Caveman vs Google Agent Development Kit (ADK)

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

	Caveman Agents	Google Agent Development Kit (ADK) Agents
Tagline	The token-efficient stack for agent-native development	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Freemium· Free: $0 · Indie: $29/mo · Team: $349/mo · Enterprise: Custom	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	Model-agnostic proxy (Claude, GPT, Gemini, 30+ others); ships CaveGemma, a fine-tuned Gemma-4 released under MIT	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	—	—
Use cases	LLM cost optimizationMulti-provider AI gatewayPrompt and output compressionCross-model request routingLLM spend observabilityAgent SDK developmentShadow-mode optimization testingEnterprise AI governance and audit	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	Vendor-neutral: works across Claude, GPT, Gemini and 30+ agent runtimes rather than locking you to one provider Open-source core (MIT-licensed Skill package and CaveGemma weights) lets you audit and self-host the primitives Free single-seat Engine tier is enough to prove the cost savings on a real workload before committing Shadow-mode plus PR-driven Autopilot means optimizations land with measured deltas, not blind swaps Per-member/per-key/per-model spend dashboard gives platform teams the accountability layer most LLM stacks lack Ed25519-signed audit receipts are a genuine differentiator for regulated environments	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	Adds a gateway or SDK dependency in the hot path of every LLM call — an extra failure surface to operate The headline '65% cost cut' is workload-dependent; savings on already-terse prompts or single-model shops will be far smaller Enterprise pricing is not published, so budgeting requires a sales conversation Value is thin for solo builders making a handful of API calls a day — this is infrastructure aimed at scaled traffic Compression and cross-model routing can subtly change model behaviour at the margins; regression testing is on you	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	caveman.so	google.github.io

Pick Caveman if

✅ Vendor-neutral: works across Claude, GPT, Gemini and 30+ agent runtimes rather than locking you to one provider
✅ Open-source core (MIT-licensed Skill package and CaveGemma weights) lets you audit and self-host the primitives
✅ Free single-seat Engine tier is enough to prove the cost savings on a real workload before committing
✅ Shadow-mode plus PR-driven Autopilot means optimizations land with measured deltas, not blind swaps

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

Compare a different pair →