tomevault-io OMEGA Memory
Persistent memory for AI coding agents. OMEGA gives your agent a knowledge graph it can query, learn from, and coordinate through across sessions.
This skill teaches you how to use OMEGA's MCP tools effectively.
Setup
pip3 install omega-memory[server]
omega setup # auto-configures your editor + downloads embedding model
omega doctor # verify everything works
Works with Claude Code, Cursor, Windsurf, Zed, and any MCP client.
Core Tools
OMEGA provides 12 MCP tools. Here's when to use each one.
Storing Memories
omega_store(content, event_type, metadata?, entity_id?)
Store decisions, lessons, and context that should persist across sessions.
| Event Type | When to Use | TTL |
|---|---|---|
decision |
Architectural choices, technology selections | 90 days |
lesson_learned |
Debugging insights, patterns that worked/failed | 90 days |
user_preference |
Code style, workflow preferences, tool choices | Permanent |
error_pattern |
Recurring errors and their fixes | 30 days |
task_completion |
Completed work with outcomes | 14 days |
checkpoint |
Mid-task state for resumption | 7 days |
omega_store("Switched from REST to GraphQL for the dashboard API — reduces N+1 queries", "decision")
omega_store("User prefers early returns, max 2 levels of nesting", "user_preference")
omega_store("pytest fixtures with db cleanup must use function scope, not session scope", "lesson_learned")
Don't store: Raw code output, tool results, transient status updates, anything shorter than a sentence.
Querying Memories
omega_query(query, mode?, limit?, entity_id?)
Search memories by meaning, not just keywords. Uses hybrid retrieval: vector similarity + full-text search + cross-encoder reranking.
| Mode | When to Use |
|---|---|
semantic (default) |
Find memories by meaning — "how did we handle auth?" |
phrase |
Exact substring match — find a specific term or identifier |
timeline |
Recent memories grouped by day — "what happened this week?" |
browse |
List by type, session, or recency — explore what's stored |
omega_query("database migration strategy")
omega_query("what decisions were made about the API", mode="timeline", days=7)
omega_query("pytest", mode="phrase")
omega_query(mode="browse", browse_by="type")
Pro tip: Query before starting work. Prior decisions and lessons save time and prevent repeating mistakes.
Session Management
omega_welcome(project?) — Call at session start. Returns recent context, active reminders, and project state. This is how your agent picks up where it left off.
omega_checkpoint() — Save current task state mid-session. If the session ends unexpectedly, the next omega_welcome restores this context.
omega_resume_task(task_id) — Resume a previously checkpointed task with full context.
Memory Maintenance
omega_reflect() — Analyze memory quality: duplicates, contradictions, coverage gaps.
omega_maintain(action) — Run maintenance operations: consolidation, compaction, health checks.
Retrieval Architecture
OMEGA's query pipeline runs 7 phases to find the most relevant memories:
- Vector similarity — Embedding search (bge-small-en-v1.5, 384-dim) via sqlite-vec
- Full-text search — FTS5 with BM25 scoring
- Strong signal short-circuit — Skip expensive phases when FTS5 finds an exact match
- Score fusion — Reciprocal Rank Fusion combines vector + text scores
- Contextual boosting — Boost results matching current file, project, or tags
- Cross-encoder reranking — ms-marco-MiniLM-L-6-v2 rescores top candidates
- Assembly — Dedup, normalize, apply minimum relevance threshold
This hybrid approach achieves 95.4% on LongMemEval (500-question benchmark).
Best Practices
What to Store
- Architectural decisions with reasoning ("chose X because Y")
- Debugging insights that took effort to discover
- User preferences stated explicitly ("always use..." / "never...")
- Cross-session context that future sessions need
What NOT to Store
- Information already in the codebase (read the code instead)
- Transient state (build output, test results)
- Anything shorter than a meaningful sentence
- Speculative conclusions from reading a single file
Query Patterns That Work
- Before starting a task:
omega_query("prior decisions about [feature area]") - Before modifying a file:
omega_query(context_file="/path/to/file.py") - After debugging:
omega_store("[root cause and fix]", "lesson_learned") - When user says "remember":
omega_store("[what they said]", "user_preference")
Anti-Patterns
| Don't | Do Instead |
|---|---|
| Store every tool result | Store only insights and decisions |
| Query with single words | Use natural language questions |
Skip omega_welcome at session start |
Always call it — it loads critical context |
Store without event_type |
Always specify type for proper TTL and dedup |
| Guess from stale memory | Query OMEGA to verify current state |
How It Works Under the Hood
- Storage: SQLite with WAL mode. Single file at
~/.omega/omega.db. - Embeddings: bge-small-en-v1.5 via ONNX Runtime (~90MB RAM). LRU cache (512 entries).
- Vector search: sqlite-vec extension for ANN similarity search.
- Text search: FTS5 with BM25 ranking.
- Dedup: Jaccard similarity with per-type thresholds (0.70-0.90). Content-level and embedding-level.
- Memory evolution: Similar memories merge (Zettelkasten-style) instead of creating duplicates.
- TTL: Automatic expiry based on event type. Permanent for preferences, 7-90 days for others.
- Privacy: Everything stays local. No cloud, no telemetry. Apache-2.0 licensed.
Links
- PyPI: omega-memory
- GitHub: omega-memory/omega-memory
- Docs: omegamax.co/docs
- Benchmarks: omegamax.co/benchmarks
Source: omega-memory/omega-memory — distributed by TomeVault.