semantica/docs/reference/context.md

title, description, icon

title	description	icon
Context Module	Agent context graphs, decision tracking, causal chains, precedent search, and policy enforcement.	brain

semantica.context is the memory and decision layer for AI agents. It stores facts with provenance, records decisions as first-class objects with causal chains, and lets agents search their own history to stay consistent across runs.

What You Get

• AgentContext — unified interface for memory, decision tracking, and graph-backed retrieval
• ContextGraph — persistent knowledge graph with centrality analysis, community detection, and decision management
• AgentMemory — low-level embedding-backed memory with TTL, tagging, and importance scoring
• DecisionRecorder — records decisions with causal chains, confidence scores, and outcome tracking
• CausalAnalyzer — traces downstream impact of any decision
• PolicyEngine — validates decisions against configurable rules before they're recorded

<img src="/assets/img/diagrams/agent-context-flow.svg" alt="AgentContext hub: AI Agent calls store/retrieve against VectorStore and record_decision against ContextGraph" style={{ width: '100%', borderRadius: '12px', margin: '0 0 24px' }} />

AgentContext

The main entry point. Wraps memory, graph, and decision tracking behind a single API.

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
)

Store and Retrieve Memories

# Store a fact — embedded and indexed automatically
memory_id = context.store(
    "GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%",
    metadata={"source": "openai_blog", "date": "2024-01"}
)

# Retrieve by semantic similarity
results = context.retrieve("LLM benchmark comparisons", top_k=5)
for r in results:
    print(f"{r['content']}  (score: {r['score']:.3f})")

Record and Search Decisions

decision_id = context.record_decision(
    category="model_selection",
    scenario="Choose LLM for production reasoning pipeline",
    reasoning="GPT-4 benchmark advantage justifies 3x cost increase",
    outcome="selected_gpt4",
    confidence=0.91,
)

# Find similar past decisions — prevents inconsistent choices
precedents = context.find_precedents("model selection reasoning", limit=5)

# Analyze downstream impact
influence = context.analyze_decision_influence(decision_id)
print(f"Decisions influenced: {len(influence.downstream_decisions)}")

Multi-Hop GraphRAG

from semantica.llms import Groq

llm    = Groq(model="llama-3.3-70b-versatile")
result = context.query_with_reasoning(
    query="What technologies have we chosen and why?",
    llm_provider=llm,
    max_hops=2,
)

print(result["response"])
for step in result["reasoning_path"]:
    print(f"  {step}")

Constructor Parameters

Parameter	Type	Default	Description
vector_store	VectorStore	required	Backend for embedding-based memory retrieval
knowledge_graph	ContextGraph	None	Enables graph-backed relationships and analytics
decision_tracking	bool	False	Activates DecisionRecorder for every decision
graph_expansion	bool	True	Auto-expands graph from stored memories
advanced_analytics	bool	True	Enables centrality and community analysis
kg_algorithms	bool	True	Adds path-finding and link prediction

Core Methods

Method	Returns	Description
store(content, metadata)	str (memory_id)	Embed and store a fact
retrieve(query, top_k)	List[Dict]	Semantic similarity search
record_decision(category, scenario, reasoning, outcome, confidence)	str (decision_id)	Record a decision with full provenance
find_precedents(scenario, category, limit)	List[Decision]	Find similar past decisions
analyze_decision_influence(decision_id)	InfluenceResult	Trace downstream impact
query_with_reasoning(query, llm_provider, max_hops)	Dict	GraphRAG with multi-hop traversal
get_context_insights()	Dict	Analytics summary

ContextGraph

The knowledge graph backing AgentContext. Can be used standalone for relationship modelling.

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

# Add nodes and edges
graph.add_node("Python",  "language",  properties={"paradigm": "multi-paradigm"})
graph.add_node("FastAPI", "framework", properties={"language": "Python"})
graph.add_edge("Python", "FastAPI", "enables")

# Decision management
decision_id = graph.add_decision_simple(
    category="technology_choice",
    scenario="Web API framework selection",
    reasoning="FastAPI's async support and auto-docs match our requirements",
    outcome="selected_fastapi",
    confidence=0.92,
    entities=["Python", "FastAPI"],
)

similar = graph.find_precedents_by_scenario("web framework", limit=3)
impact  = graph.analyze_decision_impact(decision_id)
chain   = graph.trace_decision_chain(decision_id)

ContextGraph Constructor Options

Parameter	Type	Default	Description
advanced_analytics	bool	False	PageRank, betweenness centrality
centrality_analysis	bool	False	Full centrality suite
community_detection	bool	False	Louvain community clustering
node_embeddings	bool	False	Node2Vec embeddings for structural similarity

Decision Data Structure

@dataclass
class Decision:
    decision_id:    str
    category:       str
    scenario:       str
    reasoning:      str
    outcome:        str
    confidence:     float       # 0.0 – 1.0
    decision_maker: str
    timestamp:      datetime
    entities:       List[str]
    metadata:       Dict
    causal_chain:   List[str]   # IDs of related decisions

AgentMemory (Low-Level)

For fine-grained control over memory storage, TTL, and importance scoring:

from semantica.context import AgentMemory

memory = AgentMemory(
    vector_store=VectorStore(backend="faiss", dimension=768),
    max_memories=10_000,
    ttl_days=90,
)

memory.store("Important fact", importance=0.9, tags=["compliance"])
results = memory.retrieve("fact query", top_k=5, min_importance=0.5)
memory.forget(memory_id)

PolicyEngine

Validate decisions against configurable rules before they're committed:

from semantica.context import PolicyEngine

policy = PolicyEngine()
policy.add_rule("confidence_threshold", lambda d: d.confidence >= 0.7)
policy.add_rule("requires_reasoning",   lambda d: len(d.reasoning) >= 20)

# Validate before recording
is_valid, violations = policy.validate(decision_data)
if is_valid:
    context.record_decision(**decision_data)

Real-World Patterns

Healthcare — Treatment Decisions

health_agent = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    decision_tracking=True,
)

health_agent.store("Patient has hypertension, type 2 diabetes")
health_agent.store("Patient allergic to penicillin — verified 2024-01")

decision_id = health_agent.record_decision(
    category="treatment_plan",
    scenario="Hypertension with comorbid diabetes",
    reasoning="ACE inhibitors are renoprotective in diabetic patients — preferred over beta blockers",
    outcome="prescribed_lisinopril",
    confidence=0.91,
)

# Check for similar cases
precedents = health_agent.find_precedents("hypertension diabetes", limit=5)

Finance — Loan Decisions

loan_agent = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    decision_tracking=True,
)

loan_agent.store("Applicant: credit score 750, DTI 28%, stable employment 4yr")

decision_id = loan_agent.record_decision(
    category="loan_approval",
    scenario="First-time homebuyer — 30yr fixed, 20% down",
    reasoning="Credit score above threshold, DTI within limits, stable income verified",
    outcome="approved_300k",
    confidence=0.94,
)

Embedding storage backend for memory retrieval. Graph algorithms and analytics used inside ContextGraph. Logical inference layered on top of context. W3C PROV-O lineage for every stored fact.

Cookbooks

• Context Module — memory and decision tracking · Intermediate
• Advanced Context Engineering — production FAISS + Neo4j setup · Advanced
• Decision Tracking with KG Algorithms — precedent search, policy enforcement · Advanced