An AI answer cites your competitor and leaves out your business. What can your AEO tool help you do next? Some make that gap easy to report. Others help you inspect the site, prepare changes, and check the next result.
Our free Profound and Peec alternatives article introduced several options. This comparison takes its four open-source projects and looks at the workflow each supports: Canonry, Elmo, GeoLook, and GEO Optimizer.
I build Canonry. These recommendations reflect the projects' public documentation, checked on October 6, 2026. This is a comparison of documented capabilities, rather than a benchmark of speed or citation accuracy.
The four tools at a glance
| Tool | Best fit | Documented interfaces | Deployment |
|---|---|---|---|
| Canonry | An AEO operating platform joining measurement, business data, investigation, and execution | Operational CLI, REST API, MCP, web UI, webhooks | Self-hosted instance with SQLite and your provider keys |
| Elmo | Recurring AI visibility measurement and reporting | Web UI, REST API, MCP; deployment CLI | Docker Compose with a web app, worker, and PostgreSQL |
| GeoLook | A local GEO implementation and publishing workflow | Python CLI, local web dashboard, HTTP endpoints, Claude Code skill | Local runtime with project files |
| GEO Optimizer | Technical readiness checks and fixes in development | CLI, Python API, MCP, GitHub Action | Python toolkit run locally or in CI |
Each has MIT-licensed software: Canonry from 5.0.0, Elmo, GeoLook, and GEO Optimizer. Budget separately for hosting, provider calls, collection services, and maintenance. Paid hosted products have their own terms.
1. Canonry: an operating platform for the ongoing AEO workflow
Canonry is an AEO operating platform. It connects AI visibility to the business data and tools an operator uses to investigate results, make approved changes, and measure again. Projects, answers, site findings, and schedules remain available between agent sessions. See the platform overview.
Its integrations give an investigation more context:
- Google Search Console and Bing Webmaster Tools add search and indexing evidence.
- GA4 adds traffic and conversion evidence; Google Business Profile adds local performance data.
- Server traffic integrations add crawler requests, page fetches, and referrals from Cloudflare, Vercel, Cloud Run, or WordPress logs.
- Site audits and Common Crawl backlinks help investigate technical conditions and the links around a business. The integration list shows the supported sources.
For an agency managing a hotel group, that means an agent can investigate a weak market, inspect its pages and local data, and prepare a specific change for review. Portfolio measurement keeps results for different properties and markets separate.
The interfaces support that work directly. Use the CLI for operational commands and JSON output, the REST API for application workflows, or MCP to give your existing agent typed tools. The dashboard reads the same project evidence. MCP adapts the public API; it does not give an agent extra authority. Canonry's MCP guide explains that relationship.
Execution depends on the connected system. For example, the WordPress integration can read and update pages through REST. Schema injection and some staging tasks require a manual handoff. Your agent can also prepare code changes with its own development tools. Schedules and webhooks support follow-up after approved work ships.
Choose Canonry when you want measurement, connected business evidence, and execution in one persistent operation. The tradeoff is owning the instance, its credentials, backups, and provider costs.
2. Elmo: a visibility platform for recurring measurement and reports
Elmo schedules prompts and records answers, brand mentions, citations, competitor share of voice, and query fan-out. It also documents opportunities and shareable reports. That is a useful fit when the client deliverable is a recurring view of where a brand appears in AI answers.
Elmo supports agents too. Its REST API manages brands, prompts, runs, analytics, and reports. Its MCP interface lets agents read visibility evidence and opportunities, and create or update prompts. Its CLI handles setup and deployment management, as described in the architecture guide.
Its provider integrations include collection services and direct model APIs. Select the collection method that matches the answer surface you need to observe.
Compared with Canonry, Elmo's documented focus is visibility collection and analysis. If the next step needs Search Console, server logs, or publishing, map how those systems will join the investigation. Elmo is a reasonable choice when your team already has that surrounding workflow.
The self-hosted setup uses Docker Compose, a web app, a worker, and PostgreSQL. Include those services and measurement providers in the operating budget.
3. GeoLook: a local workflow that reaches implementation and publishing
GeoLook covers answer sampling, site diagnosis, implementation tickets, content drafts, publishing, and scheduled verification. Its publishing options include WordPress drafts, GitHub, WeChat drafts, webhooks, X, and Reddit. It overlaps with several parts of Canonry's execution workflow.
GeoLook runs Python scripts and a local dashboard, with project data stored in files. It supports API-based sampling and manual answer collection. Its documentation describes a single-machine workflow without accounts or team collaboration. The README explains the setup and publishing paths.
It has HTTP endpoints behind the dashboard as well as a CLI and optional Claude Code skill. The dashboard implementation shows those API routes.
The practical comparison is how you want to operate. GeoLook suits an operator who wants a local project workspace for diagnosis, content, and publishing. Canonry suits a team that wants those investigations connected to first-party business integrations and a shared project API used by its agent and dashboard.
Choose GeoLook when its local files, scripts, and publishing channels fit your work. For each engine, check whether the measurement uses an automated API call or a manual collection step.
4. GEO Optimizer: technical checks and fixes for development workflows
GEO Optimizer checks bot access, structured data, and content signals. It can generate fixes, preserve audit history, detect regressions and drift, and produce recurring reports. Its CLI, Python API, and GitHub Action make it useful alongside development and deployment work.
Its MCP server exposes audits, fix generation, competitor comparisons, gap analysis, and validation tools. Agent access is available here too.
Readiness and visibility need separate interpretation. The project's geo monitor guide says that monitoring checks website readiness without querying model answers. Its citation commands do query providers, but the returned evidence differs: Perplexity supplies web source URLs, while its OpenAI and Anthropic checks reflect model knowledge. See the provider guide.
Compared with Canonry, this toolkit fits a more specific job: repeatable technical checks and fixes in the development process. It can complement an operating platform. A team could use GEO Optimizer in CI and Canonry to connect site findings with search data, traffic, answer evidence, and follow-up work.
Choose it when a Python toolkit fits your pipeline. Keep saved history available between jobs if you want comparisons across deployments. A higher readiness score alone does not establish more AI citations.
Where Bing fits, and how to choose
The original shortlist also included Bing Webmaster Tools. It is a free hosted Microsoft service, so it sits outside this open-source comparison. Its AI Performance reports show citation activity on supported Microsoft surfaces, including Copilot and Bing AI summaries. That first-party evidence can complement your own measurements. Microsoft describes the reporting scope here.
Use the work you need to complete to choose a starting point:
| Your team's main need | Start by evaluating |
|---|---|
| Investigate visibility with business data, prepare changes, and verify results across projects or markets | Canonry |
| Collect recurring AI answers and deliver visibility reports | Elmo |
| Run a local diagnosis, content, and publishing process | GeoLook |
| Add technical checks, regressions, and generated fixes to development | GEO Optimizer |
Before comparing results, align the questions, providers, collection surfaces, locations, and sampling dates. A browser answer, an API response, and a readiness score describe different observations. Canonry's collection method is one example of why that distinction matters.
For teams that need the full operating workflow, start with Canonry: connect the relevant accounts, inspect the evidence through API, MCP, or CLI, and prepare one bounded change for review. Measure again after it ships and report what changed.