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Orchestrators API Reference

This page documents the API for DeepCritical orchestrators.

IterativeResearchFlow

Module: src.orchestrator.research_flow

Purpose: Single-loop research with search-judge-synthesize cycles.

Methods

run

Runs iterative research flow.

Parameters: - query: Research query string - background_context: Background context (default: "") - output_length: Optional description of desired output length (default: "") - output_instructions: Optional additional instructions for report generation (default: "") <<<<<<< HEAD - message_history: Optional user conversation history in Pydantic AI ModelMessage format (default: None)

Returns: Final report string.

Note: The message_history parameter enables multi-turn conversations by providing context from previous interactions.

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Returns: Final report string.

origin/dev Note: max_iterations, max_time_minutes, and token_budget are constructor parameters, not run() parameters.

DeepResearchFlow

Module: src.orchestrator.research_flow

Purpose: Multi-section parallel research with planning and synthesis.

Methods

run

Runs deep research flow.

Parameters: - query: Research query string <<<<<<< HEAD - message_history: Optional user conversation history in Pydantic AI ModelMessage format (default: None)

Returns: Final report string.

Note: The message_history parameter enables multi-turn conversations by providing context from previous interactions.

=======

Returns: Final report string.

origin/dev Note: max_iterations_per_section, max_time_minutes, and token_budget are constructor parameters, not run() parameters.

GraphOrchestrator

Module: src.orchestrator.graph_orchestrator

Purpose: Graph-based execution using Pydantic AI agents as nodes.

Methods

run

Runs graph-based research orchestration.

Parameters: - query: Research query string <<<<<<< HEAD - message_history: Optional user conversation history in Pydantic AI ModelMessage format (default: None)

Yields: AgentEvent objects during graph execution.

Note: - research_mode and use_graph are constructor parameters, not run() parameters. - The message_history parameter enables multi-turn conversations by providing context from previous interactions. Message history is stored in GraphExecutionContext and passed to agents during execution. =======

Yields: AgentEvent objects during graph execution.

Note: research_mode and use_graph are constructor parameters, not run() parameters.

origin/dev

Orchestrator Factory

Module: src.orchestrator_factory

Purpose: Factory for creating orchestrators.

Functions

create_orchestrator

Creates an orchestrator instance.

Parameters: - search_handler: Search handler protocol implementation (optional, required for simple mode) - judge_handler: Judge handler protocol implementation (optional, required for simple mode) - config: Configuration object (optional) - mode: Orchestrator mode ("simple", "advanced", "magentic", "iterative", "deep", "auto", or None for auto-detect) - oauth_token: Optional OAuth token from HuggingFace login (takes priority over env vars)

Returns: Orchestrator instance.

Raises: - ValueError: If requirements not met

Modes: - "simple": Legacy orchestrator - "advanced" or "magentic": Magentic orchestrator (requires OpenAI API key) - None: Auto-detect based on API key availability

MagenticOrchestrator

Module: src.orchestrator_magentic

Purpose: Multi-agent coordination using Microsoft Agent Framework.

Methods

run

Runs Magentic orchestration.

Parameters: - query: Research query string

Yields: AgentEvent objects converted from Magentic events.

Note: max_rounds and max_stalls are constructor parameters, not run() parameters.

Requirements: - agent-framework-core package - OpenAI API key

See Also