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<p align="center"><b>An open agent harness for AI applications—ships with a powerful coding agent.</b></p>
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<img src=".github/assets/dexto_title.gif" alt="Dexto Demo" width="600" />
</div>
---
## 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, Web UI, REST API, 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 or Web UI.
```bash
# 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:**
- **Web UI** – Chat interface with file uploads, syntax highlighting, and MCP tool browser
- **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
```bash
# macOS / Linux / WSL (native installer, recommended)
curl -fsSL https://dexto.ai/install | bash
# 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:
- Installation guide: https://cli.dexto.ai/docs/getting-started/installation
- CLI command reference: https://cli.dexto.ai/docs/guides/cli/overview
### Run
```bash
# Start Dexto (launches setup wizard on first run)
dexto
```
**More options:**
```bash
dexto --mode cli # Terminal mode
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
```bash
# Configure defaults like LLM provider/model, API keys, or download local models
dexto setup
```
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.
```yaml
# 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 from the MCP Store in the Web UI or via `/mcp` commands in the CLI.
### Human-in-the-Loop Controls
Fine-grained control over what your agent can do:
```yaml
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.
```bash
# Continue last conversation
dexto -c
# Resume specific session
dexto -r session-abc123
# Search across all conversations
dexto search "database schema"
```
### Multi-Agent Systems
Agents can spawn specialized sub-agents to handle complex subtasks. The coding agent uses this to delegate exploration:
```yaml
# In your agent config
tools:
- type: agent-spawner
allowedAgents: ["explore-agent"]
maxConcurrentAgents: 5
defaultTimeout: 300000 # 5 minutes
```
**Built-in sub-agents:**
- **explore-agent** – Fast, read-only codebase exploration
Any agent in the [Agent Registry](#agent-registry) can be spawned as a sub-agent—including custom agents you create and register.
Sub-agents run ephemerally, auto-cleanup after completion, and forward tool approvals to the parent—so users see one unified approval flow.
---
## Run Modes
| Mode | Command | Use Case |
|------|---------|----------|
| **Web UI** | `dexto` | Chat interface with file uploads (default) |
| **CLI** | `dexto --mode cli` | Terminal interaction |
| **Web Server** | `dexto --mode server` | REST & SSE APIs |
| **MCP Server** | `dexto --mode mcp` | Expose agent as an MCP server over stdio |
Platform integrations: [Discord](examples/discord-bot/), [Telegram](examples/telegram-bot/)
<details>
<summary><strong>Dexto as an MCP Server</strong></summary>
**Transport: `stdio`**
Connect your Dexto agent as an MCP server in Claude Code or Cursor:
```bash
dexto --mode mcp --auto-approve --no-elicitation # Default agent
dexto --mode mcp --agent coding-agent --auto-approve --no-elicitation
npx dexto --mode mcp --agent coding-agent --auto-approve --no-elicitation
```
Example MCP config for Claude Code or Cursor:
```json
{
"command": "npx",
"args": ["-y", "dexto", "--mode", "mcp", "--agent", "coding-agent", "--auto-approve", "--no-elicitation"]
}
```
`--auto-approve` and `--no-elicitation` are required for MCP mode since it runs non-interactively.
**Transport: `http/sse`**
`dexto --mode server` exposes your agent as an MCP server at `/mcp` for remote clients:
```bash
dexto --mode server --port 3001
ngrok http 3001 # Optional: expose publicly
```
</details>
---
## Dexto Agents SDK
Build AI agents programmatically. Everything the CLI does, your code can too.
```bash
npm install @dexto/core
```
```typescript
import { DextoAgent } from '@dexto/core';
const agent = new DextoAgent({
llm: { provider: 'openai', model: 'gpt-5.2', apiKey: process.env.OPENAI_API_KEY }
});
await agent.start();
const session = await agent.createSession();
const response = await agent.generate('What is TypeScript?', session.id);
console.log(response.content);
// Streaming
for await (const event of await agent.stream('Write a story', session.id)) {
if (event.name === 'llm:chunk') process.stdout.write(event.content);
}
// Multimodal
await agent.generate([
{ type: 'text', text: 'Describe this image' },
{ type: 'image', image: base64Data, mimeType: 'image/png' }
], session.id);
// Switch models mid-conversation
await agent.switchLLM({ model: 'claude-sonnet-4-5-20250929' });
```
**Start a server programmatically:**
Start a Dexto server programmatically to expose REST and SSE streaming APIs to interact and manage your agent backend.
```typescript
import { DextoAgent } from '@dexto/core';
import { loadAgentConfig, startHonoApiServer } from 'dexto';
const config = await loadAgentConfig('./agents/my-agent.yml');
const agent = new DextoAgent(config);
const { server } = await startHonoApiServer(agent, 3001);
// POST /api/message, GET /api/sessions, etc.
```
This starts an HTTP server with full REST and SSE APIs, enabling integration with web frontends, webhooks, and other services. See the [REST API Documentation](https://cli.dexto.ai/api/rest/) for available endpoints.
<details>
<summary><strong>Advanced SDK Usage</strong></summary>
### Session Management
Create and manage multiple conversation sessions with persistent storage.
```typescript
const agent = new DextoAgent(config);
await agent.start();
// Create and manage sessions
const session = await agent.createSession('user-123');
await agent.generate('Hello, how can you help me?', session.id);
// List and manage sessions
const sessions = await agent.listSessions();
const history = await agent.getSessionHistory('user-123');
await agent.deleteSession('user-123');
// Search across conversations
const results = await agent.searchMessages('bug fix', { limit: 10 });
```
### LLM Management
Switch between models and providers dynamically.
```typescript
// Get current configuration
const currentLLM = agent.getCurrentLLMConfig();
// Switch models (provider inferred automatically)
await agent.switchLLM({ model: 'gpt-5.2' });
await agent.switchLLM({ model: 'claude-sonnet-4-5-20250929' });
// Switch model for a specific session
await agent.switchLLM({ model: 'gpt-5.2' }, 'session-123');
// Get supported providers and models
const providers = agent.getSupportedProviders();
const models = agent.getSupportedModels();
```
### MCP Manager
For advanced MCP server management, use the MCPManager directly.
```typescript
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();
```
### Storage & Persistence
Configure storage backends for production.
```yaml
# agents/production-agent.yml
storage:
cache:
type: redis
url: $REDIS_URL
database:
type: postgres
connectionString: $POSTGRES_CONNECTION_STRING
sessions:
maxSessions: 1000
sessionTTL: 86400000 # 24 hours
```
**Supported Backends:**
- **Cache**: Redis, In-Memory
- **Database**: PostgreSQL, SQLite, In-Memory
See the [Storage Configuration guide](https://cli.dexto.ai/docs/guides/configuring-dexto/storage) for details.
</details>
---
## Agent Registry
Pre-built agents for common use cases:
```bash
# List available agents
dexto agents list
# Install and run
dexto agents install coding-agent podcast-agent
dexto --agent coding-agent
```
**Available:** Coding, Podcast, Image Editor, Video (Sora), Database, GitHub, Triage (multi-agent), and more.
See the full [Agent Registry](https://cli.dexto.ai/docs/guides/agent-registry).
---
## Agent Configuration
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.
```yaml
# agents/production-agent.yml
llm:
provider: anthropic
model: claude-sonnet-4-5-20250929
apiKey: $ANTHROPIC_API_KEY
mcpServers:
filesystem:
type: stdio
command: npx
args: ['-y', '@modelcontextprotocol/server-filesystem', '.']
systemPrompt: |
You are a helpful assistant with filesystem access.
storage:
cache:
type: redis
url: $REDIS_URL
database:
type: postgres
connectionString: $POSTGRES_CONNECTION_STRING
permissions:
mode: manual
```
### LLM Providers
Switch between providers instantly—no code changes required.
#### Built-in Providers
| Provider | Models | Setup |
|----------|--------|-------|
| **OpenAI** | `gpt-5.2`, `gpt-5.2-pro`, `gpt-5.2-codex`, `o4-mini` | API key |
| **Anthropic** | `claude-sonnet-4-5-20250929`, `claude-opus-4-5-20250929`, extended thinking | API key |
| **Google** | `gemini-3-pro`, `gemini-2.5-pro`, `gemini-2.5-flash` | API key |
| **Groq** | `llama-4-scout`, `qwen-qwq`, `deepseek-r1-distill` | API key |
| **xAI** | `grok-4`, `grok-3`, `grok-3-fast` | API key |
| **Cohere** | `command-r-plus`, `command-r` | API key |
#### Local Models (Privacy-First)
| Provider | Models | Setup |
|----------|--------|-------|
| **Ollama** | Llama, Qwen, Mistral, DeepSeek, etc. | Local install |
| **node-llama-cpp** | Any GGUF model | Bundled (auto GPU detection: Metal, CUDA, Vulkan) |
#### Cloud Platforms
| Provider | Models | Setup |
|----------|--------|-------|
| **AWS Bedrock** | Claude, Llama, Mistral | AWS credentials |
| **Google Vertex AI** | Gemini, Claude | GCP credentials |
#### Gateway Providers
| Provider | Access | Setup |
|----------|--------|-------|
| **OpenRouter** | 100+ models from multiple providers | API key |
| **LiteLLM** | Unified API for any provider | Self-hosted or API key |
| **Glama** | Multi-provider gateway | API key |
```bash
# Switch models via CLI
dexto -m claude-sonnet-4-5-20250929
dexto -m gemini-2.5-pro
dexto -m llama-4-scout
```
Switch within interactive CLI (`/model`) or Web UI without config changes.
See the [Configuration Guide](https://cli.dexto.ai/docs/category/agent-configuration-guide).
---
## Demos & Examples
| Image Editor | MCP Store | Portable Agents |
|:---:|:---:|:---:|
| <img src=".github/assets/image_editor_demo.gif" alt="Image Editor" width="280"/> | <img src=".github/assets/mcp_store_demo.gif" alt="MCP Store" width="280"/> | <img src=".github/assets/portable_agent_demo.gif" alt="Portable Agents" width="280"/> |
| Face detection & annotation using OpenCV | Browse and add MCPs | Use agents in Cursor, Claude Code via MCP|
<details>
<summary><strong>More Examples</strong></summary>
### Coding Agent
Build applications from natural language:
```bash
dexto --agent coding-agent
# "Create a snake game and open it in the browser"
```
<img src=".github/assets/coding_agent_demo.gif" alt="Coding Agent Demo" width="600"/>
### Podcast Agent
Generate multi-speaker audio content:
```bash
dexto --agent podcast-agent
```
<img src="https://github.com/user-attachments/assets/cfd59751-3daa-4ccd-97b2-1b2862c96af1" alt="Podcast Agent Demo" width="600"/>
### Multi-Agent Triage
Coordinate specialized agents:
```bash
dexto --agent triage-agent
```
<img src=".github/assets/triage_agent_demo.gif" alt="Triage Agent Demo" width="600">
### Memory System
Persistent context that shapes behavior:
<img src=".github/assets/memory_demo.gif" alt="Memory Demo" width="600">
### Dynamic Forms
Agents generate forms for structured input:
<img src=".github/assets/user_form_demo.gif" alt="User Form Demo" width="600">
### Browser Automation
<a href="https://youtu.be/C-Z0aVbl4Ik">
<img src="https://github.com/user-attachments/assets/3f5be5e2-7a55-4093-a071-8c52f1a83ba3" alt="Amazon Shopping Demo" width="600"/>
</a>
### MCP Playground
Test tools before deploying:
<img src=".github/assets/playground_demo.gif" alt="Playground Demo" width="600">
</details>
---
<details>
<summary><strong>CLI Reference</strong></summary>
```text
Usage: dexto [options] [command] [prompt...]
Basic Usage:
dexto Start web UI (default)
dexto "query" Run one-shot query
dexto --mode cli Interactive CLI
Session Management:
dexto -c Continue last conversation
dexto -r <id> Resume specific session
Options:
-a, --agent <path> Agent config file or ID
-m, --model <model> LLM model to use
--auto-approve Skip tool confirmations
--no-elicitation Disable elicitation prompts
--mode <mode> web | cli | server | mcp
--port <port> Server port
Commands:
setup Configure global preferences
deploy Deploy current workspace to cloud
agents install <agents...> Install agents from registry
agents list List available agents
session list|history Manage sessions
search <query> Search conversation history
```
Full reference: `dexto --help`
</details>
---
## Documentation
- **[Quick Start](https://cli.dexto.ai/docs/getting-started/intro/)** – Get running in minutes
- **[Configuration Guide](https://cli.dexto.ai/docs/category/guides/)** – Agents, LLMs, tools
- **[SDK Reference](https://cli.dexto.ai/api/sdk/dexto-agent)** – Programmatic usage
- **[REST API](https://cli.dexto.ai/api/rest/)** – HTTP endpoints
---
## Telemetry
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`
---
## Contributing
See [CONTRIBUTING.md](./CONTRIBUTING.md).
---
## Community
Built by [Truffle AI](https://www.trytruffle.ai). Join [Discord](https://discord.gg/GFzWFAAZcm) for support.
If you find Dexto useful, please give us a ⭐ on GitHub—it helps a lot!
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[](https://x.com/intent/user?screen_name=Road_Kill11)
[](https://x.com/intent/user?screen_name=shaun5k_)
---
## Contributors
[](https://github.com/truffle-ai/dexto/graphs/contributors)
---
## License
Elastic License 2.0. See [LICENSE](LICENSE).