LangMem vs Letta

Detailed side-by-side comparison to help you choose the right tool

LangMem

🔴Developer

AI Knowledge Tools

LangChain memory primitives for long-horizon agent workflows.

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Starting Price

Free

Letta

🔴Developer

AI Knowledge Tools

Stateful agent platform inspired by persistent memory architectures.

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Starting Price

Free

Feature Comparison

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FeatureLangMemLetta
CategoryAI Knowledge ToolsAI Knowledge Tools
Pricing Plans11 tiers19 tiers
Starting PriceFreeFree
Key Features
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling

LangMem - Pros & Cons

Pros

  • Three-type memory model (semantic, episodic, procedural) is more sophisticated and cognitively grounded than flat fact extraction
  • Native integration with LangGraph means memory operations participate in state management and checkpointing
  • Procedural memory that modifies agent behavior based on learned patterns is a unique and powerful capability
  • Open-source with no external service dependency — memories stored in LangGraph's own persistent store

Cons

  • Tightly coupled to the LangGraph ecosystem — minimal value if you're not using LangGraph
  • Documentation is sparse and APIs are still evolving — expect breaking changes
  • Newer and less battle-tested than standalone memory products like Mem0 or Zep

Letta - Pros & Cons

Pros

  • Self-directed memory management means the agent adapts its memory strategy to each conversation instead of using fixed retrieval patterns
  • Truly persistent and stateful agents that maintain context, memory, and state across unlimited interactions
  • Multi-agent architecture with independent agent state and inter-agent communication support
  • Agent Development Environment (ADE) provides a visual interface for building and testing agents
  • Research-backed approach (MemGPT paper) with demonstrated effectiveness for long-context memory management

Cons

  • Self-directed memory management can be unpredictable — agents sometimes miss relevant memories or make unnecessary updates
  • Server-based architecture adds operational complexity compared to stateless agent frameworks
  • Transition from research project to production platform means some features are polished while others feel experimental
  • Higher learning curve than simpler frameworks — understanding the memory hierarchy is essential for effective use

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🔒 Security & Compliance Comparison

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Security FeatureLangMemLetta
SOC2
GDPR
HIPAA
SSO
Self-Hosted✅ Yes🔀 Hybrid
On-Prem✅ Yes✅ Yes
RBAC
Audit Log
Open Source✅ Yes✅ Yes
API Key Auth✅ Yes✅ Yes
Encryption at Rest
Encryption in Transit✅ Yes
Data Residency
Data Retentionconfigurableconfigurable
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