Memory & Learning

Kiro Crew has persistent memory that survives across sessions. It remembers your preferences, project context, daily activity, and corrections you teach it.

Memory Types

Preferences (preferences.md)

Your personal preferences — coding style, tools you prefer, communication style. Updated automatically by the consolidator after ~30 messages.

Projects (projects.md)

Active project context — what you're working on, key decisions, blockers. Updated alongside preferences.

Daily History (history/{date}.md)

Conversation summaries organized by date. Natural decay:

• Last 14 days: full detail (days 0–13)
• 14–60 days: first entry per day + count
• 61–180 days: date + entry count only
• 181–365 days: retained on disk but not loaded into context
• 365+ days: pruned automatically

Lessons (lessons.jsonl or vector store)

Corrections and rules you teach Kiro Crew. Two ways to create:

1. Explicit: say "remember to always use pytest" → saved immediately
2. Implicit: correct Kiro Crew during conversation → extracted during consolidation

Lessons have two scopes:

• Global (default): shared across all workspaces
• Repository-scoped: an optional repo_scope path fragment restricts a lesson to sessions whose active project is inside that repository tree

Memory Modes

Each session can operate in one of three memory modes:

Mode	Reads Memory	Writes Memory	Consolidates	Use Case
Persistent (default)	✅	✅	✅	Normal work
Incognito	✅	❌	❌	Sensitive tasks — reads context but blocks learn_add and consolidation
Temporary	❌	❌	❌	Isolated experiments — no memory interaction at all

Set via the dashboard Welcome view (ghost button), the mode icon in the chat header, Slack (!incognito / !temporary prefix), or Telegram (/incognito / /temporary). Telegram spells them as commands because it has a command grammar; the modes, the guarantees and the durability are the same on both channels, and both accept a question after the modifier to mark the conversation and answer in one message.

All modes still write session JSONL files (for history/resume). Incognito blocks learn_add and consolidation. Temporary additionally blocks memory reads — no preferences, history, or lessons are injected into the prompt.

Teaching Kiro Crew

Just tell it naturally:

• "Always use dark mode"
• "Never use rm -rf without confirmation"
• "Remember that our team uses pytest-asyncio strict mode"
• "Prefer ruff over flake8 for linting"

Kiro Crew saves these via the learn_add MCP tool. View them with learn_list or on the dashboard Overview → Lessons tab.

Workspaces

Markdown memory is workspace-scoped: each workspace stores memory/preferences.md, memory/projects.md, and memory/history/{date}.md beneath its workspace directory. Legacy JSONL lessons are also workspace-local; vector memory defaults to memory.db under the Kiro Crew data directory. Lessons are global unless their optional repo_scope restricts them to a project tree.

Vector Memory

The vector-memory subsystem is always enabled:

• Semantic memory: structured key-value store with confidence scoring
• Episodic memory: conversation fragments searchable by meaning
• Embeddings: Qwen3-Embedding-0.6B running in-process (no Ollama or any other server to install — the runtime is bundled; no data leaves your machine)

The embedding model (~610MB) downloads automatically in the background the first time the gateway starts, over HTTPS from the Kiro Crew CDN — failed downloads retry automatically with backoff, and again on the next gateway start; the Memory tab shows download progress. Once downloaded, the model loads in the background too, so nothing ever waits on it. While the model is downloading or loading, memory falls back to keyword search and switches to semantic search as soon as the model is ready — no restart needed. Requires ~610MB disk for the model and ~700MB RAM once the model is loaded.

The bundled model is qwen3-embedding:0.6b (1024 dimensions). KIROCREW_EMBED_MODEL_URL overrides memory.embed_model_url for the download URL; KIROCREW_EMBED_MODEL_PATH or memory.embed_model_path selects a local GGUF instead of the bundled model.

Consolidation

Kiro Crew automatically consolidates conversations into memory:

• Preferences/projects: every 30 messages per session
• Daily history + lessons: after 3 hours idle per session

No manual action needed — it happens in the background.

Reading Memory Programmatically

The markdown layer is readable through the CLI, so consumers depend on an interface rather than the on-disk layout:

• kirocrew memory show [preferences|projects|history] — print the markdown layer (all three when no target is given). --format json returns structured entries with path, updated_at, and content; --since YYYY-MM-DD limits history to days on or after that date.
• kirocrew memory export --include-markdown — add a markdown collection to the JSON export. Without the flag the export shape is unchanged.

Both run non-interactively (no TTY or editor needed), so they work from scheduled jobs.

Editing Memory

• Dashboard: Overview → Memory tab → edit preferences.md or projects.md
• Chat: ask Kiro Crew to update its memory files directly