Mem0 vs Letta: Which Agent Memory Wins?
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Quick take: if your agent's reasoning already runs in LangChain, CrewAI, or a custom framework and you just need persistent memory bolted on without replacing that runtime, Mem0's lightweight extraction-and-retrieval API is the more direct fit. If you're building a new agent from scratch and want memory management, deciding what to remember, what to forget, what to search, handled as a native part of the agent's own reasoning loop, Letta's stateful-agent architecture is built specifically for that. We self-hosted both and ran the same long-running support agent, one that needs to recall a customer's history across many separate sessions, to see where the architectures actually diverge.
What Mem0 and Letta actually are
Mem0 is an open-source memory layer, Apache 2.0 licensed, that sits alongside whatever agent framework already runs your logic. You call its API to add a memory after a conversation turn, and it uses an LLM to extract the salient facts worth keeping rather than storing the raw transcript, then stores those facts in a vector store you configure, Qdrant, Pinecone, Chroma, or another supported backend, with an optional graph store for tracking relationships between facts. Retrieval is a search call that returns the memories most relevant to the current context, which your agent's existing prompt-assembly step then uses however it likes.
Letta, which grew out of the MemGPT research project, is a full agent framework where memory is a first-class, built-in part of the runtime rather than an external service you call. Its agents maintain core memory, editable blocks that stay in the model's context at all times, archival memory, long-term storage searched via retrieval when needed, and a recall mechanism for past conversation history, and the agent itself can call tools that rewrite its own core memory mid-conversation. It ships a self-hostable server plus the Agent Development Environment, a visual UI for inspecting exactly what an agent currently has in each memory tier.
Comparison table
| Mem0 | Letta | |
|---|---|---|
| What it is | Memory layer / API, add-on to an existing agent | Full stateful-agent framework and runtime |
| License | Apache 2.0 | Apache 2.0 |
| Self-hosted cost | Free | Free |
| Backing storage | Vector store you configure (Qdrant, Pinecone, Chroma, etc.), optional graph store | Managed internally as part of Letta's own storage layer |
| Memory architecture | LLM-extracted facts, retrieved by search | Core (always-visible), archival (searchable), and recall memory, self-editing |
| Framework integration | Plugs into LangChain, CrewAI, AutoGen, LlamaIndex | Its own runtime; not designed to plug into another framework's agent loop |
| Visual debugging UI | None, code-first | Agent Development Environment (ADE) |
| Managed cloud option | Mem0 Platform, with a free tier | Letta Cloud |
| Best for | Adding persistent memory to an existing agent stack | Building a new agent where memory is core to the design |
Building the same support agent in both
We built a support agent that needs to remember a customer's plan tier, past issues, and stated preferences across sessions that could be days apart, then tested whether each correctly recalled those facts in a later, unrelated conversation.
With Mem0, we kept our existing LangChain agent as the reasoning layer and added two calls around it: one to store extracted facts after each conversation, one to retrieve relevant memories before composing the system prompt for a new session. Wiring it up took about 20 minutes, almost all of it configuring the vector store backend. In a later session, the agent correctly recalled the customer's plan tier and a prior complaint, pulled in through Mem0's retrieval call and injected into context by our own prompt-assembly code, exactly the division of labor Mem0 is designed around.
With Letta, there was no separate agent framework to preserve: Letta's server is the agent. We defined the agent's persona and initial core memory blocks, then let it run. Across the same multi-session test, the agent's own tool calls moved the customer's plan tier into a core memory block it chose to keep always visible, while older, less-referenced details got pushed to archival memory it searched only when a new message seemed to need them. Watching that decision happen in the Agent Development Environment, which memory tier a fact landed in and why, was a level of visibility into memory behavior Mem0's API-first design doesn't expose at all.
Integration versus ownership
The real divide is architectural, not a feature checklist. Mem0 assumes you already have an agent loop and wants to be the smallest possible addition to it: two API calls, store and retrieve, with you still deciding what the agent does with what comes back. That makes it the lower-friction choice for a team with existing agent infrastructure it has no intention of replacing.
Letta assumes the opposite: that memory management is inseparable from good agent reasoning, so it owns the agent loop and makes memory operations something the agent itself performs as part of thinking, not something that happens to it from outside. That's a more powerful model for memory quality in our testing, the agent's own judgment about what to keep active versus archive produced more coherent long-term behavior than our manually written retrieval logic did, but it comes at the cost of adopting Letta's runtime as your agent framework rather than keeping whatever you already have.
When NOT to use Mem0
Skip Mem0 if you want memory management handled as part of the agent's own reasoning rather than a fixed extract-then-retrieve pipeline step. Mem0's approach works well but it's still an external process around the agent, not a capability the agent exercises itself, and that distinction matters once memory decisions get genuinely context-dependent.
When NOT to use Letta
Skip Letta if you already have working agent infrastructure in LangChain, CrewAI, or a custom framework and don't want to replace that runtime just to gain its memory architecture. Letta isn't designed to be dropped into another framework's agent loop, so adopting it means Letta becomes your agent layer, not an addition to the one you have.
Which should you pick?
Choose Mem0 if your agent's reasoning already lives in a framework you're keeping, and you want persistent memory added with the smallest possible integration surface. Choose Letta if you're building a new agent and want memory, what to keep, what to archive, what to forget, handled as a native, self-editing part of its reasoning rather than bolted on afterward. Either way, both need a vector store underneath them for retrieval; our Qdrant vs Pinecone for AI agents comparison and our best self-hosted vector databases for RAG roundup both cover that storage layer in more depth. If you're still choosing the agent framework Mem0 would bolt onto, our best open source AI agent frameworks guide covers that decision directly.
Read the source docs before you commit either way: Mem0 covers its supported vector-store backends and graph-memory option, and Letta documents the Agent Development Environment and its memory-tier architecture in more depth than any third-party comparison can.
Frequently asked questions
Can I use Mem0 and Letta together?
Not in any meaningful way, since they solve the same problem at different layers. Letta already owns agent state and memory internally as part of its runtime, so adding Mem0 on top would mean maintaining two separate memory systems tracking overlapping facts. Pick one architecture: Mem0 if your agent's reasoning loop lives elsewhere and you want memory bolted on, or Letta if you're fine letting it run the agent itself.
Do I need a separate vector database to use either one?
For Mem0, yes, you configure a vector store, Qdrant, Pinecone, Chroma, or similar, as its backing storage for memory retrieval, plus an optional graph store for relationship tracking. Letta's archival memory also relies on vector-based retrieval internally, but it's managed as part of Letta's own storage layer rather than something you wire up as a separate service yourself.
Which one works with LangChain or CrewAI?
Mem0 is built specifically to plug into existing frameworks, LangChain, CrewAI, AutoGen, and LlamaIndex all have documented Mem0 integrations, since it's designed as an add-on layer rather than a replacement runtime. Letta is its own agent framework with its own execution model, so using it alongside LangChain or CrewAI means running Letta as the agent layer rather than integrating it into theirs.
What's the core idea behind Letta's self-editing memory?
Letta's agents can call tools that rewrite their own core memory blocks mid-conversation, deciding for themselves what's worth keeping in always-visible context versus what gets pushed to searchable archival memory. That's the core insight from the MemGPT research the project grew out of: memory management as a reasoning task the agent performs itself, not a fixed pipeline step that happens automatically around it the way Mem0's extraction step does.