Best LLM evaluation framework: DeepEval vs promptfoo

As of 2026-10-14, the sources point to DeepEval as the closest fit for a code-first LLM evaluation framework, promptfoo for declarative CLI and CI testing with red teaming, and Langfuse for evals tied to tracing and observability. The main distinction is evaluation library versus evaluation inside a platform.

Quick comparison

How the options differ

Evaluation surface

DeepEval describes itself as an open-source LLM evaluation framework with Pytest-style assertions and 50+ metrics covering LLM-as-a-judge, agent, tool-use, RAG, safety and multimodal. It supports end-to-end, trajectory-based and component-level evals.

promptfoo is an open-source CLI and library for evaluating and red-teaming LLM apps. It produces matrix views for comparing outputs across many prompts, and its test cases are declarative.

Langfuse frames evaluation as one part of a loop that connects tracing, monitoring, datasets, experiments and evaluation. Its evaluators can be LLM-as-a-judge, heuristic functions or human review, run on production data or during experiments.

Deployment and data

DeepEval states it is local-first, with evaluations running in your own environment. It integrates natively with Confident AI for shared dashboards and monitoring.

promptfoo states that it runs completely locally and talks directly with the LLM.

Langfuse documents a platform architecture using a Clickhouse OLAP database, a Redis queue for async ingestion and S3/Blob storage. Its repository topics include self-hosted.

Model support and integration

promptfoo lists OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, and custom API providers. DeepEval's cited pages do not list model providers.

Langfuse states it works with any language and framework supporting OTel instrumentation, plus 100+ integrations.

License

DeepEval's repository reports Apache-2.0 and promptfoo's reports MIT. The GitHub API reports no recognized license for Langfuse, while its site states all product features are MIT licensed. Verify the license file before adopting.

How we compared

We ranked by documented fit to the query: whether the source describes itself as an evaluation framework, how many evaluation scopes it documents, and whether it runs locally. DeepEval's own description as "the LLM Evaluation Framework" and its documented eval modes place it first. promptfoo is second for its evaluation focus plus red teaming. Langfuse is third because its sources emphasize an observability platform with evaluation as one component.

We read the GitHub repository metadata and the product sites or docs for each tool, checked 2026-10-14. We did not run, install or benchmark any tool.

Limitations

The cited sources do not state pricing for DeepEval or promptfoo. Langfuse's site states a free tier of 50k observations/month. The sources contain no head-to-head benchmarks, and no performance figures were verified. DeepEval's model support is not stated in the cited sources. Langfuse's license conflict between the API and its site is unresolved here.

For per-tool overviews of Langfuse see LLM observability tools and Opik vs Langfuse. Browse the hubs for DeepEval, promptfoo and Langfuse, or the Agent infra category. Latest launches: promptfoo 0.124.1 and Langfuse v4.57.0.