Change Management & Versioning

What Is Change Management & Versioning?

Knowledge graphs change constantly. TemporalVersionManager gives your graph a verifiable history by capturing complete state snapshots at specific points in time. It allows you to take named snapshots before consequential changes, generate detailed diffs between any two states, roll back to previous versions with a single call, and verify SHA-256 checksums before publishing data downstream.

Storage Behavior

Pass storage_path, e.g. TemporalVersionManager(storage_path="versions.db"), to persist snapshots to a SQLite database on disk. Omit storage_path and it defaults to an in-memory store that vanishes when your script finishes.

Why Use Change Management?

Change Management acts as your safety net and audit trail. Use it to:

Safeguard Ingestion: Take a snapshot before a large batch ingestion so you can instantly roll back if the data is corrupted.
Audit Trails: Maintain a verifiable log of when a change occurred, who authorized it, and exactly what nodes/edges were modified.
Release Gating: Compare staging and production graphs and verify checksums before signing off on a release.

Which Tool Do I Need?

Semantica offers multiple tracking features. It is critical to choose the right one:

Change Management (this guide): Use for whole-graph snapshots, state diffs, and full rollbacks.
Provenance: Use for granular source and lineage tracking. It answers “Which specific document did this node come from?”
Agent Memory: Use for conversational and context state. It answers “What decisions did the AI agent make during this session?”

When To Use / When Not To Use

When to Use: You have critical checkpoints (like daily feeds, partner merges, or regulatory submissions) where you need to freeze the entire state of the graph and potentially revert it.
When NOT to Use: You have a massive, multi-million node graph and want to track every minor edit. Because TemporalVersionManager snapshots the entire graph dictionary, snapshotting huge graphs too frequently will cause severe storage bloat. Use Provenance for granular tracking instead.

TemporalVersionManager integrates directly with AgentContext.flush_checkpoint() — agent checkpoints and manual snapshots share the same storage format, allowing diffs across both automated and manual workflows.

Typical Workflow

A standard change management cycle follows this progression:

Snapshot: Capture the baseline graph state.
Modify: Run your ingestion, mutations, or analysis.
Compare: Generate a diff to see what changed.
Verify: Check the SHA-256 hash to ensure data integrity.
Tag: Apply a human-readable tag (e.g., approved).
Rollback: Revert the graph state if the modifications were incorrect.

Universal Example: Employee Profile Update

Let’s look at a universally understood example: tracking an employee’s department transfer.

from semantica.change_management import TemporalVersionManager
from semantica.context import ContextGraph

# 1. Setup Graph and Version Manager
graph = ContextGraph()
graph.add_node("emp-101", "Employee", "Alice")
graph.add_node("dept-hr", "Department", "Human Resources")
graph.add_edge("emp-101", "dept-hr", "works_in")

# SQLite persistence is enabled because we provided a storage_path
vm = TemporalVersionManager(storage_path="hr_versions.db")

# 2. Snapshot the baseline
snap_v1 = vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = "v1_baseline",
    author        = "hr_system@example.com",
    description   = "Initial employee graph",
)

# 3. Modify the graph (Transfer Alice to Engineering)
graph.add_node("dept-eng", "Department", "Engineering")
graph.add_edge("emp-101", "dept-eng", "works_in")

# 4. Snapshot the post-change state
snap_v2 = vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = "v2_transfer",
    author        = "hr_admin@example.com",
    description   = "Alice transferred to Engineering",
)

# 5. Compare versions
diff = vm.compare_versions("v1_baseline", "v2_transfer")
print("Nodes added:", diff["summary"]["nodes_added"])  # 1 (Engineering)
print("Edges added:", diff["summary"]["edges_added"])  # 1 (works_in Eng)

Now let’s explore these capabilities in more depth using domain-specific scenarios.

Creating Snapshots

Take a snapshot before any consequential change: an ingestion sweep, a partner feed merge, or an automated enrichment run.

from semantica.change_management import TemporalVersionManager
from semantica.context import ContextGraph

graph = ContextGraph()
graph.add_node("apt29",         "ThreatActor",   "APT29 / NOBELIUM")
graph.add_node("cve-2024-3400", "Vulnerability", "CVE-2024-3400 PAN-OS RCE")
graph.add_edge("apt29", "cve-2024-3400", "exploits", weight=0.97)

vm = TemporalVersionManager(storage_path="cti_versions.db")

snap_pre = vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = "q3_2025_baseline",
    author        = "analyst_zhang@example.com",
    description   = "CTI baseline before Q3 OSINT sweep",
)

print(snap_pre["label"])        # "q3_2025_baseline"
print(snap_pre["checksum"])     # SHA-256 of the serialized graph
print(snap_pre["timestamp"])    # ISO datetime
print(snap_pre["author"])       # "analyst_zhang"
print(len(snap_pre["nodes"]))   # node count at snapshot time
print(len(snap_pre["edges"]))   # edge count at snapshot time

After running the ingestion, snapshot again to mark the post-change state:

graph.add_node("cve-2024-21412", "Vulnerability", "CVE-2024-21412 Windows SmartScreen bypass")
graph.add_node("apt40",          "ThreatActor",   "APT40 / BRONZE MOHAWK")
graph.add_edge("apt40", "cve-2024-21412", "exploits", weight=0.88)

snap_post = vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = "q3_2025_post_nvd_sweep",
    author        = "osint_pipeline@example.com",
    description   = "After NVD weekly sweep — 2025-07-14",
)

Comparing Two Snapshots

compare_versions returns a precise diff — nodes and edges added, removed, or modified — between any two named snapshots.

diff = vm.compare_versions("q3_2025_baseline", "q3_2025_post_nvd_sweep")

s = diff["summary"]
print("Nodes added   :", s["nodes_added"])    # 2
print("Nodes removed :", s["nodes_removed"])  # 0
print("Edges added   :", s["edges_added"])    # 1

for node in diff["nodes_added"]:
    print(" +", node.get("id"), "/", node.get("content"))

for edge in diff["edges_added"]:
    print(" +", edge.get("source"), "→", edge.get("target"), "[" + edge.get("type", "") + "]")

You can also pass snapshot dicts directly instead of labels:

diff = vm.compare_versions(snap_pre, snap_post)

Verifying Integrity

Before publishing a snapshot to a downstream system — SIEM, partner feed, regulatory submission — verify the SHA-256 checksum to confirm nothing was modified after the snapshot was written.

snap = vm.get_version("q3_2025_post_nvd_sweep")

if not vm.verify_checksum(snap):
    raise RuntimeError("Checksum mismatch on q3_2025_post_nvd_sweep — aborting publish.")

print("Integrity verified — safe to publish.")

Rolling Back

Pass the target label and require_confirmation=False (an explicit safety gate) to restore the graph to any prior snapshot.

vm.restore_snapshot(
    graph                = graph,
    target_version       = "q3_2025_baseline",
    require_confirmation = False,
)

# Record the rollback event as a new snapshot
vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = "q3_2025_rollback",
    author        = "analyst_zhang@example.com",
    description   = "Rolled back to baseline after corrupted OSINT batch",
)

restore_snapshot raises ProcessingError if require_confirmation is not explicitly False, preventing accidental rollbacks from automated scripts.

Building an Audit Changelog

List all snapshots in chronological order and diff each one against its predecessor to produce a human-readable change log.

versions = sorted(vm.list_versions(), key=lambda v: v["timestamp"])

print("Graph Change Log")
print("=" * 60)

for i, v in enumerate(versions):
    print("\n[{}] {}  (by {})".format(v["timestamp"][:10], v["label"], v["author"]))
    print("  " + v["description"])

    if i > 0:
        diff = vm.compare_versions(versions[i - 1]["label"], v["label"])
        s    = diff["summary"]
        print("  Changes: +{} nodes  -{} nodes  +{} edges  -{} edges".format(
            s["nodes_added"], s["nodes_removed"],
            s["edges_added"], s["edges_removed"],
        ))

Sample output:

Graph Change Log
============================================================

[2025-07-01] q3_2025_baseline  (by analyst_zhang@example.com)
  CTI baseline before Q3 OSINT sweep

[2025-07-14] q3_2025_post_nvd_sweep  (by osint_pipeline@example.com)
  After NVD weekly sweep — 2025-07-14
  Changes: +2 nodes  -0 nodes  +1 edges  -0 edges

[2025-07-14] q3_2025_rollback  (by analyst_zhang@example.com)
  Rolled back to baseline after corrupted OSINT batch
  Changes: -2 nodes  +0 nodes  -1 edges  +0 edges

Tagging Milestones

Attach a named tag to any snapshot to mark review gates, approved states, or regulatory submissions.

vm.tag_version("q3_2025_post_nvd_sweep", "q3-approved")

for tag_name, version_label in vm.list_tags().items():
    print(f"{tag_name:20s} → {version_label}")
# q3-approved          → q3_2025_post_nvd_sweep

Node History

Attach TemporalVersionManager to a live ContextGraph to automatically record every individual mutation — every add_node, add_edge, and update call — not just snapshot-level diffs.

vm.attach_to_graph(graph)

graph.add_node("apt29-alias", "ThreatActor", "NOBELIUM (rebranding 2021)")
graph.add_edge("apt29", "apt29-alias", "alias_of", weight=1.0)

for record in vm.get_node_history("apt29"):
    print("[{}] {} on {}  payload={}".format(
        record["timestamp"], record["operation"], record["entity_id"],
        str(record["payload"])[:60],
    ))

AgentContext Integration

Pass the version manager at AgentContext construction. Agent checkpoints and manual snapshots share the same storage, giving you one unified history.

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

graph = ContextGraph()
vm    = TemporalVersionManager(storage_path="agent_versions.db")

context = AgentContext(
    vector_store             = VectorStore(backend="faiss", dimension=768),
    knowledge_graph          = graph,
    temporal_version_manager = vm,
)

context.store("APT29 targeting NATO infrastructure in Q3 2025")
context.checkpoint("pre_analysis")

# ... agent reasoning loop ...

context.checkpoint("post_analysis")
context.flush_checkpoint("post_analysis")   # persists via vm

diff = context.diff_checkpoints("pre_analysis", "post_analysis")
print("Decisions added    :", len(diff["decisions_added"]))
print("Relationships added:", len(diff["relationships_added"]))

Common Pitfalls

Snapshotting huge graphs too frequently: TemporalVersionManager snapshots the entire graph structure. Doing this on every minor edit for a massive graph will cause severe storage bloat. Use it for milestone gating, not event sourcing.
Forgetting attach_to_graph before mutation tracking: If you want to use get_node_history(), you must call vm.attach_to_graph(graph) before any mutations happen. Otherwise, the events will not be captured.
Confusing provenance with versioning: Do not use version snapshots to answer “Where did this specific node’s data come from?”. That is the role of the Provenance module. Versioning tracks the state of the entire graph at a point in time.
Forgetting rollback confirmation requirements: Calling restore_snapshot in automated scripts will raise a ProcessingError and crash your pipeline unless you explicitly pass require_confirmation=False.
Storage growth from excessive snapshots: Over time, SQLite databases can grow large if you never prune old snapshots or if you snapshot unnecessarily.

Domain Examples

Defense — CTI Pipeline
Security — Incident Response
Life Science — Clinical Trial
Banking — Model Governance

Daily NVD and ISAC feed ingestion with pre/post snapshots, a SOC change bulletin, and an integrity gate before publishing to the SIEM.

from semantica.change_management import TemporalVersionManager
from semantica.context import ContextGraph
import datetime

graph = ContextGraph()
vm    = TemporalVersionManager(storage_path="cti_versions.db")
today = datetime.date.today().isoformat()

snap_pre = vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = f"pre_nvd_{today}",
    author        = "osint_pipeline@example.com",
    description   = "CTI baseline before NVD sweep",
)

# Ingestion
graph.add_node("cve-2025-1337",    "Vulnerability", "CVE-2025-1337 critical RCE")
graph.add_node("apt29-q3-cluster", "ThreatActor",   "APT29 Q3 2025 campaign cluster")
graph.add_edge("apt29-q3-cluster", "cve-2025-1337", "weaponizes", weight=0.91)

snap_post = vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = f"post_nvd_{today}",
    author        = "osint_pipeline@example.com",
    description   = "After NVD sweep",
)

diff = vm.compare_versions(snap_pre["label"], snap_post["label"])
s    = diff["summary"]
print(f"SOC Bulletin: +{s['nodes_added']} threat nodes, +{s['edges_added']} relationships")
for n in diff["nodes_added"]:
    print(f"  + [{n.get('type')}] {n.get('content')}")

if not vm.verify_checksum(snap_post):
    raise RuntimeError("Checksum mismatch — aborting SIEM publish")
print("Snapshot verified — publishing to SIEM feed.")

Snapshot the incident graph at each major discovery during a live IR engagement so the post-mortem can replay exactly how the investigation evolved.

from semantica.change_management import TemporalVersionManager
from semantica.context import ContextGraph

graph = ContextGraph()
vm    = TemporalVersionManager(storage_path="incident_ir042.db")

# T+0: Initial triage
graph.add_node("wkstn-047",   "Host",    "Compromised workstation WKSTN-047")
graph.add_node("attacker-ip", "Network", "Attacker ingress 185.220.101.47")
graph.add_edge("attacker-ip", "wkstn-047", "initial_access", weight=0.95)

vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = "ir042_t0_triage",
    author        = "analyst_chen@example.com",
    description   = "T+0 — one compromised host identified",
)

# T+2h: Lateral movement confirmed
graph.add_node("dc01",       "Host",    "Domain controller DC01")
graph.add_node("svc-backup", "Account", "Stolen service account SVC-BACKUP")
graph.add_edge("wkstn-047",  "svc-backup", "credential_theft", weight=0.88)
graph.add_edge("svc-backup", "dc01",       "lateral_move",     weight=0.82)

vm.create_snapshot(
    graph         = graph.to_dict(),
    version_label = "ir042_t2h_lateral",
    author        = "analyst_chen@example.com",
    description   = "T+2h — lateral movement to DC01 via stolen SVC-BACKUP",
)

diff = vm.compare_versions("ir042_t0_triage", "ir042_t2h_lateral")
s    = diff["summary"]
print(f"Post-mortem T+0→T+2h: +{s['nodes_added']} hosts/accounts, +{s['edges_added']} attack paths")
for edge in diff["edges_added"]:
    print(f"  + {edge.get('source')} → {edge.get('target')} [{edge.get('type')}]")

Version the trial knowledge graph across phases to produce a machine-verifiable diff between Phase II and Phase III submissions for regulatory review.

from semantica.change_management import TemporalVersionManager
from semantica.context import ContextGraph

graph_ph2 = ContextGraph()
graph_ph2.add_node("compound-xr401", "Compound", "XR-401")
graph_ph2.add_node("endpoint-orr",   "Endpoint", "Overall Response Rate")
graph_ph2.add_node("disease-nsclc",  "Disease",  "NSCLC")

graph_ph3 = ContextGraph()
graph_ph3.add_node("compound-xr401",  "Compound",   "XR-401")
graph_ph3.add_node("endpoint-orr",    "Endpoint",   "Overall Response Rate")
graph_ph3.add_node("endpoint-pfs",    "Endpoint",   "Progression-Free Survival")
graph_ph3.add_node("disease-nsclc",   "Disease",    "NSCLC")
graph_ph3.add_node("comparator-doce", "Comparator", "Docetaxel")

vm = TemporalVersionManager(storage_path="trial_xr401.db")

vm.create_snapshot(
    graph=graph_ph2.to_dict(), version_label="phase_ii_v1.0",
    author="clinical_data_team@example.com", description="Phase II — ORR primary, NSCLC",
)
vm.create_snapshot(
    graph=graph_ph3.to_dict(), version_label="phase_iii_v2.0",
    author="clinical_data_team@example.com", description="Phase III — PFS co-primary, Docetaxel added",
)

diff = vm.compare_versions("phase_ii_v1.0", "phase_iii_v2.0")
print("Regulatory diff Phase II → Phase III:")
for n in diff["nodes_added"]:
    print(f"  + [{n.get('type')}] {n.get('content')}")

snap = vm.get_version("phase_iii_v2.0")
assert vm.verify_checksum(snap), "Checksum failed — cannot attach to dossier"
print("Phase III snapshot verified — safe for regulatory submission.")

Version the Basel III credit risk model graph on each regulatory update and produce a machine-verifiable diff for the model governance committee before production deployment.

from semantica.change_management import TemporalVersionManager
from semantica.context import ContextGraph

graph = ContextGraph()
graph.add_node("metric-cet1",      "CapitalMetric", "CET1 Capital Ratio")
graph.add_node("metric-ltv",       "RiskParameter", "LTV Ratio")
graph.add_node("metric-pd",        "RiskParameter", "Probability of Default")
graph.add_node("metric-lgd",       "RiskParameter", "Loss Given Default")
graph.add_node("regulation-cre20", "Regulation",    "Basel III CRE20")

vm = TemporalVersionManager(storage_path="credit_risk_versions.db")

vm.create_snapshot(
    graph=graph.to_dict(), version_label="basel_v1.0",
    author="risk_model_team@example.com", description="Basel III CRE20 initial graph",
)

# Regulatory update — DSCR becomes mandatory
graph.add_node("metric-dscr", "RiskParameter", "Debt Service Coverage Ratio")
graph.add_edge("regulation-cre20", "metric-dscr", "requires", weight=1.0)

vm.create_snapshot(
    graph=graph.to_dict(), version_label="basel_v1.1",
    author="risk_model_team@example.com", description="DSCR added per EBA GL 2020/06",
)

diff = vm.compare_versions("basel_v1.0", "basel_v1.1")
s    = diff["summary"]
print(f"Model governance diff v1.0→v1.1: +{s['nodes_added']} params, +{s['edges_added']} rules")
for n in diff["nodes_added"]:
    print(f"  + [{n.get('type')}] {n.get('content')}")

snap = vm.get_version("basel_v1.1")
assert vm.verify_checksum(snap), "Checksum failed — aborting model deployment"
print("Model v1.1 verified and approved for production.")

Context Graphs — ContextGraph.to_dict() feeds create_snapshot()
Ontology Management — pair ontology versioning with graph versioning for a complete schema + data audit trail
SHACL Validation — validate graph data at each version gate before snapshotting
Provenance — combine change management with W3C PROV-O lineage for a full audit trail
Visualization — TemporalVisualizer.visualize_snapshot_comparison() and visualize_metrics_evolution() render version diffs as interactive charts

Overview

Core Concepts

Guides

Integrations

Graph Stores

Vector Stores

Change Management & Versioning

What Is Change Management & Versioning?

Storage Behavior

Why Use Change Management?

Which Tool Do I Need?

When To Use / When Not To Use

Typical Workflow

Universal Example: Employee Profile Update

Creating Snapshots

Comparing Two Snapshots

Verifying Integrity

Rolling Back

Building an Audit Changelog

Tagging Milestones

Node History

AgentContext Integration

Common Pitfalls

Domain Examples

​What Is Change Management & Versioning?

​Storage Behavior

​Why Use Change Management?

​Which Tool Do I Need?

​When To Use / When Not To Use

​Typical Workflow

​Universal Example: Employee Profile Update

​Creating Snapshots

​Comparing Two Snapshots

​Verifying Integrity

​Rolling Back

​Building an Audit Changelog

​Tagging Milestones

​Node History

​AgentContext Integration

​Common Pitfalls

​Domain Examples

​Related Guides

What Is Change Management & Versioning?

Storage Behavior

Why Use Change Management?

Which Tool Do I Need?

When To Use / When Not To Use

Typical Workflow

Universal Example: Employee Profile Update

Creating Snapshots

Comparing Two Snapshots

Verifying Integrity

Rolling Back

Building an Audit Changelog

Tagging Milestones

Node History

AgentContext Integration

Common Pitfalls

Domain Examples

Related Guides