Agentic OS is Kevin’s open-source Python agent swarm framework: agent base classes with capability registration, swarm orchestration, parallel execution, a REST, GraphQL and WebSocket API stack, and a monitoring dashboard. You install it from the repository with pip install -r requirements.txt and run agents that coordinate through Redis or in-memory message buses.
agents (capability registered)
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swarm orchestrator ---- load balance ----+----+----+
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message bus (Redis or in-memory) worker worker worker
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API stack: REST / GraphQL / WebSocket
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web dashboard: metrics + alertsThis is Kevin’s project, so everything below comes from the README at github.com/kevinbadi/AgenticOSKevsAcademy, not from guesswork. If you want to watch the build process instead of reading it, the walkthroughs live on the KevBuildsApps YouTube channel.
What Is Agentic OS and Why Python for Agent Swarms
Agentic OS is a Python codebase for running many agents as one coordinated system, which is why Python agent swarm projects keep landing on it.
The README describes it as “a powerful Python-based agentic operating system designed to evolve into an employee swarm with full API stack access and parallel agent execution capabilities.” Two things stand out in that sentence. First, the goal is an employee swarm: agents that take over repeatable work, not a single chatbot. Second, the API stack and parallel execution are treated as core, not add-ons. Python is the implementation language, so the usual ecosystem applies. If your agents need to call an external service, that service needs a real API behind it. Creator OS publishes one, with a hosted MCP server and a CLI on top, which is why the two fit together later in this post.
Agentic OS Architecture: Agent Base Classes, Swarm Orchestration, and Message Buses
The architecture has five moving parts, and each one has a clear job in the README.
| Component | What it does |
|---|---|
| Agent base classes | Extensible agent framework with capability registration, so the orchestrator knows what each agent can do. |
| Swarm orchestration | Manages and coordinates multiple agents with load balancing across them. |
| Communication protocols | Message passing with Redis or in-memory buses, depending on how you want to run it. |
| Parallel execution | Multi-threaded and multi-process task execution engine for running work at the same time. |
| API integration | A comprehensive API stack with REST, GraphQL and WebSocket support. |
| Real-time monitoring | Web dashboard with metrics and alerting. |
The capability registration is the piece worth understanding first. An agent declares what it can handle, and the orchestrator uses that to route tasks instead of you hardcoding which agent handles what. The README calls this intelligent task routing: automatic agent selection based on capabilities. There is also built-in API rate limiting and throttling, which matters the moment your swarm starts calling outside services in parallel.
How to Install Agentic OS and Run the Quick Start
The Quick Start is one step long, and that step is the one the README shows.
git clone https://github.com/kevinbadi/AgenticOSKevsAcademy
cd AgenticOSKevsAcademy
pip install -r requirements.txtThat installs the dependencies listed in the repository’s requirements file. The README’s Quick Start section covers installation. Once the dependencies are in place, the next thing to understand is not a command but a concept: how your agents get registered and how the orchestrator finds them. Read the base classes before you write anything, because capability registration is what makes the rest of the system behave. If the README’s instructions change, the repository is the source of truth, not this page.
How Swarm Orchestration and Parallel Execution Work Together
Orchestration decides who does the work; parallel execution decides how much of it happens at once.
Here is a worked example. Say you want to review 50 pages of a site for thin content. One agent per page would be wasteful and slow if the pages queue up. Instead you register a research agent with the capability “audit page,” spin up several of them, and let the orchestrator balance work across the pool. The parallel execution engine runs those agents on threads or processes. When an agent finds a problem, it publishes a message on the bus. A second agent type, registered with the capability “write fix,” picks those messages up and drafts the change.
The bus is the part people underestimate. With an in-memory bus you get a single-process swarm that is easy to debug. Switch to the Redis-backed bus and the same message-passing pattern works across processes. The README lists both as supported, so the choice is an operational one, not a rewrite.
Connecting to the REST, GraphQL, and WebSocket API Stack
Agentic OS exposes REST, GraphQL and WebSocket, and each one suits a different job.
| Interface | Good for |
|---|---|
| REST | Simple request and response calls from outside your swarm, one action per call. |
| GraphQL | Asking for exactly the fields you need when a client reads agent or task data. |
| WebSocket | A live stream of events, useful when a dashboard has to update without polling. |
In practice, an external tool calls the REST endpoint to kick off a task, a reporting client uses GraphQL to pull a shaped view of what ran, and the WebSocket feed pushes status changes into your dashboard. The README also notes rate limiting is built in, which is what keeps a burst of parallel agents from hammering whatever they call outward. When your swarm needs somewhere real to publish and measure, Creator OS has a REST API, a hosted MCP server at https://mcp.creatoros.ca/mcp and a CLI, all included in every plan. Start with the docs for the base URL and auth.
Real-Time Monitoring: The Web Dashboard, Metrics, and Alerts
The dashboard is how you find out the swarm is healthy before a user does.
The README lists a web dashboard with real-time monitoring, charts and alerts, plus system performance metrics and agent health tracking. Treat three numbers as your first alert rules. Agent count tells you whether the pool is scaling as expected. Task throughput tells you whether the orchestrator is routing or stuck. Failure rate per capability tells you which agent type is misbehaving. A quick way to see it in code is to give each agent a heartbeat message on the bus and have a monitor agent count them; if a capability’s heartbeat count drops while the queue grows, the alert is real. The README’s monitoring section is the place to check for what the dashboard renders exactly.
How to Build an SEO Agent and Social Agent on the Swarm
Two agent types make a good first build because their work is easy to measure.
For an SEO agent, register a capability like “draft article” and give it a tool that writes and schedules posts. Creator OS connects to a WordPress site or a Shopify store blog, and the AI writes SEO articles as drafts by default unless publish is set. Your agent calls it and the orchestrator tracks the result like any other task. The SEO agent kit walkthrough covers a related Claude Code setup if you want a second reference.
For a social agent, the same pattern applies across 11 platforms plus Skool. A publishing agent registers “post everywhere” and calls posts:create from the CLI or create_post through MCP:
creatoros posts:create --text "New drop Friday" \
--platforms instagram,tiktok,linkedin,x \
--scheduledAt 2026-03-06T14:00:00Z --timezone America/TorontoA second agent registers “reply to comments” and works the inbox. Keep those two capabilities separate and the orchestrator can scale replying independently of publishing. For the open-source version of this idea, Social Agents runs your socials on Creator OS and starts with npm start creatoros social-agents; the walkthrough is here and the launch video is on YouTube.
FAQ
What is the best Python framework for an agent swarm?
There is no single winner. Agentic OS is a reasonable choice because it ships agent base classes, swarm orchestration with load balancing, parallel execution and a monitoring dashboard in one repository, all described in its README. Read that file and check whether its components match your workload before you commit.
How many agents can Agentic OS run at once?
The README lists support for 100+ concurrent agents. The practical ceiling depends on your machine, whether you run multi-threaded or multi-process execution, and whether the message bus is in-memory or Redis-backed. Measure before you scale past the stated figure.
Does Agentic OS support REST, GraphQL, and WebSocket APIs?
Yes. The README lists a comprehensive API stack with REST, GraphQL and WebSocket support, plus built-in rate limiting and throttling. Use REST for single calls, GraphQL when a client needs specific fields, and WebSocket for live status updates.
Can I build an SEO agent or social media agent with Agentic OS?
You can, using the same pattern as any other agent: register a capability, give it a tool, and let the orchestrator route work to it. For social posting, Creator OS provides the REST API, MCP server and CLI that the agent calls. See the MCP docs for connection details.
How do I install the Agentic OS Python agent swarm framework?
Clone the repository from github.com/kevinbadi/AgenticOSKevsAcademy and run pip install -r requirements.txt, which is the Quick Start the README shows. Follow the repository if the steps change, since that file is maintained alongside the code.
Get Started With Creator OS
Build the swarm in AgenticOSKevsAcademy, then point its agents at real accounts. Creator OS gives you 11 platforms plus Skool in one place, with a REST API, a hosted MCP server and the @creatoros/cli package included in every plan. The CLI installs 14 Claude Code skills with npx @creatoros/cli@latest init, including post-everywhere, respond-to-comments and analytics, which is a fast way to give your swarm working tools. Watch the builds on KevBuildsApps and sign up at creatoros.ca/sign-up.
Creator OS plans start at $14.99/month for 11 platforms plus Skool, with the API, the MCP server and the CLI included.
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