DeepEval vs LangSmith
DeepEval
deepeval.comLangSmith
langchain.comSide by side
| Figure | DeepEval | LangSmith |
|---|---|---|
| Price from | $200/mo | $39/mo |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 3 |
| Spec score · AI LLM Evaluation Tools | 9.4 (#1) | 9.1 (#5) |
| Spec score · AI Agent Evaluation Tools | 9.3 (#1) | 9.1 (#4) |
| Spec score · Llm Developer Tools | 9.3 (#1) | 9.0 (#3) |
| Spec score · Prompt Engineering Tools | 9.3 (#2) | 9.1 (#4) |
| Documentation | The documentation contains installation, quickstarts, metric references, integrations, tracing, datasets, CLI, environment-variable and troubleshooting sections. | LangChain publishes reference documentation, integration guides, deployment instructions, a CLI guide and a REST API reference. |
| Headquarters | San Francisco, California, United States | San Francisco, California, United States |
| Integrations | Listed integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. | Documented integrations cover OpenAI, Anthropic, Bedrock, Gemini, Mistral, LiteLLM, OpenTelemetry, CrewAI, Vercel AI SDK, PydanticAI, AutoGen and many other frameworks and providers. |
| Purpose | DeepEval is an open-source LLM evaluation framework for building evaluation pipelines to test AI systems. | LangSmith is a commercial agent engineering platform for building, testing, deploying, monitoring, and improving AI agents. |
| Rating on G2 | 4.6 out of 5 from 5 reviews | 4.4 out of 5 from 78 reviews. |
| Self-hosting | Confident AI's enterprise service can be deployed on the customer's own infrastructure or used in the vendor cloud. | Enterprise customers can run LangSmith self-hosted or hybrid on Kubernetes in AWS, Google Cloud or Azure. |
| Tracing | DeepEval traces agent steps so they can be graded and inspected in the terminal and test runner. | Tracing records agent conversations and runs step by step, including prompts, model calls, tools, errors, latency and token usage. |
| What it does | DeepEval is an open-source framework for testing and evaluating LLM applications locally or in CI/CD. | LangSmith is LangChain's framework-agnostic platform for observing, evaluating and deploying AI agents. |
Plans
DeepEval
- DeepEvalFree
- FreeFree
- Starter$200 a month
- Team$2,000 a month
- EnterprisePriced on request
In their own words
DeepEval
DeepEval is a free, open-source framework for building evaluation pipelines to test AI systems. Its Pytest-native evaluations run in CI/CD or as Python scripts, and the framework lists more than 50 research-backed metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity and bias. It evaluates text, images and audio, including conversational and voice interactions, using methods such as G-Eval, DAG, QAG and JevEval. DeepEval can generate synthetic goldens from a knowledge base and model conversations across user personas. It also traces agent steps so they can be graded and inspected in a terminal or test runner. Integrations cover agent frameworks including LangChain, Pydantic AI, OpenAI Agents, LangGraph, LlamaIndex and CrewAI, alongside a range of model providers. By default, it sends basic telemetry to PostHog, excluding personally identifiable information and stored results; users can opt out with an environment variable. The maker describes DeepEval OS as for pre-production testing, with results in local files and an engineer-owned test runner. Enterprise capabilities are offered through Confident AI Evals, with self-hosted or maker-cloud deployment options.
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.