Fabric vs Google Agent Development Kit (ADK)

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

	Fabric Agents	Google Agent Development Kit (ADK) Agents
Tagline	An open-source framework for augmenting humans with AI, one composable prompt at a time.	Google's open-source framework for building, evaluating, and deploying production AI agents
Category	Agents	Agents
Pricing	Free· Free and open-source (MIT). Users bring their own API keys and pay each LLM provider directly; local models via Ollama or LM Studio incur no per-token cost.	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: OpenAI GPT-4o/GPT-4.1, Anthropic Claude (incl. Opus 4.7), Google Gemini, Azure OpenAI, Bedrock, Vertex AI, plus local Ollama and LM Studio models.	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM
Editorial score	—	—
Use cases	YouTube video summarisation and wisdom extractionLong-form article and PDF summarisationSecurity report and threat-intel analysisMeeting-transcript distillationBlog post and essay drafting from notesClaim and argument analysisSocial media post generationShell-scripted batch processing of documentsLocal, private LLM workflows via OllamaDrop-in Ollama-compatible API backend for other apps	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	Fully open source (MIT) with a large, actively curated pattern library covering summarisation, analysis, extraction and writing tasks. Provider-agnostic: works with commercial APIs (OpenAI, Anthropic, Gemini, Bedrock, Vertex) and local models (Ollama, LM Studio) from one CLI. Composable through Unix pipes, making it trivial to chain patterns and integrate with existing shell scripts and cron jobs. Built-in helpers like YouTube transcript extraction, streaming output, dry-run cost preview and multi-language support. REST API server mode with Ollama-compatible endpoints, so it can act as a backend for other apps. Custom patterns are just Markdown files you can version, share and fork, making prompt engineering reviewable in Git.	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	CLI-first design has a steep learning curve for non-technical users; there is no polished consumer GUI. You must bring and manage your own LLM API keys and pay provider costs; Fabric itself is unhosted. Pattern quality varies and community contributions are not always benchmarked, so output consistency depends on which pattern you pick. Windows support exists but the ergonomics still favour macOS and Linux terminal workflows. No built-in evaluation, guardrails or observability layer, so production use requires bolting on your own logging and safety tooling.	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	github.com	google.github.io

Pick Fabric if

✅ Fully open source (MIT) with a large, actively curated pattern library covering summarisation, analysis, extraction and writing tasks.
✅ Provider-agnostic: works with commercial APIs (OpenAI, Anthropic, Gemini, Bedrock, Vertex) and local models (Ollama, LM Studio) from one CLI.
✅ Composable through Unix pipes, making it trivial to chain patterns and integrate with existing shell scripts and cron jobs.
✅ Built-in helpers like YouTube transcript extraction, streaming output, dry-run cost preview and multi-language support.

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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