Agent RT
Documentation
Start with a runnable agent, then move into tools, state, policy, production operations, and extension contracts. The website documentation is organized by task instead of repository layout.
Start here
Getting started
From install to first run.
Core concepts
The mental model behind Agent RT.
Guides
Common adoption and deployment paths.
Compatibility surfaces for LangChain, LlamaIndex, OpenAI, and Anthropic applications.
↗OperationsProduction patternsBoundaries, durable execution, sandboxing, observability, evaluation, and deployment.
↗RetrievalVector databasesSwitch Chroma, Milvus, Pinecone, Qdrant, or Weaviate with environment variables, or use LangChain/LlamaIndex-compatible vector-store imports.
↗Usage detailsRuntime guideProviders, API server, CLI, sandboxes, skills, and migration entry points.
Reference
Architecture and contributor workflows.
Every major capability, what is enabled by default, what is opt-in, execution order, and how to configure it.
↗Framework comparisonAgent RT vs alternativesCompare runtime features, latency, memory, guardrails, APIs, sandboxing, and portability.
↗ExtensionsArchitectureModule boundaries and the extension contracts that keep the runtime provider-neutral.
↗ContributingDevelopmentRepository setup, test matrices, and language-specific workflows.