dongarandClaude Opus 5.5
b077906631
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It drove the orchestrator until EXECUTING and accepted EXECUTING as the end state, which with no publisher configured was the stranding this branch removes: the milestone now finishes in PR_OPEN. Return once 'start milestone 1' is sent, since the milestone runs inside it, and expect PR_OPEN or AWAIT_INPUT. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PPQ14L3LikyefCMFBqEbKE
99 lines
3.6 KiB
Python
99 lines
3.6 KiB
Python
"""End-to-end milestone against real models: plan with the planner role,
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approve through the gate, then let the executor role actually create and test
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a file on disk.
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Uses the test config's planner model for every role: it needs a model that
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holds the tool-call contract on both ends.
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"""
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import io
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from pathlib import Path
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import pytest
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from nisse_dev.control import (
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AWAIT_ARCH_SIGNOFF,
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AWAIT_INPUT,
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AWAIT_MILESTONE_SIGNOFF,
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EXECUTING,
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PR_OPEN,
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Orchestrator,
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)
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from nisse_dev.executor import LLMExecutor
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from nisse_dev.llm import LiteLLMClient, RoleMap
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from nisse_dev.oplog import OpLog
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from nisse_dev.planner import LLMPlanningAgent
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from nisse_dev.state import StateStore
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from nisse_dev.tools import Workspace
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from nisse_dev.transport import InMemoryTransport
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pytestmark = pytest.mark.integration
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def _drive_to_execution(
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store: StateStore, transport: InMemoryTransport, max_steps: int = 10
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) -> None:
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"""Play the operator. A scripted approve/approve/start sequence is not
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robust against a live model: ask_human is a legitimate move at any gate,
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and a question shifts the state machine under a fixed script. So respond
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to whatever the agent actually did."""
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for _ in range(max_steps):
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state = store.get_state()
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assert state is not None
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if state.state == EXECUTING:
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return
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gate = state.data.get("gate") or {}
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if state.state == AWAIT_INPUT:
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transport.receive("don", "Use your best judgement and proceed.")
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elif state.state == AWAIT_MILESTONE_SIGNOFF and gate.get("approved"):
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# The milestone runs to completion inside this command.
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transport.receive("don", "start milestone 1")
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return
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elif state.state in (AWAIT_ARCH_SIGNOFF, AWAIT_MILESTONE_SIGNOFF):
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transport.receive("don", "approve")
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else:
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raise AssertionError(f"unexpected state {state.state}")
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raise AssertionError(
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f"never reached execution; last message: {transport.outbound[-1]}"
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)
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def test_plan_approve_execute_produces_a_real_file(
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tmp_path: Path, litellm: LiteLLMClient, live_model: str
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) -> None:
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# Both roles use the capable model here: the point is the whole gated
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# loop over a real proxy, not local-model competence (measured separately).
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model = live_model
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roles = RoleMap(executor=model, planner=model, delegate=model, consultant=model)
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client = litellm
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store = StateStore(tmp_path / "state.db", oplog=OpLog("nisse-dev", io.StringIO()))
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workspace = Workspace(tmp_path / "work", timeout_s=60)
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transport = InMemoryTransport()
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orchestrator = Orchestrator(
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store,
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transport,
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LLMPlanningAgent(client, store, roles),
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operators={"don"},
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roles=roles,
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executor=LLMExecutor(client, store, roles, workspace, max_turns=10),
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)
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orchestrator.start()
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transport.receive(
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"don",
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"use greet. Milestone 1: create greet.py in the workspace root defining "
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"greet(name) returning 'Hello, <name>!', and verify it by running python.",
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)
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_drive_to_execution(store, transport)
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state = store.get_state()
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assert state is not None
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# Never EXECUTING at rest: with no publisher a finished milestone waits in
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# PR_OPEN, and one that stopped short waits for the operator.
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assert state.state in (PR_OPEN, AWAIT_INPUT)
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created = list(workspace.root.rglob("*.py"))
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assert created, f"executor created no Python file; said: {transport.outbound[-1]}"
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combined = "\n".join(p.read_text() for p in created)
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assert "def greet" in combined
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