Agentic AI recall systems proactively surface your saved knowledge during active work — without requiring a search query. This represents a fundamental shift from pull-based retrieval (where you stop working to search) to push-based delivery (where AI delivers relevant information to you in the moment). It's the difference between interrupting your creative flow to dig through notes and having the right insight appear exactly when you need it.

Three distinct architectural approaches have emerged in the last year: real-time in-flow recall, asynchronous overnight research, and AI-curated discovery feeds. Each reflects a fundamentally different philosophy about when, how, and why AI should deliver knowledge to you. Here's how they compare and why the timing of delivery matters more than most people think.

At a Glance: Three Models of Agentic AI Knowledge Delivery

This isn't a minor UX difference. Each model encodes a distinct answer to the question: when does a knowledge worker most need AI to intervene?

How Proactive AI Recall Systems Differ: Architecture Comparison

Liminary: Real-Time In-Flow Proactive Recall

Proactive recall — sometimes called agentic recall — is an architecture where AI surfaces relevant information based on your current activity context, without you ever typing a query. Liminary's system works through three integrated layers:

First, seamless capture across web pages, documents, and LLM chats lets you save anything with one click. There's no tagging, no folder sorting, no organizational overhead. Second, automated synthesis builds a personal knowledge graph that connects your saved ideas semantically — meaning the system understands relationships between concepts, not just keyword matches. Third, contextual proactive recall monitors your active work and surfaces relevant notes while you're writing, browsing, or researching.

The key architectural distinction is when delivery happens. Liminary pushes insights into your workflow as you're working — while you're drafting in Google Docs, reviewing a competitor's website, or writing a Slack message. A floating bar or side panel surfaces relevant snippets from your saved research, matched to your immediate context and intent.

This is what makes push-based knowledge delivery fundamentally different from search. You don't stop to look something up. Your past research finds you at the exact moment of need.

As one knowledge worker described the ideal they were looking for: "I like research, I don't want it to do that part. What I want is a tool that helps me think better, not thinks for me." Proactive recall serves exactly this need — it augments your thinking without replacing the synthesis that makes your work valuable.

ChatGPT Pulse: Asynchronous Overnight Research

ChatGPT Pulse takes a fundamentally different approach: asynchronous batch research. Rather than monitoring your active work, Pulse conducts research while you're away — analyzing your chat transcripts, connected apps, and calendar overnight to deliver "topical visual cards" each morning.

This is a scheduled intelligence model. The AI works on your behalf during downtime and presents synthesized findings at a predetermined interval. It's powerful for staying informed across topics you're tracking, but it operates on a different temporal assumption: that the best time to deliver knowledge is before your workday starts, not during the moment you need it.

The tradeoff is clear. Pulse excels at broad research synthesis you might not have thought to do yourself. But it can't know that you'll need a specific statistic at 2:47 PM while drafting paragraph four of a client proposal. By the time you're in the flow of creation, Pulse's morning briefing is hours old and may not match the specific context you're working in.

Meta Vibes: AI-Generated Content Discovery

Meta's Vibes represents a third paradigm entirely: AI-generated content discovery and creation rather than personal knowledge retrieval. Vibes is a feed of short-form AI videos that users can remix and share across platforms, replacing Meta's previous Discover feed.

This isn't a knowledge management tool — it's an entertainment and creative inspiration engine. The "knowledge" it surfaces isn't yours; it's generated from web content and AI models. Including it in this comparison matters because it illustrates a critical distinction: not all AI that "surfaces content for you" is doing the same thing. Vibes optimizes for engagement and creative remix. Proactive recall optimizes for your productivity and the quality of your output.

Real-Time vs. Async vs. Feed-Based: When AI Delivers Your Knowledge

The timing models reveal fundamentally different design philosophies about knowledge work:

Synchronous (Liminary): The system assumes the highest-value moment for knowledge delivery is during active creation. When you're writing, you're making decisions about what to include, how to frame arguments, and which evidence supports your point. Proactive recall targets this exact window — surfacing your saved research as you draft, so relevant insights arrive when your brain is primed to use them.

Asynchronous (ChatGPT Pulse): The system assumes the highest-value moment is preparation. Morning briefings help you start your day informed, with research synthesized and organized before you sit down to work. This works well for staying current across many topics but creates a gap between when you receive information and when you apply it.

Continuous feed (Meta Vibes): The system assumes no specific moment is highest-value — instead, it provides an always-available stream optimized for discovery and engagement rather than task completion.

For knowledge workers whose output quality depends on connecting ideas from their past research — analysts, strategists, researchers, consultants, writers — the synchronous model addresses a problem the other two architectures don't: the friction of retrieval during the act of creation.

Agent Memory Systems vs. Augmented Browsing: Push vs. Pull Knowledge Retrieval

Within the proactive recall space, there's a further architectural split worth understanding.

Augmented browsing tools like Recall AI take a cue-based approach: they highlight terms on web pages that match your saved items. You're browsing, you see a highlighted keyword, and you click to retrieve the related note. This is a useful pattern, but it still requires you to notice the cue and initiate retrieval. It's a pull model with visual prompts.

Agent memory systems like Liminary take a different approach: passive in-flow recall that proactively pushes relevant information into your workspace. You don't need to recognize keywords or trigger searches. The system reads your current working context — what you're writing, what page you're on, what question you seem to be working through — and delivers relevant saved knowledge without any action on your part.

This creates what functions as a unified memory layer across all applications. Your notes, saved articles, highlighted passages, and research fragments stop being static files in a folder. They become an active support system that anticipates your needs based on what you're doing right now — transforming passive knowledge into what Liminary calls a proactive "second brain" that works alongside you in real time.

The Core Shift: From "Search Your Notes" to "Your Notes Find You"

The architectural philosophy at the center of proactive recall is a move from pull (you stop to search) to push (AI delivers information into your workflow). But the critical nuance is when that push happens.

Asynchronous systems push knowledge to you on a schedule. Discovery feeds push content to you continuously. Real-time proactive recall pushes your own saved knowledge to you at the precise moment you're doing work that could benefit from it.

For knowledge workers who spend their days synthesizing information — writing reports, building strategies, connecting research threads — that temporal precision is the difference between a tool that's nice to have and a tool that fundamentally changes how you work. The goal isn't to automate thinking. It's to ensure that everything you've already learned is available to you in the moment you need it most, without breaking the flow of creation.

Frequently Asked Questions

• What is proactive AI recall?
  Proactive AI recall is an architecture where an AI system automatically surfaces relevant information from your saved research and notes based on your current working context — without requiring you to type a search query. Instead of you pulling information from a knowledge base, the system pushes relevant knowledge to you during active work like writing, browsing, or researching. As far as we know Liminary is the only tool that does that today.
  
  

• How does agentic recall differ from traditional search?
  Traditional search requires you to stop what you're doing, formulate a query, review results, and return to your task. Agentic recall eliminates this interruption by monitoring your active context — what you're writing, reading, or working on — and automatically delivering relevant saved knowledge in real time. It's the difference between going to the library and having the right book open itself on your desk as you write.
  
  

• What's the difference between synchronous and asynchronous AI research tools?
  Synchronous tools like Liminary deliver knowledge in real time as you work — surfacing relevant notes while you're actively drafting or researching. Asynchronous tools like ChatGPT Pulse conduct research during your downtime (typically overnight) and present findings at scheduled intervals, like a morning briefing. The core tradeoff is immediacy and contextual relevance vs. breadth of research synthesis.
  
  

• Is ChatGPT Pulse the same as proactive knowledge recall?
  Not quite. ChatGPT Pulse proactively conducts research on your behalf, which is a form of agentic AI. But it operates on an asynchronous schedule — delivering morning briefings rather than surfacing knowledge during the specific moment you're working on something. Proactive recall, as implemented by tools like Liminary, is specifically designed to push knowledge into your active workflow in real time, matching your immediate context and intent.
  
  

• What types of knowledge workers benefit most from proactive recall?
  Proactive recall delivers the most value for professionals whose output quality depends on their ability to connect and apply information they've previously consumed — including researchers, analysts, strategists, consultants, content creators, and anyone doing synthesis-heavy work. If your job involves writing, advising, or building arguments from accumulated knowledge, a system that automatically surfaces your past research during creation directly improves your output.

Citations

[1] https://liminary.io/blog/what-100-user-interviews-taught-us-about-how-knowledge-workers-actually-think

[2] https://liminary.io/articles/liminary-vs-recall-ai-knowledge-assistant

[3] https://openai.com/index/introducing-chatgpt-pulse/

[4] https://liminary.io/articles/human-ai-collaboration-proactive-retrieval

[5] https://about.fb.com/news/2025/09/introducing-vibes-ai-videos/