LangMem vs Zep
Detailed side-by-side comparison to help you choose the right tool
LangMem
🔴DeveloperAI Knowledge Tools
LangChain memory primitives for long-horizon agent workflows.
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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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LangMem - Pros & Cons
Pros
- ✓Native integration with LangGraph's BaseStore and LangChain agents, so memory plugs into existing pipelines without bespoke glue code
- ✓Supports semantic, episodic, and procedural memory types — including a prompt optimizer that lets agents learn from experience without fine-tuning
- ✓Offers both hot-path (synchronous) and background (asynchronous) memory formation, letting developers balance latency against memory completeness
- ✓Functional, stateless primitives can be used independently of LangGraph storage, making it adaptable to custom stacks
- ✓MIT-licensed and developed by the LangChain team, with active maintenance and alignment with LangSmith for tracing and evaluation
Cons
- ✗Tightly coupled to the LangChain/LangGraph ecosystem — teams using other frameworks face significant adaptation work
- ✗Still a relatively young library with a smaller community and fewer production case studies than core LangChain
- ✗Developers must design memory schemas, choose storage backends, and tune retrieval themselves; it is not a turnkey memory service
- ✗Documentation and examples are concentrated around LangGraph usage; standalone patterns are less thoroughly covered
- ✗Running background memory formation and storage at scale incurs additional LLM and infrastructure costs that are easy to underestimate
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
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