knowyour business, your roadmap, or the subtle nuances of your team's workflow. I've been looking into how teams try to solve this, and it usually ends in a nightmare of manual data entry or broken OAuth connections.
The team behind Almanac (a YC S26 startup) just shared how they are tackling this specific friction point. Instead of treating memory as a secondary feature, they are building a "pre-compiled knowledge layer" that functions like a brain for your company.
The architecture of a proactive AI workflow #
The core technical differentiator here is how they handle context. Most LLM agents are session-bound—they run a task, finish it, and the context vanishes. Almanac shifts this paradigm by using a dual-wiki structure to manage information:
Personal Wiki: This is strictly private. It learns your specific preferences, your personal schedule, and the people in your life. It's designed so the AI understandsyouwithout leaking your data to the rest of the team.Company Wiki: This is the shared intelligence. It ingests data from connected sources to map out company goals, product roadmaps, and current blockers.
To make this work, they aren't just dumping text into a vector database. They are spending significant compute upfront on "pre-compiling" this knowledge. This allows the agent to move beyond reactive chat and into proactive task management.
Moving from reactive chat to proactive agents #
If you've worked with LLM agents, you know the "prompt engineering" struggle: you have to constantly remind the AI who you are and what you are working on. Almanac attempts to automate this through two main features:
-
Proactive Notifications: Because the agent is constantly monitoring the background via a worker process, it can suggest actions before you ask. Imagine waking up to a notification saying, "I've drafted the fundraising pitch deck based on our last three meetings; want to review it?"
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Long-Horizon Task Management: This is where real productivity happens. Most agents fail at tasks that require waiting. Almanac stays "on" even when you aren't. If it sends an email on your behalf and receives a reply four hours later, it doesn't just see a new message—it understands that message within the context of the ongoing project and drafts a relevant follow-up immediately.
Deployment and connectivity #
Setting up these kinds of AI workflows from scratch is usually a massive headache involving custom API integrations and complex permissioning. Almanac aims to make this a beginner-friendly experience with one-click connections to:
- Gmail & Calendar
- PostHog
- Granola
- And various other workspace tools
The goal is to create an agent that feels less like a tool you use and more like a teammate who has been sitting in every meeting and reading every Slack thread. It’s a deep dive into what a true LLM agent needs to be successful in a real-world professional environment: persistent, contextual, and proactive.
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