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46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
"""Public Python API for PerfXpert's agent hierarchy.
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1:1 mirror of the MCP tools under :mod:`perfxpert.tools.agents`. Each
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callable exported here IS the same function the MCP server wraps — the
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Python API and the MCP surface share a single implementation. Use this
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module to embed PerfXpert's analysis brain in your own tooling without
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running the MCP server.
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Examples:
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>>> from perfxpert import api
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>>> verdict = api.agent_root(airgap=True, user_query="why slow?")
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>>> verdict["primary_bottleneck"]
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'mixed'
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>>> # Direct call into a Layer-2 specialist when the bottleneck is known:
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>>> techniques = api.agent_compute_specialist(
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... input={"gfx_id": "gfx942", "hot_kernels": hot},
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... airgap=True,
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... )
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Every callable honors ``PERFXPERT_AIRGAP=1`` plus the shared provider
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selection semantics from :func:`perfxpert.agents.runtime.build_session`.
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"""
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from perfxpert.tools.agents.analysis import agent_analysis
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from perfxpert.tools.agents.compute import agent_compute_specialist
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from perfxpert.tools.agents.correctness import agent_correctness
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from perfxpert.tools.agents.diff import agent_diff_specialist
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from perfxpert.tools.agents.latency import agent_latency_specialist
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from perfxpert.tools.agents.memory import agent_memory_specialist
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from perfxpert.tools.agents.recommendation import agent_recommendation
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from perfxpert.tools.agents.root import agent_root
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from perfxpert.tools.trace_diff import diff_runs as trace_diff_diff_runs
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__all__ = [
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"agent_root",
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"agent_analysis",
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"agent_recommendation",
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"agent_correctness",
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"agent_compute_specialist",
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"agent_memory_specialist",
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"agent_latency_specialist",
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"agent_diff_specialist",
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"trace_diff_diff_runs",
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]
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