Introduction

llmist is a TypeScript LLM client with streaming tool execution. Most LLM libraries buffer the entire response before parsing tool calls. llmist parses incrementally.

Your gadgets (tools) fire the instant they’re complete in the stream—giving your users immediate feedback.

Key Features

Streaming Tool Execution

Gadgets execute the moment their block is parsed—not after the response completes. Real-time UX without buffering.

for await (const event of agent.run()) {
  if (event.type === 'gadget_result')
    updateUI(event.result); // Immediate
}

Built-in Function Calling

llmist implements its own tool calling via a simple block format. No response_format: json. No native tool support needed. Works with any model from supported providers.

!!!GADGET_START:FloppyDisk
!!!ARG:filename
DOOM.ZIP
!!!ARG:megabytes
50
!!!GADGET_END

Markers are fully configurable.

Multi-Provider Support

OpenAI, Anthropic, and Gemini out of the box—extensible to any provider. Just set API keys as environment variables.

.withModel('sonnet')   // Anthropic Claude
.withModel('gpt-5')    // OpenAI
.withModel('flash')    // Google Gemini

Composable Agent API

Fluent builder, async iterators, full TypeScript inference. Hook into any lifecycle point. Your code stays readable.

const answer = await LLMist.createAgent()
  .withModel('sonnet')
  .withGadgets(FloppyDisk, DialUpModem)
  .withHooks(HookPresets.monitoring())
  .askAndCollect('How many floppies for DOOM.ZIP?');

Packages

Package	Description
llmist	Core library with agents, gadgets, and providers
@llmist/cli	Command-line interface
@llmist/testing	Testing utilities and mocks

Next Steps

• Installation - Get llmist set up
• Quick Start - Build your first agent