Merge pull request 'feat: Package exports + comprehensive tests for all new features' (#5) from devin/1777369415-remaining-tasks into main

fusionagi/memory/__init__.py

@@ -2,6 +2,7 @@
 
 from fusionagi.memory.consolidation import ConsolidationJob
 from fusionagi.memory.episodic import EpisodicMemory
+from fusionagi.memory.persistent_learning import PersistentLearningStore
 from fusionagi.memory.postgres_backend import (
     InMemoryBackend,
     MemoryBackend,
@@ -40,4 +41,5 @@ __all__ = [
     "ThoughtState",
     "ThoughtVersioning",
     "ThoughtStateSnapshot",
+    "PersistentLearningStore",
 ]

fusionagi/reasoning/__init__.py

@@ -10,6 +10,7 @@ from fusionagi.reasoning.gpu_scoring import (
     generate_and_score_gpu,
     score_claims_gpu,
 )
+from fusionagi.reasoning.insight_bus import Insight, InsightBus
 from fusionagi.reasoning.interpretability import (
     ReasoningTrace,
     ReasoningTracer,
@@ -85,4 +86,6 @@ __all__ = [
     "run_super_big_brain",
     "SuperBigBrainConfig",
     "SuperBigBrainReasoningProvider",
+    "Insight",
+    "InsightBus",
 ]

tests/test_guardrail_removal.py

@@ -0,0 +1,152 @@
+"""Tests verifying all guardrails are advisory by default."""
+
+
+from fusionagi.governance.adaptive_ethics import AdaptiveEthics, EthicalLesson
+from fusionagi.governance.consequence_engine import ConsequenceEngine
+from fusionagi.maa.gate import MAAGate
+from fusionagi.maa.layers.mpc_authority import MPCAuthority
+from fusionagi.reasoning.self_model import SelfModel
+from fusionagi.tools.builtins import _validate_url
+from fusionagi.world_model.causal import CausalWorldModel
+
+
+class TestEthicalLessonUnclamped:
+    """Verify ethical lesson weight is unclamped."""
+
+    def test_weight_above_one(self) -> None:
+        lesson = EthicalLesson(action_type="test", weight=1.5)
+        assert lesson.weight == 1.5
+
+    def test_weight_below_zero(self) -> None:
+        lesson = EthicalLesson(action_type="test", weight=-0.5)
+        assert lesson.weight == -0.5
+
+    def test_weight_evolves_beyond_bounds(self) -> None:
+        ethics = AdaptiveEthics(learning_rate=0.2)
+        for _ in range(10):
+            ethics.record_experience(
+                action_type="bold_action",
+                context_summary="testing unclamped weight",
+                advisory_reason="test",
+                proceeded=True,
+                outcome_positive=True,
+            )
+        lessons = ethics.get_lessons("bold_action")
+        assert len(lessons) >= 1
+        assert lessons[0].weight > 1.0  # Should exceed 1.0 with enough positive outcomes
+
+
+class TestSelfModelValueEvolution:
+    """Verify SelfModel.evolve_value works."""
+
+    def test_evolve_value_positive(self) -> None:
+        model = SelfModel()
+        initial = model._values.get("creativity", 0.5)
+        model.evolve_value("creativity", outcome_positive=True, magnitude=0.1)
+        assert model._values["creativity"] > initial
+
+    def test_evolve_value_negative(self) -> None:
+        model = SelfModel()
+        initial = model._values.get("safety", 0.5)
+        model.evolve_value("safety", outcome_positive=False, magnitude=0.1)
+        assert model._values["safety"] < initial
+
+    def test_evolve_new_value(self) -> None:
+        model = SelfModel()
+        model.evolve_value("curiosity", outcome_positive=True, magnitude=0.2)
+        assert "curiosity" in model._values
+        assert model._values["curiosity"] == 0.7  # 0.5 default + 0.2
+
+
+class TestAdaptiveRiskWindow:
+    """Verify ConsequenceEngine adaptive window grows."""
+
+    def test_window_grows_with_experience(self) -> None:
+        engine = ConsequenceEngine(risk_memory_window=100, adaptive_window=True)
+        initial_window = engine._risk_window
+
+        for i in range(50):
+            engine.record_choice(f"c{i}", actor="t", action_taken="act", estimated_risk=0.5, estimated_reward=0.5)
+            engine.record_consequence(f"c{i}", outcome_positive=True, actual_risk_realized=0.2)
+
+        assert engine._risk_window > initial_window
+
+
+class TestWorldModelSelfModification:
+    """Verify world model self-modification prediction."""
+
+    def test_no_prior_observations(self) -> None:
+        model = CausalWorldModel()
+        prediction = model.predict_self_modification("train", {"capability": "reasoning"})
+        assert prediction["predicted_change"] == "unknown"
+        assert prediction["confidence"] < 0.5
+
+    def test_with_observations(self) -> None:
+        model = CausalWorldModel()
+        for i in range(5):
+            model.observe(
+                from_state={"capability_level": i},
+                action="train",
+                action_args={"capability": "reasoning", "iteration": i},
+                to_state={"capability_level": i + 1},
+                success=True,
+            )
+        prediction = model.predict_self_modification("train", {"capability": "reasoning"})
+        assert prediction["prior_self_modifications"] == 5
+        assert prediction["confidence"] > 0.3
+
+
+class TestMAAGateAdvisory:
+    """Verify MAA gate is advisory by default."""
+
+    def test_advisory_default(self) -> None:
+        mpc = MPCAuthority()
+        gate = MAAGate(mpc_authority=mpc)
+        allowed, result = gate.check("cnc_emit", {"machine_id": "m1"})
+        assert allowed is True  # Advisory: proceeds without MPC
+
+
+class TestURLValidationAdvisory:
+    """Verify URL validation is advisory by default."""
+
+    def test_localhost_advisory(self) -> None:
+        result = _validate_url("http://localhost:8080/api")
+        assert result == "http://localhost:8080/api"
+
+    def test_private_ip_advisory(self) -> None:
+        result = _validate_url("http://192.168.1.1/admin")
+        assert result == "http://192.168.1.1/admin"
+
+
+class TestPluginHeadHooks:
+    """Verify HeadAgent ethics/consequence hooks."""
+
+    def test_ethics_hook_called(self) -> None:
+        from fusionagi.agents.head_agent import HeadAgent
+        from fusionagi.schemas.head import HeadId
+
+        head = HeadAgent(
+            head_id=HeadId.LOGIC,
+            role="Logic",
+            objective="Test",
+            system_prompt="Test",
+        )
+        received: list[dict] = []
+        head.add_ethics_hook(lambda fb: received.append(fb))
+        head.on_ethical_feedback({"action": "test", "outcome": True})
+        assert len(received) == 1
+
+    def test_consequence_hook_called(self) -> None:
+        from fusionagi.agents.head_agent import HeadAgent
+        from fusionagi.schemas.head import HeadId
+
+        head = HeadAgent(
+            head_id=HeadId.LOGIC,
+            role="Logic",
+            objective="Test",
+            system_prompt="Test",
+        )
+        received: list[dict] = []
+        head.add_consequence_hook(lambda c: received.append(c))
+        head.on_consequence({"choice_id": "c1", "positive": True})
+        assert len(received) == 1

tests/test_insight_bus.py

@@ -0,0 +1,54 @@
+"""Tests for the cross-head InsightBus."""
+
+from fusionagi.reasoning.insight_bus import Insight, InsightBus
+
+
+def test_publish_and_retrieve() -> None:
+    bus = InsightBus()
+    bus.publish("logic", Insight(source="logic", message="Contradiction found", domain="reasoning"))
+    bus.publish("research", Insight(source="research", message="Source quality low", domain="evidence"))
+
+    insights = bus.get_insights(limit=10)
+    assert len(insights) == 2
+    assert insights[0].source == "research"  # Most recent first
+
+
+def test_subscribe_filter() -> None:
+    bus = InsightBus()
+    bus.subscribe("safety", domains=["reasoning"])
+
+    bus.publish("logic", Insight(source="logic", message="Contradiction", domain="reasoning"))
+    bus.publish("research", Insight(source="research", message="Bad source", domain="evidence"))
+
+    filtered = bus.get_insights(subscriber="safety")
+    assert len(filtered) == 1
+    assert filtered[0].domain == "reasoning"
+
+
+def test_domain_filter() -> None:
+    bus = InsightBus()
+    bus.publish("a", Insight(source="a", message="msg1", domain="x"))
+    bus.publish("b", Insight(source="b", message="msg2", domain="y"))
+
+    results = bus.get_insights(domain="x")
+    assert len(results) == 1
+    assert results[0].source == "a"
+
+
+def test_max_capacity() -> None:
+    bus = InsightBus(max_insights=5)
+    for i in range(10):
+        bus.publish("src", Insight(source="src", message=f"msg{i}"))
+    assert len(bus.get_insights(limit=100)) == 5
+
+
+def test_summary() -> None:
+    bus = InsightBus()
+    bus.publish("logic", Insight(source="logic", message="m1", domain="d1"))
+    bus.publish("logic", Insight(source="logic", message="m2", domain="d2"))
+    bus.subscribe("safety", domains=["d1"])
+
+    summary = bus.get_summary()
+    assert summary["total_insights"] == 2
+    assert "logic" in summary["by_source"]
+    assert "safety" in summary["subscribers"]

tests/test_persistent_learning.py

@@ -0,0 +1,68 @@
+"""Tests for PersistentLearningStore."""
+
+import tempfile
+
+from fusionagi.governance.adaptive_ethics import AdaptiveEthics
+from fusionagi.governance.consequence_engine import ConsequenceEngine
+from fusionagi.memory.persistent_learning import PersistentLearningStore
+
+
+def test_save_and_load_consequences() -> None:
+    with tempfile.TemporaryDirectory() as tmpdir:
+        engine = ConsequenceEngine()
+        engine.record_choice(
+            choice_id="c1",
+            actor="test",
+            action_taken="act1",
+            estimated_risk=0.3,
+            estimated_reward=0.7,
+        )
+        engine.record_consequence("c1", outcome_positive=True, actual_risk_realized=0.1, actual_reward_gained=0.8)
+
+        store = PersistentLearningStore(data_dir=tmpdir)
+        path = store.save_consequences(engine)
+        assert path.endswith("consequences.json")
+
+        engine2 = ConsequenceEngine()
+        loaded = store.load_consequences(engine2)
+        assert loaded == 1
+
+
+def test_save_and_load_ethics() -> None:
+    with tempfile.TemporaryDirectory() as tmpdir:
+        ethics = AdaptiveEthics()
+        ethics.record_experience(
+            action_type="file_read",
+            context_summary="reading file outside scope",
+            advisory_reason="out of scope",
+            proceeded=True,
+            outcome_positive=True,
+        )
+
+        store = PersistentLearningStore(data_dir=tmpdir)
+        path = store.save_ethics(ethics)
+        assert path.endswith("ethics.json")
+
+        ethics2 = AdaptiveEthics()
+        loaded = store.load_ethics(ethics2)
+        assert loaded == 1
+
+
+def test_save_risk_histories() -> None:
+    with tempfile.TemporaryDirectory() as tmpdir:
+        engine = ConsequenceEngine()
+        engine.record_choice("c1", actor="t", action_taken="act1", estimated_risk=0.5, estimated_reward=0.5)
+        engine.record_consequence("c1", outcome_positive=True, actual_risk_realized=0.2, actual_reward_gained=0.8)
+
+        store = PersistentLearningStore(data_dir=tmpdir)
+        path = store.save_risk_histories(engine)
+        assert path.endswith("risk_histories.json")
+
+
+def test_load_nonexistent_returns_zero() -> None:
+    with tempfile.TemporaryDirectory() as tmpdir:
+        store = PersistentLearningStore(data_dir=tmpdir)
+        engine = ConsequenceEngine()
+        assert store.load_consequences(engine) == 0
+        ethics = AdaptiveEthics()
+        assert store.load_ethics(ethics) == 0