mirror of
https://github.com/Heretek-AI/MuseEngine.git
synced 2026-11-10 10:21:29 -10:00
Claude
7e25f39bc5
The original upload never called a model, its autonomous actions only logged that they had happened, and its benchmark was rigged. This replaces it with: - museengine.engine: analyze -> map -> generate -> adversarial critique -> Pareto selection, each step an LLM call with a JSON contract. Offline mode returns labelled keyword suggestions and no fake scores. - museengine.memory: plain-SQLite episodes, notes, state and audit log. - museengine.governance + agent: an autonomous loop behind an allowlist, step budget, kill switch and human approval for self-chosen goals, with every decision and real effect audited. - museengine.evaluation: blinded, order-swapped A/B eval with an exact sign test, replacing the keyword scorer and fudge constants. - Optional Anthropic adapter; the core accepts any (str) -> str callable. - pyproject, CLI, 24 offline tests, CI workflow. The original files are kept unchanged in legacy/ (minus a duplicate). Co-Authored-By: Claude Opus 5.5 (1M context) <[email protected]> Claude-Session: https://claude.ai/code/session_01YbuVfbQEpaitM5TMjBMzkF
27 lines
1.1 KiB
Python
27 lines
1.1 KiB
Python
"""MuseEngine: structured cross-domain ideation for LLM agents."""
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from .agent import MuseAgent, RunResult
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from .archetypes import Archetype, load_archetypes, load_default_archetypes
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from .engine import Analogy, Candidate, MuseEngine, ProblemStructure, Report, pareto_frontier
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from .evaluation import EvalResult, run_ab_eval, sign_test
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from .governance import (
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GovernanceError,
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Governor,
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KillSwitch,
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Policy,
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ProposedAction,
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console_approver,
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deny_all,
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)
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from .llm import LLM, BudgetExceeded, CountingLLM, LLMError, anthropic_llm, complete_json
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from .memory import Episode, MemoryStore
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from .middleware import MuseMiddleware
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__all__ = [
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"Analogy", "Archetype", "BudgetExceeded", "Candidate", "CountingLLM", "Episode", "EvalResult",
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"GovernanceError", "Governor", "KillSwitch", "LLM", "LLMError", "MemoryStore", "MuseAgent",
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"MuseEngine", "MuseMiddleware", "Policy", "ProblemStructure", "ProposedAction", "Report",
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"RunResult", "anthropic_llm", "complete_json", "console_approver", "deny_all",
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"load_archetypes", "load_default_archetypes", "pareto_frontier", "run_ab_eval", "sign_test",
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]
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