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d1a94dae8a
* refactor(dream): replace two-phase Dream class with simple cron + process_direct - Remove the heavyweight Dream class (AgentRunner-based two-phase system) from nanobot/agent/memory.py - Delete dream_phase1.md and dream_phase2.md templates - New dream.md template serves as the consolidation prompt - Cron callback uses agent.process_direct(prompt, session_key=\"dream\") instead of agent.dream.run() - Always performs git auto_commit after execution - /dream command updated to use process_direct + git commit - DreamConfig kept for backward compatibility; deprecated fields (model_override, max_batch_size, max_iterations, annotate_line_ages) are ignored but accepted in config - interval_h remains configurable via agents.defaults.dream.interval_h - Update tests and webui settings to match new architecture * feat(loop): add ephemeral mode to process_direct, skip history writes for Dream When ephemeral=True, _state_save skips enforce_file_cap (which calls raw_archive -> append_history) and consolidator.maybe_consolidate_by_tokens. This prevents Dream sessions from creating a positive feedback loop where they process their own output. The session IS still saved to disk. * fix(loop): skip extra hooks for ephemeral sessions (Dream) * feat(dream): per-run timestamped sessions with rotation for WebUI * test(config): restore DreamConfig schedule and alias tests * fix(dream): include LLM response summary in git auto-commit message The old two-phase Dream class included the Phase 1 analysis in the git commit message body. The new single-phase version lost this. Restore it by extracting resp.content from the process_direct return value and appending it to the commit message in both the cron handler and the /dream command. * fix(test): accept ephemeral kwarg in test_openai_api fake_process * refactor(dream): merge dream_session.py into MemoryStore The standalone dream_session.py module only contained three small helpers that all revolve around MemoryStore concerns (session keys, commit messages, file pruning). Fold them into MemoryStore as @staticmethod to reduce indirection and avoid a 35-line module with no independent reason to exist. * fix(test): address code review — patch correct instance, use actual function - Fix test_ephemeral_skips_raw_archive to patch loop.context.memory instead of the fixture's separate MemoryStore instance - Fix TestDreamCommitMessage to call MemoryStore.build_dream_commit_message instead of reimplementing the logic inline - Move Dream helpers in memory.py above the Consolidator section comment to avoid misleading visual boundary * fix(dream): gate cursor advancement and restrict tools maintainer edit: Dream now processes backlog from the oldest unprocessed entries, only advances the cursor after a completed ephemeral run, and uses a restricted file-only tool registry for background consolidation. * fix(dream): skip idle compact for dream sessions Dream runs use internal dream:* sessions that are pruned by Dream retention. Exclude them from AutoCompact scheduling, archive execution, and summary injection so idle-session compaction cannot truncate Dream transcripts. * fix(dream): keep batched history isolated * feat(dream): tag archived memory for single-phase Dream --------- Co-authored-by: Xubin Ren <52506698+Re-bin@users.noreply.github.com>
291 lines
10 KiB
Python
291 lines
10 KiB
Python
from types import SimpleNamespace
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from unittest.mock import MagicMock
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import pytest
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from nanobot.agent.loop import AgentLoop
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from nanobot.agent.tools.self import MyTool
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from nanobot.bus.queue import MessageBus
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from nanobot.config.schema import ModelPresetConfig
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from nanobot.providers.factory import ProviderSnapshot
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def _provider(default_model: str, max_tokens: int = 123) -> MagicMock:
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provider = MagicMock()
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provider.get_default_model.return_value = default_model
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provider.generation = SimpleNamespace(
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max_tokens=max_tokens, temperature=0.1, reasoning_effort=None
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)
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return provider
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def _make_loop(tmp_path, presets=None, active_preset=None):
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provider = _provider("base-model")
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return AgentLoop(
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bus=MessageBus(),
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provider=provider,
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workspace=tmp_path,
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model="base-model",
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context_window_tokens=1000,
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model_presets=presets or {},
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model_preset=active_preset,
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)
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def test_model_preset_getter_none_when_not_set(tmp_path) -> None:
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loop = _make_loop(tmp_path)
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assert loop.model_preset is None
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def test_model_preset_setter_updates_state(tmp_path) -> None:
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presets = {
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"fast": ModelPresetConfig(
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model="openai/gpt-4.1",
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provider="openai",
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max_tokens=4096,
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context_window_tokens=32_768,
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temperature=0.5,
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reasoning_effort="low",
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)
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}
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loop = _make_loop(tmp_path, presets=presets)
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loop.model_preset = "fast"
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assert loop.model_preset == "fast"
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assert loop.model == "openai/gpt-4.1"
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assert loop.context_window_tokens == 32_768
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assert loop.provider.generation.temperature == 0.5
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assert loop.provider.generation.max_tokens == 4096
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assert loop.provider.generation.reasoning_effort == "low"
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assert loop.subagents.model == "openai/gpt-4.1"
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assert loop.consolidator.model == "openai/gpt-4.1"
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assert loop.consolidator.context_window_tokens == 32_768
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assert loop.consolidator.max_completion_tokens == 4096
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def test_model_preset_setter_calls_runtime_model_publisher(tmp_path) -> None:
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published: list[tuple[str, str | None]] = []
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loop = AgentLoop(
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bus=MessageBus(),
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provider=_provider("base-model", max_tokens=123),
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workspace=tmp_path,
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model="base-model",
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context_window_tokens=1000,
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model_presets={"fast": ModelPresetConfig(model="openai/gpt-4.1")},
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runtime_model_publisher=lambda model, preset: published.append((model, preset)),
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)
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loop.set_model_preset("fast")
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assert published == [("openai/gpt-4.1", "fast")]
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def test_model_preset_setter_replaces_provider_from_snapshot(tmp_path) -> None:
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old_provider = _provider("base-model", max_tokens=123)
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new_provider = _provider("anthropic/claude-opus-4-5", max_tokens=2048)
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preset = ModelPresetConfig(
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model="anthropic/claude-opus-4-5",
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provider="anthropic",
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max_tokens=2048,
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context_window_tokens=200_000,
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)
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loop = AgentLoop(
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bus=MessageBus(),
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provider=old_provider,
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workspace=tmp_path,
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model="base-model",
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context_window_tokens=1000,
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model_presets={"deep": preset},
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preset_snapshot_loader=lambda name: ProviderSnapshot(
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provider=new_provider,
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model=preset.model,
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context_window_tokens=preset.context_window_tokens,
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signature=(name, preset.model),
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),
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)
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loop.set_model_preset("deep")
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assert loop.provider is new_provider
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assert loop.runner.provider is new_provider
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assert loop.subagents.provider is new_provider
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assert loop.subagents.runner.provider is new_provider
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assert loop.consolidator.provider is new_provider
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assert loop.model == "anthropic/claude-opus-4-5"
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assert loop.context_window_tokens == 200_000
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assert loop.consolidator.max_completion_tokens == 2048
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def test_model_preset_setter_failure_leaves_old_state(tmp_path) -> None:
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preset = ModelPresetConfig(model="openai/gpt-4.1", max_tokens=4096)
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loop = AgentLoop(
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bus=MessageBus(),
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provider=_provider("base-model", max_tokens=123),
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workspace=tmp_path,
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model="base-model",
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context_window_tokens=1000,
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model_presets={"fast": preset},
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preset_snapshot_loader=lambda _name: (_ for _ in ()).throw(
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RuntimeError("provider unavailable")
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),
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)
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with pytest.raises(RuntimeError, match="provider unavailable"):
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loop.set_model_preset("fast")
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assert loop.model_preset is None
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assert loop.model == "base-model"
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assert loop.subagents.model == "base-model"
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assert loop.consolidator.model == "base-model"
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assert loop.context_window_tokens == 1000
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assert loop.consolidator.max_completion_tokens == 123
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def test_active_model_preset_survives_unchanged_config_refresh(tmp_path) -> None:
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base_provider = _provider("base-model", max_tokens=123)
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fast_provider = _provider("openai/gpt-4.1", max_tokens=4096)
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default_snapshot = ProviderSnapshot(
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provider=base_provider,
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model="base-model",
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context_window_tokens=1000,
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signature=("base-model", "auto", "openai", "sk-old"),
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)
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fast_snapshot = ProviderSnapshot(
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provider=fast_provider,
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model="openai/gpt-4.1",
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context_window_tokens=32_768,
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signature=("openai/gpt-4.1", "auto", "openai", "sk-old"),
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)
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loop = AgentLoop(
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bus=MessageBus(),
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provider=base_provider,
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workspace=tmp_path,
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model="base-model",
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context_window_tokens=1000,
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provider_signature=default_snapshot.signature,
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model_presets={"fast": ModelPresetConfig(model="openai/gpt-4.1")},
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provider_snapshot_loader=lambda: default_snapshot,
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preset_snapshot_loader=lambda _name: fast_snapshot,
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)
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loop.set_model_preset("fast")
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loop._refresh_provider_snapshot()
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assert loop.model_preset == "fast"
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assert loop.provider is fast_provider
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assert loop.model == "openai/gpt-4.1"
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def test_config_model_refresh_clears_active_model_preset(tmp_path) -> None:
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base_provider = _provider("base-model", max_tokens=123)
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fast_provider = _provider("openai/gpt-4.1", max_tokens=4096)
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webui_provider = _provider("anthropic/claude-opus-4-5", max_tokens=2048)
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webui_snapshot = ProviderSnapshot(
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provider=webui_provider,
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model="anthropic/claude-opus-4-5",
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context_window_tokens=200_000,
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signature=("anthropic/claude-opus-4-5", "anthropic", "anthropic", "sk-old"),
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)
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fast_snapshot = ProviderSnapshot(
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provider=fast_provider,
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model="openai/gpt-4.1",
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context_window_tokens=32_768,
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signature=("openai/gpt-4.1", "auto", "openai", "sk-old"),
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)
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loop = AgentLoop(
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bus=MessageBus(),
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provider=base_provider,
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workspace=tmp_path,
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model="base-model",
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context_window_tokens=1000,
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provider_snapshot_loader=lambda: webui_snapshot,
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provider_signature=("base-model", "auto", "openai", "sk-old"),
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model_presets={"fast": ModelPresetConfig(model="openai/gpt-4.1")},
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preset_snapshot_loader=lambda _name: fast_snapshot,
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)
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loop.set_model_preset("fast")
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loop._refresh_provider_snapshot()
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assert loop.model_preset is None
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assert loop.provider is webui_provider
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assert loop.model == "anthropic/claude-opus-4-5"
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assert loop.context_window_tokens == 200_000
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def test_model_preset_setter_raises_on_unknown(tmp_path) -> None:
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loop = _make_loop(tmp_path)
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with pytest.raises(KeyError, match="model_preset 'missing' not found"):
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loop.model_preset = "missing"
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def test_model_preset_setter_raises_on_empty_string(tmp_path) -> None:
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loop = _make_loop(tmp_path)
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with pytest.raises(ValueError, match="model_preset must be a non-empty string"):
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loop.model_preset = ""
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def test_self_tool_inspect_shows_model_preset(tmp_path) -> None:
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presets = {
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"fast": ModelPresetConfig(model="openai/gpt-4.1"),
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}
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loop = _make_loop(tmp_path, presets=presets, active_preset="fast")
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tool = MyTool(runtime_state=loop, modify_allowed=True)
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output = tool._inspect_all()
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assert "model_preset: 'fast'" in output
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def test_self_tool_set_model_preset_via_modify(tmp_path) -> None:
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presets = {
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"fast": ModelPresetConfig(model="openai/gpt-4.1"),
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}
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loop = _make_loop(tmp_path, presets=presets)
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tool = MyTool(runtime_state=loop, modify_allowed=True)
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result = tool._modify("model_preset", "fast")
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assert "Error" not in result
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assert loop.model_preset == "fast"
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assert loop.model == "openai/gpt-4.1"
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def test_self_tool_set_model_clears_active_preset(tmp_path) -> None:
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presets = {
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"fast": ModelPresetConfig(model="openai/gpt-4.1"),
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}
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loop = _make_loop(tmp_path, presets=presets, active_preset="fast")
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tool = MyTool(runtime_state=loop, modify_allowed=True)
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result = tool._modify("model", "anthropic/claude-opus-4-5")
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assert "Error" not in result
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assert loop._active_preset is None
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assert loop.model == "anthropic/claude-opus-4-5"
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def test_from_config_injects_default_preset(tmp_path) -> None:
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from unittest.mock import patch
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from nanobot.config.schema import Config
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config = Config.model_validate({
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"agents": {"defaults": {"model": "openai/gpt-4.1", "workspace": str(tmp_path)}},
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})
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fake_provider = _provider("openai/gpt-4.1")
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with patch("nanobot.providers.factory.make_provider", return_value=fake_provider):
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loop = AgentLoop.from_config(config)
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assert loop.model == "openai/gpt-4.1"
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assert loop.model_preset is None
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assert "default" in loop.model_presets
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assert loop.model_presets["default"].model == "openai/gpt-4.1"
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def test_from_config_static_preset_loader_does_not_enable_hot_reload(tmp_path) -> None:
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from unittest.mock import patch
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from nanobot.config.schema import Config
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config = Config.model_validate({
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"agents": {"defaults": {"model": "openai/gpt-4.1", "workspace": str(tmp_path)}},
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"model_presets": {"fast": {"model": "openai/gpt-4.1-mini"}},
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})
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fake_provider = _provider("openai/gpt-4.1")
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with patch("nanobot.providers.factory.make_provider", return_value=fake_provider):
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loop = AgentLoop.from_config(config)
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assert loop._provider_snapshot_loader is None
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assert loop._preset_snapshot_loader is not None
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