Deep Agent SDK


> Note: This package requires Bun runtime. It uses Bun-specific features and TypeScript imports.

A TypeScript library for building controllable AI agents using Vercel AI SDK. This is a reimplementation of deepagentsjs without any LangChain/LangGraph dependencies.

What is Deep Agent?

Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are "shallow" and fail to plan and act over longer, more complex tasks.

Deep Agent addresses these limitations through four core architectural components:

| Component | Purpose | Implementation |
|-----------|---------|----------------|
| Planning Tool | Long-term task breakdown and tracking | write_todos for maintaining task lists |
| Sub Agents | Task delegation and specialization | task tool for spawning specialized agents |
| File System Access | Persistent state and information storage | Virtual filesystem with read_file, write_file, edit_file |
| Detailed Prompts | Context-aware instructions | Sophisticated prompting strategies |

Installation

This package requires Bun runtime:

``bash


Install Bun if you haven't already

curl -fsSL https://bun.sh/install | bash

Install the package

bun add deepagentsdk

Or install globally for CLI usage

bun add -g deepagentsdk
`

Why Bun? This package publishes TypeScript source directly and uses Bun-specific optimizations for better performance.

Quick Start

`typescript
import { createDeepAgent } from 'deepagentsdk';
import { anthropic } from '@ai-sdk/anthropic';

const agent = createDeepAgent({
model: anthropic('claude-sonnet-4-5-20250929'),
systemPrompt: 'You are an expert researcher.',
});

const result = await agent.generate({
prompt: 'Research the topic of quantum computing and write a report',
});

console.log(result.text);
console.log('Todos:', result.state.todos);
console.log('Files:', Object.keys(result.state.files));
`

Features

$3

Deep agents can return typed, validated objects using Zod schemas alongside text responses:

`typescript
import { z } from 'zod';

const agent = createDeepAgent({
model: anthropic('claude-sonnet-4-5-20250929'),
output: {
schema: z.object({
summary: z.string(),
keyPoints: z.array(z.string()),
}),
description: 'Research findings',
},
});

const result = await agent.generate({
prompt: "Research latest AI developments",
});

console.log(result.output?.summary); // string
console.log(result.output?.keyPoints); // string[]
`

$3

Stream responses with real-time events for tool calls, file operations, and more:

`typescript
for await (const event of agent.streamWithEvents({
prompt: 'Build a todo app',
})) {
switch (event.type) {
case 'text':
process.stdout.write(event.text);
break;
case 'tool-call':
console.log(Calling: ${event.toolName});
break;
case 'file-written':
console.log(Written: ${event.path});
break;
}
}
`

$3

- Planning: write_todos for task management
- Filesystem: read_file, write_file, edit_file, ls, glob, grep
- Web: web_search, http_request, fetch_url (requires Tavily API key)
- Execute: Shell command execution with LocalSandbox backend
- Subagents: Spawn specialized agents for complex subtasks

Documentation

For comprehensive guides, API reference, and examples, visit deepagentsdk.vercel.app/docs

$3

- Get Started - Installation and basic setup
- Guides - In-depth tutorials on:
- Configuration options (models, backends, middleware)
- Custom tools and subagents
- Agent memory and persistence
- Prompt caching and conversation summarization
- Web tools and API integration
- Reference - Complete API documentation

CLI

The interactive CLI is built with Ink:

`bash


Run without installing (recommended)

bunx deepagentsdk

Or install globally

bun add -g deepagentsdk
deep-agent

With options

bunx deepagentsdk --model anthropic/claude-haiku-4-5-20251001
`

API Keys: Load from environment variables (ANTHROPIC_API_KEY, OPENAI_API_KEY, TAVILY_API_KEY) or .env` file.

License

MIT