71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
"""Policy guardrails for swarms-generated agent team plans."""
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from __future__ import annotations
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from typing import Any
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class SwarmsPolicy:
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"""Clamp AutoSwarmBuilder output before nanobot executes it."""
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allowed_swarm_types = {
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# Keep this list to swarms that consume the provided nanobot agent adapters.
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"GroupChat",
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"SequentialWorkflow",
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"ConcurrentWorkflow",
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"AgentRearrange",
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"MixtureOfAgents",
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"HierarchicalSwarm",
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}
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def __init__(self, *, max_agents: int = 4, max_loops: int = 3) -> None:
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self.max_agents = max(1, max_agents)
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self.max_loops = max(1, max_loops)
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def validate_auto_config(self, raw_config: dict[str, Any]) -> dict[str, Any]:
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config = self._plain_dict(raw_config)
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swarm_type = str(
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config.get("swarm_type")
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or config.get("type")
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or config.get("architecture")
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or "GroupChat"
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)
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if swarm_type not in self.allowed_swarm_types:
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swarm_type = "GroupChat"
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config["swarm_type"] = swarm_type
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agents = list(config.get("agents") or [])[: self.max_agents]
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config["agents"] = [self._sanitize_agent_spec(item) for item in agents]
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config["max_loops"] = min(max(1, int(config.get("max_loops") or 2)), self.max_loops)
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# AutoSwarmBuilder may suggest structure, not grant capabilities.
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config.pop("tools", None)
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config.pop("mcp_url", None)
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config.pop("mcp_urls", None)
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config.pop("llm_api_key", None)
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config.pop("api_key", None)
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return config
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def _plain_dict(self, raw_config: Any) -> dict[str, Any]:
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if isinstance(raw_config, dict):
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return dict(raw_config)
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model_dump = getattr(raw_config, "model_dump", None)
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if callable(model_dump):
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payload = model_dump()
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return dict(payload) if isinstance(payload, dict) else {}
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dict_method = getattr(raw_config, "dict", None)
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if callable(dict_method):
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payload = dict_method()
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return dict(payload) if isinstance(payload, dict) else {}
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return {}
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def _sanitize_agent_spec(self, item: Any) -> dict[str, Any]:
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spec = self._plain_dict(item)
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return {
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"agent_name": str(spec.get("agent_name") or spec.get("name") or "specialist"),
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"description": str(spec.get("description") or spec.get("agent_description") or ""),
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"system_prompt": str(spec.get("system_prompt") or "")[:4000],
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"role": str(spec.get("role") or "worker"),
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}
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