Google Agent Development Kit (ADK) vs Relevance AI
Detailed side-by-side comparison to help you choose the right tool
Google Agent Development Kit (ADK)
🔴DeveloperAI Automation Platforms
Google's open-source, code-first framework for building, evaluating, and deploying AI agents. Optimized for Gemini but model-agnostic, with built-in multi-agent orchestration and Vertex AI deployment.
Was this helpful?
Starting Price
$0Relevance AI
🟢No CodeAI Agents & Autonomous Workflows
No-code platform for building AI agents and teams that automate sales, marketing, and ops workflows.
Was this helpful?
Starting Price
$0/monthFeature Comparison
Scroll horizontally to compare details.
💡 Our Take
Choose Google ADK if you have engineers who can write Python or Java and want a free, self-hostable framework with full control over agent behavior and deployment. Choose Relevance AI if you need a no-code visual builder, pre-built templates, and managed hosting without writing code. ADK is more powerful and flexible but requires development expertise; Relevance AI is faster for non-technical teams to get started.
Google Agent Development Kit (ADK) - Pros & Cons
Pros
- ✓Completely free and open-source under Apache 2.0 license
- ✓Model-agnostic — works with Gemini, GPT, Claude, and open-source models via LiteLLM
- ✓Built-in evaluation framework that LangChain and CrewAI lack out of the box
- ✓First-class Vertex AI Agent Engine deployment with managed scaling and monitoring
- ✓Backed by Google's engineering team — same framework powers Agentspace internally
- ✓Supports both Python (1.0.0+) and Java (0.1.0+), unlike most single-language competitors
- ✓Native bidirectional streaming for voice and video agent experiences
Cons
- ✗Requires Python or Java programming knowledge — no visual builder
- ✗Released April 2025, so community is smaller than LangChain's 90K+ GitHub stars
- ✗Documentation still maturing for advanced multi-agent patterns
- ✗Best deployment experience locked to Google Cloud / Vertex AI
- ✗Fewer third-party integrations than LangChain's 700+ ecosystem connectors
- ✗Steeper learning curve than no-code alternatives like Relevance AI or BuildShip
Relevance AI - Pros & Cons
Pros
- ✓Clearly focused on enterprise-scale AI agent deployment rather than only single-user assistants or simple automations.
- ✓Designed for GTM and high-growth teams, which makes the positioning specific for sales, marketing, RevOps, and operational workflows.
- ✓No-code positioning can let domain experts manage and shape agents without depending on engineering for every workflow change.
- ✓Emphasizes safe, team-managed agent usage, which is important for organizations concerned about uncontrolled agent sprawl.
- ✓Multi-agent and workforce-style positioning suggests support for coordinating specialized agents across business processes.
- ✓Enterprise and agent-sprawl messaging indicates the platform is built for governance and operational scale, not just experimentation.
Cons
- ✗Public pricing still requires verification because plan limits, usage allowances, and included features may change.
- ✗The scraped website content does not provide detailed technical architecture, deployment controls, or full security specifics.
- ✗Best fit appears to be enterprise and high-growth GTM teams, so smaller teams may find the buying process or scope heavier than needed.
- ✗The website messaging is outcome-oriented but does not show enough concrete workflow examples in the provided content to evaluate day-to-day usability.
- ✗Organizations may still need strong internal process ownership to avoid creating too many overlapping agents, even with an agent management platform.
Not sure which to pick?
🎯 Take our quiz →🔒 Security & Compliance Comparison
Scroll horizontally to compare details.
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.
Ready to Choose?
Read the full reviews to make an informed decision