AgentsProof vs AutoLang

AgentsProof and AutoLang are both large language models tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

AgentsProof

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.

AutoLang

Orbit wraps each agent run in a bounded loop: it pulls one task from a dependency-ordered backlog, hands it to whatever agent you've wired up, runs tests, lint, and type checks, and refuses to close the task until validation passes. Every run produces structured JSON — what the agent returned, how it scored against a rubric, whether a human should accept or re-queue. That audit trail is the point. The ceiling appears when your workflow needs anything beyond task-level sequencing: parallel agent execution, real-time dashboards, or integration with existing CI pipelines requires you to build the glue yourself.

Attribute	AgentsProof	AutoLang
Pricing	Paid	Free
Price	$29/month	—
Free trial	No	No
Open source	No	Yes
Has API	Yes	No
Self-hosted option	No	Yes
Platforms	Node, edge runtimes	Linux, macOS, Windows (Python)
Pros	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.	Validation gates block task closure until tests, lint, and type checks pass, so regressions that would have silently shipped surface inside the orbit instead of in production. Agent-neutral adapter contract means you can swap Claude for Codex behind the same harness and compare structured evaluation artifacts, so agent selection becomes a decision based on evidence rather than anecdote. Dependency-aware backlog sequencing ensures each agent run starts from a task whose prerequisites are already verified, which means the cascading failures that come from running tasks out of order stop accumulating. Four structured artifacts per run — result, evaluation, review recommendation, progress log — give compliance or audit teams a complete evidence trail without requiring post-hoc reconstruction. MIT licensed and self-hosted, so sensitive codebases never leave your infrastructure and there is no vendor dependency on a paid tier to retain audit history.
Cons	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.	Orbit executes one task per orbit, sequentially. Teams that need agents working in parallel on independent tasks hit this ceiling immediately — there is no built-in concurrency model, and adding it means maintaining a scheduling layer outside the harness. Integration with existing CI pipelines — GitHub Actions, Jenkins, or similar — is not provided. Teams that need orbit results to gate pull requests or trigger deployments write the integration themselves, which becomes a second system to maintain alongside Orbit. The evaluation rubric scores task focus, completion, diff signal, and validation, but the rubric definitions are fixed to what the harness ships with. Teams whose quality criteria don't map to those dimensions either accept scores that don't reflect their standards or fork the evaluation logic — at which point they own a modified harness diverging from upstream. When a team's workflow grows beyond single-repo, dependency-ordered task queues — multi-team backlogs, cross-service agents, or real-time progress visibility — Orbit's intentional smallness becomes a hard constraint. That's the condition under which teams move to a broader agent orchestration platform and treat Orbit's artifact schema as a reference rather than a production harness.

Bottom line

AgentsProof is paid while AutoLang is free; AutoLang is open source; only AgentsProof exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between AgentsProof and AutoLang?

AgentsProof is Paid, while AutoLang is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is AgentsProof better than AutoLang?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.