> For the complete documentation index, see [llms.txt](https://docs.growthfactor.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.growthfactor.ai/analyze-site-intelligence-and-selection/growthfactor-agent.md).
# Growthfactor Agent
The **Growthfactor Agent** is a conversational AI analyst built into the GrowthFactor app. Ask it a question in plain English and it carries out the work using the same tools you'd use yourself — finding sites, scoring locations, pulling demographics, running cannibalization, fetching foot traffic, and assembling trade zones — then surfaces the results back as text, tables, and map layers.
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## Opening the Agent
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Open the agent from the sidebar in any dashboard view. It opens in a side panel, next to a map of your sites.
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## What you can ask
The agent is best at the kinds of questions you'd otherwise click through several screens to answer. A few starting points:
* **Find sites** — "Find me three candidate sites in Charlotte with strong daytime population and low cannibalization."
* **Score a location** — "Score 1234 Main St, Austin, TX."
* **Pull demographics** — "What's the median household income in a 3-mile ring around 500 5th Ave, NYC?"
* **Run cannibalization** — "If I open at 100 King St, Charleston, which of my existing stores would be cannibalized?"
* **Pull foot traffic** — "What's the weekly foot-traffic trend for the Starbucks at 200 Park Ave?"
* **Compare options** — "Compare the trade-area demographics for these three addresses."
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## How results show up
* **Text and tables** — answers appear inline in the chat, with tables for multi-row results
* **Map layers** — when the agent runs an analysis that produces geography (trade zones, cannibalization, candidate sites), the resulting layer appears in the **map legend** so you can toggle it alongside your other layers
* **Streaming responses** — a thinking indicator shows between submit and the first response, and answers stream in as the agent works
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## Conversation history
The agent remembers your conversations. Every chat is saved to **History**, so you can step away and pick up right where you left off—your full thread loads back exactly as you left it, including the tables and map layers the agent produced.
Use history to:
* **Continue a thread later** — reopen a past conversation and keep going instead of starting over
* **Revisit prior work** — scroll back through a conversation to review what the agent did and how it got there
* **Iterate** — build on earlier turns ("now do the same for these other three addresses") rather than restating context
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## Submitting feature requests
You can ask the agent to pass along a feature request or piece of feedback to the GrowthFactor team. Before anything is sent, the agent **confirms the request with you**—showing you what it's about to submit so you can approve or refine it first. Nothing is filed automatically.
## Workspace context
The agent reads context from the **active workspace** you're in:
* Site Score lenses and weights
* Default trade-zone definition
* Tracked brands and categories
* Brand-specific model defaults
If you're in a sub-workspace, the agent uses **that** sub-workspace's context, not the root workspace's. To run a question against a different workspace's context, switch workspaces first.
You can also ask the agent to use a **specific trade zone** in your question (e.g., "use a 5-minute drive time"), and it will use that instead of the workspace default for that turn.
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## Tips for good results
* **Be specific about location** — full addresses or named places work better than vague descriptions
* **Specify the trade zone** if the default isn't what you want
* **Iterate** — the agent has conversation history, so you can refine ("now do the same for these other three addresses") instead of restarting
* **Share feedback** — the agent is new and improving quickly; let us know what worked and what didn't at
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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:
```
GET https://docs.growthfactor.ai/analyze-site-intelligence-and-selection/growthfactor-agent.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.