Jordan Ramos
a99799bf3d
Features: - Multi-platform bot (Slack, Telegram) - Memory system with SQLite FTS - Tool use capabilities (file ops, commands) - Scheduled tasks system - Dynamic model switching (/sonnet, /haiku) - Prompt caching for cost optimization Optimizations: - Default to Haiku 4.5 (12x cheaper) - Reduced context: 3 messages, 2 memory results - Optimized SOUL.md (48% smaller) - Automatic caching when using Sonnet (90% savings) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
149 lines
3.6 KiB
Markdown
149 lines
3.6 KiB
Markdown
# Simple Memory System
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A lightweight memory system inspired by OpenClaw, using SQLite + Markdown.
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## Features
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- **SQLite database** for fast indexing and search
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- **Markdown files** as the source of truth
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- **Full-text search** (FTS5) for keyword queries
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- **File watching** for auto-sync
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- **Chunking** for manageable pieces
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- **Daily logs** + long-term memory
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- **SOUL.md** - Agent personality and core identity
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- **User files** - Per-user preferences and context
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## File Structure
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```
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memory_workspace/
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├── SOUL.md # Agent personality/identity
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├── MEMORY.md # Long-term curated memory
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├── users/ # User-specific memories
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│ ├── alice.md # User: alice
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│ ├── bob.md # User: bob
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│ └── default.md # Default user template
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├── memory/ # Daily logs
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│ ├── 2026-02-12.md
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│ ├── 2026-02-13.md
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│ └── ...
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└── memory_index.db # SQLite index
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```
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## Usage
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```python
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from memory_system import MemorySystem
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# Initialize
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memory = MemorySystem()
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# Sync all markdown files
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memory.sync()
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# === SOUL (Agent Personality) ===
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memory.update_soul("""
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## New Trait
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- I am patient and thorough
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""", append=True)
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soul_content = memory.get_soul()
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# === User-Specific Memory ===
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memory.update_user("alice", """
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## Preferences
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- Likes Python
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- Timezone: EST
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""")
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alice_prefs = memory.get_user("alice")
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users = memory.list_users() # ['alice', 'bob', 'default']
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# Search user-specific
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results = memory.search_user("alice", "python")
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# === General Memory ===
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memory.write_memory("Important note", daily=True)
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memory.write_memory("Long-term fact", daily=False)
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# Search all memory
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results = memory.search("keyword")
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for r in results:
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print(f"{r['path']}:{r['start_line']} - {r['snippet']}")
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# Read file
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content = memory.read_file("MEMORY.md", from_line=10, num_lines=5)
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# Status
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print(memory.status())
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# Auto-sync with file watching
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memory.start_watching()
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# Cleanup
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memory.close()
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```
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## Database Schema
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### files
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- `path` - relative path to markdown file
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- `hash` - content hash for change detection
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- `mtime` - last modified timestamp
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- `size` - file size
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### chunks
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- `id` - unique chunk identifier
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- `path` - source file
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- `start_line`, `end_line` - line range
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- `text` - chunk content
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- `updated_at` - timestamp
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### chunks_fts
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- Full-text search index (FTS5)
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- Enables fast keyword search
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## How It Works
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1. **Markdown is source of truth** - all data lives in `.md` files
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2. **SQLite indexes for speed** - database only stores chunks for search
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3. **Chunking** - splits files into ~500 char paragraphs
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4. **FTS5** - SQLite's full-text search for keyword matching
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5. **File watching** - detects changes and triggers re-indexing
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6. **Hash-based sync** - only re-indexes changed files
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## Differences from OpenClaw
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**Simpler:**
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- ❌ No vector embeddings (no AI model needed)
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- ❌ No hybrid search (BM25 + vector)
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- ❌ No embedding cache
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- ❌ No session memory
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- ✅ Just FTS5 keyword search
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- ✅ Smaller, easier to understand
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**Same concepts:**
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- ✅ SQLite database
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- ✅ Markdown files
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- ✅ File watching
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- ✅ Chunking
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- ✅ Daily logs + MEMORY.md
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## Installation
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```bash
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pip install watchdog
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```
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## OpenClaw's Approach
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OpenClaw uses a more sophisticated system:
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- **Vector embeddings** for semantic search
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- **Hybrid search** combining BM25 + vector similarity
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- **Embedding cache** to avoid re-computing
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- **Multiple providers** (OpenAI, Gemini, local)
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- **Batch processing** for large indexes
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- **Session memory** (optional conversation indexing)
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This implementation strips out the complexity for a simple, fast, local-only solution.
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