LLMz is a TypeScript AI agent framework that replaces traditional JSON tool calling with executable code generation. Instead of orchestrating tools through multiple LLM roundtrips, agents write and execute TypeScript directly—enabling complex logic, loops, and multi-tool coordination in a single pass.

Powers millions of production agents at Botpress.


📚	Examples →

The Problem with Tool Calling

Traditional agentic frameworks (LangChain, CrewAI, MCP servers) rely on JSON tool calling:

{
  "tool": "getTicketPrice",
  "parameters": { "from": "quebec", "to": "new york" }
}

This breaks down quickly:

Verbose schemas: LLMs struggle with complex JSON structures
No logic: Can't express conditionals, loops, or error handling
Multiple roundtrips: Each tool call requires another LLM inference ($$$)
Fragile composition: Chaining tools is error-prone and expensive

You end up with brittle agents that cost 10-100x more than they should.

The LLMz Solution

LLMz generates and executes real TypeScript code in a secure sandbox:

const price = await getTicketPrice({ from: 'quebec', to: 'new york' })

if (price > 500) {
  throw new Error('Price too high')
}

const ticketId = await buyTicket({ from: 'quebec', to: 'new york' })
return { action: 'done', result: ticketId }

Why this works:

LLMs have seen billions of lines of TypeScript—they're exceptionally good at it
Complex logic (loops, conditionals, error handling) happens in one inference
Multiple tool calls execute synchronously without LLM roundtrips
Full type safety via Zui schemas and TypeScript inference

Real-world impact: Anthropic reduced agent costs by 98.7% using code execution patterns (source).

Quick Start

Requirements: Node.js 20+

npm install @botpress/client llmz

Platform Support

Platform	Support
Node.js 20+	✅ Full
Browser	✅ Full
AWS Lambda	✅ Full
Cloudflare Workers	✅ Full
Bun	✅ Full
Deno	✅ Full

Sandbox Execution

LLMz uses QuickJS (a lightweight JavaScript engine compiled to WebAssembly) to execute generated code in a secure, isolated sandbox. This provides:

Complete isolation: No access to filesystem, network, or host environment
Memory limits: Configurable heap size to prevent resource exhaustion
Execution timeouts: Automatic termination of runaway code
Abort signals: Support for programmatic execution cancellation
Universal compatibility: Works everywhere WebAssembly is supported

The QuickJS sandbox is bundled as a singlefile variant with WASM inlined as base64, so it works out-of-the-box with any bundler (esbuild, webpack, vite, rollup) without configuration.

Worker Mode: Autonomous Execution

import { Client } from '@botpress/client'
import { execute } from 'llmz'

const client = new Client({ botId: '...', token: '...' })

const result = await execute({
  instructions: 'Calculate sum of integers 14-1078 divisible by 3, 9, or 5',
  client,
})

console.log(result.output) // 271575

Generated code:

let sum = 0
for (let i = 14; i <= 1078; i++) {
  if (i % 3 === 0 || i % 9 === 0 || i % 5 === 0) {
    sum += i
  }
}
return { action: 'done', value: { success: true, result: sum } }

Chat Mode: Interactive Agents

import { execute } from 'llmz'
import { Text, Button } from './components'

const tools = [searchFlights, bookTicket, cancelBooking]

let state = { transcript: [] }

while (true) {
  const result = await execute({
    client,
    tools,
    chat: {
      transcript: state.transcript,
      components: { Text, Button },
    },
  })

  if (result.is('listen')) {
    // Agent yielded UI and is waiting for user input
    console.log('Agent:', result.value.components)
    const userInput = await getUserInput()
    state.transcript.push({ role: 'user', content: userInput })
  } else {
    // Agent completed the task
    break
  }
}

Generated code:

const flights = await searchFlights({ from: 'SFO', to: 'NYC' })

yield (
  <Text>
    Found {flights.length} flights. Cheapest is ${flights[0].price}. Book it?
  </Text>
)
yield <Button>Book Flight</Button>
yield <Button>Cancel</Button>

return { action: 'listen' }

Core Concepts

Execution Modes

Worker Mode: Autonomous agents that execute to completion

const result = await execute({
  instructions: 'Analyze Q4 sales data and generate report',
  client,
  tools: [fetchSales, calculateMetrics, generatePDF],
})

Chat Mode: Interactive conversations with user input

const result = await execute({
  client,
  tools,
  chat: {
    transcript: conversationHistory,
    components: { Text, Button, Form },
  },
})

Tools: Type-Safe Functions

import { Tool } from 'llmz'
import { z } from '@bpinternal/zui'

const searchFlights = new Tool({
  name: 'searchFlights',
  description: 'Search for available flights',
  input: z.object({
    from: z.string(),
    to: z.string(),
    date: z.string(),
  }),
  output: z.array(
    z.object({
      id: z.string(),
      price: z.number(),
      departure: z.string(),
    })
  ),
  handler: async ({ from, to, date }) => {
    // Your implementation
    return flights
  },
})

Tools are exposed to agents with full TypeScript signatures. Agents call them like regular async functions.

Objects: Namespaced State

Group related tools and variables:

import { ObjectInstance } from 'llmz'
import { z } from '@bpinternal/zui'

const database = new ObjectInstance({
  name: 'db',
  description: 'Database operations',
  tools: [queryUsers, updateRecord, deleteRecord],
  properties: [
    {
      name: 'connectionString',
      value: process.env.DB_URL,
      writable: false,
    },
    {
      name: 'lastQueryTime',
      value: null,
      type: z.string().nullable(),
      writable: true,
    },
  ],
})

Agents access via namespaces:

const users = await db.queryUsers({ active: true })
db.lastQueryTime = new Date().toISOString()

Exits: Structured Termination

Define how agents can complete:

import { Exit } from 'llmz'
import { z } from '@bpinternal/zui'

const TicketBooked = new Exit({
  name: 'ticket_booked',
  description: 'Successfully booked a ticket',
  schema: z.object({
    ticketId: z.string(),
    price: z.number(),
    confirmation: z.string(),
  }),
})

const result = await execute({
  client,
  tools,
  exits: [TicketBooked],
})

if (result.is(TicketBooked)) {
  console.log('Booked:', result.value.ticketId) // Fully typed
}

Agents use exits via return statements:

return {
  action: 'ticket_booked',
  ticketId: 'TKT-12345',
  price: 299,
  confirmation: 'ABC123',
}

Advanced Features

Thinking: Forced Reflection

Prevent agents from rushing to conclusions:

import { ThinkSignal } from 'llmz'

const complexAnalysis = new Tool({
  name: 'analyze',
  handler: async (data) => {
    const result = performComplexCalculation(data)
    // Force agent to reflect on results before proceeding
    throw new ThinkSignal('Analysis complete. Review data before next step.')
  },
})

Agents can also self-initiate thinking:

// Agent-generated code
const data = await fetchLargeDataset()
return { action: 'think' } // Pause to process information

Snapshots: Pause and Resume

Save execution state for long-running workflows:

import { SnapshotSignal } from 'llmz'

const approvalRequired = new Tool({
  name: 'submitForApproval',
  handler: async (request) => {
    await saveToDatabase(request)
    // Halt execution until manual approval
    throw new SnapshotSignal('Awaiting manager approval')
  },
})

// Later, resume from snapshot
const result = await execute({
  client,
  snapshot: savedSnapshot,
})

Hooks: Custom Logic Injection

const result = await execute({
  client,
  tools,
  hooks: {
    onTrace: (trace) => {
      // Non-blocking: log tool calls, errors, outputs
      logger.info(trace)
    },
    onExit: (exit) => {
      // Validate before allowing exit
      if (exit.action === 'transfer_money' && exit.amount > 10000) {
        throw new Error('Amount exceeds limit')
      }
    },
    onBeforeExecution: (code) => {
      // Inspect/modify generated code before execution
      if (code.includes('dangerousOperation')) {
        throw new Error('Blocked unsafe operation')
      }
    },
  },
})

Coming from MCP?

LLMz is not a replacement for MCP—it's complementary.

MCP (Model Context Protocol): Standardizes how AI applications connect to data sources and tools across processes/machines.

LLMz: Replaces the execution pattern after tools are exposed. Instead of making multiple LLM calls to orchestrate MCP tools via JSON, LLMz generates TypeScript code that calls those same tools in a single inference—reducing costs by up to 98%.

Production Ready

LLMz has been running in production for over a year:

Millions of active users across enterprise and consumer applications
Hundreds of thousands of deployed agents handling real-world workloads
Secure sandbox: Uses QuickJS WASM for isolated code execution
Type-safe: Full TypeScript inference and Zui validation
Observable: Comprehensive tracing and error handling

Architecture

Execution Pipeline:

Prompt Generation: Injects tools, schemas, and context into dual-mode prompts
Code Generation: LLM generates TypeScript with tool calls and logic
Compilation: Babel AST transformation with custom plugins (tracking, JSX, source maps)
Execution: Runs in QuickJS WASM sandbox with full isolation
Result Processing: Type-safe exit handling and error recovery

Security:

QuickJS WASM sandbox with complete isolation (no filesystem/network access)
Stack trace sanitization (removes internal framework details)
Configurable memory limits and execution timeouts
Tool-level permissions and rate limiting
Automatic token limit handling

Comparison

Feature	LangChain / CrewAI	MCP Servers	LLMz
Tool calling	JSON	JSON	TypeScript code
Multi-tool orchestration	Multiple LLM calls	Multiple LLM calls	Single LLM call
Complex logic	Limited	Limited	Full language support
Type safety	Partial	Schema-based	Full TypeScript + Zui
Execution environment	Python/JS runtime	Cross-process	QuickJS WASM sandbox
Cost (complex workflows)	High (many roundtrips)	High (many roundtrips)	Low (one-shot generation)
Production scale	Varies	Emerging	Battle-tested (1M+ users)

Examples

Check out the examples folder for complete working examples:

Title	Mode	Description
Basic Chat	Chat	Simple interactive chat with button-based navigation
Chat with Exits	Chat	Custom exit conditions with type-safe validation
Conditional Tools	Chat	Dynamic tool availability based on context
Small Models	Chat	Optimized prompts for smaller language models
Web Search	Chat	Integrate web search and content browsing
Tool Confirmation	Chat	User confirmation before executing tools
Guardrails	Chat	Safety constraints and content filtering
Multi-Agent	Chat	Coordinating multiple agents in one system
Variables	Chat	Stateful properties that persist across iterations
Components	Chat	Rich UI components for interactive experiences
Minimal Worker	Worker	One-shot computational task execution
File System	Worker	Automated file operations with conditional logic
Sandbox	Worker	Secure isolated code execution environment
Snapshots	Worker	Pause and resume long-running workflows
Stack Traces	Worker	Error handling and debugging patterns
Tool Chaining	Worker	Sequential multi-tool orchestration
Error Recovery	Worker	Graceful failure handling and retries
Security	Worker	Code inspection and security validation
Wrap Tools	Worker	Creating higher-order tool abstractions
RAG	Chat	Retrieval-augmented generation with knowledge bases

Contributing

git clone https://github.com/botpress/botpress
cd packages/llmz

pnpm install
pnpm test
pnpm build

Commands:

pnpm test: Run test suite (Vitest with LLM retries)
pnpm test:watch: Watch mode for development
pnpm build: Compile TypeScript and bundle (ESM + CJS)
pnpm generate: Regenerate prompt templates from markdown

License

MIT

README.md

LLMz