Memoria
VerifiedStructured memory system for AI agents. Use when the user wants to store, recall, or search memories, manage session lifecycle (wake/sleep/checkpoint), sync...
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# Memoria
Environment
Set the vault path so all commands work:
```bash export MEMORIA_VAULT=~/memory ```
If not set, pass `-v ~/memory` on every command.
Session Lifecycle
Run at the start and end of every session:
```bash memoria wake # start session, restore context memoria checkpoint --working-on "<task>" # mid-session save memoria sleep "<summary>" --next "<next steps>" # end session, write handoff ```
Storing Memories
```bash memoria remember <type> "<title>" --content "<details>" memoria sync --push # always sync after storing ```
Types: `fact`, `decision`, `preference`, `lesson`, `commitment`, `relationship`, `project`
What to capture (proactively, without being asked)
| Signal | Type | |--------|------| | Human shares personal info (name, location, health, settings) | `fact` | | A decision is made with reasoning | `decision` | | Human says "I prefer / always / never..." | `preference` | | An insight or lesson emerges | `lesson` | | A promise, goal, or deadline is set | `commitment` | | A person is mentioned with context | `relationship` | | An ongoing project is discussed | `project` |
If in doubt, store it. Better to have a memory you never look up than to forget something.
Proactive capture triggers
Listen for these patterns and store immediately:
- "I always...", "I never...", "I prefer..." -> `preference`
- "Let's go with...", "We decided...", "The plan is..." -> `decision`
- "I learned that...", "Turns out...", "The trick is..." -> `lesson`
- "My name is...", "I take...", "I live in...", "I work at..." -> `fact`
- "I need to...", "I promised...", "By next week..." -> `commitment`
- "Talk to Alice about...", "Bob said..." -> `relationship`
- "We're building...", "The project is..." -> `project`
Examples
```bash memoria remember fact "Human lives in Tokyo" --content "Mentioned during onboarding" memoria remember preference "No emojis in code" --content "Explicitly requested" memoria remember decision "Use Fly.io" --content "Chosen over Vercel for APAC latency" memoria sync --push ```
Searching
Before making decisions or giving advice, check existing memories:
```bash memoria search "<query>" ```
Other Commands
```bash memoria store <category> "<title>" --content "<body>" # store in explicit category memoria list [category] # list documents memoria get <id> # get specific document memoria status # vault stats + session state memoria sync --pull # pull Notion changes to local ```
Notion Setup
One-time configuration:
```bash memoria setup-notion --token <token> --page <page-id> ```
After setup, always run `memoria sync --push` after storing memories.
Use Cases
- Store and recall structured memories for persistent AI agent context
- Manage session lifecycle with wake, sleep, and checkpoint operations
- Search across stored memories for relevant context and past decisions
- Sync agent memory across sessions for consistent personality and knowledge
- Build AI agents with long-term memory that persists across interactions
Pros & Cons
Pros
- +Compatible with multiple platforms including claude-code, openclaw
- +Well-documented with detailed usage instructions and examples
- +Open source with permissive licensing
Cons
- -No built-in analytics or usage metrics dashboard
- -Configuration may require familiarity with ai & machine learning concepts
FAQ
What does Memoria do?
What platforms support Memoria?
What are the use cases for Memoria?
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