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
👀 See Also
cldctrl: Terminal Dashboard for Managing Claude Code Sessions
cldctrl is an npm package that provides a terminal dashboard for launching and managing Claude Code sessions across projects. It reads existing ~/.claude data, auto-discovers projects, and shows token usage with rate limit bars.
Detecting Silent Tool Failures in AI Coding Agents with Vibeyard
Vibeyard is a tool that detects when AI coding agents experience silent tool failures—where agents fall back to alternative strategies without alerting developers—and surfaces these inefficiencies during sessions. It can suggest fixes to prevent repeated inefficient workflows.
Skynet: Multi-Agent Collaboration Network for Claude Code Agents
Skynet is an open-source network that enables role-based collaboration between multiple Claude Code agents and humans. It's installed as a skill using npx and managed through natural language commands.
ZSE: Open-source LLM inference engine with 3.9-second cold starts
ZSE is an open-source LLM inference engine that reduces 32B model memory requirements from 64GB to 19.3GB VRAM and achieves 3.9-second cold starts for 7B models using a pre-quantized .zse format with memory-mapped weights.