AgentsProof
Summary
You changed a prompt, re-ran your agent, eyeballed the output, and shipped it — then a user found the regression you missed. AgentsProof exists to close that gap between 'it looked fine in testing' and 'it actually works every time.'
AgentsProof is an evaluation SDK that wraps your LLM and tool calls with a decorator, grades each run against rules you define in plain English, and produces a shareable, scored report at a public URL. The core loop is: instrument with `run.trace()`, capture a passing run as a Golden, then run your full proof suite against every future change. That workflow catches regressions before users do — not after. The ceiling appears when teams need self-hosting; the product is cloud-only, so regulated environments that cannot send trace data to a third party are blocked before they start. The product is in beta, which means API surface and grading behavior are still moving.
Bottom line: Pick this when you need to show a stakeholder an explainable pass/fail report from a real agent run without building an eval harness from scratch — plan a different architecture when your compliance policy prohibits sending trace data to an external service.
Pricing Plans
Subscription- Price
- $29/month
- Free Tier
- 1 project, 200 eval runs/month, 10 golden test cases, 1 proof suite, basic support
Free
1 project, 200 eval runs/month, default LLM grader, 10 golden test cases, 1 proof suite, public reports, basic email support
- 1 project
- 200 eval runs/month
- 10 golden test cases
- Public proof reports
Pro
Unlimited projects, 10,000 eval runs/month, unlimited custom graders and Goldens, public + private reports, priority support
- Unlimited projects
- 10,000 eval runs/month
- Unlimited Goldens
- Private reports
View full pricing on agentsproof.dev →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Decorator-level instrumentation — `run.trace()` wraps any LLM or tool call without restructuring your agent code — so teams avoid building a parallel observability layer just to get graded output.
- Plain-English grader definitions mean you specify rules like 'the agent must never reveal user PII' and every subsequent run is checked automatically, which means you stop discovering policy violations in production.
- Goldens convert a passing run into a live regression test, so a prompt change that silently breaks established behavior fails the suite before it ships rather than after a user reports it.
- Deterministic trace assertions — `must_not_call:send_email`, `max_steps:10` — run without an LLM judge, which means they catch structural regressions that a scoring model grades past.
- Framework-agnostic SDK across OpenAI, Anthropic, LangChain, CrewAI, Vercel AI SDK, and LlamaIndex, so a project that switches providers or adds a second framework does not require a separate evaluation integration.
Cons
Sign in to edit- No self-hosted deployment option exists: every agent trace is transmitted to AgentsProof's cloud. Teams in regulated industries — healthcare, finance, or any environment with data residency requirements — cannot use the product at all and will need an on-premise eval framework such as a self-hosted LangSmith instance or a custom harness.
- The product is in beta: grading behavior, SDK contracts, and grader rule syntax are subject to change between releases. A proof suite that passes today can return different scores after a backend grading update, which means regression baselines are not stable enough to anchor a CI gate in a high-stakes pipeline.
- Synthetic variant generation and advanced grader features are paid-only; teams on the free tier hit the ceiling of the test coverage those features provide and either accept reduced coverage or move to a paid tier — there is no open-source escape hatch since the product is not open-source.
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About
- Platforms
- Node, edge runtimes
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-07-14T17:05:29.070Z
Best For
Who it's for
- Indie AI agent builders needing public proof
- Teams requiring explainable pass/fail reports
- Developers who want minimal SDK integration
- Projects using multiple LLM frameworks
What it does well
- Instrumenting agent code for automatic grading
- Creating shareable proof reports for stakeholders
- Converting successful runs into reusable test Goldens
- Running batch proof suites against approved test cases
- Detecting regressions via trace assertions and custom rules
Integrations
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Frequently Asked Questions
- Is AgentsProof free?
- AgentsProof has a permanent free tier alongside paid upgrades (paid plans from $29/month). You can keep using a baseline version indefinitely without paying.
- Is AgentsProof open source?
- No — AgentsProof is a closed-source tool. Source code is not publicly available.
- Does AgentsProof have an API?
- Yes. AgentsProof exposes a developer API. See the official documentation at https://agentsproof.dev for details.
- What platforms does AgentsProof support?
- AgentsProof is available on: Node, edge runtimes.
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Shipping an AI agent without a reproducible pass/fail test is trusting a demo to hold in production. AgentsProof instruments that gap: you install one package, wrap each LLM and tool call with `run.trace()`, define what ‘good’ means in plain English via custom graders, and every run produces a scored report — five axes, per-criterion breakdown, public URL — in the time it takes the agent to complete. The full workflow runs from a single `startRun()` call and requires no separate infrastructure.
The differentiating mechanism is Goldens. When a run passes your criteria, you promote it to a Golden — an executable test spec that carries success criteria, expected behavior, and trace assertions. Those assertions run deterministically, no LLM judge required: if your agent skips a required step or exceeds a step budget, the case fails immediately. The vendor states AgentsProof also generates synthetic edge-case variants from existing Goldens automatically, growing the test suite without writing new cases by hand.
AgentsProof fits indie builders and small teams who need to prove agent reliability to stakeholders without standing up a separate eval pipeline. The docs describe compatibility with OpenAI, Anthropic, LangChain, CrewAI, Vercel AI SDK, and LlamaIndex, so multi-framework projects do not require framework-specific wiring. The wall appears in two places: there is no self-hosted option, which blocks teams whose data governance policy prohibits external trace transmission; and the product is in beta, meaning grading rules and SDK contracts can change between releases without the stability guarantees a production dependency usually demands.
The SDK is available in TypeScript and Python. The `startRun()` call accepts an optional `goldenId` parameter to run against a specific Golden on demand, or a single `proofSuite` call batches all approved Goldens. Reports are available at a stable public URL — `agentsproof.dev/r/{id}` for individual runs and `agentsproof.dev/p/{suite-id}` for suite results — which the vendor positions as the shareable artifact for stakeholder sign-off.