Quoth vs Zep
Compare Quoth and Zep for AI agent memory. Architecture, features, and pricing for persistent context management.
Last updated: April 2026
| Feature | Quoth | Zep |
|---|---|---|
| MCP Protocol Support | ||
| Long-Term Memory | ||
| Knowledge Graph | PageRank intelligence graph | Entity graph extraction |
| Agent-to-Agent Messaging | Yes (HMAC signed) | |
| Self-Learning | Bayesian pattern scoring | Fact extraction |
| Local-First Option | Yes (SQLite) | Cloud only |
| Temporal Awareness | Decay scoring | Temporal context |
| Developer Tool Integration | Claude Code hooks | LangChain, LlamaIndex |
| Open Source | Yes (CE edition) |
Quoth Pricing
Free / $29/mo Pro
Zep Pricing
Free CE / Cloud pricing varies
The Verdict
Choose Quoth for MCP-native development with Claude and self-learning capabilities. Choose Zep if you need memory for conversational AI applications with LangChain or LlamaIndex integration.
Architecture Comparison
Quoth and Zep solve similar problems — persistent AI memory — but for different ecosystems. Zep focuses on conversational memory for chatbot and RAG applications, while Quoth focuses on development memory for coding agents.
Zep's strengths: entity extraction from conversations, temporal context windows, and tight integration with LangChain and LlamaIndex. It excels at maintaining coherent conversations across sessions.
Quoth's strengths: autonomous learning from developer workflows, MCP protocol integration, multi-agent coordination, and confidence-scored pattern libraries. It excels at making AI coding assistants smarter over time.
Use Case Fit
| Use Case | Best Choice |
|---|---|
| Chatbot memory | Zep |
| Claude Code workflows | Quoth |
| Multi-agent coordination | Quoth |
| RAG applications | Zep |
| Self-learning from sessions | Quoth |
| LangChain integration | Zep |