An all-in-one toolkit to build agentic applications that turn natural language into real-world actions.

Dexto is a universal agent interface for building collaborative, context-aware AI Agents & agentic apps. It orchestrates LLMs, tools, and data into persistent, stateful systems with memory, so you can rapidly create AI assistants, digital companions & copilots that think, act and feel alive.

Dexto combines a configuration-driven framework, robust runtime, and seamless developer experience so you can build, deploy, and iterate on your agents easily.

• Framework – Define agent behavior in YAML. Instantly swap models and tools without touching code.
• Runtime – Execution with orchestration, session management, conversation memory, and multimodal support.
• Interfaces & Tooling – Native support for CLI, Web, APIs, and a TypeScript SDK.

• Autonomous Agents - Agents that plan, execute, and adapt to user goals.
• Digital Companions - AI assistants & copilots that remember context and anticipate needs.
• MCP Clients - Connect multiple tools, files, APIs, and data via MCP Servers.
• Multi-Agent Systems - Architect agents that collaborate, delegate, and solve complex tasks together.
• Agent-as-a-Service – Transform your existing SaaS products and APIs into dynamic, conversational experiences.
• Agentic Applications – Integrate Dexto as a reasoning engine to power interactive, multimodal, AI-native applications.

• Batteries Included – Session management, tool orchestration, multimodal support, and production-ready observability.
• 20+ LLMs – Instantly switch between OpenAI, Anthropic, Google, Groq, local models or bring your own.
• Run Anywhere – Local for privacy, cloud for reach, or hybrid. Same agent, any deployment.
• Native Multimodal – Text, images, files, and tools in a single conversation. Upload screenshots, ask questions, take actions.
• Persistent Sessions – Conversations, context, and memory are saved and can be exported, imported, or shared across environments.
• Flexible Interfaces – One agent, endless ways to interact: Ready-to-use CLI, WebUI, APIs, or integrate with your own UI.
• 30+ Tools & MCP – Integrate tools and connect to external servers via the Model Context Protocol (MCP).
• Pluggable Storage – Use Redis, PostgreSQL, SQLite, in-memory, S3 and more for cache, database & blob backends.

# NPM global
npm install -g dexto

# —or— build from source
# this sets up dexto CLI from the cloned code
git clone https://github.com/truffle-ai/dexto.git
cd dexto && pnpm install && pnpm install-cli

# 1. Run setup workflow - this prompts for your preferred LLM and API keys and starts the interactive CLI
dexto

# 2. Try a multi-step task
dexto "create a snake game in HTML/CSS/JS, then open it in the browser"

# 3. Launch the Dexto Web UI
dexto --mode web

In 2 -> Dexto will use filesystem tools to write code and browser tools to open it — all from a single prompt. The Web UI allows you to navigate previous conversations and experiment with different models, tools and more.

dexto --auto-approve "refactor my project using the filesystem and browser tools"

Use the --auto-approve flag to bypass confirmation prompts when you trust the tools being invoked—perfect for fast local iteration. Remove the flag when you want explicit approval again.

Dexto comes with pre-built agent recipes for common use cases. Install and use them instantly:

# List available agents
dexto list-agents

# Install specific agents
dexto install nano-banana-agent podcast-agent coding-agent

# Use an agent
dexto --agent nano-banana-agent "create a futuristic cityscape with flying cars"
dexto --agent podcast-agent "generate a podcast intro with two hosts discussing AI"
dexto --agent coding-agent "create a landing page for a coffee brand inspired by star wars"

Available Agents:

• Coding Agent – Code generation, refactoring, debugging
• Nano Banana Agent – Advanced image generation and editing using Google's Nano Banana (Gemini 2.5 Flash Image)
• Podcast Agent – Advanced podcast generation using Google Gemini TTS for multi-speaker audio content
• Sora Video Agent – AI video generation using OpenAI's Sora with custom settings, remixing, and reference support
• Database Agent – Demo agent for SQL queries and database operations
• Image Editor Agent – Image editing and manipulation
• Music Agent – Music creation and audio processing
• PDF Agent – Document analysis and conversation
• Product Researcher – Product naming and branding research
• Triage Agent – Demo multi-agent customer support routing system

Each agent is pre-configured with the right tools, prompts, and LLM settings for its domain. No setup required—just install and start building.

More ready-to-run recipes live in agents/ and the docs site.

Task: Generate an intro for a podcast about the latest in AI.

dexto --agent podcast-agent

Task: Detect all faces in this image and draw bounding boxes around them.

dexto --agent image-editor-agent

Build full-stack applications, websites, and interactive games with AI-powered coding agents. Customize them to create your own coding agents.

Task: Can you create a snake game in a new folder and open it when done?

dexto --agent coding-agent "Can you create a snake game in a new folder and open it when done?"

Dexto agents are designed to be modular, composable and portable, allowing you to run them from anywhere. In this example, we connect to dexto as an MCP server via Cursor to use our podcast agent from above.

Create multi-agent systems that can intelligently coordinate and delegate tasks among themselves based on the user query.

dexto --agent triage-agent

You can add your own Model Context Protocol (MCP) servers to extend Dexto's capabilities with new tools or data sources. Just edit your agent YAML or add it directly in the WebUI.

Create and save memories. Your agent automatically uses it to create personalized experiences.

Equip your agents from 20+ MCP Servers and start using them via chat - instantly.

• Bring your own keys
• Can't find an MCP? Contribute here!

Agents can generate structured forms when they need additional data to make it easier to collect extra info & approvals from users.

🛒 Browser Agent: Amazon Shopping Assistant

Task: Can you go to amazon and add some snacks to my cart? I like trail mix, cheetos and maybe surprise me with something else?

# Default agent has browser tools
dexto

🎮 Playground: Interactive Development Environment

A testing playground to view the tools in your MCP servers before connecting them to LLMs to see the detailed response structures.

📧 Email to Slack: Automated Email Summaries

Task: Summarize emails and send highlights to Slack

dexto --agent ./agents/examples/email_slack.yml

🖼️ Hugging Face: Image Generation

Task: Generate a photo of a baby panda.

Run dexto --help for all flags, sub-commands, and environment variables.

Dexto treats each configuration as a unique agent allowing you to define and save combinations of LLMs, servers, storage options, etc. based on your needs for easy portability. Define agents in version-controlled YAML. Change the file, reload, and chat—state, memory, and tools update automatically.

# agents/my-agent.yml
llm:
  provider: openai
  model: gpt-5-mini
  apiKey: $OPENAI_API_KEY

mcpServers:
  filesystem:
    type: stdio
    command: npx
    args: ['-y', '@modelcontextprotocol/server-filesystem', '.']
  web:
    type: stdio
    command: npx
    args: ['-y', '@modelcontextprotocol/server-brave-search']

systemPrompt: |
  You are a helpful AI assistant with access to files and web search.

Switch between providers instantly—no code changes required.

# Switch models via CLI
dexto -m claude-sonnet-4-5-20250929
dexto -m gemini-2.5-pro

See our Configuration Guide for complete setup instructions.

Install the @dexto/core library, and build applications with the DextoAgent class. Everything the CLI can do, your code can too.

npm install @dexto/core

import { DextoAgent } from '@dexto/core';

// Create and start agent
const agent = new DextoAgent({
  llm: {
    provider: 'openai',
    model: 'gpt-5-mini',
    apiKey: process.env.OPENAI_API_KEY
  }
});
await agent.start();

// Run tasks
const response = await agent.run('List the 5 largest files in this repo');
console.log(response);

// Hold conversations
await agent.run('Write a haiku about TypeScript');
await agent.run('Make it funnier');

await agent.stop();

See our TypeScript SDK docs for complete examples with MCP tools, sessions, and advanced features.

Create and manage multiple conversation sessions with persistent storage.

const agent = new DextoAgent(config);
await agent.start();

// Create and manage sessions
const session = await agent.createSession('user-123');
await agent.run('Hello, how can you help me?', undefined, 'user-123');

// List and manage sessions
const sessions = await agent.listSessions();
const sessionHistory = await agent.getSessionHistory('user-123');
await agent.deleteSession('user-123');

// Search across conversations
const results = await agent.searchMessages('bug fix', { limit: 10 });

Switch between models and providers dynamically.

// Get current configuration
const currentLLM = agent.getCurrentLLMConfig();

// Switch models (provider inferred automatically)
await agent.switchLLM({ model: 'gpt-5-mini' });
await agent.switchLLM({ model: 'claude-sonnet-4-5-20250929' });

// Switch model for a specific session id 1234
await agent.switchLLM({ model: 'gpt-5-mini' }, '1234')

// Get supported providers and models
const providers = agent.getSupportedProviders();
const models = agent.getSupportedModels();
const openaiModels = agent.getSupportedModelsForProvider('openai');

For advanced MCP server management, use the MCPManager directly.

import { MCPManager } from '@dexto/core';

const manager = new MCPManager();

// Connect to MCP servers
await manager.connectServer('filesystem', {
  type: 'stdio',
  command: 'npx',
  args: ['-y', '@modelcontextprotocol/server-filesystem', '.']
});

// Access tools, prompts, and resources
const tools = await manager.getAllTools();
const prompts = await manager.getAllPrompts();
const resources = await manager.getAllResources();

// Execute tools
const result = await manager.executeTool('readFile', { path: './README.md' });

await manager.disconnectAll();

Configure storage backends for production-ready persistence and caching.

# agents/production-agent.yml
storage:
  cache:
    type: redis
    url: $REDIS_URL
    maxConnections: 100
  database:
    type: postgres
    connectionString: $POSTGRES_CONNECTION_STRING
    maxConnections: 25

sessions:
  maxSessions: 1000
  sessionTTL: 86400000 # 24 hours

Supported Backends:

• Cache: Redis, In-Memory (fast, ephemeral)
• Database: PostgreSQL, SQLite, In-Memory (persistent, reliable)

Use Cases:

• Development: In-memory for quick testing
• Production: Redis + PostgreSQL for scale
• Simple: SQLite for single-instance persistence

See the DextoAgent API Documentation for complete method references.

Usage: dexto [options] [command] [prompt...]

Dexto CLI allows you to talk to Dexto, build custom AI Agents, build complex AI applications like Cursor, and more.

Run dexto interactive CLI with `dexto` or run a one-shot prompt with `dexto -p "<prompt>"` or `dexto "<prompt>"`
Start with a new session using `dexto --new-session [sessionId]`
Run dexto web UI with `dexto --mode web`
Run dexto as a server (REST APIs + WebSockets) with `dexto --mode server`
Run dexto as a discord bot with `dexto --mode discord`
Run dexto as a telegram bot with `dexto --mode telegram`
Run dexto agent as an MCP server with `dexto --mode mcp`
Run dexto as an MCP server aggregator with `dexto mcp --group-servers`

Check subcommands for more features. Check https://github.com/truffle-ai/dexto for documentation on how to customize dexto and other examples

Arguments:
  prompt                           Natural-language prompt to run once. If not passed, dexto will start as an interactive CLI

Options:
  -v, --version                    output the current version
  -a, --agent <name|path>          Agent name or path to agent config file
  -p, --prompt <text>              One-shot prompt text. Alternatively provide a single quoted string as positional argument.
  -s, --strict                     Require all server connections to succeed
  --no-verbose                     Disable verbose output
  --no-interactive                 Disable interactive prompts and API key setup
  -m, --model <model>              Specify the LLM model to use
  -r, --router <router>            Specify the LLM router to use (vercel or in-built)
  --new-session [sessionId]        Start with a new session (optionally specify session ID)
  --mode <mode>                    The application in which dexto should talk to you - cli | web | server | discord | telegram | mcp (default: "cli")
  --web-port <port>                optional port for the web UI (default: "3000")
  --no-auto-install                Disable automatic installation of missing agents from registry
  -h, --help                       display help for command

Commands:
  create-app                       Scaffold a new Dexto Typescript app
  init-app                         Initialize an existing Typescript app with Dexto
  setup [options]                  Configure global Dexto preferences
  install [options] [agents...]    Install agents from the registry
  uninstall [options] [agents...]  Uninstall agents from the local installation
  list-agents [options]            List available and installed agents
  which <agent>                    Show the path to an agent
  mcp [options]                    Start Dexto as an MCP server. Use --group-servers to aggregate and re-expose tools from configured MCP servers.

• Quick Start – Get up and running in minutes.
• Configuration Guide – Configure agents, LLMs, and tools.
• Building with Dexto – Developer guides and patterns.
• API Reference – REST APIs, WebSocket, and SDKs.

We collect anonymous usage data (no personal/sensitive info) to help improve Dexto. This includes:

• Commands used
• Command execution time
• Error occurrences
• System information (OS, Node version)
• LLM Models used

To opt-out:

Set env variable DEXTO_ANALYTICS_DISABLED=1

We welcome contributions! Refer to our Contributing Guide for more details.

Dexto is built by the team at Truffle AI.
Join our Discord to share projects, ask questions, or just say hi!

If you enjoy Dexto, please give us a ⭐ on GitHub—it helps a lot!

Thanks to all these amazing people for contributing to Dexto!

Elastic License 2.0. See LICENSE for full terms.