@agentskit/runtime

@agentskit/runtime

Run autonomous agents in 5 lines — no UI, no boilerplate, just results.

Why

• ReAct loop handled for you — observe, think, act, repeat: the runtime drives the full cycle and stops when the agent decides it's done
• Structured, inspectable results — every run returns the final content, step count, all tool calls made, and total duration; no black boxes
• Production-ready lifecycle — lazy tool init/dispose, AbortSignal cancellation, memory persistence, and AgentEvent emissions for observability

Install

npm install @agentskit/runtime @agentskit/adapters

Quick example

import { createRuntime } from '@agentskit/runtime'
import { openai } from '@agentskit/adapters'
import { webSearch, filesystem } from '@agentskit/tools'

const runtime = createRuntime({
  adapter: openai({ apiKey: process.env.OPENAI_API_KEY, model: 'gpt-4o' }),
  tools: [webSearch(), ...filesystem({ basePath: './workspace' })],
  systemPrompt: 'You are a helpful research assistant.',
})

const result = await runtime.run('Research the latest advances in quantum computing')
console.log(result.content)
console.log(`Completed in ${result.steps} steps, ${result.durationMs}ms`)

With skills

import { createRuntime } from '@agentskit/runtime'
import { anthropic } from '@agentskit/adapters'
import { researcher } from '@agentskit/skills'

const runtime = createRuntime({
  adapter: anthropic({ apiKey: process.env.ANTHROPIC_API_KEY!, model: 'claude-sonnet-4-6' }),
})

const result = await runtime.run('Summarize REST vs GraphQL', { skill: researcher })
console.log(result.content)

Next steps

• Add RAG: pass retriever from @agentskit/rag — see @agentskit/rag README
• Add observability with @agentskit/observability observers for LangSmith or OpenTelemetry

Ecosystem

Package	Role
@agentskit/core	createRuntime contracts, Retriever
@agentskit/adapters	LLM adapters
@agentskit/tools	Tool modules
@agentskit/skills	Pre-built skills
@agentskit/rag	retriever for context injection

Docs

Full documentation