apps/docs/skills/reactive-agents/SKILL.md
Orient to the Reactive Agents framework, understand the builder API shape, and select the right capability skills for your task.
npx skillsauth add tylerjrbuell/reactive-agents-ts reactive-agentsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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After loading this skill you know: (1) what the framework does, (2) the canonical builder chain pattern, (3) which capability skills to load for the task at hand.
Reactive Agents is an Effect-TS layered runtime for building autonomous AI agents in TypeScript. Agents are composed via a fluent ReactiveAgentBuilder — each .withX() call wires in an optional capability layer. The runtime ships 25 packages covering reasoning, memory, tools, MCP, guardrails, identity, observability, orchestration, cost, verification, eval, A2A networking, and web framework integrations (React, Vue, Svelte).
Six LLM providers are supported: anthropic, openai, gemini, ollama, litellm, and test.
import { ReactiveAgents } from "@reactive-agents/runtime";
const agent = await ReactiveAgents.create()
.withName("my-agent")
.withProvider("anthropic") // required
.withModel("claude-sonnet-4-6") // optional — uses provider default if omitted
.withReasoning({ defaultStrategy: "adaptive", maxIterations: 10 })
.withTools() // enables all built-in tools
.withMemory({ tier: "enhanced", dbPath: "./agent.db" })
.withObservability({ verbosity: "normal", live: true })
.build(); // always await — returns Promise<ReactiveAgent>
const result = await agent.run("Your task here");
console.log(result.output);
console.log(result.metadata.stepsCount, result.metadata.strategyUsed);
| Building... | Load these skills |
|---|---|
| Any agent (start here) | builder-api-reference |
| Task agent (research, analysis, coding) | reasoning-strategy-selection, tool-creation |
| Agent that runs shell commands | shell-execution-sandbox |
| Agent with persistent memory | memory-patterns, context-and-continuity |
| Agent using MCP tools | mcp-tool-integration |
| Always-on / scheduled agent | gateway-persistent-agents |
| Multi-agent workflow | multi-agent-orchestration |
| Agent embedded in a web app | ui-integration, interaction-autonomy |
| Production / multi-tenant agent | identity-and-guardrails, cost-budget-enforcement |
| Agent with output quality guarantees | reasoning-strategy-selection, quality-assurance |
| Agent-to-agent networking | a2a-agent-networking |
| Custom provider behavior or local models | provider-patterns |
Load a recipe skill for a full working example:
| Recipe | What it builds |
|---|---|
| recipe-research-agent | Research/analysis agent with memory + verification |
| recipe-code-assistant | Code generation + sandboxed shell execution |
| recipe-persistent-monitor | Always-on monitoring via gateway + crons |
| recipe-orchestrated-workflow | Multi-agent pipeline, lead/worker pattern |
| recipe-saas-agent | Multi-tenant agent with identity + cost controls |
| recipe-embedded-app-agent | Agent in React/Vue/Svelte with streaming UI |
ReactiveAgents.create() // blank builder
ReactiveAgents.fromConfig(config) // from AgentConfig object
ReactiveAgents.fromJSON(json) // from JSON string
ReactiveAgents.runOnce("task", builder) // build + run + dispose in one call
builder.buildEffect() // returns Effect<ReactiveAgent> for Effect runtimes
.build() is async — always await it; forgetting causes silent "agent is undefined" errors.withProvider() is required — there is no default provider.withTools() with no args enables 5 standard tools: web-search, http-get, file-read, file-write, code-execute. Shell execution is opt-in only via .withTerminalTools(); use allowedTools to restrict standard tools"plan-execute-reflect" — not "plan-execute" (throws StrategyNotFoundError)"standard" and "enhanced" — not "1" and "2" (those are deprecated)"groq" and "openrouter" are not valid provider names — use "litellm" for proxy/router providerstesting
Enable output verification (hallucination detection, semantic entropy, self-consistency), add post-run verification steps, and run LLM-scored evals across 5 quality dimensions.
data-ai
Configure per-provider behavior, understand streaming quirks, and use the 7-hook adapter system for optimal performance across LLM providers.
data-ai
Configure the 4-layer memory system with SQLite/FTS5/vec storage for persistent agent knowledge that survives sessions.
testing
Set per-request, per-session, daily, and monthly spend limits, configure rate limiting and circuit breakers, and isolate costs per user or tenant.