google/adk-docs▌

Quick reference for Python ADK API, agent types, tools, orchestration, and state management. \n \n Covers Python ADK SDK only; includes agent definitions, tool patterns, callbacks, plugins, state management, artifacts, and context caching \n Provides a searchable index of all ADK documentation pages with URLs for fetching reference materials \n Intended as a lookup resource while writing or modifying code in existing projects, not for bootstrapping new agents \n Reference files include python.md

Essential reference guide for ADK agent development, covering spec-driven workflow, code preservation rules, and operational best practices. \n \n Read DESIGN_SPEC.md first as your primary source of truth; all implementation decisions must align with it \n Follow four mandatory phases: understand spec, build and implement, evaluate (using /adk-eval-guide ), and deploy with human approval \n Preserve all unrelated code when making modifications; only alter explicitly targeted segments and never c

Comprehensive evaluation methodology guide for ADK agents covering metrics, schemas, and iteration workflows. \n \n Provides eight evaluation criteria (tool trajectory, response matching, rubric-based scoring, hallucination detection, safety) with configurable thresholds and judge model options \n Includes evalset schema documentation with multi-turn conversation support, tool use trajectory specification, and session state initialization patterns \n Outlines the eval-fix loop: start small, run

Scaffold new ADK agent projects and add deployment, CI/CD, and infrastructure to existing ones. \n \n Provides guided project creation with templates for standard agents, agent-to-agent (A2A) coordination, and RAG with data ingestion \n Supports multiple deployment targets: Agent Engine (managed), Cloud Run (container), GKE (Kubernetes), or prototype-only (code without deployment scaffolding) \n Includes optional CI/CD pipeline setup via GitHub Actions or Google Cloud Build, session storage conf

Comprehensive deployment guide for ADK agents across Google Cloud platforms with CI/CD, infrastructure, and troubleshooting. \n \n Covers three deployment targets (Agent Engine, Cloud Run, GKE) with a decision matrix comparing scaling, networking, session state, and cost models \n Includes quick-deploy CLI commands, scaffolded project workflows with make commands, and full CI/CD pipeline setup via GitHub Actions or Cloud Build with Workload Identity Federation \n Provides platform-specific detai

Observability setup guide for ADK agents covering tracing, logging, analytics, and third-party integrations. \n \n Four observability tiers: Cloud Trace (always enabled, distributed tracing), Prompt-Response Logging (GenAI interactions to GCS/BigQuery), BigQuery Agent Analytics (structured agent events), and third-party platforms (AgentOps, Phoenix, MLflow, Weave, Arize, Monocle, Freeplay) \n Cloud Trace automatically configured in scaffolded projects and Agent Engine deployments; captures execu