How OMEGA Compares
An honest, data-backed comparison of AI agent memory systems. Every claim on this page is sourced and verifiable.
The Players
Eight approaches to AI agent memory, from full cloud platforms to flat text files. Each with different trade-offs.
OMEGA
~5 ★Persistent memory for AI coding agents
- MCP Tools
- 12 (action-composited)
- Database
- SQLite (built-in, zero config)
- Cloud Required
- No
- LongMemEval
- 95.4%
- License
- Apache-2.0
- Pricing
- Free forever (open source)
Mem0
~47.3K ★Memory layer for AI applications
- MCP Tools
- 9 (cloud) / 4 (local OpenMemory)
- Database
- Proprietary cloud / PostgreSQL + Qdrant (local)
- Cloud Required
- Cloud: Yes (API key). Local: OpenAI API key for embeddings
- LongMemEval
- Not published
- License
- Apache-2.0
- Pricing
- Free: 10K memories. Pro (graph): $249/mo
Zep / Graphiti
~22.7K ★Temporal knowledge graph for agents
- MCP Tools
- 9-10
- Database
- Neo4j 5.26+ (external dependency)
- Cloud Required
- Graphiti: No. Zep Cloud: Yes
- LongMemEval
- 71.2%
- License
- Apache-2.0 (Graphiti). Proprietary (Zep Cloud)
- Pricing
- Graphiti: Free (self-host). Cloud: Free 1K episodes, then $25-475/mo
Letta (MemGPT)
~21.1K ★Stateful agent framework with memory
- MCP Tools
- 7 (community wrapper)
- Database
- PostgreSQL / SQLite
- Cloud Required
- No (local CLI available)
- LongMemEval
- Not published
- License
- Apache-2.0
- Pricing
- Open source. Cloud: app.letta.com
Claude Native
CLAUDE.md + auto-memory in Claude Code
- MCP Tools
- 0 (filesystem, not MCP)
- Database
- None (text files)
- Cloud Required
- No
- LongMemEval
- Not published
- License
- Proprietary (part of Claude Code)
- Pricing
- Free (included with Claude Code)
doobidoo/mcp-memory
~2K ★Knowledge graph memory server for MCP
- MCP Tools
- 8
- Database
- JSON files on disk
- Cloud Required
- No
- LongMemEval
- Not published
- License
- MIT
- Pricing
- Free (open source)
Supermemory
~16.4K ★Consumer knowledge sync across LLMs
- MCP Tools
- 2
- Database
- Cloudflare D1 / Vectorize
- Cloud Required
- Yes (cloud-native)
- LongMemEval
- Not published
- License
- MIT
- Pricing
- Free tier. Pro: $12/mo
OpenMemory (Mem0)
Part of Mem0 ★Local-first memory by Mem0 team
- MCP Tools
- 4
- Database
- PostgreSQL + Qdrant (via Docker)
- Cloud Required
- Docker + OpenAI API key
- LongMemEval
- Not published
- License
- Apache-2.0
- Pricing
- Free (self-host)
Feature Comparison
Side-by-side capabilities. Hover rows with * for details. All data verified February 2026.
LongMemEval Leaderboard
LongMemEval (ICLR 2025) tests 500 questions across 5 memory capabilities. Only systems with published scores are shown.
Token Efficiency
Memory systems vary wildly in how many tokens they inject per query. Fewer tokens = faster responses, lower costs, more room for your actual code.
Tokens per query
How much context each system injects into the LLM
Hybrid semantic + BM25, top 5–10 results
Graph query + entity extraction
Full memory dump into context block
MEMORY.md capped at 200 lines
Monthly cost at scale
10,000 sessions/month · GPT-4 Turbo input pricing ($0.01/1K tokens)
Context cost = tokens consumed by memory injection into LLM input. OMEGA's local ONNX embeddings add $0 to embedding costs. Observational Memory approaches (e.g. Mastra) pack all memories into a single context block on every query.
Where OMEGA Fits
OMEGA is a good fit if you…
- ✓Want memory that works offline, no API keys needed
- ✓Need your data to stay on your machine
- ✓Use Claude Code, Cursor, or any MCP-compatible client
- ✓Want graph traversal, temporal queries, and relationship tracking
- ✓Run multiple AI agents that need to coordinate
- ✓Care about benchmark performance on memory tasks
Consider alternatives if you…
- ✕Want a fully hosted SaaS - OMEGA is self-hosted first (consider Mem0 Cloud)
- ✕Want a full agent framework, not just memory (consider Letta)
- ✕Only need basic session notes (Claude native memory is fine)
Sources & Verification
All data on this page was verified in February 2026 from official documentation, GitHub repositories, and published research papers. Benchmark scores are self-reported by each project unless noted otherwise.
OMEGA's 95.4% LongMemEval score was achieved using the standard LongMemEval methodology (Wang et al., ICLR 2025) with GPT-4.1 as the evaluation model. Full results and methodology are documented on the benchmarks page. Learn how OMEGA works under the hood or read the detailed breakdown: OMEGA vs Mem0 vs Zep.