The agent toolkit JavaScript actually deserves.
A 10KB core. Twelve plug-and-play packages. Zero lock-in. Six formal contracts that make every adapter, tool, skill, memory, retriever, and runtime substitutable.
Documentation · Discord · Manifesto · Origin · Architecture
You started building an AI agent last week. You're three libraries deep, two of them fight each other, and nothing you wrote is reusable. This is for you.
We don't need another framework. We need a kit.
Building a real AI agent in JavaScript today means cobbling together five libraries that don't fit. Vercel AI SDK is a beautiful chat SDK with no runtime. LangChain.js drags in 200MB and leaks abstractions at every layer. MCP solves tool interop and nothing else. assistant-ui has 53 components and no opinion about how to compose them.
AgentsKit is the missing kit: small, contracted, composable. Six packages you can use alone, twelve that combine without ceremony. You stay in plain JavaScript the entire time.
Origin story for the long version. Manifesto for the principles.
npm install @agentskit/react @agentskit/adaptersimport { useChat, ChatContainer, Message, InputBar } from '@agentskit/react'
import { anthropic } from '@agentskit/adapters'
import '@agentskit/react/theme'
export default function Chat() {
const chat = useChat({ adapter: anthropic({ apiKey: KEY, model: 'claude-sonnet-4-6' }) })
return (
<ChatContainer>
{chat.messages.map(m => <Message key={m.id} message={m} />)}
<InputBar chat={chat} />
</ChatContainer>
)
}Streaming, tool calls, default styling, abortable. No setup. No boilerplate.
Before — the typical "JS agent" stack:
// Pick your favorite: LangChain, raw fetch, Vercel AI SDK + custom runtime,
// MCP client + custom UI, manual ReAct loop, hand-rolled streaming...
// Then wire memory. Then wire tools. Then wire delegation. Then debug.After — AgentsKit:
import { createRuntime } from '@agentskit/runtime'
import { openai } from '@agentskit/adapters'
import { webSearch, filesystem } from '@agentskit/tools'
const runtime = createRuntime({
adapter: openai({ apiKey: KEY, model: 'gpt-4o' }),
tools: [webSearch(), ...filesystem({ basePath: './workspace' })],
})
const result = await runtime.run('Research the top 3 AI frameworks and save a summary')That's an autonomous agent. With a tool registry. With memory. With observability hooks. Two imports, six lines.
Swap providers in one line — every other line stays the same:
import { anthropic, openai, gemini, ollama, deepseek, grok } from '@agentskit/adapters'
useChat({ adapter: anthropic({ apiKey, model: 'claude-sonnet-4-6' }) })
useChat({ adapter: openai({ apiKey, model: 'gpt-4o' }) })
useChat({ adapter: ollama({ model: 'llama3.1' }) }) // local, no key| AgentsKit | Vercel AI SDK | LangChain.js | assistant-ui | |
|---|---|---|---|---|
| Core size | 10KB gzip, zero deps | ~30KB | hundreds of MB transitively | n/a (UI only) |
| Agent runtime | First-class (ReAct, tools, skills, delegation, memory, RAG) | None | Yes, but heavy | None |
| Provider swap | One line | Route-handler-shaped | Per-class wiring | BYO backend |
| UI surfaces | React + Ink + headless | React | None | React |
| Formal contracts | Six versioned ADRs | Implicit | Implicit | Implicit |
| Edge-ready | Yes (10KB core, no Node-only deps) | Mostly | No | n/a |
We are honest about this:
- You only need a single OpenAI streaming call. Use the
openaiSDK directly — AgentsKit is overkill. - You're shipping a chat SDK to consumers, not an agent. Vercel AI SDK is purpose-built for that and excellent.
- You need Python. AgentsKit is JavaScript-first by design. Use a Python framework.
- You require enterprise-grade observability today. AgentsKit's observability layer is good but young; LangSmith/Arize/Helicone are more mature integrations right now.
- You can't accept a v0.x semver story. We're pre-1.0 with formal contracts already locked, but real production teams may want a stable v1.0.0 first.
Pick what you need. Every package works alone. Combinations work without glue code.
| Package | What it does | Stability |
|---|---|---|
@agentskit/core |
Types, contracts, primitives | stable |
@agentskit/adapters |
Provider adapters (OpenAI, Anthropic, Gemini, Ollama, DeepSeek, Grok, …) | stable |
@agentskit/react |
React hooks + headless UI | stable |
@agentskit/ink |
Terminal UI (Ink) components | stable |
@agentskit/cli |
CLI: chat, init, run | stable |
@agentskit/runtime |
Autonomous agent runtime (ReAct loop, delegation) | stable |
@agentskit/tools |
Web search, filesystem, shell | stable |
@agentskit/skills |
Pre-built behavioral prompts | stable |
@agentskit/memory |
Chat + vector memory (SQLite, Redis, file) | stable |
@agentskit/rag |
Plug-and-play RAG | stable |
@agentskit/observability |
Console, LangSmith, OpenTelemetry | beta |
@agentskit/sandbox |
Secure code execution | beta |
@agentskit/eval |
Agent evaluation and benchmarking | beta |
@agentskit/templates |
Authoring toolkit for skills/tools | stable |
The whole catalog is one npx @agentskit/cli init away.
import { planner, researcher, coder } from '@agentskit/skills'
const result = await runtime.run('Build a landing page about quantum computing', {
skill: planner,
delegates: {
researcher: { skill: researcher, tools: [webSearch()], maxSteps: 3 },
coder: { skill: coder, tools: [...filesystem({ basePath: './src' })], maxSteps: 8 },
},
})The planner decomposes the task. The researcher and coder execute their parts. Delegation happens through a tool the model already knows how to call — no special syntax to learn.
npm install -g @agentskit/cli
agentskit chat --provider ollama --model llama3.1
agentskit chat --provider openai --tools web_search,shell --skill researcherThe same useChat mental model. Real keyboard input. Real streaming. Real tools.
The full public API fits in under 2,000 tokens. Paste the agent-friendly reference into your LLM context and start generating real AgentsKit code immediately. We treat agents as first-class consumers of our docs.
graph TD
core["@agentskit/core\n(zero deps · 5 KB)"]
adapters["@agentskit/adapters\nOpenAI · Anthropic · Gemini\nOllama · DeepSeek · Grok"]
react["@agentskit/react\nReact hooks + headless UI"]
ink["@agentskit/ink\nTerminal UI (Ink)"]
runtime["@agentskit/runtime\nReAct loop · delegation"]
tools["@agentskit/tools\nweb search · filesystem · shell"]
skills["@agentskit/skills\nresearcher · coder · planner"]
memory["@agentskit/memory\nSQLite · Redis · file · vector"]
rag["@agentskit/rag\nplug-and-play RAG"]
observability["@agentskit/observability\nLangSmith · OpenTelemetry"]
sandbox["@agentskit/sandbox\nE2B · WebContainer"]
eval["@agentskit/eval\nbenchmarking · metrics"]
templates["@agentskit/templates\nskill/tool authoring"]
cli["@agentskit/cli\nchat · init · run"]
core --> adapters
core --> react
core --> ink
core --> runtime
core --> tools
core --> skills
core --> memory
core --> rag
core --> observability
core --> sandbox
core --> eval
core --> templates
cli --> core
cli --> adapters
cli --> ink
cli --> runtime
cli --> skills
cli --> tools
cli --> memory
classDef foundation fill:#1e293b,stroke:#6366f1,color:#f8fafc,font-weight:bold
classDef ui fill:#0f172a,stroke:#22d3ee,color:#f8fafc
classDef agent fill:#0f172a,stroke:#a78bfa,color:#f8fafc
classDef data fill:#0f172a,stroke:#34d399,color:#f8fafc
classDef ops fill:#0f172a,stroke:#fb923c,color:#f8fafc
classDef entry fill:#0f172a,stroke:#f472b6,color:#f8fafc
class core foundation
class react,ink ui
class adapters,runtime,tools,skills agent
class memory,rag,templates data
class observability,sandbox,eval ops
class cli entry
Legend: purple = provider/execution layer · cyan = UI layer · green = data layer · orange = ops layer · pink = CLI entry point
Six ADRs define the substrate:
| ADR | Contract |
|---|---|
| 0001 | Adapter — LLM provider seam |
| 0002 | Tool — function the model calls |
| 0003 | Memory — chat history + vector store + embed |
| 0004 | Retriever — context fetching |
| 0005 | Skill — declarative persona |
| 0006 | Runtime — the loop that composes them all |
Read these once and you can predict how every package behaves.
@agentskit/core is at v1.0.0 — API frozen at the minor level, deprecations carry a cycle, contracts pinned to ADRs. Other packages track their own cadence and individual stability tiers.
Concretely, as of the Phase 1 release:
- 538 tests across 14 packages
- 5.17 KB gzipped core — 48% under the 10 KB manifesto budget (enforced in CI)
- Seven formal contracts pinned to ADRs 0001–0007
- 74 documentation routes including 13 copy-paste recipes and 3 migration guides
See the Phase 1 release notes for what shipped, and the Master PRD for what's next.
AgentsKit is built in the open and ships because contributors show up. Every package, every doc, every example is fair game.
- How to contribute → — start here
- Good-first-issues — curated, tractable tickets
- Help-wanted — larger scoped work
- Discussions — ask, propose, share
- RFC template — open before touching a contract
CONTRIBUTING.md— dev setup + PR checklist
Thanks to everyone who's shipped a line of code, docs, or feedback.
MIT — see LICENSE.