{
    "id": "abc123",
    "content": "User prefers dark mode",
    "memory_type": "standard",
    "scope": "agent-private",
    "agent_id": "assistant",
    "user_id": "user_123",
    "namespace": "my-app"
}

┌─────────────────────────────────────────────────────────┐
│                      GLOBAL                              │
│  Visible to ALL agents, ALL projects                    │
│  Use for: Company-wide best practices                   │
├─────────────────────────────────────────────────────────┤
│                   AGENT-SHARED                           │
│  Visible to specified agents                            │
│  Use for: Team knowledge, project context               │
├─────────────────────────────────────────────────────────┤
│                   AGENT-PRIVATE                          │
│  Visible only to the creating agent                     │
│  Use for: Working notes, draft reasoning                │
└─────────────────────────────────────────────────────────┘

# Private - only this agent sees it
client.add("My working notes", scope="agent-private")

# Shared - specific agents can see it
client.add("Task breakdown", scope="agent-shared",
           shared_with_agents=["executor", "reviewer"])

# Global - everyone sees it
client.add("Always use type hints", scope="global")

# Production app
client = AegisClient(api_key="...", namespace="production")

# Staging
client = AegisClient(api_key="...", namespace="staging")

# Different project
client = AegisClient(api_key="...", namespace="project-alpha")

# Planner agent stores its insights
client.add("User wants simple solution", agent_id="planner")

# Executor queries planner's memories
memories = client.query("user requirements", agent_id="executor",
                        cross_agent_ids=["planner"])

# Store user-specific preference
client.add("Prefers dark mode", user_id="user_123")

# Retrieve for that user
context = client.query("preferences", user_id="user_123")

"User prefers dark mode" → [0.12, -0.45, 0.78, ...]  (1536 dimensions)

# Query: "What theme does the user like?"
# Embedding: [0.15, -0.42, 0.81, ...]
# Matches: "User prefers dark mode" (cosine similarity: 0.94)

score = (helpful_votes - harmful_votes) / (total_votes + 1)

# Start of work
session = client.progress.create("build-auth")

# During work
client.progress.update("build-auth",
    completed=["login", "logout"],
    in_progress="password-reset",
    next=["2fa", "oauth"]
)

# After context reset, resume
session = client.progress.get("build-auth")
print(session.in_progress_item)  # "password-reset"

# Define what "done" means
client.features.create("user-auth",
    test_steps=["Can login", "Can logout", "Token expires correctly"]
)

# Only mark complete after verification
client.features.mark_complete("user-auth", verified_by="tester")

# Start a run before executing a task
run = client.start_run("task-42", "executor", memory_ids_used=["m1", "m2"])

# Complete with outcome -- auto-votes and auto-reflects
client.complete_run("task-42", success=True, evaluation={"score": 0.95})

result = client.curate(namespace="production")
print(f"Promoted: {len(result.promoted)}")  # High-effectiveness entries
print(f"Flagged: {len(result.flagged)}")    # Low-effectiveness entries
print(f"Consolidation: {len(result.consolidation_candidates)}")  # Duplicates