The AthenaHQ alternative for developers

AgentGEO is an AthenaHQ alternative built for developers. AthenaHQ packages GEO for marketing teams: dashboards, content recommendations, and a workflow for improving AI visibility. AgentGEO unbundles it. One API and one MCP server return the raw answers ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini actually give — answer text, citations, sources, surfaceKey, providerFields — and your own agent turns that evidence into recommendations. You own the data; the advice comes from something that knows your product.

AthenaHQ is a genuinely strong product for its audience. A marketing team that wants answer-engine optimization served as a guided workflow — see the problem, get the recommendation, track the fix — is well served by it. AgentGEO is aimed at a different workflow: the one that runs inside your own agent and your own codebase.

The best AthenaHQ alternative for builders

Both products exist because AI answers now decide who gets discovered — that premise is shared. The split is in what you receive: a GEO platform hands you its conclusions about your visibility; the answer-access layer hands you the answers themselves and lets your own tools conclude. For developers, four things fall out of that:

Own every answer and citation

Each response is plain JSON: the answerText an engine gave, the sources[] it cited with title, URL, and position, and the surfaceKey and providerFields that produced it. Persist it and you have the evidence base — queryable, diffable, joinable — that any recommendation worth acting on should trace back to. There is no export button because there is nothing to export from; the data starts in your hands.

Ship it inside your product

An API and MCP server can be embedded where a workflow platform cannot: a visibility panel in your own SaaS, a GEO module in an agency deliverable, a nightly pipeline that feeds your content backlog. If the output of GEO work should surface in *your* interfaces rather than a vendor's, the primitive you need is the data layer, not another login.

Pay for calls, not seats

AgentGEO meters usage — a subscription plus overage under a spend cap — because the heavy user of this data is a script or an agent, not a login. The free tier requires no credit card, and pricing follows fetch volume. An automation that runs every night costs what its calls cost, whether one person or ten watch the results.

Your agent, connected over MCP

One command — claude mcp add agentgeo -- npx -y agentgeo-mcp --key ag_live_... — and Claude Code, Cursor, Codex, or any MCP client can pull live engine answers as a native tool. That is the piece a hosted platform cannot structurally offer: the raw data arriving inside the agent that also holds your docs, your positioning, and your code. See what a GEO data layer is for why we build at this layer.

AthenaHQ vs AgentGEO at a glance

A respectful side-by-side — these tools sit at different layers, and the right pick depends on who is doing the work. Find yourself in a row.

Recommendations your agent writes, from data you own

A platform's recommendations are generated from its model of your business — the category it slots you into, the pages it sees, the playbook it applies to everyone in your segment. That produces reasonable generic advice, and for many teams reasonable generic advice is enough.

An agent connected to AgentGEO over MCP starts from a different position. It holds your actual context — the product, the docs, the positioning, even the codebase — and it can pull the actual evidence: what each of six engines answers for your money queries, and which sources they cite. Ask it why Perplexity never cites your pricing page and it can read the fetched citations, read the page itself, and propose a fix specific to *your* site — then open the pull request. The recommendation loop closes inside your own tools, grounded in raw data you keep. That is answer engine optimization done by something that actually knows you.

The evidence layer under any GEO workflow

Whatever writes your recommendations — agent, script, or human — it needs the same inputs. AgentGEO returns all of them:

• Six engines, one interface: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini, chosen per request with the surfaces array.
• The verbatim answer each engine gives for a query — the ground truth of your AI visibility.
• Citations and sources (title, URL, position): who gets referenced, and where you are missing.
• `surfaceKey` and `providerFields` metadata, so advice can account for which model said what.
• Managed-scraper engine, maintained for you — no headless browsers, proxies, or CAPTCHA fights under your pipeline.

One POST, six engines

The integration is a single authenticated call. Widen the surfaces array to ask every engine at once.

curl -X POST https://api.agentgeo.org/v1/fetches \
  -H "Authorization: Bearer ag_live_..." \
  -H "Content-Type: application/json" \
  -d '{"query": "best email marketing platform", "surfaces": ["gemini"]}'

# → {
#     "answers": [
#       { "surfaceKey": "gemini", "answerText": "...",
#         "sources": [{ "title": "...", "url": "https://...", "position": 1 }] }
#     ]
#   }

The Python guide and Node guide cover the full loop — multi-engine fan-out, storage, and feeding the results to an agent.

Start feeding your agent real answers

If you want GEO recommendations delivered as a managed workflow, AthenaHQ is a strong choice for a marketing team and we will say so plainly. If you want them written by your own agent, grounded in raw answers and citations you own, AgentGEO is the data layer that makes it possible.