Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code
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Updated
May 25, 2026 - TypeScript
Daytona is a Secure and Elastic Infrastructure for Running AI-Generated Code
Zero, your trustworthy AI teammate for real work.
A free and open-source toolkit for running other people's code in your applications.
The fastest Trust Layer for AI Agents
Ruby's most capable AI runtime
Secure autonomous AI agent framework and platform. Build AI teams by describing what you want. Orchestrate agents that can do everything a human can do.
Composable agent runtime with enforced isolation boundaries
AI-native application framework and runtime, simply write a YAML file.
AutomatosX is an orchestrates AI agents, workflows, and memory
Local-first AI runtime console for cloud/local models, Hecate Chat, supervised coding-agent sessions, task approvals, usage visibility, and OpenTelemetry.
Android 16 fork. AI as a platform primitive. Twelve capabilities, one shared runtime, every app. OEM-pluggable. Apache 2.0.
Production-grade TypeScript AI runtime focused on reliability, governance, and reproducible LLM systems. Multi-provider gateway, agents, RAG, workflows, policy engine, audit trails, and deterministic testing — built for teams shipping AI in production.
Benchmarked agent execution runtime for Python. Sub-10ms cold starts, real-time streaming, time-travel debugging, and self-growing tool libraries. Compare 3 sandbox backends: Docker (OpenSandbox), MicroVM, and in-process AST.
A self-evolving, AI-native language and platform for intelligent agents and autonomous software.
Unified execution runtime for LLM and ML programs.
Local-first AI runtime for Apple Silicon with CLI and macOS operator workflows for LoRA training, benchmarking, and evaluation.
Jupyter notebooks for testing Prisma AIRS AI Runtime with your LLM
An open-source AI runtime framework focused on task execution, traceability, and delivery closure.
Autonomous AI Agent Infrastructure Platform — OpenAI-compatible AI gateway with MCP support, multi-agent orchestration, tool calling, observability, memory, RAG, AI workflows, and unified infrastructure for any LLM provider.
Modular AI runtime with LLM reasoning, memory, and pluggable action modules.
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