Letta vs LangGraph
Detailed side-by-side comparison to help you choose the right tool
Letta
🔴DeveloperAI Agents
Stateful AI agent platform from the MemGPT team, providing long-term memory, tools, and a managed runtime for production agents.
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FreeLangGraph
🔴DeveloperAI 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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FreeFeature Comparison
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💡 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
- ✓Built by the team that invented MemGPT-style stateful memory
- ✓Memory blocks are inspectable and editable — no black-box embeddings vault
- ✓Model-agnostic: switch between Claude, GPT, Gemini, and local Ollama freely
- ✓MCP support layers Letta's memory on top of the broader tool ecosystem
- ✓Generous Free tier for prototyping stateful agents
Cons
- ✗Memory editing adds tokens to every turn — costs grow on long sessions
- ✗Dashboard debugging is less mature than dedicated tracing tools
- ✗Hosted runtime locks you into Letta's data model unless you self-host
- ✗Memory tuning still benefits from periodic human-curated summaries
LangGraph - Pros & Cons
Pros
- ✓Open-source library is MIT-licensed and runs anywhere without platform lock-in
- ✓Native checkpointing makes durable, resumable, human-in-the-loop agents straightforward
- ✓First-class multi-agent patterns: supervisor, hierarchical, sequential, parallel branches
- ✓Tight integration with LangSmith for production observability, evaluations, and replays
- ✓Active maintenance from the LangChain team with frequent releases and strong community
Cons
- ✗More verbose than LangChain for simple agents — explicit state schemas and edge functions add overhead
- ✗LangSmith trace pricing ($2.50/1k base traces) is a real cost at production scale
- ✗LCU + deployment-minute billing makes pricing harder to predict than seat-only competitors
- ✗Steeper learning curve than role-based frameworks like CrewAI for newcomers
- ✗Best documented in Python; JavaScript SDK exists but lags in features
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