Memory-first agent SDK for persistent, learning AI agents
By Tanmay Verma, Founder · Last verified 02 Jun 2026
In short
Letta — Memory-first agent SDK for persistent, learning AI agents. Best for Developers building long-lived AI agents that need persistent memory, AI researchers studying continual learning and memory in LLMs, Building personalized assistants that learn user preferences over time. Free to start; paid plans from $2/mo.
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Letta tackles the critical memory problem in AI agents, offering a novel approach with sleep-time compute and git-based memory. It's a solid pick for researchers and developers building long-lived, learning agents, but production readiness and pricing transparency remain unclear.
Last verified: June 2026
Letta is a promising framework for anyone frustrated by stateless AI agents that forget everything between sessions. Its memory-first approach, with features like git-based memory and sleep-time compute, allows agents to learn from experience and improve over time. This is a game-changer for applications requiring personalized, long-term interaction, such as personal assistants, tutoring bots, or autonomous research agents. The model-agnostic nature means you can plug in any LLM, avoiding vendor lock-in. However, Letta is still early-stage—code-first and aimed at developers, with no low-code or no-code options. The website emphasizes research and open lab culture, which may signal less mature documentation or support. Pricing is not mentioned, so expect open-source self-hosting or contact-based plans. The closest alternative is MemGPT (from the same team), but Letta seems to be the evolved product. If you need a production-ready memory solution today, consider Pinecone or LangChain's memory modules, but if you want cutting-edge research and flexibility, Letta is worth exploring.
Skip Letta if Skip Letta if you need a hosted SLA-backed API with no upfront setup, or if your use case demands ephemeral stateless agents.
Across the latest 6 updates: 5 feature updates and 1 launch.
Letta released the Context Constitution: principles for AI agents managing context to learn from experience.
Letta announces next phase: agents with persistent memory, real computer access, and Letta Code runtime with git-backed memory, skills, subagents.
How to build agents that learn and remember, moving beyond stateless LLM paradigm.
Guide to designing and managing context windows via context engineering for AI agents.
Memory blocks as abstraction for context window management, structuring context into discrete functional units.
Explains why traditional RAG is insufficient for building agent memory.
How likely is Letta to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
Letta is an open AI lab building machines with real memory that can continually learn and self-improve. The flagship product, Letta Code, is a memory-first, model-agnostic coding agent that persists context across sessions, enabling agents to remember past mistakes and adapt over time. Unlike traditional stateless agents, Letta agents maintain a git-based memory, utilize sleep-time compute for offline learning, and are governed by the Context Constitution—a set of principles for experiential AI. The Letta SDK allows developers to build on top of this harness, creating agents that are portable across models and deeply personalized. Letta is ideal for researchers and developers seeking to create AI that behaves more like a human, with continuous learning and identity. It positions itself as the solution to AI's memory problem, differentiating from chatbots and stateless agents by offering persistent memory and offline self-improvement.
Tell us what you want to build — we'll match the AI tools that fit your goal, budget & existing stack.
Concrete scenarios for the personas Letta actually fits — and what changes day-one when you adopt it.
Install Letta Code via npm, connect your OpenAI API key, and start a persistent agent in the CLI. The agent learns your project's conventions over time through skill learning and context repositories.
Outcome: A personalized coding agent that suggests relevant code patterns based on your past work, reducing boilerplate.
Use Context-Bench to benchmark LLMs on agentic context management, then deploy agents with sleep-time compute and observe memory palace updates.
Outcome: Quantitative understanding of how context engineering affects agent performance over long sessions.
Create an agent via Letta Code, train it on past conversations, and teleport its memory from desktop to mobile (remote environments). The agent accumulates relationship history.
Outcome: A companion app that remembers user preferences, past topics, and emotional context across devices.
Memory quality depends heavily on model choice—smaller models hallucinate their own memory. Server architecture is heavier than stateless agent libraries; deployment and ops cost is real. MemGPT paradigm is one opinion about how to do memory—alternative approaches (pure RAG, episodic memory, graph memory) may be better for some products. The free tier only supports Letta Code with your own API keys; cloud plans have usage quotas.
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
For each published Letta tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free (Open Source)
Free (Apache 2.0)
Free (Cloud)
$0/mo
Pro
$20/mo
Ideal for
Individual developers and hobbyists who want included LLM inference (Letta Auto) with weekly and monthly quotas; up to 20 stateful agents.
What this tier adds
Adds Letta Auto weekly + monthly quota and pay-as-you-go overage; supports up to 20 stateful agents instead of 3.
The company stage and team size where Letta's pricing actually pencils out — and where peers do it cheaper.
Letta's open-source tier is free (Apache 2.0) with your own API keys—costs only what you spend on LLM providers. The $20/mo Pro plan targets individual users with included Letta Auto inference. Heavy coding users often exceed $100/mo in total usage, making external coding plans more cost-effective. For teams, contact sales for per-seat pricing; no public team tier yet.
How long it actually takes to get something useful out of Letta — broken out by persona, not the marketing-page minute.
Developers: under 5 minutes to install Letta Code via npm (requires Node.js 18+), connect an API key, and start a persistent agent in CLI. Non-developers: desktop app download and provider connection takes about 10 minutes. Cloud features (Constellation) require sign-in but are free for up to 3 agents.
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
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Last calculated: June 2026
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