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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
parent 7af1d60db8
commit 849987f0e6
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21 changed files with 3006 additions and 172 deletions
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49
trustgraph-base/trustgraph/messaging/translators/agent.py
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@ -16,6 +16,14 @@ class AgentRequestTranslator(MessageTranslator):
collection=data.get("collection", "default"),
streaming=data.get("streaming", False),
session_id=data.get("session_id", ""),
conversation_id=data.get("conversation_id", ""),
pattern=data.get("pattern", ""),
task_type=data.get("task_type", ""),
framing=data.get("framing", ""),
correlation_id=data.get("correlation_id", ""),
parent_session_id=data.get("parent_session_id", ""),
subagent_goal=data.get("subagent_goal", ""),
expected_siblings=data.get("expected_siblings", 0),
)
def from_pulsar(self, obj: AgentRequest) -> Dict[str, Any]:
@ -28,6 +36,14 @@ class AgentRequestTranslator(MessageTranslator):
"collection": getattr(obj, "collection", "default"),
"streaming": getattr(obj, "streaming", False),
"session_id": getattr(obj, "session_id", ""),
"conversation_id": getattr(obj, "conversation_id", ""),
"pattern": getattr(obj, "pattern", ""),
"task_type": getattr(obj, "task_type", ""),
"framing": getattr(obj, "framing", ""),
"correlation_id": getattr(obj, "correlation_id", ""),
"parent_session_id": getattr(obj, "parent_session_id", ""),
"subagent_goal": getattr(obj, "subagent_goal", ""),
"expected_siblings": getattr(obj, "expected_siblings", 0),
}
@ -40,24 +56,15 @@ class AgentResponseTranslator(MessageTranslator):
def from_pulsar(self, obj: AgentResponse) -> Dict[str, Any]:
result = {}
# Check if this is a streaming response (has chunk_type)
if hasattr(obj, 'chunk_type') and obj.chunk_type:
if obj.chunk_type:
result["chunk_type"] = obj.chunk_type
if obj.content:
result["content"] = obj.content
result["end_of_message"] = getattr(obj, "end_of_message", False)
result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
else:
# Legacy format (non-streaming)
if obj.answer:
result["answer"] = obj.answer
if obj.thought:
result["thought"] = obj.thought
if obj.observation:
result["observation"] = obj.observation
# Include completion flags for legacy format too
result["end_of_message"] = getattr(obj, "end_of_message", False)
result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
if obj.content:
result["content"] = obj.content
result["end_of_message"] = getattr(obj, "end_of_message", False)
result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
if getattr(obj, "message_id", ""):
result["message_id"] = obj.message_id
# Include explainability fields if present
explain_id = getattr(obj, "explain_id", None)
@ -76,11 +83,5 @@ class AgentResponseTranslator(MessageTranslator):
def from_response_with_completion(self, obj: AgentResponse) -> Tuple[Dict[str, Any], bool]:
"""Returns (response_dict, is_final)"""
# For streaming responses, check end_of_dialog
if hasattr(obj, 'chunk_type') and obj.chunk_type:
is_final = getattr(obj, 'end_of_dialog', False)
else:
# For legacy responses, check if answer is present
is_final = (obj.answer is not None)
is_final = getattr(obj, 'end_of_dialog', False)
return self.from_pulsar(obj), is_final