AgentSwarms: Free Hands-On Playground for Learning Agentic AI
AgentSwarms is a free interactive playground for learning agentic AI by building real agents — no local setup, no API keys needed to start. It's aimed at developers who want to move from reading about agents to actually building them.
Curriculum Overview
Five tracks with 40+ in-depth lessons and 30+ runnable agents. The curriculum covers six core topics:
- Lesson 1 – Prompts & System Messages: How system prompts shape an agent's personality, role, and constraints. Covers few-shot vs zero-shot patterns and temperature effects.
- Lesson 2 – RAG & Knowledge Bases: Grounding answers in documents with real citations. Explains why retrieval beats fine-tuning for facts, and when RAG fails.
- Lesson 3 – Tools & Function Calling: Connecting agents to APIs, MCP servers, and webhooks. OpenAI tool-call schema, MCP servers in 5 minutes, designing safe idempotent tools.
- Lesson 4 – Guardrails & HITL: Input/output filters, PII redaction, prompt-injection defense, approval inboxes, cost & rate-limit guardrails.
- Lesson 5 – Multi-Agent Swarms: Building researcher → writer → reviewer pipelines with explicit handoffs and shared memory. Orchestrator vs peer-to-peer patterns.
- Lesson 6 – Observability & Evals: Inspecting every token, tool call, and dollar spent. Reading execution traces, token/latency/cost dashboards, building eval suites.
How It Works
No installs, no API keys to start. Process in four steps:
- Try a Live Demo: Pick a template (Product Support, Research Assistant, Code Reviewer) — fully working agent provisioned in seconds.
- Follow the Guided Tour: Side-panel lessons with suggested prompts walk through RAG, guardrails, and approvals checkpoint by checkpoint.
- Fork & Experiment: Tweak system prompt, swap models (AgentSwarms AI, OpenAI, Gemini, Grok, Claude), wire up your own knowledge base.
- Build Your Own: Compose agents, chain into swarms, watch traces in the observability dashboard.
Key Terms Cheat Sheet
The source includes a glossary of agentic AI terms:
- Agent: LLM with system prompt, tools, memory for multi-step reasoning.
- RAG: Inject relevant document chunks into prompt for citation.
- Tool / Function call: Typed action the model can invoke.
- Guardrail: Input/output filters (PII, profanity, cost caps).
- HITL: Human-in-the-loop approval before risky actions.
- MCP: Model Context Protocol for exposing tools/data.
- Swarm: Multiple specialized agents with handoffs.
- Eval: Test suite scoring accuracy, format, safety, cost.
Pricing: Free forever for learners — no credit card required. Learn Mode (zero setup) is free; Build Mode lets you bring your own API keys.
📖 Read the full source: HN AI Agents
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