This directory contains specific developer guides for the ADK Python implementation. For the official ADK documentation, visit adk.dev.

• Creating Agents with Configurations - Building and wiring multi-agent graphs from external YAML configuration files.
• LlmAgent Single-Turn Mode - Guide on using LlmAgent in single-turn mode.
• LlmAgent Task Mode - Guide on using LlmAgent in task mode.
• ManagedAgent - Guide on using ManagedAgent with server-side tools.
• RemoteA2aAgent Task Mode - Guide on using RemoteA2aAgent in task mode.

• App - The top-level container binding a root agent to app-wide plugins and configuration.

• BaseArtifactService - Storing binary payloads outside the conversation history, with versioning and user-scoped filenames.

• AuthConfig and authenticated tools - Declaring the credentials a tool needs, and the pause-for-consent handshake.

• BaseCodeExecutor - Executing model-generated code safely across local, container, GKE, and managed sandbox backends.

• Event and NodeInfo - Understanding Event and NodeInfo in workflows.
• RequestInput - How to use RequestInput for human-in-the-loop interactions.

• Live model callbacks - Inspecting or blocking content on a live bidirectional session.

• Model Armor - Screening user input and model output with Google Cloud Model Armor.

• AntigravityAgent - Runs a Google Antigravity SDK agent as an ADK agent node.

• LiveRequestQueue - Streaming content, realtime audio, and stream control signals to live agents.

• BaseMemoryService - Storing finished sessions and recalling them from later conversations.

• BaseLlm and LLMRegistry - The model interface, how a model name resolves to an implementation, and how to plug in your own.

• BasePlanner - Guiding model execution with structured planning instructions, thinking configurations, and Plan-Re-Act thought tagging.

• ReflectAndRetryModelPlugin - Self-healing, concurrent-safe error recovery for model failures.
• ReflectAndRetryToolPlugin - Self-healing, concurrent-safe error recovery for tool failures.

• Runner and InMemoryRunner - Managing session lifecycles, state resolution, and streaming agent execution events.
• Runner Live Streaming - Real-time bidirectional audio/text streaming and non-blocking background tool execution with Gemini Multimodal Live API.

• Session and BaseSessionService - The session lifecycle, state scoping, and choosing a session service.
• State - Session state and the app:, user:, and temp: prefixes that decide what is shared and what is stored.

• Node as tool - Exposing workflows and deterministic nodes as agent tools with isolated runtime branching and resume support.
• to_mcp_server - Expose an ADK agent as an MCP server so any MCP host can drive it as a single tool (the MCP counterpart of to_a2a).

• Workflow - Graph-based orchestration of complex, multi-step agent interactions.
• Workflow Graphs - Understanding nodes, edges, and graph structures in workflows.
• Function Nodes - Wrapping Python functions and generators as workflow nodes.
• JoinNode - Synchronizing parallel execution paths in workflows.
• RetryConfig - Configuring retry policies for resilient workflow nodes.
• ParallelWorker - Processing lists of items concurrently in workflows.
• Dynamic Nodes - Scheduling and executing nodes dynamically at runtime.