
Memory
- 138 installs
- 22 repo stars
- Updated January 31, 2026
- rjyo/memory-search
Give coding agents durable, searchable long-term memory so prior sessions, decisions, and codebase context can be retrieved across tasks.
About
Implements searchable persistent memory for AI coding agents: capturing salient facts and session outcomes, indexing them for semantic lookup, exposing retrieval tools to the agent loop, and reducing repeated rediscovery of project context across long-running development workflows.
- Long-term agent memory
- Semantic memory search
- Cross-session recall
- Context retrieval hooks
- Persistent knowledge store
Memory by the numbers
- 138 all-time installs (skills.sh)
- +1 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #3,548 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 138 |
|---|---|
| repo stars | ★ 22 |
| Last updated | January 31, 2026 |
| Repository | rjyo/memory-search ↗ |
What it does
Give coding agents durable, searchable long-term memory so prior sessions, decisions, and codebase context can be retrieved across tasks.
Files
Memory
A hybrid search memory system for your project. Save important information and find it later using semantic search.
First run: If slow on first use, run bunx memory-search --warmup to pre-download the embedding model (~300MB).Memory File Structure
project/
├── MEMORY.md # Long-term: preferences, patterns, decisions
└── memory/
└── YYYY-MM-DD.md # Daily: session notes, context, progress---
Searching Memory
When the user asks about past decisions, preferences, or wants to recall information:
bunx memory-search "QUERY_HERE"Search Examples
bunx memory-search "database choice decision"
bunx memory-search "error handling pattern"
bunx memory-search "user preferences coding style"
bunx memory-search "authentication implementation"Output Format
Returns matching snippets with:
- path: File where the match was found
- lines: Line range (startLine-endLine)
- score: Relevance score (0-1)
- snippet: The matching text
After Searching
1. Present the relevant results to the user 2. If they want more detail, use Read to get the full file content 3. If no results found, offer to search with different terms
---
Saving to Memory
When the user says "remember this", "save this", "note that", or wants to store information:
Decide Which File
MEMORY.md (Permanent):
- User preferences ("I prefer TypeScript")
- Project decisions ("We chose PostgreSQL for X reason")
- Coding patterns ("Always use async/await")
- Architecture decisions
- Important URLs, contacts, credentials references
memory/YYYY-MM-DD.md (Daily):
- What was worked on today
- Bugs found and fixed
- Ideas to explore later
- Meeting notes
- Temporary context
How to Save
1. Read existing file first (if it exists) to avoid overwriting 2. Append new content with a timestamp or section header 3. Use clear, searchable language (will be vector-searched later) 4. Run sync after saving to update the search index
bunx memory-search --syncExample: MEMORY.md
## User Preferences
- Prefers Bun over Node for TypeScript projects
- Uses pnpm as package manager
- Likes minimal dependencies
## Project Decisions
### 2024-01-15: Database Choice
Chose SQLite over PostgreSQL because:
- Single-user application
- No need for concurrent writes
- Simpler deploymentExample: memory/2024-01-15.md
# 2024-01-15
## Session Notes
### 10:30 - Authentication Setup
- Implemented JWT auth flow
- Added refresh token rotation
- TODO: Add rate limiting
### 14:00 - Bug Fix
- Fixed race condition in user creation
- Root cause: missing transaction wrapper---
Tips
- Use descriptive queries, not single keywords
- The search is semantic (understands meaning, not just exact words)
- If unsure which file to use, ask the user
- Always sync after saving new content