Google Agent Development Kit (ADK) vs SWE-agent

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

	Google Agent Development Kit (ADK) Agents	SWE-agent Agents
Tagline	Google's open-source framework for building, evaluating, and deploying production AI agents	Open-source autonomous agent framework that lets LLMs fix GitHub issues and find security vulnerabilities by using a custom agent-computer interface.
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).	Free· Free and open-source; you pay your own LLM API costs
Model	Gemini (default) plus Claude, GPT-4/5, Llama, and other providers via LiteLLM	Multi-model (GPT-4o, Claude Sonnet, DeepSeek, local via LiteLLM)
Editorial score	—	7.0 / 10
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	github-issue-fixingautonomous-codingswe-benchctf-securityagent-research
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	Open-source under MIT with a strong research pedigree (Princeton/Stanford) Model-agnostic via LiteLLM - swap GPT-4o, Claude, or local models freely Reproducible SWE-bench harness makes it a credible baseline for agent research EnIGMA mode extends the same loop to CTF-style security tasks
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	Officially in maintenance mode; team now recommends mini-swe-agent No hosted product, GUI, or managed service - CLI and YAML only LLM API costs on long-horizon tasks can be significant Setup requires Docker and comfort with Python tooling
Website	google.github.io	swe-agent.com

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 SWE-agent if

✅ Open-source under MIT with a strong research pedigree (Princeton/Stanford)
✅ Model-agnostic via LiteLLM - swap GPT-4o, Claude, or local models freely
✅ Reproducible SWE-bench harness makes it a credible baseline for agent research
✅ EnIGMA mode extends the same loop to CTF-style security tasks

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