Research & Learning

Basic Memory turns your AI into a research partner that remembers everything. Instead of losing insights in chat history, every research conversation produces real markdown notes -- linked together, searchable, and always available for the next session.

Whether you're exploring a new field, tracking a long-running project, or just trying to make sense of what you've been reading, Basic Memory gives you a shared knowledge base where both you and your AI can build on prior work.

How Research Works in Basic Memory

The key insight: when you ask your AI to research something, it doesn't just answer in chat. It creates detailed notes in your knowledge base with structured observations, tags, and relations to other notes. Those notes persist. Next time you pick up the thread -- even weeks later -- the AI reads your existing research and builds on it.

This means your knowledge compounds. A note about quantum computing links to your note about cryptography. A synthesis note ties together three papers you read last month. Your AI can navigate these connections to give you better answers over time.

Starting a Research Project

Tell your AI what you're exploring and ask it to start building notes:

You: "I'm starting a research project on renewable energy storage.
     Create an overview note covering the main approaches --
     battery technology, hydrogen, thermal storage, and gravity-based systems."

The AI creates a detailed note with observations categorized by approach, tagged for easy discovery, and ready to link to future deep-dives.

To get consistent structure across all your research notes, set up a schema:

You: "Create a schema for my research notes. Each one should have
     sections for key findings, sources, open questions, and connections
     to other topics."

Now every research note the AI writes follows the same format. You can validate notes against the schema later, and your whole research library stays organized without extra effort. See Schema System for details.

Building Connected Knowledge

The real power shows up when notes start linking to each other. As you go deeper, ask the AI to connect new findings to what you already have:

You: "Research solid-state batteries and connect it to my
     renewable energy storage overview."

The AI creates a new note with technical detail on solid-state batteries and adds relations back to your overview. Your knowledge graph grows naturally -- each note is both a standalone document and a node in a larger web.

Over time, you can ask for synthesis:

You: "Look at my energy storage notes and create a synthesis --
     what are the main trade-offs between approaches, and where
     are the biggest open questions?"

The AI reads across your existing notes, identifies patterns, and produces a new note that ties everything together. This is where having persistent, connected knowledge pays off -- the AI isn't starting from scratch each time.

Finding and Exploring Your Research

This is where Basic Memory v0.19 really shines. Once you've built up a collection of research notes, you need to find things -- and not just by exact title.

Semantic Search

Semantic search finds conceptually related notes, even when the wording doesn't match:

You: "Find everything I've learned about energy density improvements"

This returns notes about battery chemistry, hydrogen storage capacity, and thermal systems -- anything conceptually related to energy density, regardless of whether those exact words appear. It's the difference between keyword matching and actual understanding.

Tag and Metadata Search

Filter your research library by tags or metadata:

You: "Show me all research notes tagged battery-technology"

You: "Find notes with status: needs-review"

You: "Search for tag:open-question in my energy storage project"

Combine these with semantic search when you need precision. For example, find all notes tagged literature-review that relate to a specific concept. See Semantic Search for the full query syntax.

Exploring Connections

Use build_context to follow the links between notes:

You: "Show me what connects to my solid-state batteries note"

The AI traces relations outward, showing you the web of connected research -- what links in, what links out, and how topics cluster together.

Keeping Research Organized

Note Types

Use note types to categorize your research:

• research -- Primary research notes on specific topics
• literature-review -- Summaries and analysis of papers or books
• synthesis -- Notes that pull together findings across multiple topics
• question -- Open research questions to investigate

You: "Create a literature-review note on the latest solid-state
     battery papers I've been reading"

Schemas for Consistency

Once you find a structure that works, lock it in with a schema. A research schema might require:

• A "Key Findings" section with categorized observations
• A "Sources" section for references
• An "Open Questions" section
• A "Relations" section linking to related topics

The AI follows the schema automatically. When you come back to your research after a break, everything is in the same format and easy to scan.

Learning Workflows

Daily Capture

When you read something interesting, tell your AI:

You: "I just read a paper on perovskite solar cells. Create a note
     with the key findings and connect it to my energy research."

Don't worry about perfect organization in the moment. The tags and relations make it findable later.

Weekly Synthesis

Once a week, ask for a rollup:

You: "Review my research notes from this week. What themes are emerging?
     What should I dig into next?"

The AI reads your recent notes, identifies patterns, and suggests directions -- all saved as a new synthesis note in your knowledge base.

Best Practices

1. Let the AI write detailed notes. Don't settle for short summaries. The more detail in each note, the more useful it is when the AI references it later.
2. Ask for connections explicitly. "Connect this to my existing research on X" tells the AI to create relations, which makes your knowledge graph navigable.
3. Use schemas for recurring note types. If you write a lot of literature reviews or research summaries, a schema keeps them consistent without extra effort.
4. Search semantically first, filter second. Start broad with semantic search, then narrow down with tags or metadata when you need specifics.
5. Synthesize regularly. The most valuable notes are often the ones that connect other notes together. Ask for synthesis often.
6. Tag generously. Tags are cheap and make filtering powerful. Use them for topics, status (needs-review, complete), and anything else you might want to filter on later.

Next Steps