Google Cloud Agent Evaluation vs LangSmith
Google Cloud Agent Evaluation
docs.cloud.google.comLangSmith
langchain.comSide by side
| Figure | Google Cloud Agent Evaluation | LangSmith |
|---|---|---|
| Price from | Free | $39/mo |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 3 |
| Spec score · AI Agent Evaluation Tools | 7.9 (#15) | 9.1 (#4) |
| Founded | 1998 | 2023 |
| Headquarters | Mountain View, California, United States | San Francisco, California, United States |
| Integrations | Google provides evaluation notebook launch options for Colab, Colab Enterprise, Agent Platform Workbench, and GitHub. | Documented integrations cover OpenAI, Anthropic, Bedrock, Gemini, Mistral, LiteLLM, OpenTelemetry, CrewAI, Vercel AI SDK, PydanticAI, AutoGen and many other frameworks and providers. |
| Purpose | Agent evaluation measures and helps improve agents' performance, safety, and quality. | LangSmith is a commercial agent engineering platform for building, testing, deploying, monitoring, and improving AI agents. |
| Support | Google Cloud Basic Support includes documentation, community forums, billing assistance, and Active Assist; customers can upgrade for tailored technical support. | The Developer plan includes community support, and the Enterprise plan lists a support SLA. |
Plans
In their own words
Google Cloud Agent Evaluation
Google Cloud Agent Evaluation helps measure and improve agent performance, safety, and quality. Its workflow runs from defining evaluation cases through inference, behavior-trace capture, metric calculation, result analysis, and optimization. It can generate varied, multi-turn test scenarios from agent instructions and tool definitions, and intercept tool calls to supply custom behavior, mocked data, or simulated errors such as HTTP 503 responses and latency spikes. Prebuilt or custom raters score traces with reference-based Exact Match or reference-free Helpfulness metrics. Teams can also evaluate production-traffic traces or external logs without a managed test environment. Prompt optimization identifies failure points and proposes focused system-instruction changes. The service supports tool-call checks, safety evaluations, regression runs, trace ingestion, and custom models. Access is through API or web, with notebook launch options for Colab, Colab Enterprise, Agent Platform Workbench, and GitHub. Pricing is usage-based: $0.00003 per 1k input characters and $0.00009 per 1k output characters for computation-based metrics; model-based metrics also incur underlying autorater model costs. New Google Cloud customers get $300 in trial credits.
LangSmith
LangSmith is a commercial agent-engineering platform for building, evaluating, deploying, monitoring, and improving AI agents. It supports offline evaluations on curated datasets and real-time scoring of production interactions. Evaluation methods include human review, heuristic checks, LLM-as-judge evaluators, pairwise comparisons, and custom Python or TypeScript evaluators. Annotation queues route runs to subject-matter experts for feedback that can calibrate evaluations, improve prompts, or augment datasets. The Playground compares prompt versions or model providers, and LangSmith chat can auto-improve prompts. Observability includes agent and retrieval tracing, token and general cost tracking, trajectory monitoring, online evaluations, and webhook or PagerDuty alerts. Traces can arrive through SDKs or an API from LangGraph, custom Python, or other stacks; SDKs are listed for Python, TypeScript, Go, and Java. Hosting ranges from cloud to hybrid and self-hosted Enterprise options. The Developer plan costs nothing, Plus costs $39 a month, and Enterprise pricing is custom.