{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"google-adk","slug":"google-adk","name":"Google ADK","type":"framework","url":"https://github.com/google/adk-python","page_url":"https://unfragile.ai/google-adk","categories":["ai-agents"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"google-adk__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.hierarchical.agent.types","name":"multi-agent orchestration with hierarchical agent types","description":"Supports composition of specialized agent types (LoopAgent, SequentialAgent, ParallelAgent) that can be nested and orchestrated together. Each agent type implements a distinct execution pattern: LoopAgent iterates until exit conditions, SequentialAgent chains agents linearly with state passing, and ParallelAgent executes multiple agents concurrently. The framework manages state hierarchy, context propagation, and inter-agent communication through an InvocationContext that tracks execution scope and agent relationships.","intents":["I need to build agents that coordinate with each other in specific patterns (sequential, parallel, or looping)","I want to decompose complex tasks into specialized sub-agents with clear execution semantics","I need to manage state and context as it flows between multiple agents in a hierarchy"],"best_for":["teams building complex multi-step AI workflows with distinct agent responsibilities","developers implementing hierarchical agent systems where sub-agents handle specific domains"],"limitations":["Parallel execution adds complexity for stateful operations — requires careful handling of concurrent state mutations","Agent composition depth can impact observability — debugging deeply nested agent chains requires tracing through multiple context layers","No built-in load balancing for parallel agents — all agents execute on same resource pool"],"requires":["Python 3.9+","Understanding of agent execution patterns (loop, sequential, parallel)","LLM provider configuration (OpenAI, Anthropic, Vertex AI, or Ollama)"],"input_types":["agent configuration objects","invocation context with user input","tool definitions and schemas"],"output_types":["structured agent responses","execution history with state transitions","tool call results and artifacts"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_1","uri":"capability://data.processing.analysis.schema.based.structured.output.with.provider.specific.response.formatting","name":"schema-based structured output with provider-specific response formatting","description":"Enables agents to request structured outputs by defining JSON schemas that are passed to LLM providers with native support for structured outputs (Anthropic's json_mode, OpenAI's response_format with JSON schema, Vertex AI's structured output). The framework handles schema validation, response parsing, and fallback to text parsing when provider doesn't support structured outputs natively. Schemas are defined as Pydantic models or raw JSON schemas and automatically converted to provider-specific formats.","intents":["I need the LLM to return structured data (JSON objects, lists) that I can directly deserialize without parsing","I want to enforce a specific output schema and validate responses match the expected structure","I need to use provider-specific structured output features without rewriting code for each provider"],"best_for":["developers building agents that need deterministic, parseable outputs for downstream processing","teams using multiple LLM providers and wanting provider-agnostic schema handling"],"limitations":["Structured output support varies by provider — older models or non-Google providers may fall back to text parsing with lower reliability","Schema complexity impacts token usage and latency — deeply nested schemas with many fields increase processing overhead","Validation failures require retry logic — framework doesn't auto-retry on schema validation failure"],"requires":["Python 3.9+","Pydantic for schema definition (optional, raw JSON schemas also supported)","LLM provider that supports structured outputs (Anthropic, OpenAI, Vertex AI recommended)"],"input_types":["Pydantic model classes","JSON schema objects","Python type hints"],"output_types":["validated Python objects matching schema","JSON strings","structured data ready for database insertion"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_10","uri":"capability://automation.workflow.telemetry.and.observability.with.tracing.and.bigquery.analytics","name":"telemetry and observability with tracing and bigquery analytics","description":"Implements comprehensive telemetry collection through tracing (execution traces with timing and error information) and BigQuery analytics (sends execution events to BigQuery for analysis). Traces capture agent invocations, tool calls, LLM requests, and latencies. BigQueryAnalyticsPlugin automatically sends execution telemetry to BigQuery tables for querying and analysis. Integrates with standard observability patterns and supports custom telemetry collection through plugin system.","intents":["I want to trace agent execution with timing information for performance analysis","I need to send execution telemetry to BigQuery for analytics and dashboarding","I want to monitor agent performance and identify bottlenecks","I need to collect execution metrics for cost analysis and optimization"],"best_for":["teams running production agents needing performance monitoring","organizations using BigQuery for analytics and wanting agent telemetry integration","developers optimizing agent performance and identifying bottlenecks"],"limitations":["BigQuery dependency — analytics features require BigQuery access and incur query costs","Telemetry overhead — tracing and analytics collection add latency and storage overhead","Data volume — high-volume agent execution can generate large telemetry datasets","Privacy considerations — telemetry may contain sensitive information, requires careful data handling"],"requires":["Python 3.9+","BigQuery project and dataset for analytics (optional but recommended)","Appropriate BigQuery permissions for telemetry tables","Tracing infrastructure (OpenTelemetry or similar)"],"input_types":["agent execution events","tool call results","LLM responses","timing information"],"output_types":["execution traces with latencies","BigQuery telemetry tables","performance metrics","analytics dashboards"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_11","uri":"capability://automation.workflow.configuration.driven.agent.definition.with.yaml.json.config.files","name":"configuration-driven agent definition with yaml/json config files","description":"Supports defining agents through configuration files (YAML or JSON) rather than code, enabling non-developers to configure agents. Agent configuration files specify agent type, LLM provider, tools, instructions, and execution parameters. The framework parses configuration files and instantiates agents at runtime. Supports configuration inheritance and templating for reusable configurations. Enables rapid iteration on agent behavior without code changes.","intents":["I want to define agents through configuration files without writing code","I need to enable non-technical users to configure agent behavior","I want to version control agent configurations separately from code","I need to rapidly iterate on agent behavior without redeploying code"],"best_for":["teams with non-technical stakeholders needing to configure agents","organizations wanting to separate agent configuration from code","developers needing rapid iteration on agent behavior"],"limitations":["Configuration expressiveness limits — complex agent logic may require code, not all scenarios fit configuration model","Validation complexity — configuration validation errors may be hard to debug","Schema evolution — changing configuration schema requires migration of existing configs","Limited debugging — configuration-driven agents may be harder to debug than code-based agents"],"requires":["Python 3.9+","YAML or JSON configuration files","Understanding of agent configuration schema"],"input_types":["YAML/JSON configuration files","agent type specifications","tool definitions","instruction templates"],"output_types":["instantiated agent objects","configuration validation results","agent behavior specifications"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_12","uri":"capability://memory.knowledge.context.caching.for.repeated.agent.invocations.with.cost.optimization","name":"context caching for repeated agent invocations with cost optimization","description":"Implements context caching at the framework level to reduce costs and latency for repeated agent invocations with similar context. Caches are created for frequently-used context (system instructions, knowledge bases, tool definitions) and reused across invocations. Supports provider-specific caching (Anthropic prompt caching, Vertex AI cached content) and framework-level caching. Automatically manages cache lifecycle and invalidation.","intents":["I want to reduce costs for agents that repeatedly use the same context (instructions, knowledge bases)","I need to improve latency for repeated agent invocations by caching context","I want to optimize token usage by caching large context blocks"],"best_for":["teams running high-volume agent workloads with repeated context","cost-conscious organizations wanting to optimize LLM API costs","developers building agents with large static context (knowledge bases, instructions)"],"limitations":["Cache invalidation complexity — determining when cached context is stale requires careful management","Provider support varies — not all providers support context caching, fallback to non-cached requests","Cache overhead — cache creation and management add latency on first request","Limited cache control — framework may not expose fine-grained cache control options"],"requires":["Python 3.9+","LLM provider with context caching support (Anthropic, Vertex AI recommended)","Repeated agent invocations with similar context for caching to be beneficial"],"input_types":["agent context (instructions, knowledge bases)","tool definitions","system prompts"],"output_types":["cached context references","cost savings metrics","cache hit/miss statistics"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_13","uri":"capability://automation.workflow.deployment.to.cloud.run.vertex.ai.agent.engine.and.gke.with.configuration.management","name":"deployment to cloud run, vertex ai agent engine, and gke with configuration management","description":"Provides deployment templates and configuration management for deploying agents to Google Cloud infrastructure (Cloud Run, Vertex AI Agent Engine, GKE). The framework handles containerization, environment configuration, and service setup. Deployment configurations specify resource requirements, scaling policies, and environment variables. The framework supports blue-green deployments and canary releases through configuration.","intents":["I want to deploy agents to Google Cloud with minimal configuration","I need to manage different configurations for different deployment targets","I want to implement blue-green or canary deployments for agents"],"best_for":["teams deploying agents on Google Cloud infrastructure","developers managing multiple deployment environments","builders implementing CI/CD pipelines for agents"],"limitations":["Deployment templates are Google Cloud-specific; no support for other cloud providers","Configuration management is manual; no automatic environment detection","Blue-green and canary deployments require external orchestration (e.g., Cloud Deploy)","No built-in rollback mechanisms; requires manual intervention for failed deployments"],"requires":["Python 3.9+","Google Cloud project with appropriate permissions","Docker for containerization","Deployment configuration file (YAML)"],"input_types":["agent code","deployment configuration (YAML)","environment variables"],"output_types":["deployed agent service","service endpoint (URL)","deployment status"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_14","uri":"capability://text.generation.language.llm.flow.orchestration.with.provider.abstraction.and.multi.provider.support","name":"llm flow orchestration with provider abstraction and multi-provider support","description":"Abstracts LLM provider differences through a BaseLlm interface that normalizes request/response handling across OpenAI, Anthropic, Vertex AI, and Ollama. The framework handles provider-specific features (function calling schemas, structured output formats, caching mechanisms) transparently. Agents can switch providers through configuration without code changes. The framework manages API key rotation, rate limiting, and fallback providers.","intents":["I want to switch between LLM providers without changing agent code","I need to use multiple LLM providers in the same agent for cost optimization","I want to handle provider-specific features (function calling, structured output) transparently"],"best_for":["teams using multiple LLM providers for cost/performance optimization","developers building provider-agnostic agents","builders implementing fallback strategies across providers"],"limitations":["Provider abstraction hides provider-specific optimizations; may not leverage all provider capabilities","API key management is manual; no built-in credential rotation","Rate limiting is not enforced; requires external rate limiting service","Fallback provider logic is not automatic; requires explicit configuration"],"requires":["Python 3.9+","API keys for one or more LLM providers (OpenAI, Anthropic, Vertex AI, Ollama)","Provider configuration (model name, API endpoint, parameters)"],"input_types":["LLM request (prompt, tools, structured output schema)","provider configuration"],"output_types":["LLM response (text, function calls, structured output)","provider metadata (model, tokens used, cost)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_2","uri":"capability://tool.use.integration.tool.framework.with.mcp.openapi.bigquery.and.python.function.integration","name":"tool framework with mcp, openapi, bigquery, and python function integration","description":"Provides a unified tool abstraction that supports multiple tool sources: Python functions decorated with @tool, OpenAPI/REST specifications parsed into callable tools, Model Context Protocol (MCP) servers for standardized tool interfaces, and native BigQuery tools for data querying. Tools are registered in a schema-based function registry that generates provider-specific function calling schemas (OpenAI function_calling format, Anthropic tool_use format). The framework handles tool authentication, parameter validation, and execution with optional human-in-the-loop confirmation.","intents":["I want to expose Python functions as LLM-callable tools without writing boilerplate function calling code","I need to integrate REST APIs as tools by providing OpenAPI specs instead of manual schema definition","I want to use standardized MCP servers as tool sources for better interoperability","I need to query BigQuery from agents with automatic schema inference and result formatting"],"best_for":["developers building agents that need to call external APIs and Python functions","teams using MCP servers and wanting native integration without custom adapters","data teams building agents that query BigQuery and need seamless data access"],"limitations":["Tool authentication requires explicit credential management — no automatic credential discovery from environment in all cases","OpenAPI spec parsing may fail on non-standard specs — complex or custom OpenAPI extensions not guaranteed to work","Tool confirmation (HITL) adds latency — requires human approval before tool execution, blocking agent progress","BigQuery tools require dataset schema knowledge — automatic schema inference may be incomplete for complex nested schemas"],"requires":["Python 3.9+","Tool definitions (Python functions, OpenAPI specs, or MCP server URLs)","Appropriate credentials for tool execution (API keys, service accounts, BigQuery permissions)","LLM provider with function calling support"],"input_types":["Python function signatures with type hints","OpenAPI 3.0+ specification documents","MCP server endpoints","BigQuery dataset and table references"],"output_types":["tool execution results","structured tool call schemas","formatted API responses","BigQuery query results as structured data"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_3","uri":"capability://memory.knowledge.session.management.with.event.based.state.persistence.and.resumability","name":"session management with event-based state persistence and resumability","description":"Manages agent execution state through a session system that persists all events (agent invocations, tool calls, LLM responses) to a database. Sessions support resumability — interrupted executions can be resumed from the last checkpoint without re-running completed steps. The framework implements event compaction to reduce storage overhead and session rewind to replay execution from specific points. State is organized hierarchically with parent-child relationships between agents, and the database session service handles concurrent access and state consistency.","intents":["I need to persist agent execution state so interrupted runs can resume without losing progress","I want to replay agent execution from specific points for debugging or re-evaluation","I need to track complete execution history including all tool calls and LLM responses for audit trails","I want to manage multiple concurrent agent sessions without state conflicts"],"best_for":["teams building long-running agents that may be interrupted (network failures, timeouts)","developers needing complete execution audit trails for compliance or debugging","systems requiring session replay for testing or re-evaluation of agent decisions"],"limitations":["Database dependency — session management requires external database (SQL-based), no in-memory-only option for production","Event compaction complexity — aggressive compaction may lose granular execution details needed for debugging","Resumability limitations — some LLM provider state (streaming tokens, partial responses) may not resume cleanly","Storage overhead — event-based persistence grows with execution length, requires periodic cleanup or archival"],"requires":["Python 3.9+","SQL database (PostgreSQL, MySQL, or compatible) for session storage","Database schema initialization (framework provides migration scripts)","Appropriate database credentials and network access"],"input_types":["agent execution events","tool call results","LLM responses","user inputs and confirmations"],"output_types":["session state snapshots","execution history with timestamps","resumable checkpoints","compacted event logs"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_4","uri":"capability://automation.workflow.plugin.system.with.callbacks.for.agent.and.tool.lifecycle.hooks","name":"plugin system with callbacks for agent and tool lifecycle hooks","description":"Provides a plugin architecture that intercepts agent and tool execution at multiple lifecycle points through callback hooks. Plugins implement BasePlugin interface and register callbacks for events like before/after agent invocation, before/after tool execution, on LLM responses, and on errors. The PluginManager chains multiple plugins and ensures callbacks execute in order. Built-in plugins include GlobalInstructionPlugin (injects instructions into all agent prompts), LoggingPlugin (logs execution events), and BigQueryAnalyticsPlugin (sends telemetry to BigQuery). Plugins can modify execution flow, inject context, or collect observability data.","intents":["I want to inject global instructions or system prompts into all agents without modifying agent code","I need to log and monitor all agent and tool executions for debugging and observability","I want to send execution telemetry to analytics systems (BigQuery, monitoring platforms) without coupling agents to those systems","I need to implement custom middleware that intercepts agent execution for validation, filtering, or transformation"],"best_for":["teams building multi-agent systems needing cross-cutting concerns (logging, monitoring, instruction injection)","developers implementing custom observability or analytics without modifying agent code","organizations needing to enforce global policies (instruction templates, safety checks) across all agents"],"limitations":["Plugin ordering matters — callback execution order is sequential and can impact behavior, no dependency resolution","Limited hook points — not all execution stages have callbacks, custom behavior may require agent subclassing","Performance overhead — each plugin callback adds latency, many plugins can accumulate significant overhead","State isolation — plugins share execution context, potential for unintended side effects between plugins"],"requires":["Python 3.9+","Understanding of agent execution lifecycle","Plugin implementation extending BasePlugin class"],"input_types":["agent configuration","invocation context","tool definitions","LLM responses"],"output_types":["modified agent context","execution logs","telemetry events","validation results"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_5","uri":"capability://text.generation.language.llm.provider.abstraction.with.streaming.context.caching.and.live.interactions","name":"llm provider abstraction with streaming, context caching, and live interactions","description":"Abstracts multiple LLM providers (OpenAI, Anthropic, Vertex AI, Ollama) behind a BaseLlm interface that handles request/response processing, streaming, and provider-specific features. Supports streaming responses with token-by-token delivery, context caching (Anthropic's prompt caching, Vertex AI's cached content) to reduce latency and costs for repeated context, and live streaming for real-time interactions. The framework manages provider-specific request formatting, response parsing, and error handling. Function calling requests are translated to each provider's native format (OpenAI function_calling, Anthropic tool_use).","intents":["I want to switch between LLM providers without rewriting agent code","I need streaming responses for real-time agent output to users","I want to use context caching to reduce latency and costs when agents repeatedly use the same context","I need to handle provider-specific features (vision, function calling, structured output) transparently"],"best_for":["teams using multiple LLM providers and wanting provider-agnostic code","developers building real-time agent interfaces requiring streaming responses","cost-conscious teams wanting to leverage context caching for repeated queries"],"limitations":["Provider feature parity is incomplete — some providers lack certain features (e.g., not all support context caching), fallback behavior may degrade performance","Streaming adds complexity — requires async handling and careful error management during token streaming","Context caching overhead — cache creation and management add latency on first request, only beneficial for repeated context","Provider-specific bugs — issues in provider APIs may require workarounds in framework code"],"requires":["Python 3.9+","API keys for at least one LLM provider (OpenAI, Anthropic, Vertex AI, or Ollama)","Async runtime support for streaming (asyncio)","Network access to provider APIs"],"input_types":["agent prompts and messages","tool/function schemas","context for caching","streaming configuration"],"output_types":["LLM responses (text or structured)","token streams","function calling requests","usage statistics"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_6","uri":"capability://planning.reasoning.evaluation.framework.with.test.cases.metrics.and.user.personas","name":"evaluation framework with test cases, metrics, and user personas","description":"Provides an evaluation system for testing agent behavior through evaluation sets (collections of test cases), metrics (quantitative measures of agent performance), and user personas (simulated users for testing). Evaluation cases define inputs, expected outputs, and success criteria. The framework supports conformance testing to verify agents meet specifications and includes built-in metrics for common scenarios. Evaluations can be run against agents to measure performance, and results are stored for comparison across versions.","intents":["I want to test agent behavior systematically with predefined test cases and success criteria","I need to measure agent performance using quantitative metrics (accuracy, latency, cost)","I want to simulate different user personas to test agent behavior across user types","I need to verify agents meet conformance requirements before deployment"],"best_for":["teams building production agents needing systematic testing and quality assurance","developers measuring agent performance improvements across versions","organizations requiring conformance testing for compliance or quality gates"],"limitations":["Metric definition requires domain knowledge — building meaningful metrics for complex agent behaviors is non-trivial","Evaluation cost — running evaluations against LLM providers incurs API costs, can be expensive at scale","Limited metric coverage — built-in metrics cover common scenarios, custom metrics require implementation","Persona simulation limitations — simulated personas may not capture real user behavior nuances"],"requires":["Python 3.9+","Evaluation test cases with defined inputs and expected outputs","LLM provider access for running evaluations","Metric definitions (built-in or custom)"],"input_types":["evaluation test cases","agent configurations","user personas","metric definitions"],"output_types":["evaluation results with pass/fail status","performance metrics (accuracy, latency, cost)","comparison reports across versions","conformance test reports"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_7","uri":"capability://automation.workflow.development.web.ui.with.function.call.visualization.and.execution.tracing","name":"development web ui with function call visualization and execution tracing","description":"Provides a FastAPI-based web interface for developing and debugging agents locally. The UI visualizes agent execution including function calls, LLM responses, and state transitions. Developers can inspect function call parameters and results, trace execution flow through multi-agent hierarchies, and replay execution from specific points. The UI connects to the agent runtime via API endpoints and provides real-time execution monitoring. Includes CLI commands for starting the development server and managing recordings/replays.","intents":["I want to visually debug agent execution and see function calls, LLM responses, and state changes","I need to trace execution flow through multi-agent hierarchies to understand agent interactions","I want to replay agent execution from specific points for testing and debugging","I need a local development interface for iterating on agent behavior"],"best_for":["developers actively building and debugging agents","teams needing visual execution tracing for complex multi-agent systems","developers iterating on agent prompts and tool definitions"],"limitations":["Development-only tool — not suitable for production monitoring, lacks production-grade security and scalability","Local execution only — requires local agent runtime, doesn't support remote agent debugging","UI complexity — deeply nested agent hierarchies may be hard to visualize and navigate","Recording overhead — execution recording adds latency and storage overhead"],"requires":["Python 3.9+","FastAPI and web framework dependencies","Local agent runtime running","Web browser for UI access"],"input_types":["agent execution events","function call results","LLM responses","execution traces"],"output_types":["visual execution flow diagrams","function call details","state snapshots","execution recordings"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_8","uri":"capability://tool.use.integration.agent.to.agent.protocol.a2a.for.inter.agent.communication","name":"agent-to-agent protocol (a2a) for inter-agent communication","description":"Implements a protocol for agents to communicate with and invoke other agents directly. Agents can call other agents as tools, passing structured inputs and receiving structured outputs. The protocol handles agent discovery, request routing, and response formatting. Supports both local agent invocation (same process) and remote agent invocation (via API). Enables building agent networks where agents can delegate tasks to specialized agents.","intents":["I want agents to call other agents as tools for task delegation","I need to build agent networks where agents can discover and invoke each other","I want to enable remote agent invocation for distributed agent systems"],"best_for":["teams building complex agent networks with specialized sub-agents","developers implementing distributed agent systems across multiple services","organizations needing agent-to-agent delegation for task decomposition"],"limitations":["Protocol complexity — A2A adds complexity to agent design and debugging","Latency overhead — remote agent invocation adds network latency compared to local tool calls","Error handling complexity — cascading failures across agent network require careful error propagation","Limited standardization — A2A protocol is framework-specific, not compatible with other agent frameworks"],"requires":["Python 3.9+","Agent definitions for both caller and callee agents","Network access for remote agent invocation","API endpoints for remote agents"],"input_types":["agent invocation requests with structured inputs","agent discovery information","routing configuration"],"output_types":["agent responses with structured outputs","execution status and results","error information"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__cap_9","uri":"capability://automation.workflow.deployment.to.google.cloud.with.vertex.ai.agent.engine.and.cloud.run","name":"deployment to google cloud with vertex ai agent engine and cloud run","description":"Provides deployment configurations and utilities for deploying agents to Google Cloud Platform. Supports deployment to Vertex AI Agent Engine (managed agent service), Cloud Run (containerized agents), and GKE (Kubernetes). The framework includes deployment configuration templates, Docker image building utilities, and environment setup scripts. Handles credential management, service account configuration, and API enablement for deployed agents.","intents":["I want to deploy agents to Vertex AI Agent Engine for managed execution","I need to containerize agents and deploy to Cloud Run for serverless execution","I want to deploy agents to GKE for Kubernetes-based orchestration","I need to manage credentials and service accounts for deployed agents"],"best_for":["teams deploying agents to Google Cloud Platform","organizations using Vertex AI and wanting native agent integration","developers needing managed or serverless agent deployment"],"limitations":["Google Cloud-specific — deployment utilities are tailored to GCP, limited support for other cloud providers","Configuration complexity — deployment requires understanding of GCP services (Cloud Run, Vertex AI, GKE)","Cost implications — deployed agents incur GCP service costs (Vertex AI, Cloud Run, BigQuery)","Cold start latency — Cloud Run deployments may have cold start delays on first invocation"],"requires":["Python 3.9+","Google Cloud Project with appropriate APIs enabled","gcloud CLI configured with credentials","Docker for Cloud Run deployments","kubectl for GKE deployments"],"input_types":["agent code and configuration","deployment configuration files","Docker image specifications","Kubernetes manifests (for GKE)"],"output_types":["deployed agent endpoints","deployment status and logs","service URLs","monitoring dashboards"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"google-adk__headline","uri":"capability://tool.use.integration.ai.agent.development.framework","name":"ai agent development framework","description":"Google's Agent Development Kit (ADK) is a comprehensive framework designed for building AI agents, enabling tool use, multi-agent orchestration, and seamless integration with Google services, making AI agent development as intuitive as software development.","intents":["best AI agent development framework","AI framework for multi-agent orchestration","Google framework for building AI agents","how to create AI agents with Google ADK","top tools for AI agent development"],"best_for":["developers building AI agents","teams needing multi-agent systems"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Understanding of agent execution patterns (loop, sequential, parallel)","LLM provider configuration (OpenAI, Anthropic, Vertex AI, or Ollama)","Pydantic for schema definition (optional, raw JSON schemas also supported)","LLM provider that supports structured outputs (Anthropic, OpenAI, Vertex AI recommended)","BigQuery project and dataset for analytics (optional but recommended)","Appropriate BigQuery permissions for telemetry tables","Tracing infrastructure (OpenTelemetry or similar)","YAML or JSON configuration files","Understanding of agent configuration schema"],"failure_modes":["Parallel execution adds complexity for stateful operations — requires careful handling of concurrent state mutations","Agent composition depth can impact observability — debugging deeply nested agent chains requires tracing through multiple context layers","No built-in load balancing for parallel agents — all agents execute on same resource pool","Structured output support varies by provider — older models or non-Google providers may fall back to text parsing with lower reliability","Schema complexity impacts token usage and latency — deeply nested schemas with many fields increase processing overhead","Validation failures require retry logic — framework doesn't auto-retry on schema validation failure","BigQuery dependency — analytics features require BigQuery access and incur query costs","Telemetry overhead — tracing and analytics collection add latency and storage overhead","Data volume — high-volume agent execution can generate large telemetry datasets","Privacy considerations — telemetry may contain sensitive information, requires careful data handling","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=google-adk","compare_url":"https://unfragile.ai/compare?artifact=google-adk"}},"signature":"HV4xY421B814XsIgkWeJ6RY+rPlLxNYoouyBzZC/WScxHMCppIDaKvWOgoIjn1ej10R3odKqUl+sC4uL7VxPBA==","signedAt":"2026-06-20T15:14:33.430Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-adk","artifact":"https://unfragile.ai/google-adk","verify":"https://unfragile.ai/api/v1/verify?slug=google-adk","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}