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Agent Runtimes

Datalayer

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🤖 🚀 Agent Runtimes

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Agent Runtimes is a unified library for deploying, managing, and interacting with AI agents across multiple protocols and frameworks. It provides both a Python server for hosting agents and React components for seamless integration into web and desktop applications.

What is Agent Runtimes?​

Agent Runtimes solves the complexity of deploying AI agents by providing:

  1. Protocol Abstraction: One agent, multiple protocols - deploy your agent once and access it through ACP, Vercel AI SDK, AG-UI, MCP-UI, or A2A without changing your code.

  2. Framework Flexibility: Write agents using your preferred framework (Pydantic AI, LangChain, Jupyter AI) while maintaining a consistent API.

  3. Cloud Runtime Management: Built-in integration with Datalayer Cloud Runtimes for launching and managing compute resources with Zustand-based state management.

  4. UI Components: Pre-built React components (ChatBase, ChatSidebar, ChatFloating) that connect to agents and execute tools directly in the browser.

  5. Tool Ecosystem: Seamless integration with MCP (Model Context Protocol) tools, custom tools, and built-in utilities for Jupyter notebooks and Lexical documents.

Agent Runtimes Chat Web

Agent Runtimes Chat CLI

🌟 Features​

Multi-Protocol Support​

  • ACP (Agent Client Protocol): WebSocket-based standard protocol
  • Vercel AI SDK: Compatible with Vercel's AI SDK for React/Next.js
  • AG-UI: Lightweight web interface (Pydantic AI native)
  • MCP-UI: Interactive UI resources protocol with React/Web Components
  • A2A: Agent-to-agent communication

Multi-Agent Support​

  • Pydantic AI: Type-safe agents (fully implemented)
  • LangChain: Complex workflows (adapter ready)
  • Jupyter AI: Notebook integration (adapter ready)

Built-in Features​

  • 🔌 Flexible Architecture: Easy to add new agents and protocols
  • 🛠️ Tool Support: MCP, custom tools, built-in utilities
  • 📊 Observability: OpenTelemetry integration
  • 💾 Persistence: DBOS support for durable execution
  • 🔒 Context Optimization: LLM context management

Documentation​

The detailed guides for architecture, use cases, interactive chat, key concepts, and runtime configuration are now in Docusaurus docs: