Letta Review (2026): Pricing, Features & Honest Verdict

TLDR

Letta is an open platform for building stateful AI agents: agents with deep persistent memory that learn over time, from the UC Berkeley team behind MemGPT. If agent memory is core to what you are building, this is the most credible option we found. Best for: engineering teams building production agents. Price: Free plan: yes, Pro $20/mo. Rating: 8.1/10.

What is Letta?

Letta is a platform for building stateful AI agents. The key word is stateful. Most agent frameworks treat each session as a blank slate: the model starts fresh, forgets the last conversation, and repeats the same mistakes. Letta gives agents persistent, tiered memory so they remember what happened, carry context across sessions, and improve over time. It does this through memory blocks and a memory hierarchy that the agent manages itself, deciding what to keep in active context and what to store for later recall.

The platform comes from the team behind MemGPT, the UC Berkeley research project that framed the LLM as an operating system with managed memory. That research lineage is the whole reason Letta is worth taking seriously: the people building it are the ones who published the foundational ideas on agent memory. Letta ships an open-source core, a hosted cloud, an Agent Development Environment for building and inspecting agents, and SDKs. It is model-agnostic, so agents run on OpenAI, Anthropic, Mistral, or local models, and you can bring your own API keys. We dug into Letta because agent memory is suddenly a crowded category, and Letta is the entrant with the deepest technical roots in it.

What Are Letta’s Key Features?

Tiered persistent memory

This is the core. Letta agents have a memory hierarchy: a limited active context plus external storage the agent reads from and writes to on its own. The agent decides what is important enough to keep in working memory and what to archive, so it can hold long-running context without blowing past the model’s context window. This is the original MemGPT insight, that you manage the context window like an operating system pages memory in and out of RAM. In practice it means an agent can remember a user’s preferences, past decisions, and earlier conversations weeks later, which a stateless chatbot simply cannot do, and it does so without you manually stuffing the whole history into every prompt.

Memory blocks and self-improvement

Memory in Letta is structured into blocks rather than dumped into one undifferentiated log. Agents can update their own memory blocks, learn from past sessions, and avoid repeating earlier mistakes. The roadmap leans into continual learning and sleep-time compute, where an agent processes and consolidates what it learned offline between sessions, closer to how a person reflects after the fact than how a typical chatbot resets.

Model-agnostic and open source

Letta is not tied to one model vendor. You can run agents on OpenAI, Anthropic, Mistral, or local models, and bring your own API keys so inference bills go straight to your provider account. The core is open source, which means you can self-host the runtime and inspect how memory actually works rather than trusting a black box. For teams worried about lock-in, this is a meaningful difference from closed memory APIs.

Agent Development Environment and SDK

Letta is more than an API. The Agent Development Environment (ADE) lets you build agents, watch their memory update in real time, and debug behavior visually, which is genuinely useful when an agent does something unexpected and you need to see why. Being able to literally watch which memory block changed after a turn turns debugging from guesswork into inspection. The SDK covers programmatic agent creation for production use, and Letta Code applies the same memory-first approach to a coding agent that does not reset between sessions. Together they make Letta a full platform for stateful agents rather than a single endpoint you call.

How Much Does Letta Cost?

Letta starts free, with a limited number of stateful agents, limited Letta Auto usage, and bring-your-own-key support so you can route inference through your own provider account. The open-source core is also free to self-host. Paid plans are Pro at $20/mo, which adds up to 20 stateful agents plus a weekly and monthly Letta Auto quota with pay-as-you-go overage, and Max at $200/mo for power users who run agents heavily every day. Enterprise is custom, managed per-seat pricing with dedicated support.

One honest gripe: the gap between Pro at $20/mo and Max at $200/mo is wide, with nothing in between. If you outgrow 20 agents or the Pro quota but are not a 10x-the-price power user, you are in an awkward spot. Compared with the memory-layer competition, Mem0 has a free Hobby tier and a $19/mo Starter, Zep starts at $25/mo with graph memory at every tier, and Memori, a newer entrant, is free with paid plans from $19/mo. Letta’s $20 Pro sits right alongside those.

The difference is what you are paying for. Mem0, Zep, and Memori sell a memory layer you call from your existing code. Letta sells a full stateful-agent runtime with memory baked in. If you only need to add recall to an app you already built, the lighter tools are cheaper and simpler. If memory and agent behavior are tightly coupled in your product, Letta’s all-in-one approach can be worth the heavier commitment. Price the workload, not just the sticker.

Who is Letta Best For?

Use Letta if you are an engineering team building production AI agents where deep, persistent memory is central to the product, not a nice-to-have. The MemGPT pedigree, the depth of the memory system, and the model-agnostic open-source core make it the most credible choice when you want agents that genuinely learn over time. It is also a strong fit if avoiding vendor lock-in matters, since you can self-host and swap models freely.

Skip Letta if you just need to bolt light memory onto an existing chatbot or no-code workflow. In that case a drop-in memory API like Mem0 or Zep is faster to integrate and easier to reason about. The stateful-agent model is a heavier concept, and committing to the full Letta runtime is more framework lock-in than calling a simple memory endpoint. Match the tool to how deep your memory needs actually go.

Best Letta Alternatives

Mem0

Mem0 is the popular drop-in memory layer for agents, designed to be called from your existing code rather than adopted as a full runtime. It has a free Hobby tier with 10,000 memories, a Starter at $19/mo, and a Pro at $249/mo that unlocks graph memory. Mem0 is faster to integrate and lighter than Letta, but it is a memory API, not an agent platform. Choose Mem0 if you want to add recall to an app you already built; choose Letta if memory and agent behavior are deeply intertwined.

Zep

Zep is a memory layer built on a temporal knowledge graph, with its Graphiti engine and graph traversal available at every tier starting at $25/mo. It is strong when you need entity relationships and time-aware recall, and its flat graph-at-every-tier pricing undercuts Mem0’s gated graph features. Like Mem0, it is a focused memory layer rather than a full stateful-agent framework, so it is the pick when graph memory is the priority and you do not want Letta’s broader runtime.

Memori

Memori is a newer entrant that treats agent memory as structured relational data with schema and history, rather than vector chunks, and posts strong recall benchmarks. It is free to start with paid plans from $19/mo. Memori is interesting if you want explainable, queryable memory and care about token cost, but it launched in mid-2026 into a packed market with a thin track record. Letta has far more research history and a deeper agent runtime; Memori is the leaner, newer bet.

Final Verdict: Is Letta Worth It?

Letta earns its rating on credibility and depth. The MemGPT team built the foundational research on agent memory, and Letta is the productized version of that work: tiered persistent memory, self-improving agents, an open-source model-agnostic core, and a real development environment. In a category that filled up fast in 2026, Letta is the entrant with the strongest technical roots, and the free tier plus $20 Pro plan make it cheap to evaluate seriously.

The honest caveats are about fit, not quality. The stateful-agent model is a heavier mental model than a drop-in memory API, the jump from $20 Pro to $200 Max is steep, and committing to the full runtime is real framework lock-in. If you only need light recall in an existing app, Mem0 or Zep will get you there faster. But if you are building agents where memory is the product, Letta is the one we would bet on, and it is worth the learning curve.