Inspect or modify Letta Code's own memory, model, context window, system prompt, compaction, permissions, toolsets, mods, skills, channels, schedules, agent secrets, and local runtime settings. Use when the user asks how this agent or conversation is configured, or asks you to change how you behave or how the harness…
Submits user-approved feedback about Letta Code or the current agent to the Letta team. Load when the user is upset, frustrated, dissatisfied, reports poor agent behavior, or asks to send feedback. Works with cloud-hosted and local agents. Ask before submitting unless the user already explicitly requested submission.
Diagnose and repair MemFS repository setup, remote sync, authentication failures, optional backup remotes, or merge/rebase conflicts. Do not load for routine memory reads or edits.
Moves the current agent conversation to Cloud, Desktop Local, or another Cloud-registered environment while coordinating machine-local files and setup. Use when the user says "let's continue this task on cloud", asks to continue or move work on another connected computer, wants to teleport between environments, or…
Uses MCP servers connected to the current Letta Cloud agent (cloud MCP). Load when the user asks to use a connected MCP server, list the agent's MCP servers or MCP tools, run an MCP tool connected in ADE/chat, or mentions cloud MCP, server-side MCP, agent MCP, or letta cloud-mcp.
Comprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.
Guide for using the Letta Conversations API to manage isolated message threads on agents. Use when building multi-user chat applications, session management, or any scenario requiring separate conversation contexts on a single agent.
Builds and debugs Letta Code channels, including first-party channel adapters and dynamic user channel plugins under /.letta/channels. Use when adding Telegram, WhatsApp, Bluesky, Slack, Discord, or custom channel support; testing channel routing, pairing, MessageChannel, runtime dependencies, or channel plugin…
Manage Letta AI agent fleets declaratively with kubectl-style CLI. Use when creating, updating, or managing multiple Letta agents with shared configurations, memory blocks, tools, folders, canary deployments, multi-tenancy, and bulk operations.
Clone ChatGPT saved memory into Letta, then optionally enrich it with broader conversation history. Designed for a slick onboarding flow that extracts hidden saved-memory/context blocks, builds Letta-ready previews, and only asks questions at meaningful checkpoints.
Build applications with the Letta API — a model-agnostic, stateful API for building persistent agents with memory and long-term learning. Covers SDK patterns for Python and TypeScript. Includes 24 working code examples.
Configure LLM models and providers for Letta agents and servers. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings, setting up BYOK providers, or configuring self-hosted deployments with environment variables.
Migrates deprecated Letta Filesystem folders/files to MemFS using markdown document corpora, chunking, local lexical search, and QMD semantic search via the memfs-search skill. Use when replacing folders.files.upload, working with PDFs or document QA, or emulating openfile, grepfile, and searchfile behavior.
Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand. Use when recalling what a past assistant knew, searching old conversations, rendering specific chats, seeding reference memory from export sidecars, or mining historical context without doing a full import.
Configures Letta agents' own runtime behavior, including model, context window, system prompt, reasoning, conversation overrides, compaction settings, and compaction prompts. Use when an agent or user asks to self-modify, tune summarization/compaction, change identity/system instructions, adjust model settings, or…
Sets Letta Desktop and Letta Code agent profile images by writing profile.png into an agent MemFS repository. Use when the user asks to add, change, generate, or fix an agent avatar, profile picture, profile image, or Desktop agent photo.
Create and contribute skills to the communal knowledge base. Use when creating new skills, updating existing skills, or contributing learnings back to the repository.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.