Skip to main content
Tgtoolglade.
Side-by-side
Browse all comparisons →

Letta vs LangGraph

LettaLangGraph

Bottom line: Letta for teams that need persistent agent memory; LangGraph for engineering teams building production agents.

LettaLetta

Stateful AI agents with long-term memory (formerly MemGPT).

Visit
LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

Visit
Votes00
PricingFreemiumFreemium
CategoryAgent FrameworksAgent Frameworks
Tags
agentsmemoryopen-sourcestateful-agentsllm-framework
agentsorchestrationllm-frameworkopen-sourceworkflow
Best for
  • Teams that need persistent agent memory
  • Builders of long-running assistants
  • Researchers exploring stateful agents
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
Pros
  • Purpose-built for the agent memory problem
  • Strong research pedigree (MemGPT paper)
  • OS-style tiered memory persists across sessions
  • Open-source core, free to self-host
  • Managed Letta Cloud with free and paid tiers
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
Cons
  • Narrower than general agent frameworks
  • Often used alongside other tooling, not a full stack
  • Renamed from MemGPT, some older docs use old name
  • Cloud pricing and tiers still evolving
  • Python-focused
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks

Comparison generated from each tool's listing. Add or remove tools above to change it.