Merge pull request 'Add executor tool loop (A12/A15) — step 5b' (#12) from executor-loop into main
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This commit was merged in pull request #12.
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dev-agent committed 2026-09-07 20:35:33 +00:00
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"""End-to-end milestone against real models: plan with the planner role,
approve through the gate, then let the executor role actually create and test
a file on disk.
Gated on NISSE_LIVE_PLANNER_MODEL like the planning test — it needs a model
that holds the tool-call contract on both ends.
"""
import io
import os
from pathlib import Path
import pytest
from nisse_dev.control import (
AWAIT_ARCH_SIGNOFF,
AWAIT_INPUT,
AWAIT_MILESTONE_SIGNOFF,
EXECUTING,
Orchestrator,
)
from nisse_dev.envfile import load_env_file
from nisse_dev.executor import LLMExecutor
from nisse_dev.llm import LiteLLMClient, RoleMap
from nisse_dev.oplog import OpLog
from nisse_dev.planner import LLMPlanningAgent
from nisse_dev.state import StateStore
from nisse_dev.tools import Workspace
from nisse_dev.transport import InMemoryTransport
load_env_file(Path(__file__).resolve().parents[2] / ".env")
SENTINEL = "NISSE_LIVE_PLANNER_MODEL"
pytestmark = [
pytest.mark.integration,
pytest.mark.skipif(
not os.environ.get(SENTINEL),
reason=f"no live planner route configured ({SENTINEL} unset)",
),
]
def _drive_to_execution(
store: StateStore, transport: InMemoryTransport, max_steps: int = 10
) -> None:
"""Play the operator. A scripted approve/approve/start sequence is not
robust against a live model: ask_human is a legitimate move at any gate,
and a question shifts the state machine under a fixed script. So respond
to whatever the agent actually did."""
for _ in range(max_steps):
state = store.get_state()
assert state is not None
if state.state == EXECUTING:
return
gate = state.data.get("gate") or {}
if state.state == AWAIT_INPUT:
transport.receive("don", "Use your best judgement and proceed.")
elif state.state == AWAIT_MILESTONE_SIGNOFF and gate.get("approved"):
transport.receive("don", "start milestone 1")
elif state.state in (AWAIT_ARCH_SIGNOFF, AWAIT_MILESTONE_SIGNOFF):
transport.receive("don", "approve")
else:
raise AssertionError(f"unexpected state {state.state}")
raise AssertionError(
f"never reached execution; last message: {transport.outbound[-1]}"
)
def test_plan_approve_execute_produces_a_real_file(tmp_path: Path) -> None:
assert os.environ.get("NISSE_LLM_TOKEN"), (
f"{SENTINEL} is set, so the live path is expected to work, "
"but NISSE_LLM_TOKEN is missing"
)
# Both roles use the capable model here: the point is the whole gated
# loop over a real proxy, not local-model competence (measured separately).
model = os.environ[SENTINEL]
roles = RoleMap(executor=model, planner=model, delegate=model, consultant=model)
client = LiteLLMClient.from_env()
store = StateStore(tmp_path / "state.db", oplog=OpLog("nisse-dev", io.StringIO()))
workspace = Workspace(tmp_path / "work", timeout_s=60)
transport = InMemoryTransport()
orchestrator = Orchestrator(
store,
transport,
LLMPlanningAgent(client, store, roles),
operators={"don"},
roles=roles,
executor=LLMExecutor(client, store, roles, workspace, max_turns=10),
)
orchestrator.start()
transport.receive(
"don",
"use greet. Milestone 1: create greet.py in the workspace root defining "
"greet(name) returning 'Hello, <name>!', and verify it by running python.",
)
_drive_to_execution(store, transport)
state = store.get_state()
assert state is not None
assert state.state in (EXECUTING, AWAIT_INPUT)
created = list(workspace.root.rglob("*.py"))
assert created, f"executor created no Python file; said: {transport.outbound[-1]}"
combined = "\n".join(p.read_text() for p in created)
assert "def greet" in combined