mirror of
https://github.com/khodges42/nightShift.git
synced 2026-06-14 18:18:36 +00:00
K. Hodges
646c655314
- Added operational run logging via nightshift/runlog.py. - CLI now streams progress during run / run --all. - Runs write .nightshift/runs/<run-id>/run.log and aggregate .nightshift/nightshift.log. - Web dashboard now shows the last 100 run log lines. - Added agent temperature config. - Added minimal openai_compatible backend and temperature passing for it. - Added Ollama temperature handling. - Added scoped repo lookup tools in nightshift/repo_tools.py: list_files, read_file, grep. - Planner agents can request lookup context with lookup_requests; NightShift saves files-inspected.md and reruns the planner with retrieved context. - Added repo_context stage type that writes context-pack.md. - Marked phases 23-27 complete in docs/design.md:990.
191 lines
7.3 KiB
Python
191 lines
7.3 KiB
Python
from pathlib import Path
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import tempfile
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import unittest
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from unittest.mock import MagicMock, patch
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from nightshift.agents import AgentExecutor, build_prompt_bundle, parse_review_output
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from nightshift.agents import AgentInvocation, format_agent_invocation
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from nightshift.artifacts import ArtifactStore
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from nightshift.config import AgentConfig, StageConfig
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from nightshift.tasks import parse_tasks
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TASK_MD = """# Tasks
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- [ ] TASK-001: Add fake agent coverage
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Description:
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Exercise fake command agents.
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Acceptance Criteria:
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- Prompt includes task details
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- Agent output is stored
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"""
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class AgentExecutorTests(unittest.TestCase):
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def test_build_prompt_bundle_includes_task_and_acceptance_criteria(self) -> None:
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task = parse_tasks(TASK_MD)[0]
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prompt = build_prompt_bundle(
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system_prompt="System rules",
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stage=StageConfig(id="plan", type="agent", agent="planner"),
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task=task,
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project_context="Project context",
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previous_outputs={"prior": "Earlier output"},
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retry_notes=["Retry note"],
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)
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self.assertIn("System rules", prompt)
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self.assertIn("TASK-001", prompt)
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self.assertIn("- Prompt includes task details", prompt)
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self.assertIn("Earlier output", prompt)
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self.assertIn("Retry note", prompt)
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def test_build_prompt_bundle_includes_task_context(self) -> None:
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task = parse_tasks(TASK_MD)[0]
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prompt = build_prompt_bundle(
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system_prompt="System rules",
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stage=StageConfig(id="plan", type="agent", agent="planner"),
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task=task,
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project_context="Project context",
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task_context="Task context body",
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previous_outputs={},
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retry_notes=[],
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retry_context="- No retries",
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)
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self.assertIn("## Task Context", prompt)
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self.assertIn("Task context body", prompt)
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self.assertIn("- No retries", prompt)
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def test_command_agent_writes_output_and_returns_pass(self) -> None:
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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prompt_path = root / "planner.md"
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prompt_path.write_text("Plan carefully.", encoding="utf-8")
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artifacts = ArtifactStore(root, ".nightshift", run_id="test-run")
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executor = AgentExecutor(
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root,
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{
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"planner": AgentConfig(
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id="planner",
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backend="command",
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command='python -c "import sys; print(sys.stdin.read())"',
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system_prompt=Path("planner.md"),
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)
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},
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artifacts,
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)
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task = parse_tasks(TASK_MD)[0]
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stage = StageConfig(id="plan", type="agent", agent="planner", output="plan.md")
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result = executor.run_stage(stage, task)
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self.assertEqual(result.status, "pass")
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output = (root / result.output_path).read_text(encoding="utf-8")
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self.assertIn("TASK-001", output)
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self.assertIn("Plan carefully.", output)
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def test_review_output_parser_accepts_structured_status(self) -> None:
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status, reason, next_stage, context_update = parse_review_output(
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"status: retry\nreason: Needs changes\nnext_stage: implement\ncontext_update: Fix tests\n"
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)
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self.assertEqual(status, "retry")
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self.assertEqual(reason, "Needs changes")
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self.assertEqual(next_stage, "implement")
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self.assertEqual(context_update, "Fix tests")
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def test_ollama_agent_invocation_uses_model_without_real_ollama(self) -> None:
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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prompt_path = root / "planner.md"
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prompt_path.write_text("Plan carefully.", encoding="utf-8")
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artifacts = ArtifactStore(root, ".nightshift", run_id="test-run")
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executor = AgentExecutor(
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root,
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{
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"planner": AgentConfig(
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id="planner",
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backend="ollama",
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command=None,
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model="tiny-model",
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system_prompt=Path("planner.md"),
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)
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},
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artifacts,
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)
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task = parse_tasks(TASK_MD)[0]
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stage = StageConfig(id="plan", type="agent", agent="planner", output="plan.md")
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completed = type(
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"Completed",
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(),
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{"returncode": 0, "stdout": "ollama output", "stderr": ""},
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)()
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with patch("nightshift.agents.subprocess.run", return_value=completed) as run:
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result = executor.run_stage(stage, task)
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self.assertEqual(result.status, "pass")
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run.assert_called_once()
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self.assertEqual(run.call_args.args[0], ["ollama", "run", "tiny-model"])
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output = (root / result.output_path).read_text(encoding="utf-8")
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self.assertIn("ollama run tiny-model", output)
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def test_openai_compatible_agent_sends_temperature(self) -> None:
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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prompt_path = root / "planner.md"
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prompt_path.write_text("Plan carefully.", encoding="utf-8")
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artifacts = ArtifactStore(root, ".nightshift", run_id="test-run")
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executor = AgentExecutor(
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root,
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{
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"planner": AgentConfig(
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id="planner",
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backend="openai_compatible",
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command=None,
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model="tiny-model",
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base_url="http://localhost:11434/v1",
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temperature=0.2,
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system_prompt=Path("planner.md"),
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)
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},
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artifacts,
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)
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task = parse_tasks(TASK_MD)[0]
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stage = StageConfig(id="plan", type="agent", agent="planner", output="plan.md")
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response = MagicMock()
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response.__enter__.return_value.read.return_value = (
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b'{"choices":[{"message":{"content":"api output"}}]}'
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)
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with patch("nightshift.agents.request.urlopen", return_value=response) as urlopen:
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result = executor.run_stage(stage, task)
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self.assertEqual(result.status, "pass")
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request_obj = urlopen.call_args.args[0]
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body = request_obj.data.decode("utf-8")
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self.assertIn('"temperature": 0.2', body)
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self.assertIn("api output", (root / result.output_path).read_text(encoding="utf-8"))
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def test_agent_artifact_format_tolerates_missing_streams(self) -> None:
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invocation = AgentInvocation(
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agent_id="planner",
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command="ollama run model",
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prompt="prompt",
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exit_code=0,
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stdout=None, # type: ignore[arg-type]
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stderr=None, # type: ignore[arg-type]
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duration_seconds=0.1,
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)
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output = format_agent_invocation("plan", invocation)
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self.assertIn("Agent: `planner`", output)
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self.assertIn("## stderr", output)
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if __name__ == "__main__":
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unittest.main()
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