Google Agent Development Kit (ADK) vs Stele

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

	Google Agent Development Kit (ADK) Agents	Stele Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Shared project memory for AI coding agents across Claude Code, Cursor, Codex, and Copilot
Category	Agents	Agents
Pricing	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).	Freemium· Free: $0/ forever · Pro: $12/ month · Team: $20/ user / mo · Enterprise: Contact sales
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	—
Editorial score	—	—
Use cases	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	shared memory across AI coding agentscross-tool project context handofftask queue for multi-agent workflowsagent onboarding from an existing codebasetracking failed refactor attemptscoordinating Claude Code and Cursor on one repoMCP-based agent memory backendarchitectural decision log for AI agents
Pros	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	MCP-native, so it works with Claude Code, Cursor, Codex, and Copilot without per-tool adapters One-line CLI install with automatic onboarding from an existing repo Shared task queue prevents two agents from duplicating the same work Graph-based memory captures failed attempts, not just successful ones, so agents stop repeating dead ends Free tier is genuinely usable for solo developers (unlimited public projects, one private) Self-hosted SQLite edition on the roadmap for teams that need local data Export and deletion controls on private project graphs
Cons	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	Currently hosted-only; the promised self-hosted build is not yet shipped Value depends on running multiple agents or switching tools often, less compelling for single-agent users Shared memory across agents can propagate a bad decision if not curated Early-stage product with limited public track record and small community Private project quota on the free tier is tight for anyone with several client repos
Website	google.github.io	stele-ai.dev

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

Pick Stele if

✅ MCP-native, so it works with Claude Code, Cursor, Codex, and Copilot without per-tool adapters
✅ One-line CLI install with automatic onboarding from an existing repo
✅ Shared task queue prevents two agents from duplicating the same work
✅ Graph-based memory captures failed attempts, not just successful ones, so agents stop repeating dead ends

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