
DeepEval
8.3#1 in llm evaluation toolsFrom $200 a monthFree planWebAPILinuxmacOS
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| Fact | DeepEval | TruLens |
|---|---|---|
| Founders | Y Combinator identifies Jeffrey Ip and Kritin Vongthongsri as Confident AI's founders. | Snowflake's announcement names Anupam Datta, Shayak Sen and Will Uppington as TruEra's three co-founders. |
| Framework integrations | Official integrations include LangChain, LlamaIndex, CrewAI, OpenAI Agents, LangGraph, PydanticAI, Google ADK, AgentCore, Strands and OpenTelemetry. | Official integrations instrument LangChain, LangGraph, LlamaIndex and NeMo Guardrails applications. |
| Integrations | Listed integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. | The documentation lists integrations and providers for LangChain, LangGraph, LlamaIndex, MCP, OpenAI, Anthropic, Amazon Bedrock, HuggingFace, LiteLLM, Google, and Snowflake Cortex. |
| Notable limit | The maker describes DeepEval OS as limited to pre-production testing, with results in local files and an engineer-owned test runner. | Conversation features are not supported with the SnowflakeConnector, including conversation grouping, conversation-level metrics, retrieval APIs, and dashboard views. |
| Purpose | DeepEval is an open-source LLM evaluation framework for building evaluation pipelines to test AI systems. | TruLens evaluates and traces AI agents to help find failures and reduce costs while maintaining quality. |
| Release activity | The public changelog records frequent releases, feature additions, bug fixes and breaking changes by year. | The GitHub releases page shows TruLens 2.13.1 released on August 20 and documents fixes for dill compatibility and lazy Hugging Face imports. |
DeepEval suits developers building repeatable AI evaluations in Python or CI/CD, especially those testing multiple modalities or agent workflows. Its free OS offering is for pre-production testing rather than production evaluation.
TruLens suits teams building AI agents, RAG systems, MCP apps, or summarizers who need trace inspection and evaluation. It is aimed at Python-based workflows and can instrument custom apps and listed frameworks.