AI chatbot with persistent conversation history per visitor
SleekAI can log every conversation in your own WordPress database, ties them to the visitor session or to the logged-in user from wp_users, and feeds the recent turns back into the model on the next visit. Bring your own provider key.
Why memory changes how a chatbot is used
A chatbot without memory restarts every conversation as if you have never met. The visitor has to re-introduce their goal, re-state their context, and re-ask follow-ups. That works for a one-shot FAQ widget. It fails the moment the bot is supposed to support a real workflow like onboarding, lesson plans, multi-step troubleshooting, or shopping with a long consideration window.
On return visits the recent turns are passed back into the prompt as context, so the bot picks up where the last session ended. Logged-in users see the same history across devices because it ties to wp_users, not to a cookie alone.
The hard part is privacy and pruning. History without limits becomes a liability and balloons the token bill. Generic chatbots either skip history entirely or stuff the whole transcript into every prompt, which gets expensive fast.
Features
Picks up where you left off
On return visits the recent conversation turns are loaded back into the prompt, so the bot remembers what the visitor was working on. No more starting from scratch with every page refresh or new session.
User-controlled retention
Visitors can delete their own history from the chat UI. Old conversations expire automatically so the table does not grow without bound.
Answers from your data
Map post titles, content, post meta, taxonomies or a WP_Query into the instruction, and the bot answers from what your site actually publishes.
Where persistent history earns its keep
Long sales cycles
Considered purchases happen over weeks. The bot remembers product preferences and constraints, so each new conversation continues the same thread instead of restarting cold.
Tutoring and courses
Lessons build on each other. The bot recalls the last topic, the visitor’s reported skill level, and which questions they struggled with, then tailors the next session to that progress.
Multi-step support
Troubleshooting often spans days. With persistent history the bot remembers which steps were already tried, which logs were already pulled, and where to continue diagnosing the issue.
How persistent history works
Pick a model
Store your OpenAI, Anthropic, Google or OpenRouter key once in the shared AI settings, or add a compatible endpoint, and choose the chatbot’s model.
Set display conditions
Decide which pages, post types, roles or logged-in states show the chatbot.
Map your data
Choose the posts, fields, taxonomies or users the bot should know and reference them in the instruction.
Review and refine
Turn on logs, read what visitors ask and tighten the instruction or the page behind it.
Why memory turns chats into relationships
A chatbot that remembers is a chatbot that gets used. The single biggest predictor of repeat engagement is whether the visitor has to re-explain themselves on the second visit. If they do, the second visit rarely happens. If they do not, the bot starts to feel like a colleague who actually paid attention.
That shift in posture changes the kinds of tasks visitors trust the bot with. Persistent history is what makes long sales cycles, multi-week tutoring, and complex support flows possible. Without memory the bot is a glorified FAQ widget.
With memory it is the first touchpoint of an ongoing relationship. Anything less feels broken in the same way that an email client without sync feels broken. Operationally the history table is also a research goldmine. Sales teams see how customers describe their needs in their own words.
Support teams see which issues recur. Editorial teams see which questions get asked over and over and prioritize new content accordingly. Done right, the history table earns its keep three times over and the chatbot stops being a feature and becomes a system.
Frequently asked questions
How does the bot handle anonymous users?
A different browser starts fresh.
Will history grow my database without bound?
No. Retention windows prune old rows automatically. The schema is designed to be queryable for both auditing and reporting.
Which AI providers does it support?
OpenAI, Anthropic, Google and OpenRouter, plus custom OpenAI-compatible endpoints. You bring your own API key and the provider bills you directly.
Where does the chatbot get its answers?
From the WordPress data you map into its instruction: post titles and content, post meta, ACF and Meta Box fields, taxonomies, terms, users and user meta, or a WP_Query. Values resolve on every request.
Can it act on a visitor’s behalf?
No. A chatbot answers questions.
Can I control where the chatbot appears?
Yes. Display conditions cover post type, post, post status, post template, taxonomy, term, user role, logged-in state and WordPress conditional tags such as front page, archive, search and 404.
Are conversations stored?
Only if you turn on logging for that chatbot. SleekAI then stores each visitor message and each completed reply, which you read and delete on the chatbot’s Logs page, and a retention period can delete old logs.
Can I run more than one chatbot?
Yes. Each chatbot has its own instruction, data, display conditions and styling, so different sections of a site can get different bots.
Pricing
Explore our flexible licensing options tailored to your needs. Upgrade your license anytime to access more features, or opt for a lifetime license for ongoing value, including lifetime updates and lifetime support. Our hassle-free upgrade process ensures that our platform can grow with you, starting from whichever plan you choose.
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What’s included
SleekAI
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SleekView
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