A better way to inspect and test AI Agents traces
Hi everyone! My team and I just open-sourced a bunch of cool agent dev tools: Invariant Explorer to visually inspect and understand AI traces and a testing framework, building on pytest.
A better way to inspect and test AI Agents traces launched on December 17, 2024 with 5 votes and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
A better way of testing, inspecting, and analyzing AI Agent traces. - invariantlabs-ai/explorer
Note: The hosted version of Invariant Explorer has been shut down in January 2026. If you're interested in Invariant Labs' continued work on AI security, check out Snyk's AI Security offering . # install Invariant package pip install invariant-ai # pull and launch Explorer application invariant explorer You can then access your Explorer instance via http://localhost . Data will be stored at ./data of the current working directory. To get started, run the following command. Make sure you first install Docker Compose . ./run.sh up To open the Explorer interface go to http://localhost . This will automatically launch a local Explorer instance on your machine. All data will be stored in ./data…from github.com
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A registry of agent benchmarks (including many OSS agent trajectories)2024 · explorer.invariantlabs.ai · ▲6If 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.
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