Yet Another Agent Memory System
Project description
YAAMS
Yet Another Agent Memory System. A local-first, high-recall memory store for your personal digital exhaust - messages, email, calendar, GitHub activity, notes - searchable in seconds and synthesizable into grounded, cited answers by the LLM of your choice.
YAAMS is Tier 1 of a two-tier memory architecture: the firehose. Tier 2 is the cognitive-ledger: curated atomic notes you keep forever. YAAMS ingests everything, lets you query across the lot, and promotes the gems upstream when you're ready.
Each tier is split between a public engine and a private store:
| Tier | Engine (public) | Store (private, your data) |
|---|---|---|
| Tier 1 | YAAMS - this repo | a single SQLite file at db_path |
| Tier 2 | cognitive-ledger - sibling repo | a markdown tree of curated atomic notes |
Engines ship code, no data. Stores live outside the repo, are gitignored by default, and never get pushed.
Suite
YAAMS is one of four tools in the hugr
memory suite. The suite gives you one install (brew install damsleth/tap/hugr), one verb surface (hugr query, hugr ingest,
hugr promote, ...), and one CLI contract (output classes, exit
codes, action envelopes - see
hugr/CONVENTIONS.md).
Power users can still call yaams directly and get byte-identical
JSON.
What
query
|
v
ingest -> normalize -> embed -> retrieve -> fuse -> answer (cited)
| | | |
iMessage SQLite BGE-M3 Tier 2 boost
Signal + FTS5 + spaCy (cognitive-ledger)
Email + vec
Teams
Calendar
GitHub
Obsidian
- Local-only by default. Embeddings, NER, and LLM synthesis run on your machine. Nothing leaves the host unless you point an adapter at a hosted backend.
- Append-only. Raw items are immutable. Re-ingest is idempotent.
- Cited answers. Every synthesized claim points back to the source items.
- Pluggable LLM.
claude,codex,ollama, any subprocess, ordummy. - Single SQLite file. No daemons, no servers, no cloud bill.
Why
Language models forget everything between sessions, and the answer is not "shove more raw chat logs into the context window." The answer is a memory system you can grep, query, and audit, that lives on your machine and that you fully control.
YAAMS gives you that for the high-volume side of your life: every iMessage, every email, every calendar event, every GitHub issue you have touched. It normalizes them into a common schema, embeds them, and lets you ask questions like "what did we decide about the deploy in May?" and get back a grounded answer with citations - in seconds, offline.
Quickstart
Requires Python 3.11+ and macOS (Linux works for everything except the iMessage adapter).
Homebrew (recommended):
brew install damsleth/tap/yaams
PyPI via pipx:
pipx install yaams
Bleeding-edge from main:
brew install --HEAD damsleth/tap/yaams
Then bootstrap a config and run the dry-run ingest:
mkdir -p ~/.config/yaams
cp "$(brew --prefix)/share/yaams/config.yaml.example" ~/.config/yaams/config.yaml
$EDITOR ~/.config/yaams/config.yaml
yaams init-db
yaams ingest --dry-run
If you cloned the repo instead of installing the package, run the
all-in-one bootstrap script (scripts/install_phase_a.sh) - it creates
.venv, runs the config wizard, downloads the spaCy NER model, and runs
init-db + ingest --dry-run in sequence.
Verify the setup:
yaams --version # 0.1.1
yaams stats # zero items - that is expected before first ingest
yaams ingest --dry-run # see what each adapter would pick up
Then do a real ingest:
yaams ingest
The first run downloads the embedding model (BAAI/bge-m3, ~2GB). YAAMS
will prompt before downloading; after the cache is populated, subsequent
runs are fully offline.
Ask a question:
yaams query "what did we decide about the deploy in May"
yaams query --answer "open items from the kickoff meeting"
CLI
yaams <command> [options]
Bare yaams lists commands. Global flags: --version, --help.
Per-command help: yaams <command> --help.
For the full feature walkthrough — every command, the entity-curation workflow, query flags, and best practices — see the User Guide.
| command | what it does |
|---|---|
init-db |
create the SQLite schema (idempotent) |
ingest |
run ingest for all enabled sources (or --source <name>) |
stats |
print item counts per source, plus last ingest run timing |
query |
full-text + vector search; --answer for synthesized response |
feedback |
log relevance signals against a prior query result |
signals |
inspect recent query history |
consolidate |
group conversational items into sessions |
promote |
review and accept atomic-note candidates into the Tier 2 ledger |
reset-db |
drop and recreate the database (asks first) |
version |
print version (--json for machine-readable) |
Ingest sources
| source | config key | what it ingests |
|---|---|---|
imessage |
ingest.imessage |
iMessage conversations from local chat.db |
signal |
ingest.signal |
Signal Desktop messages (1:1 + groups, attachment metadata) |
email |
ingest.email |
.emlx (Apple Mail) or .mbox |
notes |
ingest.notes |
Obsidian vault markdown |
tier2_ledger |
ingest.tier2_ledger |
curated atomic notes from cognitive-ledger |
github |
ingest.github |
GitHub issues and PRs across your repos |
calendar / calendar_<profile> |
ingest.calendar |
Outlook calendar via owa-cal |
teams / teams_<profile> |
ingest.teams |
Microsoft Teams via Graph API |
Run one source: yaams ingest --source imessage.
Query
yaams query "budget discussion"
yaams query --top-k 20 "deploy"
yaams query --no-vector "fast FTS-only path" # skips embedder load
yaams query --source imessage "holiday plans"
yaams query --since 2025-06-01 --until 2025-09-01 "summer"
yaams query --no-consolidations "raw items only"
yaams query --format json "search term"
yaams query --answer "what are the open items from the kickoff"
Promote
Surface candidate atomic notes from recent items, review them interactively, and write accepted ones into your ledger inbox:
yaams promote generate # scan last 30 days
yaams promote generate --days 60
yaams promote review # interactive: a/e/r/s/q
Nothing is promoted without your explicit acceptance. Accepted notes land in
promote.inbox_path (default ~/yaams/ledger-inbox/).
Configure
config.yaml lives in the repo root (or ~/.config/yaams/config.yaml, or
wherever $YAAMS_CONFIG points). It is gitignored: it carries your
personal entity dictionary, source paths, and addresses. Edit
config.yaml.example instead when contributing structural changes.
Rerun the wizard at any time:
python scripts/configure_phase_a.py --config config.yaml
Key blocks:
db_path: ~/yaams/data.db
ingest:
since: '2025-01-01T00:00:00Z'
imessage:
chat_db_path: ~/Library/Messages/chat.db
email:
sources:
- type: emlx
path: ~/Library/Mail/V10
embed:
model: BAAI/bge-m3
device: mps # or cpu
synth:
backend: claude # claude | codex | ollama | subprocess | dummy
model: claude-sonnet-4-6
On Apple Silicon, use the Homebrew arm64 Python explicitly - PyTorch 2.4+ has no x86_64 macOS wheels:
/opt/homebrew/bin/python3.12 -m venv .venv
Scheduling
YAAMS is meant to run unattended. See docs/scheduling.md
for a launchd agent that runs a single nightly yaams ingest across all
enabled sources, plus the macOS Full Disk Access setup required for the
imessage adapter to work under launchd.
Privacy and security
YAAMS reads and stores sensitive personal data. The defaults keep everything local, but you are responsible for protecting the database file at rest - enable full-disk encryption (FileVault / LUKS / BitLocker) on the host machine.
See SECURITY.md for the full threat model, data classification, and vulnerability disclosure flow. See docs/privacy-security.md for the operational detail on what is written, what is not, and how to scrub.
Documentation
- User Guide - the end-to-end manual: every feature, the entity-curation workflow, query power-flags, best practices, and troubleshooting.
- Implementation status - architecture and what's shipped.
- Scheduling - unattended nightly ingest via
launchd. - Privacy and security - what's written, what isn't, and how to scrub.
Contributing
See CONTRIBUTING.md for development setup, the test suite, schema-migration rules, and commit conventions.
License
MIT. Copyright 2026 Carl Joakim Damsleth.
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