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AgentsProof
Trace, grade and share AI agent eval reports from a few SDK calls, with custom graders and golden cases
0Visit AgentsProof
https://agentsproof.dev
About AgentsProof
AgentsProof exists to replace vibe-checking an AI agent with a shareable artifact. The integration is intentionally tiny: install one npm package, call `ap.startRun()` with the input and a goal string that anchors grading, wrap each LLM and tool call in `run.trace()`, and call `run.complete()` — which returns a public URL to a proof report. The report scores the run out of 100 with a breakdown across goal completion, tool accuracy, step efficiency, output quality, safety and anomaly detection, alongside the traced step sequence. Beyond the default LLM grader you write custom graders as plain-English rules that are checked against every run automatically, and you define golden test cases — approved input-output pairs the agent must keep satisfying — grouped into proof suites. The problem it targets is the specific failure mode of shipping agents: someone changes a prompt, something breaks silently, and a user notices before the team does. Because a proof report has a public URL, it also works as external evidence — a way to show a customer or a reviewer that the agent behaves, rather than asserting it. Published drop-in support covers OpenAI, Anthropic, LangChain, CrewAI, the Vercel AI SDK and LlamaIndex, with TypeScript and Python SDKs. The product is in beta, and free-tier eval runs return a 402 from the SDK when the monthly limit is reached rather than silently degrading.
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Key Features
AgentsProof Pros & Cons
✅ Pros
- +Integration really is a few lines, so there is no adoption cliff
- +Public reports double as customer-facing evidence, not just internal telemetry
- +Free tier's 200 runs a month is enough for a solo builder's CI
⚠️ Cons
- −Still in beta
- −Custom graders and private reports are both paid-only, which limits free CI use
- −Public-by-default reports need care with sensitive inputs
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