WIN-JHFT4D3SIVT\caoxiaozhu
4251a79062
Introduce a manifest-backed agent registry surface and align graph tests with the new runtime prompt and tool indexing behavior.
115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
from app.agents.prompts import SUB_COMMANDER_PROMPTS_BY_KEY
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from app.agents.registry.models import (
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AgentManifest,
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CapabilityManifest,
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SpecialistTemplateManifest,
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SubCommanderManifest,
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)
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from app.agents.state import AgentRole
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from app.agents.tools import SUB_COMMANDER_TOOLSETS
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TOP_LEVEL_AGENT_DEFAULT_SUB_COMMANDERS: dict[str, tuple[str, ...]] = {
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AgentRole.MASTER.value: (),
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AgentRole.SCHEDULE_PLANNER.value: (
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"schedule_analysis",
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"schedule_planning",
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),
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AgentRole.EXECUTOR.value: (
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"executor_tasks",
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"executor_forum",
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),
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AgentRole.LIBRARIAN.value: (
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"librarian_retrieval",
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"librarian_graph",
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),
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AgentRole.ANALYST.value: (
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"analyst_progress",
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"analyst_insights",
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),
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}
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TOP_LEVEL_AGENT_DISPLAY_NAMES: dict[str, str] = {
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AgentRole.MASTER.value: "Master",
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AgentRole.SCHEDULE_PLANNER.value: "Schedule Planner",
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AgentRole.EXECUTOR.value: "Executor",
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AgentRole.LIBRARIAN.value: "Librarian",
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AgentRole.ANALYST.value: "Analyst",
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}
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TOP_LEVEL_AGENT_ROUTING_HINTS: dict[str, tuple[str, ...]] = {
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AgentRole.MASTER.value: (
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"Route user requests to the most suitable top-level runtime agent or answer directly.",
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),
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AgentRole.SCHEDULE_PLANNER.value: (
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"Handle planning-oriented requests using schedule analysis and schedule planning sub-commanders.",
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),
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AgentRole.EXECUTOR.value: (
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"Handle execution-oriented requests using task and forum sub-commanders.",
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),
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AgentRole.LIBRARIAN.value: (
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"Handle knowledge retrieval and graph-context requests using librarian sub-commanders.",
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),
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AgentRole.ANALYST.value: (
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"Handle reporting and insight requests using analyst sub-commanders.",
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),
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}
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SUB_COMMANDER_PARENT_AGENT_IDS: dict[str, str] = {
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"schedule_analysis": AgentRole.SCHEDULE_PLANNER.value,
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"schedule_planning": AgentRole.SCHEDULE_PLANNER.value,
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"executor_tasks": AgentRole.EXECUTOR.value,
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"executor_forum": AgentRole.EXECUTOR.value,
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"librarian_retrieval": AgentRole.LIBRARIAN.value,
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"librarian_graph": AgentRole.LIBRARIAN.value,
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"analyst_progress": AgentRole.ANALYST.value,
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"analyst_insights": AgentRole.ANALYST.value,
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}
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BUILTIN_AGENT_MANIFESTS: tuple[AgentManifest, ...] = tuple(
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AgentManifest(
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agent_id=role.value,
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display_name=TOP_LEVEL_AGENT_DISPLAY_NAMES[role.value],
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role_value=role.value,
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system_prompt_key=role.value,
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routing_hints=list(TOP_LEVEL_AGENT_ROUTING_HINTS[role.value]),
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default_sub_commanders=list(TOP_LEVEL_AGENT_DEFAULT_SUB_COMMANDERS[role.value]),
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skill_context_key=role.value.replace("agent_", ""),
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)
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for role in AgentRole
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)
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_capability_tool_names = tuple(
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dict.fromkeys(
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tool.name
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for tools in SUB_COMMANDER_TOOLSETS.values()
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for tool in tools
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)
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)
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BUILTIN_CAPABILITY_MANIFESTS: tuple[CapabilityManifest, ...] = tuple(
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CapabilityManifest(
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capability_id=tool_name,
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tool_name=tool_name,
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)
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for tool_name in _capability_tool_names
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)
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BUILTIN_SUB_COMMANDER_MANIFESTS: tuple[SubCommanderManifest, ...] = tuple(
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SubCommanderManifest(
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sub_commander_id=sub_commander_id,
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parent_agent_id=SUB_COMMANDER_PARENT_AGENT_IDS[sub_commander_id],
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prompt_text=SUB_COMMANDER_PROMPTS_BY_KEY[sub_commander_id],
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capability_ids=list(
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dict.fromkeys(tool.name for tool in tools)
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),
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)
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for sub_commander_id, tools in SUB_COMMANDER_TOOLSETS.items()
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)
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BUILTIN_SPECIALIST_TEMPLATE_MANIFESTS: tuple[SpecialistTemplateManifest, ...] = ()
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