Technology · Agentic → Memory · Open Source · wiki:deep

Letta

Taxonomy

Letta (formerly MemGPT) builds stateful agents with memory that can learn and improve over time. Active development has moved: current source lives in letta-ai/letta-code (agent harness, terminal UI, App Server, channels, desktop/web runtime). The letta-ai/letta repo README points there; an archive branch holds the retired Letta V1 API server.

Install/run today via npm install -g @letta-ai/letta-code → letta (TUI) / letta server, plus desktop, chat.letta.com, Slack/Telegram/Discord channels, Agent SDK, and Letta Cloud for cross-device memory/identity.

RepoDocsStack: agentic-memory

Why it matters here

Memory for an agentic OS is not only “store embeddings” — it is agents that keep identity and memory across sessions while tools and channels change. Letta is the research pack for MemGPT-lineage stateful agents and self-hosted/cloud runtimes. Prefer LangMem when you only need LangGraph store tools; prefer Mem0/Zep when you want a memory service plugged into many frameworks.

How it works

  1. Install @letta-ai/letta-code (or use desktop / chat.letta.com / Cloud).
  2. Run interactive letta or letta server for local/self-hosted agents.
  3. Connect channels (Slack, Telegram, Discord, custom) or embed via Agent SDK.
  4. Memory/identity persist per Letta’s runtime (Cloud keeps them across machines).
  5. Do not build new work on the archived V1 API in letta’s archive branch.

Related: mem0 · langmem · zep · topics/31-memory-architecture

Flow

Scroll inside the canvas to pan

When to reach for it

  • Use when: you want stateful MemGPT-style agents with a first-party harness, channels, and optional Cloud.
  • Skip when: you only need a memory library behind an existing LangGraph/Crew agent (langmem / mem0), or a temporal knowledge graph (graphiti / Zep).
  • Prefer instead: letta-code over historical letta tags for new installs; Mem0 for framework-agnostic memory API.

Limits

  • Repo split: starring/cloning letta-ai/letta alone misses the active harness — follow letta-code.
  • V1 API retired: reproducibility tags remain; active projects must migrate.
  • Cloud vs self-host: cross-computer memory nudges toward Cloud — clarify tenancy before production.
  • Not the company ledger: agent memory ≠ declared process/authority.

Linked from

Catalog backlinks — what points here (wiki “what links here”).

Features

Stacks

Peers

What we checked

Claims below are backed by science sources on disk.

Platform / stateful agents

letta-ai/letta repository · MODERATE

“The platform for building stateful agents”

MemGPT research lineage

MemGPT: Towards LLMs as Operating Systems · STRONG

“To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems.”

Research site / positioning

MemGPT research site · MODERATE

“Use Letta to create coding agents, personal assistants, AI coworkers, and stateful agents embedded in your own applications.”

In this research stack

Features and peers linked from the catalog map — not a second product surface.

Features it supports

Same cell

Stack

Document management

No DM vendor crosswalk edges yet.

Source

Primary repo github.com/letta-ai/letta-code · Open Source

technologies/letta/README.md

Research inventory

9 tags · 12 out · 13 in · 3 artifacts · 1 gaps · 34 corpus docs

Catalog tags

landscape.layer
Agentic
landscape.subcategory
Memory
license_tag
Open Source
maps.dm
absent
maps.features
9
one_liner
Agentic
review.depth
science
slug
letta
title
Letta

Artifacts

  • dm_map · absent
  • features_map · present · technologies/letta/features.md
  • readme · present · technologies/letta/README.md

Out · alternative_to

Out · maps_to

In · alternative_to

In · in_stack

In · maps_to

Corpus tags

category
Agentic → Memory
dedication
open-source
feature
learning-adaptation
memory-state-persistence
os-company-model-memory
r2-10-sleep-improvement
r2-31-memory-architecture
r2-35-context-engineering
r2-36-importance-consolidation
r2-master-synthesis
wt-data-context
kind
company_review
map_edge
tech_features
tech_quote
tech_readme
tech_science_source
tech_section
needs_deepen
false
quality
ok
section
Evidence
Features map
GitHub map
How it works
Limits & failure modes
Links
Scientific notes
What it is
When to use / skip
Why it matters here
slug
letta
source_id
arxiv-2310-08560
letta-github
memgpt-research-site
surface
interface-review
technology
letta

Corpus documents (34)

company_review · 2

  • Letta · interface review
  • Letta

map_edge · 9

  • letta → learning-adaptation
  • letta → memory-state-persistence
  • letta → os-company-model-memory
  • letta → r2-10-sleep-improvement
  • letta → r2-31-memory-architecture
  • letta → r2-35-context-engineering
  • letta → r2-36-importance-consolidation
  • letta → r2-master-synthesis
  • … +1 more

tech_features · 1

  • Letta · features

tech_quote · 8

  • Letta · arxiv-2310-08560
  • Letta · arxiv-2310-08560
  • Letta · arxiv-2310-08560
  • Letta · letta-github
  • Letta · letta-github
  • Letta · letta-github
  • Letta · memgpt-research-site
  • Letta · memgpt-research-site

tech_readme · 1

  • Letta

tech_science_source · 3

  • Letta · arxiv-2310-08560
  • Letta · letta-github
  • Letta · memgpt-research-site

tech_section · 10

  • Letta · Evidence
  • Letta · Features map
  • Letta · GitHub map
  • Letta · How it works
  • Letta · Limits & failure modes
  • Letta · Links
  • Letta · Scientific notes
  • Letta · What it is
  • … +2 more