DeepEval vs OpenAgent Eval
DeepEval
deepeval.comOpenAgent Eval
openagenthq.github.ioSide by side
| Figure | DeepEval | OpenAgent Eval |
|---|---|---|
| Price from | $200/mo | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 0 |
| Spec score · AI Agent Evaluation Tools | 9.3 (#1) | 7.3 (#25) |
| Integrations | Listed integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. | Documented LLM providers include OpenAI, Anthropic, Gemini, Groq, OpenRouter, Ollama, and a mock provider. |
| License | The repository and package identify DeepEval as Apache Software License 2.0. | The project is licensed under Apache License 2.0. |
| Local operation | DeepEval can run entirely locally without Confident AI or an account. | The project says it runs on the user's machine without telemetry or required network calls. |
| Metrics | The project advertises more than 50 metrics spanning quality, agents, RAG, conversations, safety, voice and multimodal use cases. | Built-in metrics cover retrieval, generation, latency, and token cost. |
| Purpose | DeepEval is an open-source LLM evaluation framework for building evaluation pipelines to test AI systems. | OpenAgent Eval is an open-source, local-first framework for evaluating RAG systems and 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.
OpenAgent Eval
OpenAgent Eval has no write-up on Specifiction yet; its figures are above.