Google Agent Development Kit (ADK) vs Sharper

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

	Google Agent Development Kit (ADK) Agents	Sharper Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	AI office assistant that turns your documents and connected apps into a cited, agentic knowledge base
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 tier available via 'Start for free'; paid plans referenced on the pricing page but tier prices load dynamically and were not visible at review time.
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	Cross-source internal knowledge searchCited research briefsDrafting stakeholder emails from internal dataWeekly competitor and market digestsAutomated daily briefingsFirst-draft slide decks from a document folderMeeting and calendar-aware task planningMarketing content refresh and repurposingOperations status and reporting automation
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	Connects to common workplace sources (Notion, Slack, Google Drive, Gmail, Outlook, Calendar) so answers draw on real internal context instead of just the open web Proof Ledger cites the exact passages behind each claim, making outputs auditable for research and compliance-sensitive teams Execution Chain exposes the intermediate tool calls and steps, so users can inspect and adjust the workflow rather than trust a black-box answer Four explicit modes (Research, Review, Build, Run) map cleanly to common knowledge-work jobs instead of forcing everything through a chat box Scheduled Operations turn one-off prompts into recurring briefs, digests and reports without extra automation tooling Free tier lets individuals and small teams pilot the office-assistant workflow before committing
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	Underlying model family is not disclosed, so buyers cannot compare reasoning quality or data-handling assumptions against competitors Pricing tiers load dynamically and were not visible at review time, making budget planning and procurement conversations harder No public documentation of an API or developer surface, so it is difficult to embed Sharper into custom products or pipelines Broad connector access to email, chat and drive creates a wide data-exposure surface that will need security review at most companies Marketing positioning overlaps heavily with Glean, Notion AI, Microsoft Copilot and ChatGPT connectors, and the site does not spell out concrete differentiators on evaluation benchmarks
Website	google.github.io	sharper-ai.co

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 Sharper if

✅ Connects to common workplace sources (Notion, Slack, Google Drive, Gmail, Outlook, Calendar) so answers draw on real internal context instead of just the open web
✅ Proof Ledger cites the exact passages behind each claim, making outputs auditable for research and compliance-sensitive teams
✅ Execution Chain exposes the intermediate tool calls and steps, so users can inspect and adjust the workflow rather than trust a black-box answer
✅ Four explicit modes (Research, Review, Build, Run) map cleanly to common knowledge-work jobs instead of forcing everything through a chat box

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