nodus-memory 0.1.0

Persistent agent memory: nodes, MAS path addressing, scoring, embedding, and recall

nodus-memory

Persistent, searchable, scored agent memory for Nodus AI systems.

Provides typed memory nodes with MAS (Memory Address Space) hierarchical path addressing, semantic + tag + impact scoring, embedding support, and sync/async recall. No required external dependencies — the in-memory store and NoOp embedder work out of the box.

Note: An earlier version of this repo provided nodus-lang bindings (attach_to_runtime, import "nodus-memory" in .nd scripts). That implementation is preserved in the git history. The current package is the standalone Tier 2 memory library.

Status: v0.1.0 — prepared, not yet published.

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Install

pip install nodus-memory

# With pgvector + SQLAlchemy backend:
pip install "nodus-memory[pgvector]"

# With OpenAI embeddings:
pip install "nodus-memory[openai]"

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What it provides

Component	Purpose
MemoryNode	Content node with tags, path, embedding, scores, feedback
MemoryLink	Directed causal/associative link between nodes
build_path / parse_path / glob_match	MAS hierarchical addressing
score_nodes / update_feedback	Composite relevance scoring
EmbeddingProvider / NoopEmbeddingProvider	Embedding protocol + fallback
InMemoryStore	Thread-safe in-process store (dev + tests)
recall / recall_async	Sync and async retrieval with optional embedder

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Quick start

from nodus_memory import MemoryNode, InMemoryStore, recall

store = InMemoryStore()

node = MemoryNode(
    content="The capital of France is Paris.",
    tags=["geography", "europe"],
    path="/tenant/alice/fact/paris-capital",
)
store.put(node)

results = recall(query="France capital", store=store, limit=5)
for r in results:
    print(r.node.content, r.score.total)

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MemoryNode

from nodus_memory import MemoryNode, VALID_NODE_TYPES, VALID_MEMORY_TYPES

node = MemoryNode(
    content="Important fact",
    tags=["fact", "geography"],
    path="/tenant/alice/fact/paris",    # MAS path
    node_type="fact",                   # from VALID_NODE_TYPES
    memory_type="semantic",             # from VALID_MEMORY_TYPES
    embedding=[0.1, 0.2, ...],          # optional float list
)
node.id            # UUID str (auto-generated)
node.created_at    # datetime UTC
node.weight        # float 0.0–1.0 (scoring weight)
node.impact_score  # float (feedback-driven)

---

MAS path addressing

from nodus_memory import build_path, parse_path, glob_match

path = build_path(tenant="alice", namespace="work", type="fact", node_id="paris")
# "/alice/work/fact/paris"

parsed = parse_path(path)
# {"tenant": "alice", "namespace": "work", "type": "fact", "node_id": "paris"}

glob_match("/alice/work/fact/paris", "/alice/work/**")   # True
glob_match("/alice/work/fact/paris", "/alice/home/**")   # False

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Scoring and feedback

from nodus_memory import score_nodes, update_feedback, MemoryScore

nodes = store.list_all()
query_embedding = embedder.embed(["capital city"])
scored = score_nodes(nodes, query_embedding=query_embedding, query_tags=["geography"])

for result in scored:
    print(result.node.content, result.score.total)
    # result.score.semantic, .tag, .impact, .weight, .recency

# Record outcome to adjust future scores
update_feedback(node, success=True)    # increases weight + impact
update_feedback(node, success=False)   # decreases weight

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Embedding

from nodus_memory import EmbeddingProvider, NoopEmbeddingProvider

# Zero-vector fallback — no embedding dep needed
embedder = NoopEmbeddingProvider()

# OpenAI (requires nodus-memory[openai])
from nodus_memory import OpenAIEmbeddingProvider
embedder = OpenAIEmbeddingProvider(api_key="sk-...", model="text-embedding-3-small")

# Custom embedder — implement the protocol
class MyEmbedder:
    dimensions = 768
    def embed(self, texts: list[str]) -> list[list[float]]: ...

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Recall

from nodus_memory import recall, recall_async

# Sync
results = recall(
    query="capital of France",
    store=store,
    embedder=embedder,    # optional
    tags=["geography"],   # optional tag filter
    limit=10,
)

# Async
results = await recall_async(query="...", store=store, embedder=embedder)

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Design

• No required dependencies. Core models, MAS, scoring, and in-memory store are pure stdlib. Embedding providers and pgvector are optional extras.
• Protocol-based store. Any class satisfying MemoryStore (put, get, list, delete) works as a backend.
• Thread-safe. InMemoryStore uses threading.Lock.
• Auto-detects nodus-native-memory-engine. When installed, hot-path scoring operations use the Rust extension automatically.

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Development

pip install -e ".[dev]"
pytest tests/ -q

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License

MIT — see LICENSE.