Calmo vs Google Agent Development Kit (ADK)

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Calmo Agents	Google Agent Development Kit (ADK) Agents
Tagline	Agent-native SRE platform that runs autonomous incident response, root cause analysis, and postmortems across your existing observability stack.	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Paid· 14-day free trial; pricing on request	Free· Framework itself is free and open-source (Apache 2.0). Costs come from the underlying model provider (e.g. Gemini API / Vertex AI usage) and any hosting infrastructure (Cloud Run, GKE, Agent Engine).
Model	Multi-model (Claude, GPT, Gemini, BYO)	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	6.8 / 10	—
Use cases	incident-responseroot-cause-analysisalert-triagepostmortemssre-automation	Multi-agent research assistantCustomer support triage agentRAG chatbot backed by Vertex AI SearchCode review and refactoring agentBigQuery natural-language analytics agentDocument processing pipelineVoice/streaming conversational agentInternal tool-use agent orchestrating APIsEvaluation and regression testing of LLM workflowsEnterprise workflow automation on Agent Engine
Pros	Autonomous, parallel hypothesis testing against live production data Model-agnostic with bring-your-own-model option for regulated deployments Plugs into existing stack (PagerDuty, Datadog, Grafana, GitHub, K8s) Turns investigations into reusable institutional knowledge	Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly Rich multi-agent primitives out of the box: SequentialAgent, ParallelAgent, LoopAgent, and hierarchical sub-agents Tight Google Cloud integration for deployment (Cloud Run, GKE, Agent Engine) plus native BigQuery/Vertex Search tools Evaluation harness with trajectory-level scoring is included, not a separate paid add-on First-class MCP (Model Context Protocol) client and server support
Cons	No public pricing; clearly aimed at sales-led enterprise deals Value depends on having mature observability and playbooks already in place Closed source with no documented public API on the marketing site	Documentation and examples lean heavily on Gemini + Google Cloud; non-Google paths work but feel like second-class citizens API surface is still evolving — breaking changes between minor versions have been common through 2025-2026 Multi-agent orchestration primitives are powerful but the graph/callback model has a real learning curve compared to a plain prompt loop Agent Engine deployment is convenient but locks you into GCP billing and quotas TypeScript/Kotlin support lags the Python SDK in features and community examples
Website	getcalmo.com	google.github.io

Pick Calmo if

✅ Autonomous, parallel hypothesis testing against live production data
✅ Model-agnostic with bring-your-own-model option for regulated deployments
✅ Plugs into existing stack (PagerDuty, Datadog, Grafana, GitHub, K8s)
✅ Turns investigations into reusable institutional knowledge

Pick Google Agent Development Kit (ADK) if

✅ Genuinely open-source (Apache 2.0) with active Google engineering behind it, not a hosted-only product
✅ Multi-language: first-class Python, Java, and Go SDKs — rare among agent frameworks that are usually Python-only
✅ Built-in dev UI (`adk web`) with trace inspection, event stream, and session replay speeds up debugging enormously
✅ Model-agnostic via LiteLLM — Gemini is default but Claude, GPT, and local models plug in cleanly

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