. More counter-intuitively, the publisher of Claude-Flow claims that the use of Claude Code in swarm mode saves up to 32.3% of tokens. Official figures also speak of an increase in speed of 2.8 to 4.4 compared to the classic use of Claude Code, thanks to the parallelization of agents. Finally, Claude-Flow recently offers cloud integration via the Flow Nexus platform. In cloud mode, swarms can be deployed in isolated environments (in sandbox mode). Basic access is free, for the moment.
Productivity gains and the accuracy of the results already make Claude-Flow a tool of choice for teams looking to make the most of Claude Code. The approach of agentics in swarm mode should, most certainly, continue to develop and may well inspire publishers of owner code agents (Cursor, Windsurf, etc.).
