LangSmith vs Sensei
LangSmith
langchain.comSensei
sensei.shSide by side
| Figure | LangSmith | Sensei |
|---|---|---|
| Price from | $39/mo | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 3 | 0 |
| Spec score · AI Agent Evaluation Tools | 9.1 (#4) | 6.6 (#28) |
| CI/CD | The evaluation workflow integrates with pytest, Vitest and GitHub workflows for automated regression checks. | The repository lists CI/CD integration among Sensei's capabilities. |
| Evaluation | Evaluation supports offline datasets, online production evaluations, human annotation queues and side-by-side experiment comparison. | It evaluates agents across task execution, reasoning, and self-improvement. |
| Integrations | Documented integrations cover OpenAI, Anthropic, Bedrock, Gemini, Mistral, LiteLLM, OpenTelemetry, CrewAI, Vercel AI SDK, PydanticAI, AutoGen and many other frameworks and providers. | The repository documents HTTP, stdio, OpenAI-compatible, and LangServe adapters for connecting agents. |
| Purpose | LangSmith is a commercial agent engineering platform for building, testing, deploying, monitoring, and improving AI agents. | Sensei is an open-source qualification engine for testing, evaluating, and certifying AI agents across professional skills. |
Plans
In their own words
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.