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Add multi-pattern orchestrator with plan-then-execute and supervisor (#739)

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Introduce an agent orchestrator service that supports three
execution patterns (ReAct, plan-then-execute, supervisor) with
LLM-based meta-routing to select the appropriate pattern and task
type per request. Update the agent schema to support
orchestration fields (correlation, sub-agents, plan steps) and
remove legacy response fields (answer, thought, observation).
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cybermaggedon 2026-03-31 00:32:49 +01:00 • committed by GitHub
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21 changed files with 3006 additions and 172 deletions
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29
trustgraph-base/trustgraph/schema/services/agent.py
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@ -1,5 +1,6 @@
from dataclasses import dataclass, field
from typing import Optional
from ..core.topic import topic
from ..core.primitives import Error
@ -8,6 +9,14 @@ from ..core.primitives import Error
# Prompt services, abstract the prompt generation
@dataclass
class PlanStep:
goal: str = ""
tool_hint: str = "" # Suggested tool for this step
depends_on: list[int] = field(default_factory=list) # Indices of prerequisite steps
status: str = "pending" # pending, running, completed, failed
result: str = "" # Result of step execution
@dataclass
class AgentStep:
thought: str = ""
@ -15,6 +24,9 @@ class AgentStep:
arguments: dict[str, str] = field(default_factory=dict)
observation: str = ""
user: str = "" # User context for the step
step_type: str = "" # "react", "plan", "execute", "decompose", "synthesise"
plan: list[PlanStep] = field(default_factory=list) # Plan steps (for plan-then-execute)
subagent_results: dict[str, str] = field(default_factory=dict) # Subagent results keyed by goal
@dataclass
class AgentRequest:
@ -27,6 +39,16 @@ class AgentRequest:
streaming: bool = False # Enable streaming response delivery (default false)
session_id: str = "" # For provenance tracking across iterations
# Orchestration fields
conversation_id: str = "" # Groups related requests into a conversation
pattern: str = "" # Selected pattern: "react", "plan-then-execute", "supervisor"
task_type: str = "" # Task type from config: "general", "research", etc.
framing: str = "" # Domain framing text injected into prompts
correlation_id: str = "" # Links fan-out subagents to parent for fan-in
parent_session_id: str = "" # Session ID of the supervisor that spawned this subagent
subagent_goal: str = "" # Specific goal for a subagent (set by supervisor)
expected_siblings: int = 0 # Number of sibling subagents in this fan-out
@dataclass
class AgentResponse:
# Streaming-first design
@ -39,11 +61,10 @@ class AgentResponse:
explain_id: str | None = None # Provenance URI (announced as created)
explain_graph: str | None = None # Named graph where explain was stored
# Legacy fields (deprecated but kept for backward compatibility)
answer: str = ""
# Orchestration fields
message_id: str = "" # Unique ID for this response message
error: Error | None = None
thought: str = ""
observation: str = ""
############################################################################