AbstractMemory

AbstractMemory is a Python library for durable, append-only agent memory. It provides two layers:

• Layer 1 — triple truth: append-only, temporal, provenance-aware triple assertions with deterministic structured queries and optional vector/semantic retrieval, over in-memory, SQLite, or LanceDB backends.
• Layer 2 — the memory system: a MemorySystem facade that turns those triples plus an append-only journal into a usage-weighted memory graph: typed record formation, stimulus-driven reconstruction (working memory that emerges from use), attention and decay, identity cores for long-lived entities, valence/gradation (how experience felt), diary conventions, sleep/consolidation, and a replay stream for observability.

Storage never decays and nothing is ever deleted; only retrieval strength changes. Reads are pure — rendering a memory does not strengthen it; only committed use does.

Status

• Pre-1.0: the API is versioned and tested, and details may still evolve. The current version is in pyproject.toml.
• The authoritative export list is src/abstractmemory/__init__.py; docs/api.md documents it.
• Requires Python 3.10+.

Ecosystem (AbstractFramework)

AbstractMemory is a component of the AbstractFramework ecosystem. It has no dependency on AbstractCore or AbstractRuntime; embeddings for semantic retrieval can come from any OpenAI-compatible /embeddings endpoint (OpenAICompatTextEmbedder), from an AbstractGateway deployment (AbstractGatewayTextEmbedder), or from your own TextEmbedder implementation.

flowchart LR
  APP["Your app or agent"] --> MS["MemorySystem (layer 2)"]
  MS --> ST["Triple store (layer 1)"]
  MS --> J["Journal (append-only)"]
  ST --> IM["InMemoryTripleStore"]
  ST --> SQL["SQLiteTripleStore"]
  ST --> LDB["LanceDBTripleStore"]
  SQL --> F[("one SQLite file")]
  J --> F
  MS -. "optional embeddings" .-> E["TextEmbedder (OpenAI-compatible / Gateway / custom)"]

Related projects:

• AbstractFramework: https://github.com/lpalbou/abstractframework
• AbstractCore: https://github.com/lpalbou/abstractcore
• AbstractRuntime: https://github.com/lpalbou/abstractruntime

Install

From source (recommended inside the AbstractFramework monorepo):

python -m pip install -e .

Optional LanceDB backend:

python -m pip install -e ".[lancedb]"

PyPI (packaged release):

python -m pip install AbstractMemory
python -m pip install "AbstractMemory[lancedb]"

The distribution name is AbstractMemory (pip is case-insensitive); the import name is abstractmemory. The [apple]/[gpu] extras are no-op compatibility aliases; [all], [all-apple], and [all-gpu] install the LanceDB backend.

Quick example — layer 1 (triples)

from abstractmemory import InMemoryTripleStore, TripleAssertion, TripleQuery

store = InMemoryTripleStore()
store.add([
    TripleAssertion(
        subject="Scrooge",
        predicate="related_to",
        object="Christmas",
        scope="session",
        owner_id="sess-1",
        provenance={"span_id": "span_123"},
    )
])

hits = store.query(TripleQuery(subject="scrooge", scope="session", owner_id="sess-1"))
assert hits[0].object == "christmas"      # terms are canonicalized (trim + lowercase)
assert hits[0].assertion_id is not None   # stores stamp read-side identity on results

Quick example — layer 2 (the memory system)

from abstractmemory import (
    MemorySystem, MemoryRecordInput, SQLiteTripleStore, SQLiteJournal, Stimulus,
)

store = SQLiteTripleStore("memory.sqlite3")
journal = SQLiteJournal("memory.sqlite3")   # sidecar tables in the same file
system = MemorySystem(store=store, journal=journal)

# Form a typed record (idempotent by key; forming is not using).
[record_id] = system.remember_many(
    [MemoryRecordInput(kind="episode", title="Pool outage",
                       digest="The connection pool saturated at noon.",
                       keywords=("pool", "outage"))],
    scope="session", owner_id="s1", idempotency_key="turn-1",
)

# Reconstruct working memory for a cue (pure read), then commit what you used.
result = system.reconstruct(Stimulus(cue_text="pool outage"), scopes=[("session", "s1")])
system.commit_selection(result.trace_id, [h.record_id for h in result.handles[:2]])

Documentation

• Getting started: docs/getting-started.md
• Architecture: docs/architecture.md
• The memory system (cognitive model): docs/memory-system.md
• API reference: docs/api.md
• Stores/backends: docs/stores.md
• Operator guide (entity homes): docs/operator.md
• FAQ: docs/faq.md
• Troubleshooting: docs/troubleshooting.md
• Development: docs/development.md

Project

• Changelog: CHANGELOG.md
• Contributing: CONTRIBUTING.md
• Code of conduct: CODE_OF_CONDUCT.md
• Security: SECURITY.md
• License: LICENSE
• Acknowledgments: ACKNOWLEDGMENTS.md

Design principles

• Append-only, no deletion: updates are new assertions; belief revision is closure records (retract/supersede/reinterpreted); forgetting is decay of retrieval strength plus closures and silencing — the substrate is lossless.
• Reads are pure: reconstruction, inspection, replay, and the entity card deposit nothing. commit_selection is the only strengthening path.
• One seq axis: the journal assigns a monotonic seq to every record; any past state is reproducible by anchoring reads at as_of.
• Works-or-loud: degraded paths are labeled #FALLBACK in result warnings; invalid inputs raise actionable errors instead of silently meaning something else.
• No heavy dependencies: SQLite persistence and vector scoring use the standard library; LanceDB and embedders are optional.