Prove me wrong: 95% of open source eval libs are just abandoned GitHub repos with fancy README files and good marketing!
I know because I’ve used them, plus I keep hearing the same story from builders-
"We picked [popular eval library] because it's open source. Now we're getting NaN scores for half our metrics and our evaluation has been 'running' for hours on 100 samples. Are we missing anything?
No, you're not. You've been sold a lie in the name of "OPEN SOURCE"-
❌ Unmaintained code, documentation that hasn't worked since v0.1.3
❌ "Community-driven" with zero support when your eval hangs for 8 hours
❌ "Free" until you need expensive APIs to make it function
❌ Breaks with every model except GPT-4
❌ "Production-ready" that can't handle 100 test samples without crashing
Let me tell you 2 hard truths: Your "free" tool costs more than enterprise software and works worse. And to burst your bubble- open source doesn't mean compromising on quality.
What enterprise-grade open source should look like (and why teams move to [Future AGI](https://futureagi.com/) and STAY):
✅ Easy setup. Copy-paste quickstart. Runs in your cloud or local.
✅ Turing models + multimodal evals. Fast, accurate, pinpoint error finds with clear explanations, not fuzzy scores.
✅ Built-in observability. Unified traces, logs, and dashboards from day one
✅ Zero latency impact. Fully async and non-blocking, so evals never slow prod or melt hardware.
✅ Enterprise best practices. Curated metrics, consistent results, and actionable insights- no analysis paralysis.
✅ Broad compatibility + flexible SDK. Works with LangChain/LangGraph/LlamaIndex; supports OpenAI, Azure OpenAI, Anthropic, Bedrock, and local/vLLM. Clean SDK + CLI for custom checks and pipelines.
Clear choice- Want to experiment with confidence? Start with our open source version. Need enterprise features? Upgrade seamlessly when you're ready.
Your time is too valuable. Your AI is too important. Your standards should be higher.
If this post has hit a nerve, comment “me too”/DM me and I’ll help you migrate in minutes. Or kick the tires now: https://github.com/future-agi/ai-evaluation
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