Letta vs Zep
Detailed side-by-side comparison to help you choose the right tool
Letta
🔴DeveloperAI Knowledge Tools
Letta is the open-source successor to MemGPT — a stateful agent platform with persistent memory, tool use, and a visual Agent Development Environment.
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FreeZep
🔴DeveloperAI Knowledge Tools
Enterprise agent memory built on temporal Context Graphs (Graphiti) with millisecond retrieval, SOC 2 Type II, and HIPAA BAA.
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FreeFeature Comparison
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Letta - Pros & Cons
Pros
- ✓Stateful by design — agents remember across sessions without prompt-stuffing
- ✓Visual ADE makes memory behavior inspectable and debuggable
- ✓Truly open source (Apache 2.0); self-hostable on commodity infra
- ✓Provider-agnostic so you can swap models without rewriting agents
- ✓Direct lineage from the MemGPT paper gives strong technical credibility
Cons
- ✗More moving parts than a stateless agent loop; not the right tool for one-shot tasks
- ✗Cloud pricing not fully transparent in static HTML; verify before signup
- ✗Memory management adds latency vs. raw chat completions
- ✗Production deployment of self-host requires managing vector store + database
- ✗Smaller community than LangChain or CrewAI
Zep - Pros & Cons
Pros
- ✓Temporal knowledge graph captures when facts changed — better than "last-message wins" vector memory
- ✓~200ms retrieval keeps memory viable in latency-sensitive agent flows
- ✓Credit-based pricing makes storage and retrieval free — predictable for read-heavy agents
- ✓SOC 2 Type II + HIPAA BAA + DPA make procurement realistic at regulated enterprises
- ✓First-class MCP server integrates with Claude Desktop, Cursor, and OpenAI Agents SDK out of the box
Cons
- ✗Credit math (1 credit per 350 bytes per Episode) is hard to forecast until you measure real payloads
- ✗Free tier (1,000 credits/mo, no rollover) is tight even for evaluation
- ✗Webhooks, analytics, and custom extraction live only on Flex Plus ($375/mo) and above
- ✗Most compliance value (audit retention, BYOK/BYOC) is gated behind Enterprise pricing
- ✗Temporal graph modeling adds upfront design work vs throwing chat history into a vector DB
Not sure which to pick?
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