Google Agent Development Kit vs Voiceflow

Google Agent Development Kit (ADK) is an open-source framework for building, debugging, evaluating, and deploying AI agents. It is available in Python, TypeScript, Go, Java, and Kotlin. Graph-based workflows can combine deterministic code with adaptive AI reasoning, and hierarchical agents can delegate work to specialized agents. ADK supports Gemini and adapters for other model providers, including locally running models. Its integrations catalog includes tools and connectors for MCP, Google Cloud, databases, search, observability, and third-party services. Teams can create multi-turn evaluation datasets and run evaluations locally through the CLI or development UI. ADK also supports live agents with bidirectional text and audio streaming, manages session context, and connects to longer-term memory services. Deployment options include Google Cloud Agent Runtime, Cloud Run, GKE, container-friendly infrastructure, and local Docker or Podman environments. Security guidance covers identity, authorization, guardrails, sandboxed code execution, and network controls. ADK is free; model and cloud-service use may require separate access or billing. Agent Config is narrower: it currently supports only Gemini models and requires Python.

Voiceflow is a platform for building, testing, deploying, and monitoring chat and voice agents across channels and languages. Its builder combines agentic playbooks, deterministic workflows, shared instructions, and guardrails. Teams can review transcripts and call recordings, run evaluations and tests, and track analytics. Agents can reach users through a native chat widget, mobile apps, phone calls, SMS, or custom interfaces built with the Conversations API. Native integrations include Zendesk, Salesforce, HubSpot, Shopify, Airtable, Google Sheets, Gmail, and Twilio. Agents can also connect to external REST APIs, custom Functions, and MCP servers. Teams can choose from major LLM providers or bring their own model. Voiceflow says customer data is not used to train AI or machine-learning models and integrated model providers are configured for zero data retention. Its security information describes encryption at rest with customer-managed keys and encryption in transit using TLS and mTLS. The company was founded in 2019. Agencies and partners have a free-trial plan with usage-based billing; business pricing is available on request. Support includes implementation that can be self-serve or fully managed.