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Core Concepts

Understanding the key concepts behind Vibe AIGC.

The Intent-Execution Gap

Traditional AI content generation suffers from a fundamental problem: the Intent-Execution Gap. Users have complex, multi-dimensional creative visions, but current single-shot models require precise prompt engineering to achieve desired results.

Vibe AIGC addresses this by:

  1. Accepting high-level intent (Vibes) instead of detailed prompts
  2. Automatically decomposing intent into executable steps
  3. Adapting dynamically based on execution feedback

Vibe

A Vibe is a high-level representation of creative intent:

from vibe_aigc import Vibe

vibe = Vibe(
    description="Main creative goal",      # What you want
    style="aesthetic preferences",          # How it should feel
    constraints=["limit1", "limit2"],      # Boundaries
    domain="content type",                  # Context
    metadata={"key": "value"}              # Additional info
)

Unlike prompts, Vibes are:

  • Multi-dimensional: Capture style, constraints, and context
  • Composable: Can be refined through feedback
  • Abstract: Don't specify implementation details

MetaPlanner

The MetaPlanner is the central orchestrator that:

  1. Receives a Vibe from the user
  2. Uses LLM reasoning to decompose it into tasks
  3. Generates a hierarchical WorkflowPlan
  4. Manages execution and adaptation
from vibe_aigc import MetaPlanner

planner = MetaPlanner()
plan = await planner.plan(vibe)      # Planning only
result = await planner.execute(vibe)  # Plan + execute

WorkflowPlan

A WorkflowPlan is the executable decomposition of a Vibe:

from vibe_aigc import WorkflowPlan, WorkflowNode

# Plans contain hierarchical nodes
plan = WorkflowPlan(
    id="plan-001",
    source_vibe=vibe,
    root_nodes=[node1, node2, node3]
)

WorkflowNode

WorkflowNodes are individual tasks in the workflow:

from vibe_aigc import WorkflowNode, WorkflowNodeType

node = WorkflowNode(
    id="research",
    type=WorkflowNodeType.ANALYZE,
    description="Research the topic",
    dependencies=["setup"],  # Must complete first
    children=[sub_node1]     # Hierarchical decomposition
)

Node Types

Type Purpose
ANALYZE Information gathering and analysis
GENERATE Content creation
TRANSFORM Modify existing content
VALIDATE Quality checks
COMPOSITE Groups of related tasks

Execution Flow

graph TD
    A[User Vibe] --> B[MetaPlanner]
    B --> C[WorkflowPlan]
    C --> D[WorkflowExecutor]
    D --> E{Node Type}
    E -->|Independent| F[Parallel Execution]
    E -->|Dependent| G[Sequential Execution]
    F --> H[Results]
    G --> H
    H --> I{Success?}
    I -->|Yes| J[Complete]
    I -->|No| K[Adaptive Replan]
    K --> B

Feedback Loops

The system supports multiple feedback mechanisms:

  1. Progress Callbacks: Real-time execution updates
  2. Adaptive Replanning: Automatic recovery from failures
  3. Checkpointing: Save/restore execution state

Parallel Execution

Independent nodes execute concurrently:

     ┌─── Node A ───┐
     │              │
Start├─── Node B ───┼─── End
     │              │
     └─── Node C ───┘

Nodes with dependencies wait:

Node A ──→ Node B ──→ Node C
          (waits)    (waits)

Next Steps