Letta vs LangGraph

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

Letta

πŸ”΄Developer

AI Agent Framework

Framework and cloud service for building stateful AI agents that remember, learn continuously, and improve over time β€” the successor to MemGPT.

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Starting Price

Free

LangGraph

πŸ”΄Developer

AI agent framework

LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.

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Starting Price

Free

Feature Comparison

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FeatureLettaLangGraph
CategoryAI Agent FrameworkAI agent framework
Pricing Plans327 tiers8 tiers
Starting PriceFreeFree
Key Features
  • β€’ Persistent agents instead of stateless chat sessions
  • β€’ Memory palace for viewing an agent’s memory
  • β€’ Background memory agents / dream agents that refine prompts, context, and skills over time
  • β€’ Graph-based workflow orchestration
  • β€’ Deterministic state machine execution
  • β€’ Human-in-the-loop workflows

πŸ’‘ Our Take

Choose Letta if you want a memory-first agent platform with hosted pricing, Letta Code, AgentFile portability, and a REST API for stateful agents. Choose LangGraph if you need a lower-level graph runtime with explicit state transitions.

Letta - Pros & Cons

Pros

  • βœ“Persistent, editable memory is built into the agent runtime
  • βœ“ADE exposes agent state and tool calls for debugging
  • βœ“Python, TypeScript, REST, multiple model providers, and MCP offer flexible integration
  • βœ“Open-source self-hosting avoids mandatory platform lock-in

Cons

  • βœ—Stateful agents add privacy, retention, deletion, and migration responsibilities
  • βœ—Cloud plan and model usage can create separate cost layers
  • βœ—Memory quality still depends on prompts, models, and application-specific evaluation

LangGraph - Pros & Cons

Pros

  • βœ“Open-source library is MIT-licensed and runs anywhere without platform lock-in
  • βœ“Native checkpointing makes durable, resumable, human-in-the-loop agents straightforward
  • βœ“First-class multi-agent patterns: supervisor, hierarchical, sequential, parallel branches
  • βœ“Tight integration with LangSmith for production observability, evaluations, and replays
  • βœ“Active maintenance from the LangChain team with frequent releases and strong community

Cons

  • βœ—More verbose than LangChain for simple agents β€” explicit state schemas and edge functions add overhead
  • βœ—LangSmith trace pricing ($2.50/1k base traces) is a real cost at production scale
  • βœ—LCU + deployment-minute billing makes pricing harder to predict than seat-only competitors
  • βœ—Steeper learning curve than role-based frameworks like CrewAI for newcomers
  • βœ—Best documented in Python; JavaScript SDK exists but lags in features

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πŸ”’ Security & Compliance Comparison

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Security FeatureLettaLangGraph
SOC2β€”βœ… Yes
GDPRβ€”βœ… Yes
HIPAAβ€”β€”
SSOβ€”βœ… Yes
Self-HostedπŸ”€ HybridπŸ”€ Hybrid
On-Premβœ… Yesβœ… Yes
RBACβ€”βœ… Yes
Audit Logβ€”βœ… Yes
Open Sourceβœ… Yesβœ… Yes
API Key Authβœ… Yesβœ… Yes
Encryption at Restβ€”βœ… Yes
Encryption in Transitβœ… Yesβœ… Yes
Data Residencynot publicly documentedβ€”
Data Retentionconfigurableconfigurable
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