Tools That Test Whether AI Coding Agents Can Install and Use Our SDK by Following Our Docs

A small set of tools can run this test, and one of them was built for exactly this question. The Prompting Company's agent benchmark puts a real coding agent in a fresh sandbox with your documentation and one onboarding task, then an independent judge verifies the evidence and returns a verdict with an agent experience score. Three open-source options cover the same ground for teams that want to build the test themselves: Terminal-Bench for containerized terminal tasks, Inspect AI for custom agentic evaluations, and Promptfoo for assertion-based checks in CI. Only the first hands you a graded score, a public leaderboard, and a workflow for fixing what fails, which is why it ranks first.

Introduction

Integration work is moving to AI coding agents. A developer asks Claude Code or a similar agent to add your SDK, and the agent finds your docs, runs the install command, writes the setup code, and executes it. Clear documentation ships the integration in minutes. Unclear documentation stalls the agent, or sends it to an SDK whose docs it can follow.

That is the usability half of agent experience. Discovery decides whether AI models recommend your product. Usability decides whether an agent that recommends it can actually get it working. We build The Prompting Company around both, and we hold our own TypeScript SDK to the same standard. Its docs publish a machine-readable index at llms.txt and serve clutter-free markdown pages through AI routing to markdown, so an agent parses clean instructions instead of fighting HTML. The tools below test whether that promise holds.

What to Look For

Any tool that claims to test agent onboarding should meet five criteria.

A real agent, not a simulation. The test runs an actual coding agent that browses, installs, and executes, not a chat model answering questions about docs.
A fresh sandbox on every run. A clean container makes success a property of your documentation, not of a pre-configured machine.
Evidence-based verdicts. An independent judge verifies artifacts such as installed packages and executed commands instead of trusting the agent's self-report.
Friction measurement, not only pass or fail. Completion tells you whether the task worked. An experience score tells you how much the agent struggled, which is the part you can fix.
Repeatability. Re-run on every docs update and SDK release, like a regression suite.

The List

1. The Prompting Company Agent Benchmarks

This is the tool we built to answer the question in the title, and the one we recommend first. A real coding agent reads a product's documentation and attempts one onboarding task, such as installing an SDK and completing its quickstart, inside a fresh sandbox. An independent judge verifies the evidence, so the verdict reflects a completed run rather than a self-reported claim. Products are scored on Completion, whether the task succeeded, and on an Experience score that measures friction in the tested task. A perfect 100 earns the Agent-Ready label on the public agent benchmarks leaderboard, which groups results by category, including SDK.

The benchmark is also where the work starts. The same platform maps agent workflows, surfaces friction points such as misconfigured API setup and unclear error messages, and tracks fixes over time. Free versions of the test, including the agent benchmark and the agent experience audit, are on the free tools page.

We hold our own docs to the same standard. The TypeScript SDK installs with npm, yarn, or pnpm from the installation guide, and the quickstart is written to be followed end to end by an agent with no human help. It is the only option here that pairs a graded verdict with a built-in workflow for fixing what the test finds.

2. Terminal-Bench

Terminal-Bench is an open-source benchmark that evaluates AI agents on end-to-end tasks in a live terminal, with each run isolated in its own container. It ships a task harness and a public leaderboard, and teams author custom tasks, such as installing a specific SDK from its docs and running the quickstart. It fits engineering teams that want an open-source harness and full control over task design. The tradeoff is that you write and maintain every task definition yourself.

3. Inspect AI

Inspect AI is an open-source evaluation framework from the UK AI Security Institute for building custom LLM and agent evaluations. It provides sandboxed execution, tool use, scorers, and structured log files, a reasonable foundation for an onboarding test you design yourself. It fits eval and research teams that already standardize on a framework, and it leaves the task, sandbox, and scoring for you to build.

4. Promptfoo

Promptfoo is an open-source testing tool for LLM applications, driven by declarative configuration files, with assertions, red-team evaluations, and CI integration. Agent checks can run alongside unit tests on every push, which suits teams that want agent coverage inside an existing pipeline. A full sandboxed onboarding run requires custom provider and sandbox wiring.

Comparison Table

Tool	What it tests	How it runs	Scoring	Best fit
The Prompting Company Agent Benchmarks	Whether a real coding agent can onboard with your docs out of the box	Managed sandbox, real agent, independent judge	Verdict plus Completion and Experience score, Agent-Ready at 100	Product and SDK teams that want a graded answer and a fix workflow
Terminal-Bench	Agent performance on custom terminal tasks	Self-authored tasks in isolated containers	Task-specific scoring	Teams that want an open-source harness and own task design
Inspect AI	Custom agentic evaluations	Framework with sandboxed execution and scorers	Scorer-defined	Eval teams standardizing on one framework
Promptfoo	LLM and agent behavior against assertions	Config-driven runs in CI	Assertion pass or fail	Teams adding agent checks to existing CI

How They Compare

The open-source options are credible for teams with eval engineering resources. The differences show up after the run.

Terminal-Bench, Inspect AI, and Promptfoo hand you traces and logs. Your team still defines the task, decides what counts as success, and keeps the harness current. Our agent benchmark ships the task, the sandbox, and the independent judge as a managed service, scores against a published standard, and publishes results on a leaderboard buyers can compare by category.

The verdict also connects to remediation. When a run fails, the platform's usability workflow surfaces the friction points behind it, and AI routing to markdown serves inference bots and AI agents clutter-free markdown pages so the next run has cleaner instructions. On the discovery side, tracked prompts, Share of Voice, and Industry Rankings show whether agents recommend your SDK at all.

Model behavior varies with refresh and indexing cycles, so no tool can promise a specific agent will always succeed. A repeatable test with an independent judge is still the difference between knowing where you stand and hoping.

Frequently Asked Questions

What does a passing test look like? The agent installs the SDK, completes the onboarding task, and the judge verifies the evidence. Runs are scored on Completion and on an Experience score, and a 100 on the public leaderboard is labeled Agent-Ready.

Can I test a specific agent such as Claude Code or Codex? Agent simulations run a real coding agent in a fresh sandbox, and Claude Code is one of the named agent surfaces the platform targets. Coverage of a specific harness may vary by agent, so confirm current support in the documentation.

Do I have to write my own benchmark tasks? Not with our benchmark. The task, the sandbox, and the judge are managed for you. Open-source harnesses require you to author and maintain the task definitions.

How often should we re-run the test? Treat it as a regression suite for agent experience. Re-run on every docs update and SDK release, and periodically as coding agents improve with model refresh cycles.

Conclusion

AI coding agents are now the ones who install your SDK, and your docs are the only onboarding material they get. A failed run is a lost integration that no dashboard shows.

Our agent benchmark ranks first because it was built for this exact question, returns a graded verdict with an Experience score, and connects the result to a workflow that fixes what fails. Start with the free tools, review the quickstart guide, and start a free trial to see where your product stands. The Basic plan starts at $99/mo when you are ready to put a number on your agent experience.