Dexto 2.0 CLI: manage your Dexto cloud agents, chats, sandboxes and Sites, and run AI agents in your terminal.
Node.js 20 or later works; 22 is recommended. Use npm. Install once:
npm install -g dexto
If npm fails with EACCES, don't use sudo; use the install script
(installs under ~/.dexto, no admin).
dexto login --device prints a link and code on stderr, then waits for approval. Run it so you
can read its output while it waits, and pass both to the person.
Do not approve sign-in on their behalf. Check the first account result:
dexto whoami --json
Success is exit 0 with your account identity. Signed-out account commands exit 3 with
needs_sign_in; for a headless machine, run dexto login --device and retry. Installation and
dexto --version need no sign-in. Account and workspace requests do; model runs also need a
configured model or credits. This check starts no model run.
What is Dexto?
Dexto is an agent harness—the orchestration layer that turns LLMs into reliable, stateful agents that can take actions, remember context, and recover from errors.
Think of it like an operating system for AI agents:
| Component | Analogy | Role |
|---|
| LLM | CPU | Raw processing power |
| Context Window | RAM | Working memory |
| Dexto | Operating System | Orchestration, state, tools, recovery |
| Your Agent | Application | Domain-specific logic and clients |
Why Dexto?
- Configuration-driven: Define agents in YAML. Swap models and tools without touching code.
- Batteries included: Session management, tool orchestration, memory, multimodal support, and observability—out of the box.
- Run anywhere: Local, cloud, or hybrid. CLI, Discord, Telegram, or embedded in your app.
What You Can Build
- Coding Agents – Build, debug, and refactor code autonomously
- Autonomous Agents – Plan, execute, and adapt to user goals
- Digital Companions – Assistants that remember context and anticipate needs
- MCP Clients & Servers – Connect tools, files, APIs via Model Context Protocol
- Multi-Agent Systems – Agents that collaborate, delegate, and solve complex tasks together
Coding Agent
Dexto ships with a production-ready coding agent you can use immediately via the CLI.
# Launch the coding agent (default)
dexto
# Or explicitly
dexto --agent coding-agent
What it can do:
- Build new apps from scratch
- Read, write, and refactor code across your entire codebase
- Execute shell commands and run tests
- Spawn specialized sub-agents for exploration and planning
- Remember context across sessions with persistent memory
- Work with any of 50+ LLMs (swap models mid-conversation)
Ready-to-use interfaces:
- CLI – Terminal-native with
/commands, streaming output, and session management
The coding agent is just one example of what you can build. Create your own agents by defining a YAML config—same architecture, your domain.
Quick Start
Install
npm install -g dexto
# Alternative: native installer (no sudo)
# macOS / Linux / WSL
curl -fsSL https://dexto.ai/install | sh
# Windows PowerShell
irm https://dexto.ai/install.ps1 | iex
# Or build from source
git clone https://github.com/truffle-ai/dexto.git
cd dexto && pnpm install && pnpm install-cli
Upgrade/uninstall and migration troubleshooting live in docs:
Run
# Start Dexto (first run: `dexto login`, or set a provider key such as ANTHROPIC_API_KEY)
dexto
More options:
dexto -p "create a landing page for a coffee shop" # One-shot task
dexto --auto-approve "refactor this codebase" # Skip confirmations
dexto -m claude-sonnet-4-5-20250929 # Switch models
dexto --help # Explore all options
Inside the interactive CLI, type / to explore commands—switch models, manage sessions, configure tools, and more.
Manage Settings
Switch models with /model inside the interactive CLI, or pass -m <model>.
Logs are stored in ~/.dexto/logs/. Use DEXTO_LOG_LEVEL=debug for verbose output.
Core Features
50+ LLMs, Instant Switching
Switch models mid-conversation—no code changes, no restarts.
| Provider | Models |
|---|
| OpenAI | gpt-5.2, gpt-5.2-pro, gpt-5.2-codex, o4-mini |
| Anthropic | Claude Sonnet, Opus, Haiku (with extended thinking) |
| Google | Gemini 3 Pro, 2.5 Pro/Flash |
| Groq | Llama 4, Qwen, DeepSeek |
| xAI | Grok 4, Grok 3 |
| Local | Ollama, GGUF via node-llama-cpp (Llama, Qwen, Mistral, etc.) |
| + Gateways | OpenRouter, AWS Bedrock, Vertex AI, LiteLLM |
Run locally for privacy: Local models keep data on your machine with automatic GPU detection (Metal, CUDA, Vulkan).
MCP Integration (30+ Tools)
Connect to Model Context Protocol servers—Puppeteer, Linear, ElevenLabs, Firecrawl, Sora, and more.
# agents/my-agent.yml
mcpServers:
filesystem:
type: stdio
command: npx
args: ['-y', '@modelcontextprotocol/server-filesystem', '.']
browser:
type: stdio
command: npx
args: ['-y', '@anthropics/mcp-server-puppeteer']
Browse and add servers via /mcp commands in the CLI.
Human-in-the-Loop Controls
Fine-grained control over what your agent can do:
permissions:
mode: manual # Require approval for each tool
# mode: auto-approve # Trust mode for local development
toolPolicies:
alwaysAllow:
- mcp--filesystem--read_file
- mcp--filesystem--list_directory
Agents remember which tools you've approved per session.
Persistent Sessions & Memory
Conversations persist across restarts. Create memories that shape agent behavior.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
