claude-mem vs Google Agent Development Kit (ADK)

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

	claude-mem Agents	Google Agent Development Kit (ADK) Agents
Tagline	Persistent, cross-agent memory for Claude Code and other coding assistants	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Freemium· Pro: ? · Team Cloud: $333	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 — works with the models used by the connected agent (Claude, GPT, Gemini, etc.) via MCP	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	—	—
Use cases	Persistent memory for Claude Code sessionsCross-IDE context sharing via MCPSemantic search over past agent decisionsTeam knowledge routing between engineersRecording architectural decisions and dead endsSelf-hosted local memory store for coding agentsMobile dashboard for agent activityScoped per-project agent memory	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	Open-source core (Apache-2.0) means the memory engine can be self-hosted and audited Cross-agent by design via MCP — memories carry across Claude Code, Cursor, ChatGPT, Gemini, and other MCP clients Offline-first with a local vector store, so nothing has to leave the machine unless cloud sync is enabled Structured capture of observations, decisions, and dead ends is more useful than raw transcript logging Scoped memory per project and per team member avoids the usual 'one giant global memory' contamination Includes 11 bundled skills and a live mobile dashboard out of the box	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	Value depends on the user already living inside MCP-capable coding agents — casual chat users get little from it Team plan at $333/seat/month is a steep jump from the $20 individual tier Branding is split between claude-mem, CMEM, and cmem.ai, which makes docs and pricing harder to navigate Memory quality is only as good as what the agent chooses to record — noisy sessions can pollute future context Cloud sync introduces a trust boundary that some enterprises will need to review before adopting	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	cmem.ai	google.github.io

Pick claude-mem if

✅ Open-source core (Apache-2.0) means the memory engine can be self-hosted and audited
✅ Cross-agent by design via MCP — memories carry across Claude Code, Cursor, ChatGPT, Gemini, and other MCP clients
✅ Offline-first with a local vector store, so nothing has to leave the machine unless cloud sync is enabled
✅ Structured capture of observations, decisions, and dead ends is more useful than raw transcript logging

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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