context-memory-graph 0.1.0

Deterministic memory infrastructure for institutional knowledge.

context-graph

Deterministic memory infrastructure for institutional knowledge. context-graph is a zero-dependency Python library that models decisions, events, and outcomes as a directed graph. It provides a built-in scoring engine for retrieving the most relevant context based on signal overlap, time decay, and graph connectivity. Everything is fully deterministic — same inputs always produce the same outputs.

Installation

pip install context-graph

Quick Start

from context_graph import Graph, Node, Edge
from context_graph.storage import MemoryStorage

graph = Graph(storage=MemoryStorage())

# Add nodes representing an incident chain
incident = graph.add_node(Node(
    type="event",
    content="Production API latency spike",
    signals={"service": "api-gateway", "severity": "high"},
))
root_cause = graph.add_node(Node(
    type="signal",
    content="Connection pool exhaustion on primary DB",
    signals={"service": "api-gateway", "component": "database"},
))
decision = graph.add_node(Node(
    type="decision",
    content="Increased pool size from 20 to 100 and added circuit breaker",
    signals={"service": "api-gateway", "action": "config-change"},
))

# Connect them
graph.add_edge(Edge(source_id=incident.id, target_id=root_cause.id, relation="caused_by"))
graph.add_edge(Edge(source_id=root_cause.id, target_id=decision.id, relation="led_to"))

# Later: retrieve relevant context for a similar situation
results = graph.similar_context({"service": "api-gateway", "severity": "high"}, limit=5)
for node in results:
    print(f"[{node.type}] {node.content}")

Scoring

Nodes are ranked by a deterministic formula:

effective_score = base_confidence * time_decay * signal_boost * edge_boost

Factor	Description
base_confidence	The node's stored confidence_score (0.0--1.0)
time_decay	exp(-decay_rate * age_hours) — older nodes score lower
signal_boost	Fraction of query signals matching the node's signals
edge_boost	1 + log(1 + edge_count) * edge_weight_factor

All parameters are tuneable via ScoringConfig.

Storage Backends

Backend	Use case	Persistence
MemoryStorage	Tests, prototyping, ephemeral sessions	In-memory only
SQLiteStorage	Production, persistent graphs	File-based (*.db)

from context_graph.storage import SQLiteStorage

graph = Graph(storage=SQLiteStorage("my_graph.db"))

Serialization

Export and import full graphs as JSON:

from context_graph.core.serialization import dump_graph, load_graph

dump_graph(graph, "snapshot.json")
graph = load_graph("snapshot.json", storage=MemoryStorage())

Development

git clone https://github.com/dishajain-code/context-lib.git
cd context-lib
pip install -e ".[dev]"
pytest --cov=context_graph

See CONTRIBUTING.md for more details.

License

Apache-2.0