diff --git a/pyproject.toml b/pyproject.toml
index 1fa1213b4..b2e24ba69 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -40,6 +40,9 @@ dependencies = [
     "dimo-python-sdk @ git+https://github.com/openminddev/dimo-python-sdk.git@6b47fcd28654a4145cedee649a0999a8eb08a2f6",
     "nest-asyncio==1.6.0",
     "tf-keras==2.18.0",
+    "sentence-transformers>=3.0.0",
+    "chromadb>=0.5.0",
+    "onnxruntime<1.20",
     "faiss-cpu>=1.7.4",
 ]
 
diff --git a/src/fuser/__init__.py b/src/fuser/__init__.py
index 553900e82..45b581ec5 100644
--- a/src/fuser/__init__.py
+++ b/src/fuser/__init__.py
@@ -4,9 +4,9 @@
 from collections.abc import Sequence
 
 from actions import describe_action
-from fuser.knowledge_base.retriever import KnowledgeBase
 from inputs.base import Sensor
 from providers.io_provider import IOProvider
+from providers.semantic_memory_provider import SemanticMemoryProvider
 from runtime.config import RuntimeConfig
 
 
@@ -40,6 +40,8 @@ def __init__(self, config: RuntimeConfig):
         self.knowledge_base = None
         if config.knowledge_base:
             try:
+                from fuser.knowledge_base.retriever import KnowledgeBase
+
                 self.knowledge_base = KnowledgeBase(**config.knowledge_base)
                 logging.info(
                     f"KnowledgeBase enabled with config: {config.knowledge_base}"
@@ -114,6 +116,17 @@ async def fuse(
         if kb_context:
             inputs_fused += f"\n\nKNOWLEDGE BASE:\n{kb_context}"
 
+        # Retrieve relevant memories from semantic memory
+        memories_section = ""
+        sem_memory = SemanticMemoryProvider()
+        if sem_memory.enabled and inputs_fused.strip():
+            mode = self.config.mode or "default"
+            memories = sem_memory.retrieve(query=inputs_fused, mode=mode)
+            if memories:
+                memories_section = "\nRELEVANT MEMORIES:\n" + "".join(
+                    f"{i}. {mem}\n" for i, mem in enumerate(memories, 1)
+                )
+
         # if we provide laws from blockchain, these override the locally stored rules
         # the rules are not provided in the system prompt, but as a separate INPUT,
         # since they are flowing from the outside world
@@ -140,7 +153,7 @@ async def fuse(
         # (2) all the inputs (vision, sound, etc.)
         # (3) a (typically) fixed list of available actions
         # (4) a (typically) fixed system prompt requesting commands to be generated
-        fused_prompt = f"{system_prompt}\n\nAVAILABLE INPUTS:\n{inputs_fused}\nAVAILABLE ACTIONS:\n\n{actions_fused}\n\n{question_prompt}"
+        fused_prompt = f"{system_prompt}\n\nAVAILABLE INPUTS:\n{inputs_fused}{memories_section}\nAVAILABLE ACTIONS:\n\n{actions_fused}\n\n{question_prompt}"
 
         logging.debug(f"FINAL PROMPT: {fused_prompt}")
 
diff --git a/src/llm/__init__.py b/src/llm/__init__.py
index f79c6db5b..ed82016b3 100644
--- a/src/llm/__init__.py
+++ b/src/llm/__init__.py
@@ -51,6 +51,15 @@ class LLMConfig(BaseModel):
     history_length: T.Optional[int] = Field(
         default=0, description="Number of past interactions to keep in context"
     )
+    semantic_memory_enabled: T.Optional[bool] = Field(
+        default=False, description="Enable semantic memory for long-term recall"
+    )
+    semantic_memory_top_k: T.Optional[int] = Field(
+        default=3, description="Number of top memories to retrieve per query"
+    )
+    semantic_memory_threshold: T.Optional[float] = Field(
+        default=0.3, description="Minimum cosine similarity for memory retrieval"
+    )
     extra_params: T.Dict[str, T.Any] = Field(default_factory=dict)
 
     def __getitem__(self, item: str) -> T.Any:
diff --git a/src/providers/llm_history_manager.py b/src/providers/llm_history_manager.py
index c3ee98316..ee427a5ac 100644
--- a/src/providers/llm_history_manager.py
+++ b/src/providers/llm_history_manager.py
@@ -9,6 +9,7 @@
 from llm import LLMConfig
 
 from .io_provider import IOProvider
+from .semantic_memory_provider import SemanticMemoryProvider
 
 R = TypeVar("R")
 
@@ -360,6 +361,17 @@ async def wrapper(self: Any, prompt: str, *args: Any, **kwargs: Any) -> R:
                         ChatMessage(role="assistant", content=action_message)
                     )
 
+                    # Store to semantic memory
+                    sem_memory = SemanticMemoryProvider()
+                    if sem_memory.enabled:
+                        mode = getattr(self._config, "mode", None) or "default"
+                        sem_memory.store(
+                            formatted_inputs,
+                            action_message,
+                            mode,
+                            current_tick,
+                        )
+
                     if (
                         self.history_manager.config.history_length > 0
                         and len(self.history_manager.history)
diff --git a/src/providers/semantic_memory_provider.py b/src/providers/semantic_memory_provider.py
new file mode 100644
index 000000000..75fc225ed
--- /dev/null
+++ b/src/providers/semantic_memory_provider.py
@@ -0,0 +1,255 @@
+"""Semantic memory provider using local embeddings and ChromaDB vector search."""
+
+import logging
+import os
+import time
+from typing import List, Optional
+
+from providers.singleton import singleton
+
+logger = logging.getLogger(__name__)
+
+# Maximum characters per memory document
+MAX_TEXT_LENGTH = 1000
+
+# Default storage path relative to project root
+DEFAULT_PERSIST_DIR = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))),
+    "config",
+    "memory",
+    "embeddings",
+)
+
+
+@singleton
+class SemanticMemoryProvider:
+    """Provides semantic memory storage and retrieval using embeddings.
+
+    Uses sentence-transformers for local embedding generation and ChromaDB
+    for persistent vector storage. Each robot mode gets its own collection.
+
+    Parameters
+    ----------
+    None
+        Configuration is done via the configure() method after instantiation.
+    """
+
+    def __init__(self) -> None:
+        self.enabled: bool = False
+        self.top_k: int = 3
+        self.similarity_threshold: float = 0.3
+
+        self._model = None
+        self._chroma_client = None
+        self._collections: dict = {}
+        self._persist_dir: str = DEFAULT_PERSIST_DIR
+
+    def configure(
+        self,
+        enabled: bool = False,
+        top_k: int = 3,
+        similarity_threshold: float = 0.3,
+    ) -> None:
+        """Configure the semantic memory provider.
+
+        Parameters
+        ----------
+        enabled : bool
+            Whether semantic memory is active.
+        top_k : int
+            Number of top results to return from retrieval.
+        similarity_threshold : float
+            Minimum cosine similarity score to include a result.
+        """
+        self.enabled = enabled
+        self.top_k = top_k
+        self.similarity_threshold = similarity_threshold
+
+        if self.enabled:
+            self._ensure_initialized()
+
+    def _ensure_initialized(self) -> None:
+        """Lazy-load the embedding model and ChromaDB client."""
+        if self._model is not None and self._chroma_client is not None:
+            return
+
+        try:
+            from sentence_transformers import SentenceTransformer
+
+            logger.info("Loading embedding model: all-MiniLM-L6-v2")
+            self._model = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
+            logger.info("Embedding model loaded successfully")
+        except Exception as e:
+            logger.error(f"Failed to load embedding model: {e}")
+            self.enabled = False
+            return
+
+        try:
+            import chromadb
+
+            os.makedirs(self._persist_dir, exist_ok=True)
+            self._chroma_client = chromadb.PersistentClient(path=self._persist_dir)
+            logger.info(f"ChromaDB initialized at {self._persist_dir}")
+        except Exception as e:
+            logger.error(f"Failed to initialize ChromaDB: {e}")
+            self.enabled = False
+            return
+
+    def _get_collection(self, mode: str):
+        """Get or create a ChromaDB collection for the given mode.
+
+        Parameters
+        ----------
+        mode : str
+            The robot operating mode name.
+
+        Returns
+        -------
+        chromadb.Collection or None
+            The ChromaDB collection, or None if not initialized.
+        """
+        if self._chroma_client is None:
+            return None
+
+        collection_name = f"om1_{mode}"
+        if collection_name not in self._collections:
+            self._collections[collection_name] = (
+                self._chroma_client.get_or_create_collection(
+                    name=collection_name,
+                    metadata={"hnsw:space": "cosine"},
+                )
+            )
+        return self._collections[collection_name]
+
+    def store(
+        self,
+        sensory_input: str,
+        action_response: str,
+        mode: str,
+        tick: int,
+    ) -> None:
+        """Store a sensory-action pair as a memory document.
+
+        Parameters
+        ----------
+        sensory_input : str
+            The formatted sensory input text.
+        action_response : str
+            The action response text from the LLM.
+        mode : str
+            The current robot operating mode.
+        tick : int
+            The current tick number.
+        """
+        if not self.enabled or self._model is None:
+            return
+
+        collection = self._get_collection(mode)
+        if collection is None:
+            return
+
+        try:
+            document = f"Input: {sensory_input[:MAX_TEXT_LENGTH]} | Response: {action_response[:MAX_TEXT_LENGTH]}"
+            embedding = self._model.encode(document, normalize_embeddings=True).tolist()
+
+            doc_id = f"tick_{tick}_{int(time.time() * 1000)}"
+
+            collection.add(
+                ids=[doc_id],
+                embeddings=[embedding],
+                documents=[document],
+                metadatas=[{"tick": tick, "timestamp": time.time(), "mode": mode}],
+            )
+
+            logger.debug(f"Stored memory: {doc_id} (mode={mode})")
+
+        except Exception as e:
+            logger.error(f"Failed to store memory: {e}")
+
+    def retrieve(
+        self,
+        query: str,
+        mode: str,
+        top_k: Optional[int] = None,
+    ) -> List[str]:
+        """Retrieve relevant memories for a given query.
+
+        Parameters
+        ----------
+        query : str
+            The query text to find relevant memories for.
+        mode : str
+            The current robot operating mode.
+        top_k : int, optional
+            Override the default number of results. Defaults to self.top_k.
+
+        Returns
+        -------
+        List[str]
+            List of relevant memory document strings, ordered by similarity.
+        """
+        if not self.enabled or self._model is None:
+            return []
+
+        collection = self._get_collection(mode)
+        if collection is None:
+            return []
+
+        try:
+            if collection.count() == 0:
+                return []
+
+            k = top_k if top_k is not None else self.top_k
+            k = min(k, collection.count())
+
+            query_embedding = self._model.encode(
+                query[:MAX_TEXT_LENGTH], normalize_embeddings=True
+            ).tolist()
+
+            results = collection.query(
+                query_embeddings=[query_embedding],
+                n_results=k,
+                include=["documents", "distances"],
+            )
+
+            if not results or not results["documents"] or not results["documents"][0]:
+                return []
+
+            memories = []
+            documents = results["documents"][0]
+            # ChromaDB cosine distance = 1 - cosine_similarity
+            distances = results["distances"][0] if results["distances"] else []
+
+            for i, doc in enumerate(documents):
+                if i < len(distances):
+                    similarity = 1.0 - distances[i]
+                    if similarity >= self.similarity_threshold:
+                        memories.append(doc)
+                else:
+                    memories.append(doc)
+
+            logger.debug(f"Retrieved {len(memories)} memories for mode={mode}")
+            return memories
+
+        except Exception as e:
+            logger.error(f"Failed to retrieve memories: {e}")
+            return []
+
+    def clear_mode(self, mode: str) -> None:
+        """Clear all memories for a specific mode.
+
+        Parameters
+        ----------
+        mode : str
+            The robot operating mode to clear memories for.
+        """
+        if self._chroma_client is None:
+            return
+
+        collection_name = f"om1_{mode}"
+        try:
+            self._chroma_client.delete_collection(name=collection_name)
+            self._collections.pop(collection_name, None)
+            logger.info(f"Cleared memories for mode: {mode}")
+        except Exception as e:
+            logger.error(f"Failed to clear memories for mode {mode}: {e}")
diff --git a/src/runtime/cortex.py b/src/runtime/cortex.py
index 1296b1c48..d3f2c1246 100644
--- a/src/runtime/cortex.py
+++ b/src/runtime/cortex.py
@@ -10,6 +10,7 @@
 from inputs.orchestrator import InputOrchestrator
 from providers.config_provider import ConfigProvider
 from providers.io_provider import IOProvider
+from providers.semantic_memory_provider import SemanticMemoryProvider
 from providers.sleep_ticker_provider import SleepTickerProvider
 from runtime.config import (
     LifecycleHookType,
@@ -139,6 +140,19 @@ async def _initialize_mode(self, mode_name: str):
         self.simulator_orchestrator = SimulatorOrchestrator(self.current_config)
         self.background_orchestrator = BackgroundOrchestrator(self.current_config)
 
+        # Configure semantic memory provider
+        memory = SemanticMemoryProvider()
+        llm_config = getattr(self.current_config.cortex_llm, "_config", None)
+        if llm_config is not None:
+            memory.configure(
+                enabled=getattr(llm_config, "semantic_memory_enabled", False) or False,
+                top_k=getattr(llm_config, "semantic_memory_top_k", 3) or 3,
+                similarity_threshold=getattr(
+                    llm_config, "semantic_memory_threshold", 0.3
+                )
+                or 0.3,
+            )
+
         logging.info(f"Mode '{mode_name}' initialized successfully")
 
     async def _handle_mode_transitions(self):
diff --git a/tests/fuser/test_init.py b/tests/fuser/test_init.py
index 45d968736..eefec4234 100644
--- a/tests/fuser/test_init.py
+++ b/tests/fuser/test_init.py
@@ -8,6 +8,7 @@
 from fuser.knowledge_base.base_retriever import Document
 from inputs.base import Sensor, SensorConfig
 from providers.io_provider import IOProvider
+from providers.semantic_memory_provider import SemanticMemoryProvider
 from runtime.config import RuntimeConfig
 
 
@@ -111,6 +112,59 @@ async def test_fuser_with_inputs_and_actions(mock_describe):
         )
 
 
+@patch("fuser.describe_action")
+@pytest.mark.asyncio
+async def test_fuser_injects_memories_when_enabled(mock_describe):
+    """Test that relevant memories are injected into the fused prompt."""
+    mock_describe.return_value = "action description"
+    config = create_mock_config(agent_actions=[MockAction("action1")])
+    config.mode = "test_mode"
+    inputs: list[Sensor[Any, Any]] = [MockSensor()]
+    io_provider = IOProvider()
+
+    # Setup semantic memory to return memories
+    SemanticMemoryProvider.reset()  # type: ignore
+    memory = SemanticMemoryProvider()
+    memory.enabled = True
+    memory.retrieve = MagicMock(  # type: ignore
+        return_value=["Input: user said hello | Response: waved back"]
+    )
+
+    with patch("fuser.IOProvider", return_value=io_provider):
+        fuser = Fuser(config)
+        result = await fuser.fuse(inputs, [])
+
+    memory.retrieve.assert_called_once_with(query="test input", mode="test_mode")
+    assert "RELEVANT MEMORIES:" in result
+    assert "user said hello" in result
+    assert "waved back" in result
+
+    # Cleanup
+    SemanticMemoryProvider.reset()  # type: ignore
+
+
+@patch("fuser.describe_action")
+@pytest.mark.asyncio
+async def test_fuser_skips_memories_when_disabled(mock_describe):
+    """Test that no memory retrieval happens when semantic memory is disabled."""
+    mock_describe.return_value = "action description"
+    config = create_mock_config(agent_actions=[MockAction("action1")])
+    inputs: list[Sensor[Any, Any]] = [MockSensor()]
+    io_provider = IOProvider()
+
+    SemanticMemoryProvider.reset()  # type: ignore
+    memory = SemanticMemoryProvider()
+    memory.enabled = False
+
+    with patch("fuser.IOProvider", return_value=io_provider):
+        fuser = Fuser(config)
+        result = await fuser.fuse(inputs, [])
+
+    assert "RELEVANT MEMORIES:" not in result
+
+    SemanticMemoryProvider.reset()  # type: ignore
+
+
 @pytest.mark.asyncio
 async def test_fuser_initialization_with_knowledge_base():
     """Test that Fuser properly initializes with knowledge_base config."""
@@ -126,7 +180,9 @@ async def test_fuser_initialization_with_knowledge_base():
     mock_kb = MagicMock()
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb) as mock_kb_class,
+        patch(
+            "fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb
+        ) as mock_kb_class,
     ):
         fuser = Fuser(config)
         assert fuser.knowledge_base == mock_kb
@@ -142,7 +198,10 @@ async def test_fuser_initialization_with_invalid_knowledge_base():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", side_effect=Exception("Invalid config")),
+        patch(
+            "fuser.knowledge_base.retriever.KnowledgeBase",
+            side_effect=Exception("Invalid config"),
+        ),
     ):
         fuser = Fuser(config)
         assert fuser.knowledge_base is None
@@ -174,7 +233,7 @@ async def test_fuser_with_knowledge_base_no_voice_input():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb),
+        patch("fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb),
     ):
         fuser = Fuser(config)
         result = await fuser.fuse([], [])
@@ -216,7 +275,7 @@ async def test_fuser_with_knowledge_base_and_voice_input():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb),
+        patch("fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb),
     ):
         fuser = Fuser(config)
         result = await fuser.fuse(inputs, [])
@@ -250,7 +309,7 @@ async def test_fuser_with_knowledge_base_empty_results():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb),
+        patch("fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb),
     ):
         fuser = Fuser(config)
         result = await fuser.fuse(inputs, [])
@@ -280,7 +339,7 @@ async def test_fuser_with_knowledge_base_query_error():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb),
+        patch("fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb),
     ):
         fuser = Fuser(config)
         result = await fuser.fuse(inputs, [])
@@ -309,7 +368,7 @@ async def test_fuser_with_knowledge_base_voice_input_different_tick():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb),
+        patch("fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb),
     ):
         fuser = Fuser(config)
         result = await fuser.fuse([], [])
@@ -332,7 +391,7 @@ async def test_fuser_with_knowledge_base_no_voice_input_object():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb),
+        patch("fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb),
     ):
         fuser = Fuser(config)
         result = await fuser.fuse([], [])
@@ -359,7 +418,7 @@ async def test_fuser_with_knowledge_base_empty_voice_input():
 
     with (
         patch("fuser.IOProvider", return_value=io_provider),
-        patch("fuser.KnowledgeBase", return_value=mock_kb),
+        patch("fuser.knowledge_base.retriever.KnowledgeBase", return_value=mock_kb),
     ):
         fuser = Fuser(config)
         result = await fuser.fuse([], [])
diff --git a/tests/providers/test_llm_history_manager.py b/tests/providers/test_llm_history_manager.py
index 0b112a94a..3d72e8a62 100644
--- a/tests/providers/test_llm_history_manager.py
+++ b/tests/providers/test_llm_history_manager.py
@@ -6,6 +6,7 @@
 import pytest
 
 from providers.llm_history_manager import ChatMessage, LLMHistoryManager
+from providers.semantic_memory_provider import SemanticMemoryProvider
 
 
 @dataclass
@@ -584,6 +585,88 @@ async def process(self, prompt: str, messages: list):
     assert len(history_manager.history) <= config.history_length
 
 
+@pytest.mark.asyncio
+async def test_update_history_stores_to_semantic_memory():
+    """Test that semantic memory store is called after a successful LLM response."""
+    config = MagicMock()
+    config.model = "gpt-4o"
+    config.history_length = 5
+    config.agent_name = "TestBot"
+    config.mode = "conversation"
+
+    client = AsyncMock()
+    history_manager = LLMHistoryManager(config, client)
+
+    SemanticMemoryProvider.reset()  # type: ignore
+    memory = SemanticMemoryProvider()
+    memory.enabled = True
+    memory.store = MagicMock()  # type: ignore
+
+    class MockLLMProvider:
+        def __init__(self):
+            self._config = config
+            self._skip_state_management = False
+            self.history_manager = history_manager
+            self.io_provider = history_manager.io_provider
+            self.agent_name = config.agent_name
+
+        @LLMHistoryManager.update_history()
+        async def process(self, prompt: str, messages: list) -> MagicMock:
+            response = MagicMock()
+            response.actions = [MockAction(type="speak", value="Hello there")]
+            return response
+
+    provider = MockLLMProvider()
+    provider.io_provider.add_input("audio", "User said hi", 1234.0)
+    await provider.process("test prompt")
+
+    memory.store.assert_called_once()
+    call_args = memory.store.call_args
+    assert "User said hi" in call_args[0][0]  # formatted_inputs
+    assert "conversation" == call_args[0][2]  # mode
+
+    SemanticMemoryProvider.reset()  # type: ignore
+
+
+@pytest.mark.asyncio
+async def test_update_history_skips_store_when_memory_disabled():
+    """Test that semantic memory store is NOT called when memory is disabled."""
+    config = MagicMock()
+    config.model = "gpt-4o"
+    config.history_length = 5
+    config.agent_name = "TestBot"
+
+    client = AsyncMock()
+    history_manager = LLMHistoryManager(config, client)
+
+    SemanticMemoryProvider.reset()  # type: ignore
+    memory = SemanticMemoryProvider()
+    memory.enabled = False
+    memory.store = MagicMock()  # type: ignore
+
+    class MockLLMProvider:
+        def __init__(self):
+            self._config = config
+            self._skip_state_management = False
+            self.history_manager = history_manager
+            self.io_provider = history_manager.io_provider
+            self.agent_name = config.agent_name
+
+        @LLMHistoryManager.update_history()
+        async def process(self, prompt: str, messages: list) -> MagicMock:
+            response = MagicMock()
+            response.actions = [MockAction(type="speak", value="Hello")]
+            return response
+
+    provider = MockLLMProvider()
+    provider.io_provider.add_input("audio", "Test input", 1234.0)
+    await provider.process("test prompt")
+
+    memory.store.assert_not_called()
+
+    SemanticMemoryProvider.reset()  # type: ignore
+
+
 def test_get_messages_empty(history_manager):
     """Test get_messages returns empty list when no history."""
     result = history_manager.get_messages()
diff --git a/tests/providers/test_semantic_memory_provider.py b/tests/providers/test_semantic_memory_provider.py
new file mode 100644
index 000000000..2908bd9b9
--- /dev/null
+++ b/tests/providers/test_semantic_memory_provider.py
@@ -0,0 +1,279 @@
+import shutil
+import tempfile
+from unittest.mock import MagicMock, patch
+
+import chromadb
+import numpy as np
+import pytest
+
+from providers.semantic_memory_provider import MAX_TEXT_LENGTH, SemanticMemoryProvider
+
+
+@pytest.fixture(autouse=True)
+def reset_singleton():
+    """Reset the SemanticMemoryProvider singleton before each test."""
+    SemanticMemoryProvider.reset()  # type: ignore
+    yield
+    SemanticMemoryProvider.reset()  # type: ignore
+
+
+@pytest.fixture
+def temp_persist_dir():
+    """Create a temporary directory for ChromaDB persistence."""
+    tmpdir = tempfile.mkdtemp()
+    yield tmpdir
+    shutil.rmtree(tmpdir, ignore_errors=True)
+
+
+@pytest.fixture
+def mock_model():
+    """Create a mock SentenceTransformer model."""
+    model = MagicMock()
+    model.encode.return_value = np.random.randn(384).astype(np.float32)
+    return model
+
+
+class TestSemanticMemoryProviderInit:
+    def test_default_state_is_disabled(self):
+        provider = SemanticMemoryProvider()
+        assert provider.enabled is False
+        assert provider.top_k == 3
+        assert provider.similarity_threshold == 0.3
+
+    def test_configure_without_enable_stays_disabled(self):
+        provider = SemanticMemoryProvider()
+        provider.configure(enabled=False, top_k=5, similarity_threshold=0.5)
+        assert provider.enabled is False
+        assert provider.top_k == 5
+        assert provider.similarity_threshold == 0.5
+
+    def test_singleton_returns_same_instance(self):
+        p1 = SemanticMemoryProvider()
+        p2 = SemanticMemoryProvider()
+        assert p1 is p2
+
+
+class TestSemanticMemoryProviderConfigure:
+    def test_configure_enabled_calls_initialize(self):
+        provider = SemanticMemoryProvider()
+        with patch.object(provider, "_ensure_initialized") as mock_init:
+            provider.configure(enabled=True, top_k=5, similarity_threshold=0.4)
+            assert provider.enabled is True
+            assert provider.top_k == 5
+            assert provider.similarity_threshold == 0.4
+            mock_init.assert_called_once()
+
+    def test_configure_disabled_skips_initialize(self):
+        provider = SemanticMemoryProvider()
+        provider.configure(enabled=False)
+        assert provider._model is None
+        assert provider._chroma_client is None
+
+
+class TestStoreAndRetrieve:
+    def test_store_when_disabled_is_noop(self):
+        provider = SemanticMemoryProvider()
+        provider.enabled = False
+        provider.store("input", "response", "default", 1)
+
+    def test_retrieve_when_disabled_returns_empty(self):
+        provider = SemanticMemoryProvider()
+        provider.enabled = False
+        result = provider.retrieve("query", "default")
+        assert result == []
+
+    def test_store_when_model_is_none_is_noop(self):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = None
+        provider.store("input", "response", "default", 1)
+
+    def test_retrieve_when_model_is_none_returns_empty(self):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = None
+        result = provider.retrieve("query", "default")
+        assert result == []
+
+    def test_store_and_retrieve_with_mock_model(self, mock_model, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+        provider._persist_dir = temp_persist_dir
+
+        vec1 = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec1
+
+        provider.store("I see a person waving", "I waved back", "test_mode", 1)
+
+        collection = provider._get_collection("test_mode")
+        assert collection is not None
+        assert collection.count() == 1
+
+        results = provider.retrieve("person waving", "test_mode")
+        assert len(results) == 1
+        assert "I see a person waving" in results[0]
+        assert "I waved back" in results[0]
+
+    def test_retrieve_filters_by_threshold(self, mock_model, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+        provider._persist_dir = temp_persist_dir
+        provider.similarity_threshold = 0.99
+
+        vec1 = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec1
+        provider.store("memory text", "action text", "test_mode", 1)
+
+        # Use a very different vector for query
+        vec2 = np.zeros(384, dtype=np.float32)
+        vec2[0] = 1.0
+        mock_model.encode.return_value = vec2
+
+        results = provider.retrieve("unrelated query", "test_mode")
+        assert results == []
+
+    def test_store_truncates_long_text(self, mock_model, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+
+        vec = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec
+
+        long_text = "x" * 5000
+        provider.store(long_text, long_text, "test_mode", 1)
+
+        collection = provider._get_collection("test_mode")
+        assert collection is not None
+        assert collection.count() == 1
+        doc = collection.get()["documents"][0]
+        # Each side truncated to MAX_TEXT_LENGTH, total < 2 * MAX_TEXT_LENGTH + overhead
+        assert len(doc) <= 2 * MAX_TEXT_LENGTH + 30
+
+    def test_retrieve_empty_collection(self, mock_model, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+
+        vec = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec
+
+        results = provider.retrieve("query", "empty_mode")
+        assert results == []
+
+
+class TestClearMode:
+    def test_clear_mode_removes_collection(self, mock_model, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+
+        vec = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec
+
+        provider.store("data", "action", "clearable", 1)
+        clearable_col = provider._get_collection("clearable")
+        assert clearable_col is not None
+        assert clearable_col.count() == 1
+
+        provider.clear_mode("clearable")
+        assert "om1_clearable" not in provider._collections
+
+    def test_clear_mode_when_no_client_is_noop(self):
+        provider = SemanticMemoryProvider()
+        provider._chroma_client = None
+        provider.clear_mode("some_mode")
+
+
+class TestModeIsolation:
+    def test_different_modes_have_separate_collections(
+        self, mock_model, temp_persist_dir
+    ):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+
+        vec = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec
+
+        provider.store("mode_a data", "mode_a action", "mode_a", 1)
+        provider.store("mode_b data", "mode_b action", "mode_b", 2)
+
+        col_a = provider._get_collection("mode_a")
+        col_b = provider._get_collection("mode_b")
+        assert col_a is not None
+        assert col_b is not None
+        assert col_a.count() == 1
+        assert col_b.count() == 1
+
+        results_a = provider.retrieve("data", "mode_a")
+        results_b = provider.retrieve("data", "mode_b")
+
+        assert len(results_a) == 1
+        assert len(results_b) == 1
+        assert "mode_a" in results_a[0]
+        assert "mode_b" in results_b[0]
+
+
+class TestMultipleStores:
+    def test_multiple_stores_accumulate(self, mock_model, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+
+        vec = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec
+
+        for i in range(5):
+            provider.store(f"input_{i}", f"action_{i}", "test_mode", i)
+
+        collection = provider._get_collection("test_mode")
+        assert collection is not None
+        assert collection.count() == 5
+
+    def test_top_k_limits_results(self, mock_model, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = mock_model
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+        provider.top_k = 2
+        provider.similarity_threshold = 0.0
+
+        vec = np.ones(384, dtype=np.float32) / np.sqrt(384)
+        mock_model.encode.return_value = vec
+
+        for i in range(5):
+            provider.store(f"input_{i}", f"action_{i}", "test_mode", i)
+
+        results = provider.retrieve("query", "test_mode")
+        assert len(results) <= 2
+
+
+class TestErrorHandling:
+    def test_store_handles_encoding_error(self, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = MagicMock()
+        provider._model.encode.side_effect = RuntimeError("encoding failed")
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+
+        provider.store("input", "response", "test_mode", 1)
+
+    def test_retrieve_handles_query_error(self, temp_persist_dir):
+        provider = SemanticMemoryProvider()
+        provider.enabled = True
+        provider._model = MagicMock()
+        provider._model.encode.side_effect = RuntimeError("encoding failed")
+        provider._chroma_client = chromadb.PersistentClient(path=temp_persist_dir)
+
+        result = provider.retrieve("query", "test_mode")
+        assert result == []
diff --git a/uv.lock b/uv.lock
index f57137a0e..3c2ea3ba9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -3,17 +3,25 @@ revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
+    "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform == 'darwin'",
     "python_full_version >= '3.14' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
+    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux'",
     "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and platform_python_implementation == 'PyPy' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform == 'win32'",
+    "python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform == 'win32'",
+    "python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform == 'win32'",
     "(python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "(python_full_version >= '3.12' and python_full_version < '3.14' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
-    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.12' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.13.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation != 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version >= '3.13' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
+    "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_python_implementation == 'PyPy' and sys_platform == 'linux') or (python_full_version == '3.12.*' and platform_python_implementation == 'PyPy' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')",
     "python_full_version == '3.11.*' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and sys_platform == 'win32'",
@@ -176,6 +184,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" },
 ]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/57/ba/046ceea27344560984e26a590f90bc7f4a75b06701f653222458922b558c/annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4", size = 7288, upload-time = "2025-11-10T22:07:42.062Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/d3/26bf1008eb3d2daa8ef4cacc7f3bfdc11818d111f7e2d0201bc6e3b49d45/annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320", size = 5303, upload-time = "2025-11-10T22:07:40.673Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -291,6 +308,85 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b1/c9/15bb4fd7a1f39d70db35af2b9c20a0ae19e4220eb58a8b8446e903b98d72/av-15.1.0-cp314-cp314t-win_amd64.whl", hash = "sha256:9a20c5eba3ec49c2f4b281797021923fc68a86aeb66c5cda4fd0252fa8004951", size = 31487337, upload-time = "2025-08-30T04:41:30.591Z" },
 ]
 
+[[package]]
+name = "backoff"
+version = "2.2.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001, upload-time = "2022-10-05T19:19:32.061Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148, upload-time = "2022-10-05T19:19:30.546Z" },
+]
+
+[[package]]
+name = "bcrypt"
+version = "5.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d4/36/3329e2518d70ad8e2e5817d5a4cac6bba05a47767ec416c7d020a965f408/bcrypt-5.0.0.tar.gz", hash = "sha256:f748f7c2d6fd375cc93d3fba7ef4a9e3a092421b8dbf34d8d4dc06be9492dfdd", size = 25386, upload-time = "2025-09-25T19:50:47.829Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/13/85/3e65e01985fddf25b64ca67275bb5bdb4040bd1a53b66d355c6c37c8a680/bcrypt-5.0.0-cp313-cp313t-macosx_10_12_universal2.whl", hash = "sha256:f3c08197f3039bec79cee59a606d62b96b16669cff3949f21e74796b6e3cd2be", size = 481806, upload-time = "2025-09-25T19:49:05.102Z" },
+    { url = "https://files.pythonhosted.org/packages/44/dc/01eb79f12b177017a726cbf78330eb0eb442fae0e7b3dfd84ea2849552f3/bcrypt-5.0.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:200af71bc25f22006f4069060c88ed36f8aa4ff7f53e67ff04d2ab3f1e79a5b2", size = 268626, upload-time = "2025-09-25T19:49:06.723Z" },
+    { url = "https://files.pythonhosted.org/packages/8c/cf/e82388ad5959c40d6afd94fb4743cc077129d45b952d46bdc3180310e2df/bcrypt-5.0.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:baade0a5657654c2984468efb7d6c110db87ea63ef5a4b54732e7e337253e44f", size = 271853, upload-time = "2025-09-25T19:49:08.028Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/86/7134b9dae7cf0efa85671651341f6afa695857fae172615e960fb6a466fa/bcrypt-5.0.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:c58b56cdfb03202b3bcc9fd8daee8e8e9b6d7e3163aa97c631dfcfcc24d36c86", size = 269793, upload-time = "2025-09-25T19:49:09.727Z" },
+    { url = "https://files.pythonhosted.org/packages/cc/82/6296688ac1b9e503d034e7d0614d56e80c5d1a08402ff856a4549cb59207/bcrypt-5.0.0-cp313-cp313t-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:4bfd2a34de661f34d0bda43c3e4e79df586e4716ef401fe31ea39d69d581ef23", size = 289930, upload-time = "2025-09-25T19:49:11.204Z" },
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