How OMEGA Compares
An honest, data-backed comparison of AI agent memory systems. Every claim on this page is sourced and verifiable.
TL;DR: OMEGA leads with 95.4% on LongMemEval (vs Zep's 71.2%), runs fully local with zero cloud dependency, and requires no API keys. Mem0 offers managed cloud. Zep excels at temporal graphs but needs Neo4j. Letta is a full agent framework. Choose based on your architecture: local-first (OMEGA), cloud-managed (Mem0), knowledge graph (Zep), or agent runtime (Letta).
The Players
Twelve approaches to AI agent memory, from full cloud platforms to flat text files. Each with different trade-offs.
OMEGA
55+ ★Local-first intelligence layer for AI agents
- MCP Tools
- 18 (action-composited)
- Database
- SQLite (built-in, zero config)
- Cloud Required
- No
- LongMemEval
- 95.4%
- License
- Apache-2.0
- Pricing
- Free forever (open source)
Mastra OM
~22.5K ★In-context observational memory for agents
- MCP Tools
- 0 (framework-integrated, not MCP)
- Database
- LibSQL (local SQLite). Observations stored as text in context.
- Cloud Required
- No
- LongMemEval
- 94.87%
- License
- Apache-2.0
- Pricing
- Free (open source)
Mem0
~51.7K ★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
~24.4K ★Temporal knowledge graph for agents
- MCP Tools
- 8-9
- 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)
~21.8K ★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
OpenAI (ChatGPT)
Context-injected memory for ChatGPT
- MCP Tools
- 0 (product feature, no API)
- Database
- OpenAI servers (proprietary)
- Cloud Required
- Yes (ChatGPT subscription)
- LongMemEval
- Not published
- License
- Proprietary
- Pricing
- $20-200/mo (ChatGPT Plus/Pro)
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)
Cognee
~14.8K ★Knowledge graph pipeline for AI memory
- MCP Tools
- 0 (Python SDK)
- Database
- Neo4j / NetworkX + Qdrant / Weaviate
- Cloud Required
- OpenAI key required for embeddings
- LongMemEval
- Not published
- License
- Apache-2.0
- Pricing
- Free (open source). Cloud beta.
Hindsight (Vectorize)
Multi-strategy memory with retrieval pipelines
- MCP Tools
- 0 (SDK-based)
- Database
- Vectorize cloud (proprietary)
- Cloud Required
- Yes (API key required)
- LongMemEval
- Not published
- License
- Proprietary
- Pricing
- Free tier available. Paid plans.
doobidoo/mcp-memory
~1.6K ★Knowledge graph memory server for MCP
- MCP Tools
- 12
- Database
- JSON files on disk
- Cloud Required
- No
- LongMemEval
- Not published
- License
- MIT
- Pricing
- Free (open source)
Supermemory
~20.8K ★Consumer knowledge sync across LLMs
- MCP Tools
- 3
- Database
- Cloudflare D1 / Vectorize
- Cloud Required
- Yes (cloud-native)
- LongMemEval
- Not published
- License
- MIT
- Pricing
- Free tier. Pro: $19/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 April 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 April 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 and How OpenAI Memory Actually Works.
Head-to-Head Comparisons
OMEGA vs Mastra
In-context observational memory vs local-first intelligence layer
View comparison →OMEGA vs OpenAI Memory
Reverse-engineered ChatGPT architecture vs purpose-built agent memory
View comparison →OMEGA vs Supermemory
Consumer knowledge management vs coding agent memory
View comparison →OMEGA vs Letta (MemGPT)
Full agent framework vs composable memory layer
View comparison →OMEGA vs LangChain Memory
Conversation buffers vs persistent semantic memory
View comparison →OMEGA vs Mem0
Cloud memory platform vs local-first memory layer
View comparison →OMEGA vs ContextStream
Codebase and tool indexing vs persistent memory with full lifecycle and coordination
View comparison →OMEGA vs Pieces
Workstream capture and snippet management vs agent-facing compound intelligence
View comparison →OMEGA vs Zep (Graphiti)
Neo4j temporal knowledge graph vs lightweight SQLite memory
View comparison →OMEGA vs OpenMemory (Mem0)
Dockerized local Mem0 vs zero-dependency local memory
View comparison →OMEGA vs Claude Native Memory
Flat MEMORY.md files vs structured semantic memory with search and forgetting
View comparison →OMEGA vs XTrace
Browser memory extension vs local-first intelligence layer for developers
View comparison →OMEGA vs Paperclip
Agent team orchestration vs sovereign intelligence with memory and code-level coordination
View comparison →OMEGA vs Cognee
Knowledge graph pipeline vs local-first memory with zero external dependencies
View comparison →OMEGA vs Hindsight (Vectorize)
Cloud multi-strategy memory vs sovereign local-first intelligence layer
View comparison →OMEGA vs Claude-Mem
Community memory wrapper vs integrated intelligence layer with coordination and learning
View comparison →Frequently Asked
Which AI agent memory framework should I use: Mem0, Zep, Letta, or OMEGA?
It depends on your architecture needs. OMEGA is best for local-first, zero-cloud memory with the highest LongMemEval benchmark (95.4%). Mem0 is best if you want a managed cloud service. Zep/Graphiti excels at temporal knowledge graphs but requires Neo4j. Letta is a full agent framework, not just memory. For coding agents using Claude Code, Cursor, or Windsurf, OMEGA's MCP-native approach with zero external dependencies makes it the simplest to set up (pip install omega-memory).
What is the best open-source self-hosted AI memory server with no cloud dependency?
OMEGA is the only AI agent memory system that runs fully local with zero external dependencies: no API keys, no cloud accounts, no Docker, no Neo4j. It uses SQLite for storage and ONNX for local embeddings, with AES-256 encryption at rest. Install with pip install omega-memory. Alternatives like Mem0's OpenMemory require Docker plus an OpenAI API key, and Zep's Graphiti requires Neo4j.
How do I give Claude Code persistent memory across sessions?
Claude Code's built-in MEMORY.md provides basic session memory but is limited to 200 lines and has no semantic search. For full persistent memory, install OMEGA as an MCP server (pip install omega-memory). OMEGA automatically captures decisions, preferences, and project context across sessions with semantic retrieval, contradiction detection, and intelligent forgetting. No API keys or cloud accounts required.
What is the difference between RAG and persistent memory for AI agents?
RAG (Retrieval-Augmented Generation) retrieves chunks from static documents. Persistent memory stores and retrieves decisions, preferences, and learned patterns that evolve over time. RAG answers 'what does the documentation say?' while persistent memory answers 'what did we decide last Tuesday and why?' Most production AI agents need both: RAG for reference data, persistent memory for accumulated context.
Why does my AI coding agent keep making the same mistakes?
AI coding agents are stateless by default. Each session starts from zero context, so the agent re-discovers your conventions, re-asks your preferences, and repeats past errors. The fix is persistent memory: a system that captures decisions and lessons across sessions and surfaces them automatically. OMEGA's hook system auto-captures this context and injects it at session start, so your agent never forgets what it learned.
What are the LongMemEval benchmark scores for AI memory systems?
As of April 2026, published LongMemEval (ICLR 2025) scores are: OMEGA 95.4% (GPT-4.1), Mastra Observational Memory 94.87% (GPT-5-mini actor), Emergence AI 86% (RAG-based), and Zep/Graphiti 71.2% (GPT-4o). Mem0, Letta, and most other memory systems have not published LongMemEval scores.