Vibe Features

Vibe Agent uses a high-level planner to architect specialized multi-agent workflows. This page covers the technical design behind graph generation, tool integration, and flow lifecycle management.

Initialization Parameters

The VibeAgent accepts the following configuration parameters:

Parameter	Type	Description
planner_provider	str	LLM provider for the planner (openai, anthropic, gemini).
planner_api_key	str	API key for the planner provider.
planner_model	str	Specific model version (e.g., gpt-4o, gemini-2.0-flash).
text_model	str	(Optional) Preferred model string for text tasks.
image_model	str	(Optional) Preferred model string for image tasks.
speech_model	str	(Optional) Preferred model string for speech tasks.
recognition_model	str	(Optional) Preferred model string for transcription tasks.
processors	dict	Mapping of strings to post-processing functions.
context_files	list	Custom list of code files to provide to the planner for context.

Quick Example

from intelli.flow import VibeAgent

# 1. Initialize the architect
vf = VibeAgent(planner_provider="openai", planner_api_key="your_key")

# 2. Build the graph from a vibe
flow = await vf.build("Research AI trends and generate a summary report")

# 3. Execute the flow
results = await flow.start()

The Generated Blueprint

The blueprint is the bridge between natural language and the Flow execution engine.

Task Definition

Each task in the blueprint follows this schema:

• name: Unique identifier for the task node.
• desc: The instruction to be executed by the agent.
• agent: AgentSpec containing agent_type, provider, and mission.
• model_params: Dictionary of model-specific parameters (e.g., temperature, max_tokens).
• post_process: Key of a function defined in the VibeAgent.processors map.

Global Configuration

• max_workers: Integer defining the maximum parallel task execution.
• output_memory_map: Maps task outputs to shared memory keys for downstream access.
• map_paths: Defines the dependency edges between tasks (DAG structure).

Advanced Methods

Flow Persistence

Use save_bundle to export the generated blueprint for production use without re-running the planner.

# Exports flow_spec.json and vibeflow_graph.png
vf.save_bundle(save_dir="./prod_flow", spec=vf.last_spec, flow=flow)

Direct Blueprint Execution

Reconstruct a flow directly from a saved blueprint to save latency and costs.

spec = vf.load_spec("./prod_flow/flow_spec.json")
flow = vf.build_from_spec(spec)

Blueprint Modification

Modify existing architectures using natural language instructions.

# existing_spec is loaded from a path or vf.last_spec
updated_flow = await vf.edit(spec_path="./path/to/spec.json", instruction="Add a validation step")

Runtime Features

Environment Variable Injection

Vibe Agent supports secret resolution at runtime using the ${ENV:VAR_NAME} syntax. This ensures that API keys or sensitive endpoints are not persisted in the flow_spec.json file.

Automatic Output Management

Configure the execution engine to handle file persistence automatically:

• flow.auto_save_outputs = True: Automatically writes images and audio files to disk.
• flow.output_dir = "./path": Sets the target directory for all generated assets.