dexto

<a href="https://dexto.ai"> <div align="center"> <picture> <source media="(prefers-color-scheme: light)" srcset=".github/assets/dexto_logo_light.svg"> <source media="(prefers-color-scheme: dark)" srcset=".github/assets/dexto_logo_dark.svg"> <img alt="Dexto" src=".github/assets/dexto_logo_dark.svg" width="55%" style="max-width: 1000px; padding: 48px 8px;"> </picture> </div> </a> <p align="center"> <img src="https://img.shields.io/badge/Status-Beta-yellow"> <img src="https://img.shields.io/badge/License-Elastic%202.0-blue.svg"> <a href="https://discord.gg/GFzWFAAZcm"><img src="https://img.shields.io/badge/Discord-Join%20Chat-7289da?logo=discord&logoColor=white"></a> <a href="https://deepwiki.com/truffle-ai/dexto"><img src="https://deepwiki.com/badge.svg"></a> </p> <!-- Keep these links. Translations will automatically update with the README. --> <p align="center"> <a href="https://zdoc.app/de/truffle-ai/dexto">Deutsch</a> | <a href="https://zdoc.app/en/truffle-ai/dexto">English</a> | <a href="https://zdoc.app/es/truffle-ai/dexto">Español</a> | <a href="https://zdoc.app/fr/truffle-ai/dexto">français</a> | <a href="https://zdoc.app/ja/truffle-ai/dexto">日本語</a> | <a href="https://zdoc.app/ko/truffle-ai/dexto">한국어</a> | <a href="https://zdoc.app/pt/truffle-ai/dexto">Português</a> | <a href="https://zdoc.app/ru/truffle-ai/dexto">Русский</a> | <a href="https://zdoc.app/zh/truffle-ai/dexto">中文</a> </p> <p align="center"><b>An open agent harness for AI applications—ships with a powerful coding agent.</b></p> <div align="center"> <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! [](https://x.com/intent/user?screen_name=dexto_ai) [](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).