Learn Agentic AI from zero — no experience needed
Free forever for learners — no credit card
An AI agent is software that uses an LLM to think, plan, and take real actions — search your docs, query a database, send an email, call an API, then check its own work. AgentSwarms teaches you how to build them, hands-on, in your browser. No installs, no scattered YouTube tutorials, no math degree.
Read a concept → click Run → see a real agent do it. From your first prompt to a team of agents working together (a "swarm") — in one guided playground.
Learn Mode: zero setup, free gatewayBuild Mode: bring your own API keysSee how →
Six lessons. From "what's an agent?" to "I shipped a swarm."
Every lesson is interactive. You read a concept, then run a live agent that demonstrates it — prompts and all.
Prompts & System Messages
Learn how an agent's personality, role, and constraints are shaped by the system prompt. See the same model behave like a teacher, a lawyer, or a sarcastic pirate — just by changing words.
- Anatomy of a great system prompt
- Few-shot vs zero-shot patterns
- How temperature changes creativity vs accuracy
RAG & Knowledge Bases
Watch a generic chatbot transform into a domain expert by grounding its answers in your documents. Real citations, real docs, no hallucinations.
- Why retrieval beats fine-tuning for facts
- Chunking, embeddings, and citations
- When RAG fails — and how to detect it
Tools & Function Calling
Give your agent superpowers. Connect it to APIs, MCP servers, and webhooks so it can actually do things — fetch data, send emails, run SQL.
- OpenAI tool-call schema, plain English
- MCP servers in 5 minutes
- Designing safe, idempotent tools
Guardrails & HITL
Production agents need brakes. Add input/output filters, PII detection, content safety, and human-in-the-loop approvals for risky actions.
- PII redaction and prompt-injection defense
- Approval inboxes for high-risk actions
- Cost & rate-limit guardrails
Multi-Agent Swarms
One agent is a worker. A swarm is a team. Build researcher → writer → reviewer pipelines with explicit handoffs and shared memory.
- Orchestrator vs peer-to-peer patterns
- Routing and handoff messages
- When to split an agent into a swarm
Observability & Evals
If you can't trace it, you can't trust it. Inspect every token, tool call, and dollar spent — and learn how to evaluate agent quality systematically.
- Reading execution traces like a pro
- Token, latency & cost dashboards
- Building your first eval suite
Learn by doing, in four steps
No installs. No API keys to start. Open a demo, follow the guided prompts, then make it your own.
Try a Live Demo
Start with the Templates gallery. Click any template — Product Support, Research Assistant, Code Reviewer — and a fully working agent is provisioned for you in seconds.
Follow the Guided Tour
Each demo opens in the Playground with a side-panel lesson. Suggested prompts walk you through RAG, guardrails, and approvals one checkpoint at a time.
Fork & Experiment
Tweak the system prompt, swap models (AgentSwarms AI, OpenAI, Gemini, Grok, Claude…), wire up your own knowledge base. Break things — that's how you learn.
Build Your Own
Apply what you learned. Compose your own agents, chain them into a swarm, and watch your traces light up in the observability dashboard.
The Agentic AI vocabulary, demystified
Every term you'll hear in agent papers, blog posts, and Twitter threads — explained in one line.
- Agent
- An LLM with a system prompt, optional tools, and memory — capable of multi-step reasoning toward a goal.
- RAG
- Retrieval-Augmented Generation. Inject relevant chunks from your docs into the prompt so the model can cite real sources.
- Tool / Function call
- A typed action the model can invoke (search_web, send_email, query_db). The agent decides when to call it.
- Guardrail
- Rules that filter input or output — PII redaction, profanity blocks, schema validation, cost caps.
- HITL
- Human-in-the-Loop. The agent pauses for human approval before doing something risky (refunds, deletes, sends).
- MCP
- Model Context Protocol. A standard way to expose tools and data sources to any compatible agent.
- Swarm
- Multiple specialized agents that hand off work to each other — researcher → writer → reviewer.
- Eval
- A test suite for agents. Score outputs on accuracy, format, safety, cost — not just vibes.
Your first agent is 60 seconds away
Sign up free, pick a template, and start the guided tour. By the end of the day you'll understand what makes agents tick — and you'll have built one yourself.