概览
主要功能
- 拥有持久内存的状态代理
- 自编辑内存块
- 支持多个 LLM 提供商
- 工具和函数调用
- 代理开发环境（ADE）
- REST API 和 Python/TypeScript SDK
价格
- 模型
- Free
- 分类
- 人工智能代理存储
- 评分
- 5.0 / 5 (6)
使用场景
拥有记忆的个人 AI 助手
创建能够记住用户偏好、过去的对话和会话间上下文的助手，与此同时提供更个性化和持续的交互。
具备上下文意识的客户服务代理
部署客户代理，能够回忆客户的历史、先前票据和累积知识，从而解决问题时不需要用户重复说明。
无人操作工作流自动化
创建具有使用工具调用的状态代理，可在执行多步workflow 时保持状态并从之前运行中学习以改善可靠性。
代理原型化和调试
使用代理开发环境和 SDK，可视化地检查内存块、推理和工具使用，同时在状态代理行为上迭代。
优点 & 缺点
优点
- 会话之间保持长期的持久内存
- 模型中立，支持多个 LLM 提供商
- 开源基础，持续开发
- 可视化工具用于检查代理状态和内存
- 开源基础，持续开发
- 可视化工具用于检查代理状态和内存
缺点
- 需要技术设置和开发人员的专家知识
- 内存管理增加了简单的 LLM 调用上的复杂性
- 与主流代理框架相比，稍小的生态系统
对决战绩
在万神殿中参与了 6 对决。
Last 5 battles
- #1
AI Agent Memory Showdown — November 15, 2025
Nov 15, 2025 · #1 of 4
- #2
AI Agent Memory Showdown — April 1, 2025
Apr 1, 2025 · #2 of 4
- #4
AI Agent Memory Showdown — March 19, 2025
Mar 19, 2025 · #4 of 4
- #1
AI Agent Memory Showdown — July 11, 2024
Jul 11, 2024 · #1 of 4
- #2
AI Agent Memory Showdown — February 16, 2024
Feb 16, 2024 · #2 of 4
评测
6 个评分的平均值。
登录以留下评测。
Use it every day
Honestly didn't expect to like it this much. Stateful agents with persistent memory is exactly what I needed, and visual tools for inspecting agent state and memory. I do wish memory management adds complexity over simple LLM calls, but I reach for it almost every day now and it just clicks.
Does the job
Pretty happy overall. Stateful agents with persistent memory just works and open-source foundation with active development. but no dealbreakers — I'd recommend it to a friend without hesitating.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on self-editing memory blocks, and visual tools for inspecting agent state and memory caught me off guard. still, I'd recommend giving it a real trial.
Years in this space
I've evaluated a lot of these over the years. What stands out here is rEST API and Python/TypeScript SDKs — handled better than most — and persistent long-term memory across sessions. Memory management adds complexity over simple LLM calls is my one real gripe. Worth the time if this is your use case.
Skeptical, then convinced
I went in skeptical — most tools in this space overpromise. It actually delivers on tool and function calling, and visual tools for inspecting agent state and memory caught me off guard. Requires technical setup and developer expertise is why this isn't a perfect score, still, I'd recommend giving it a real trial.
Solid for our team
We rolled this out across the team last quarter and visual tools for inspecting agent state and memory. Self-editing memory blocks fits neatly into how we already work, and tool and function calling removed a step we used to do by hand. but it has held up under daily use.
问答
What are the cons of using Letta?
Cons include requiring technical setup and developer expertise, added complexity due to memory management, and a smaller ecosystem compared to mainstream agent frameworks.
Asked by Tomáš Novák · Nov 25, 2025
What are the pros of using Letta?
Pros include persistent long-term memory, model-agnostic support for multiple LLM providers, and an open-source foundation with active development.
Asked by Yara Mansour · Nov 23, 2025
What are key features of Letta?
Key features include stateful agents with persistent memory, self-editing memory blocks, and support for multiple LLM providers.
Asked by Emeka Obi · Oct 13, 2025
What is Letta?
Letta 是一个开发者平台，对于创建能够在会话中保留上下文、通过互动学习和改进行为的 AI 代理。
Asked by Yosef Mizrahi · Oct 7, 2025
