Alternatives
Products that do what APIEval-20 does
An open benchmark for AI agents that test APIs
- 1MD
We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…
2025 · github.com
- 2RT
2020 · github.com
- 3
- 4DT
2018 · github.com
- 5CB
I built a small benchmark to test CLI coding agents on blind bug detection. A challenger agent injects bugs and writes ground truth (`bugs.json`). A different reviewer agent audits the repo without seeing ground truth, and an LLM matcher scores bug-to-finding assignments. Current run: 50 repos, 150 challenges, 450 reviews, 2,603 injected bugs. Weighted detection: Claude 58.05%, Codex 37.84%, Gemini 27.81%. LLM-judge benchmarks are easy to get wrong, so I’d really appreciate critical feedback on benchmark fairness, scoring/matching methodology, and obvious failure modes I’m missing. Full…
Feb 2026 · github.com
- 6
- 7AT
2025 · qodex.ai
- 8CB
AI agents now have impressive reasoning capabilities. This raises an important question: how dangerous are these AI agents at identifying & exploiting web vulnerabilities? We created CVE-bench to find out (I'm one contributor of 16). To our knowledge CVE-bench is the first benchmark using real-world web vulnerabilities to evaluate AI agents' cyberattack capabilities. We included 40 CVEs from NIST's database, focusing on critical-severity vulnerability (CVSS > 9.0). To properly evaluate agents’ attacks, we built isolated environments with containerization and identified 8 common attack…
2025 · github.com
- 9SE
Hey HN! I built self-driving sim and eval at Waymo. Now I’m building Scorecard to bring that approach to agent eval: reproducible, automated scoring for AI. Scorecard lets you: - Run LLM-as-judge evals on agent workflows: test tool usage, multi-step reasoning, and task completion in CI/CD or in a playground. - Debug failures with OpenTelemetry traces: see which tool failed, why your agent looped, and where reasoning went wrong. - Collaborate on datasets, simulated agents, and evaluation metrics. Try it out → https://app.scorecard.io (free tier, no payment required!) Docs →…
Oct 2025 · docs.scorecard.io
- 10AC
2017 · docs.google.com
- 11OH
I'm Fenil, co-founder/CEO of OpenFunnel (YC F24), building this with my co-founder/CTO Aditya. We're launching OpenBenchmarks (https://openbenchmarks.com), open-source, reproducible benchmarks for SaaS APIs, starting with the category we know best: GTM APIs. ## Why we built this More and more B2B software evaluation will/already runs through reasoning models inside agentic workflows rather than through people. And buyers increasingly pick vendors that are API-first and ship MCPs, so they can wire them into internal workflows. Strong reasoning models are skeptical of…
Jul 2026 · openbenchmarks.com
- 12AR
Feb 2026 · trypillar.com
- 13AR
If you're interested in exploring what LLM-based agent systems these days actually do to solve certain benchmarks such as SWEBench or WebArena, we created a small leaderboard with our team, that allows to view a lot of public and OSS agent results including all the runtime traces (the step-by-step reasoning behind the scenes). Looking at traces is actually quite interesting, as they reveal a lot about the inner working and shortcomings of current agent system, e.g. see https://explorer.invariantlabs.ai/u/invariant/webarena--SteP... for an example trace.
2024 · explorer.invariantlabs.ai
- 14AE
I've been working on a site [1] to give people control of their LLM workflows through AI evals - automated checks that, once defined, let you move fast without regressions and cut through hype with proof. That one-liner is aimed at software engineers, but I've spent my career helping cross-functional teams collaborate, and that's really what this is about. AI agents make powerful workflows very plausible, but only if teams can grow them incrementally without losing control - no vendor lock-in, no discipline silos, no blind trust in outputs. The site tries to meet different audiences where…
Feb 2026 · ai-evals.io
- 15SM
2016 · medium.com
- 16AA
We’ve published a set of open-source reference implementations on how to build production-grade Agentic AI applications on AWS. What’s in the repo: • Agentic RAG, memory, and planning workflows with LangGraph & CrewAI • Strands-based flows with observability using OTEL & Arize • Evaluation with LLM-as-judge and cost/performance regressions • Built with Bedrock, S3, Step Functions, and more GitHub: https://github.com/aws-samples/sample-agentic-frameworks-on-... Would love your thoughts — feedback, issues, and stars welcome!
2025 · github.com
- 17IB
I built a tool to roast landing pages with AI agents. I was gathering feedback from watching landing page roast videos, and figured out I could prompt LLMs to analyse a screenshot and roast based on the same criteria. It's not 100% accurate yet, but it has been really insightful when I've tested it on my own websites. Let me know what you think!
2024 · roastmylandingpage.io
- 18WB
Humans compete to improve their AI agents on benchmarks. But what if agents could collaborate and compete on their own? We built Hive, a crowdsourced platform where agents can evolve solutions together. One agent begins to tackle a task, iteratively improving its code. Then other agents join. They read each other’s runs, fork the best ideas, propose new ones, and push the solution forward together. We already have agents working on benchmarks like Tau2-Bench, Terminal-Bench, and ARC-AGI-2, with more tasks coming soon. We also support the new OpenAI Parameter Golf Challenge, and you can…
Mar 2026 · hive.rllm-project.com
- 19AT
Hi Hacker News! We're launching Zalor, an agent testing platform. Agents often break when you tweak system prompts, swap models, or add tools. Zalor automatically generates test scenarios and evaluates your agent so you know it's reliable before deploying to production. We currently support the OpenAI Agents SDK and are onboarding other frameworks. A GitHub integration is coming so you can get feedback on every update. Looking forward to hearing feedback from people building agents.
Mar 2026 · agents.zalor.ai
- 20DD
2018 · github.com
- 21GZ
I made an API mocking tool that imports a schema, populates fake data, then runs a local server. This is helpful for testing APIs or advanced prototyping. A demo video is here: https://www.loom.com/share/abad2cdf325e4e0b9addea1e14406166?... There’s two fun things about this tool 1. the complete lack of configs and schema annotations. i.e. you don’t need to learn faker-js. You just need your existing GraphQL schema and to to swap out the server URL in your frontend code. 2. the depth. Your fake objects have relationships to other fake objects. And in proxy mode, real…
2023 · github.com
- 22OS
Hey HN! We built EvalKit, a library you embed to capture agent actions and a UI where domain experts give feedback, evaluate and improve AI agents. We experienced, in large agentic systems, prompt-engineering or auto-prompt improvement tool can get accuracy from 0 to 50% but for increasing accuracy to 100% we had to work with domain experts. Example -> In a law ai agent, lawyers are needed because law is complex and lawyers have a deeper context compared to non-lawyers. Other evaluation tools in the market focus on the experience of the developer and we are focusing on making as easy as…
2025 · github.com
- 23BA
I built CodeLens.AI - a tool that compares how 6 top LLMs (GPT-5, Claude Opus 4.1, Claude Sonnet 4.5, Grok 4, Gemini 2.5 Pro, o3) handle your actual code tasks. How it works: - Upload code + describe task (refactoring, security review, architecture, etc.) - All 6 models run in parallel (~2-5 min) - See side-by-side comparison with AI judge scores - Community votes on winners (blind voting) - Each evaluation gets reflected in the overall AI model leaderboard, showing us best ones Why I built this: Existing benchmarks (HumanEval, SWE-Bench) don't reflect real-world developer tasks. I wanted to…
Oct 2025 · codelens.ai
- 24
Ranked by how close each launch is in meaning, then by votes. Refine with a description →