deepeval vs future-agi
Pytest for LLM apps: 40+ research-backed metrics — G-Eval, RAG suite, agent task completion, hallucination — as unit tests you run in CI, judged by any LLM including local ones. — versus — Self-hostable platform for the whole agent-quality loop: tracing, evals, simulations, datasets, guardrails and an LLM gateway — one feedback loop from prototype to production. Apache 2.0.
deepeval is the pytest-shaped primitive you wire into CI; Future AGI is the platform that wants to own the whole loop those tests live in.
| deepeval | future-agi | |
|---|---|---|
| Stars | 17k | 1.6k |
| Forks | 1.8k | 469 |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 4 days ago | yesterday |
| Topics | evals | evals |
| Curated connections | 4 | 2 |
deepeval — the curator's take
The most complete general-purpose eval framework: pytest ergonomics ('assert_test' in CI), metrics with explanations grounded in the research they implement, component-level tracing for agents, plus benchmark harnesses (MMLU, HumanEval) when you need them. If you're evaluating LLM apps in Python and don't know where to start, start here. NOT vendor-neutral: the free framework feeds Confident AI's platform for reports and regression tracking — usable without it, but the gravity is real. And LLM-as-judge metrics inherit judge bias: treat scores as regression signals, not absolute truth. RAG-only shops may prefer Ragas' tighter focus.
future-agi — the curator's take
The consolidation bet: evals + observability + guardrails + simulation in one loop instead of Langfuse-plus-four-friends, with every evaluator inspectable — no black-box scoring. When NOT: its own banner says nightly, expect rough edges — pin versions and file issues; all-in-one platforms trade each component being second-best for the loop being closed; teams already invested in a tracing stack get more from adding evals to it than migrating.