GetCito vs Elmo: The Best Open-Source AI Visibility Tool?

GetCito and Elmo are the two best-known open-source AI visibility tools, and on paper they look alike: both are MIT-licensed, both track your brand across AI answer engines, and both sit around 120 GitHub stars. The difference shows up after launch. GetCito has had one substantive code commit since November 2025. Elmo ships changes most weeks. If you plan to actually run the tool, that gap is the whole decision.

Key takeaways

Star counts and commit history reflect the public GitHub repositories as of June 2026. The chart below tracks both over time.

GetCito bills itself as the world's first open-source AIO, AEO, and GEO tool. The pitch is real in one sense: the code is on GitHub under the MIT license, so you can read it and host it yourself. Its most distinctive feature is an AI Crawlability Clinic that checks how well AI bots can reach and parse your pages, and the team behind it also sells GEO playbooks and consulting.

The product is built on Next.js with Firebase for storage and Azure OpenAI as its main model provider. If you want a fast read on whether AI crawlers can see your site, the crawlability angle is a genuinely useful idea.

The problem is what happened after launch, which is almost nothing.

Elmo is an open-source, self-hosted AI visibility platform. It tracks how your brand shows up across AI models, records which sources get cited, and benchmarks you against competitors, with every metric computed by code you can read. It covers an unusually wide set of engines: ChatGPT, Claude, Gemini, Grok, Perplexity, Copilot, DeepSeek, Mistral, and Google's AI Mode and AI Overviews, pulling responses through the models' own APIs or OpenRouter.

The self-hosted core is free under the MIT license, with unlimited prompts and all models. The trade-off is that you run the infrastructure (Docker and PostgreSQL) and bring your own LLM API keys, which carry their own usage costs. A managed cloud option is listed as coming soon. For a wider field of options, see our roundup of the best AI visibility tools and the open-source category in our directory.

This is where the two projects separate, so it is worth being specific.

Open GetCito's GitHub repository and look at the commit history. After the initial commit on November 3, 2025, the log is eight automated Dependabot pull requests (all on the same launch day) and two edits to the README, the most recent in March 2026. That is it. No feature work, no bug-fix commits from a human, and no tagged releases at all. The "activity" you see in the repo is a bot bumping package versions, not people building a product.

Elmo's history looks like the opposite. The project has been committed to most weeks since its first commit in July 2025, with dozens of changes landing in any given month and an open issue tracker that the team works through. When a new engine appears or an API changes, an actively maintained tool can follow it. A tool frozen at its launch commit cannot.

For software you are going to depend on, this matters more than any single feature. AI engines change constantly. Models get renamed, APIs shift, and new answer surfaces appear every few months. The tool that keeps pace is the one whose code keeps moving.

GetCito's repository also reads like a project that was generated in a single burst rather than grown over time. A few tells stand out, and you can confirm each one by browsing the repo.

The project root holds more than 40 markdown files with names like IMPLEMENTATION_SUMMARY.md, FIX_SUMMARY.md, DUAL_ANALYTICS_SYSTEM_IMPLEMENTATION.md, and QUERIES_OVERVIEW_INFINITE_RENDER_FIX.md. These are the kind of step-by-step recap files an AI coding agent leaves behind after a long session, not documentation a team writes for users. A maintained project folds that material into real docs or deletes it.

Sitting next to those are roughly a dozen loose test-*.js scripts in the same root folder (test-perplexity.js, test-ai-query.js, test-firebase-admin.js, and so on), plus a stray test-provider-fix.html. Real test suites live in a tests directory and run in CI. One-off scripts left at the top level are usually throwaway checks from a build sprint that nobody cleaned up.

None of this proves the tool does not work. It does suggest the repo was shipped once and left alone, which lines up exactly with the commit history.

GetCito is run by a digital marketing agency that offers AEO and GEO consulting. The open-source tool functions mostly as a calling card for that service, which helps explain why the code stopped moving the day after launch. The agency's incentive is to win consulting clients, not to maintain a free product.

Elmo's product is the tool itself. The open-source repo is the thing they are making, not an advertisement for something else, which is why development continues week to week. If you want the full feature-by-feature breakdown, see the Elmo vs GetCito comparison in our directory.

If your goal is a one-time crawlability check and you like GetCito's clinic concept, it is free to try and the code is yours to read. Just go in knowing the project has barely changed since it launched, so do not expect fixes or new engine support.

If you want a tool you will run on an ongoing basis, across many engines, that keeps up as AI search changes, Elmo is the better bet. It is the same license, similar in popularity, broader in coverage, and actually maintained.

Whichever you pick, the thing that separates a useful open-source tool from an abandoned one is upkeep. In a field that changes this fast, a tool that stops being maintained stops being useful, whatever its license says. To go deeper on the practice itself, start with our guide to answer engine optimization, then see how to track your brand in AI search.