Spaces:
Running
Running
File size: 4,571 Bytes
026ee5d d45d242 026ee5d d45d242 f173aad d45d242 f173aad d45d242 026ee5d d45d242 026ee5d f173aad d45d242 f173aad d45d242 026ee5d d45d242 026ee5d f173aad 026ee5d f173aad d45d242 026ee5d d45d242 026ee5d d45d242 026ee5d d45d242 026ee5d d45d242 026ee5d 7a4d2bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
# 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`
<!--codeinclude-->
[IterativeResearchFlow.run](../src/orchestrator/research_flow.py) start_line:134 end_line:140
<!--/codeinclude-->
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: "")
- `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.
**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`
<!--codeinclude-->
[DeepResearchFlow.run](../src/orchestrator/research_flow.py) start_line:778 end_line:778
<!--/codeinclude-->
Runs deep research flow.
**Parameters**:
- `query`: Research query string
- `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.
**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`
<!--codeinclude-->
[GraphOrchestrator.run](../src/orchestrator/graph_orchestrator.py) start_line:177 end_line:177
<!--/codeinclude-->
Runs graph-based research orchestration.
**Parameters**:
- `query`: Research query string
- `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.
## Orchestrator Factory
**Module**: `src.orchestrator_factory`
**Purpose**: Factory for creating orchestrators.
### Functions
#### `create_orchestrator`
<!--codeinclude-->
[create_orchestrator](../src/orchestrator_factory.py) start_line:44 end_line:50
<!--/codeinclude-->
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`
<!--codeinclude-->
[MagenticOrchestrator.run](../src/orchestrator_magentic.py) start_line:101 end_line:101
<!--/codeinclude-->
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
- [Architecture - Orchestrators](../architecture/orchestrators.md) - Architecture overview
- [Graph Orchestration](../architecture/graph_orchestration.md) - Graph execution details
|