feat: add Mem0 memory integration with config, implementation, docs, tests, and dependency

docs/user_guide/en/modules/memory.md

@@ -32,12 +32,23 @@ memory:
         model: text-embedding-3-small
 ```
 
+### Mem0 Memory Config
+```yaml
+memory:
+  - name: agent_memory
+    type: mem0
+    config:
+      api_key: ${MEM0_API_KEY}
+      agent_id: my-agent
+```
+
 ## 3. Built-in Store Comparison
 | Type | Path | Highlights | Best for |
 | --- | --- | --- | --- |
 | `simple` | `node/agent/memory/simple_memory.py` | Optional disk persistence (JSON) after runs; FAISS + semantic rerank; read/write capable. | Small conversation history, prototypes. |
 | `file` | `node/agent/memory/file_memory.py` | Chunks files/dirs into a vector index, read-only, auto rebuilds when files change. | Knowledge bases, doc QA. |
 | `blackboard` | `node/agent/memory/blackboard_memory.py` | Lightweight append-only log trimmed by time/count; no vector search. | Broadcast boards, pipeline debugging. |
+| `mem0` | `node/agent/memory/mem0_memory.py` | Cloud-managed by Mem0; semantic search + graph relationships; no local embeddings or persistence needed. Requires `mem0ai` package. | Production memory, cross-session persistence, multi-agent memory sharing. |
 
 All stores register through `register_memory_store()` so summaries show up in UI via `MemoryStoreConfig.field_specs()`.
 
@@ -98,6 +109,14 @@ This schema lets multimodal outputs flow into Memory/Thinking modules without ex
 - **Retrieval** – Returns the latest `top_k` entries ordered by time.
 - **Write** – `update()` appends the latest snapshot (input/output blocks, attachments, previews). No embeddings are generated, so retrieval is purely recency-based.
 
+### 5.4 Mem0Memory
+- **Config** – Requires `api_key` (from [app.mem0.ai](https://app.mem0.ai)). Optional `user_id`, `agent_id`, `org_id`, `project_id` for scoping.
+- **Important**: `user_id` and `agent_id` are mutually exclusive in Mem0 API calls. If both are configured, two separate searches are made and results merged. For writes, `agent_id` takes precedence. Agent-generated content is stored with `role: "assistant"`.
+- **Retrieval** – Uses Mem0's server-side semantic search. Supports `top_k` and `similarity_threshold` via `MemoryAttachmentConfig`.
+- **Write** – `update()` sends conversation messages to Mem0 via the SDK. Agent outputs use `role: "assistant"`, user inputs use `role: "user"`.
+- **Persistence** – Fully cloud-managed. `load()` and `save()` are no-ops. Memories persist across runs and sessions automatically.
+- **Dependencies** – Requires `mem0ai` package (`pip install mem0ai`).
+
 ## 6. EmbeddingConfig Notes
 - Fields: `provider`, `model`, `api_key`, `base_url`, `params`.
 - `provider=openai` uses the official client; override `base_url` for compatibility layers.

entity/configs/__init__.py

@@ -10,6 +10,7 @@
     EmbeddingConfig,
     FileMemoryConfig,
     FileSourceConfig,
+    Mem0MemoryConfig,
     MemoryAttachmentConfig,
     MemoryStoreConfig,
     SimpleMemoryConfig,
@@ -43,6 +44,7 @@
     "FunctionToolConfig",
     "GraphDefinition",
     "HumanConfig",
+    "Mem0MemoryConfig",
     "MemoryAttachmentConfig",
     "MemoryStoreConfig",
     "McpLocalConfig",

entity/configs/node/memory.py

@@ -279,6 +279,75 @@ def from_dict(cls, data: Mapping[str, Any], *, path: str) -> "BlackboardMemoryCo
     }
 
 
+@dataclass
+class Mem0MemoryConfig(BaseConfig):
+    """Configuration for Mem0 managed memory service."""
+
+    api_key: str = ""
+    org_id: str | None = None
+    project_id: str | None = None
+    user_id: str | None = None
+    agent_id: str | None = None
+
+    @classmethod
+    def from_dict(cls, data: Mapping[str, Any], *, path: str) -> "Mem0MemoryConfig":
+        mapping = require_mapping(data, path)
+        api_key = require_str(mapping, "api_key", path)
+        org_id = optional_str(mapping, "org_id", path)
+        project_id = optional_str(mapping, "project_id", path)
+        user_id = optional_str(mapping, "user_id", path)
+        agent_id = optional_str(mapping, "agent_id", path)
+        return cls(
+            api_key=api_key,
+            org_id=org_id,
+            project_id=project_id,
+            user_id=user_id,
+            agent_id=agent_id,
+            path=path,
+        )
+
+    FIELD_SPECS = {
+        "api_key": ConfigFieldSpec(
+            name="api_key",
+            display_name="Mem0 API Key",
+            type_hint="str",
+            required=True,
+            description="Mem0 API key (get one from app.mem0.ai)",
+            default="${MEM0_API_KEY}",
+        ),
+        "org_id": ConfigFieldSpec(
+            name="org_id",
+            display_name="Organization ID",
+            type_hint="str",
+            required=False,
+            description="Mem0 organization ID for scoping",
+            advance=True,
+        ),
+        "project_id": ConfigFieldSpec(
+            name="project_id",
+            display_name="Project ID",
+            type_hint="str",
+            required=False,
+            description="Mem0 project ID for scoping",
+            advance=True,
+        ),
+        "user_id": ConfigFieldSpec(
+            name="user_id",
+            display_name="User ID",
+            type_hint="str",
+            required=False,
+            description="User ID for user-scoped memories. Mutually exclusive with agent_id in API calls.",
+        ),
+        "agent_id": ConfigFieldSpec(
+            name="agent_id",
+            display_name="Agent ID",
+            type_hint="str",
+            required=False,
+            description="Agent ID for agent-scoped memories. Mutually exclusive with user_id in API calls.",
+        ),
+    }
+
+
 @dataclass
 class MemoryStoreConfig(BaseConfig):
     name: str

pyproject.toml

@@ -39,6 +39,7 @@ dependencies = [
     "filelock>=3.20.1",
     "markdown>=3.10",
     "xhtml2pdf>=0.2.17",
+    "mem0ai>=1.0.9",
 ]
 
 [build-system]

runtime/node/agent/memory/builtin_stores.py

@@ -3,6 +3,7 @@
 from entity.configs.node.memory import (
     BlackboardMemoryConfig,
     FileMemoryConfig,
+    Mem0MemoryConfig,
     SimpleMemoryConfig,
     MemoryStoreConfig,
 )
@@ -34,6 +35,19 @@
 )
 
 
+def _create_mem0_memory(store):
+    from runtime.node.agent.memory.mem0_memory import Mem0Memory
+    return Mem0Memory(store)
+
+
+register_memory_store(
+    "mem0",
+    config_cls=Mem0MemoryConfig,
+    factory=_create_mem0_memory,
+    summary="Mem0 managed memory with semantic search and graph relationships",
+)
+
+
 class MemoryFactory:
     @staticmethod
     def create_memory(store: MemoryStoreConfig) -> MemoryBase:

runtime/node/agent/memory/mem0_memory.py

@@ -0,0 +1,203 @@
+"""Mem0 managed memory store implementation."""
+
+import logging
+import time
+import uuid
+from typing import Any, Dict, List
+
+from entity.configs import MemoryStoreConfig
+from entity.configs.node.memory import Mem0MemoryConfig
+from runtime.node.agent.memory.memory_base import (
+    MemoryBase,
+    MemoryContentSnapshot,
+    MemoryItem,
+    MemoryWritePayload,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _get_mem0_client(config: Mem0MemoryConfig):
+    """Lazy-import mem0ai and create a MemoryClient."""
+    try:
+        from mem0 import MemoryClient
+    except ImportError:
+        raise ImportError(
+            "mem0ai is required for Mem0Memory. Install it with: pip install mem0ai"
+        )
+
+    client_kwargs: Dict[str, Any] = {}
+    if config.api_key:
+        client_kwargs["api_key"] = config.api_key
+    if config.org_id:
+        client_kwargs["org_id"] = config.org_id
+    if config.project_id:
+        client_kwargs["project_id"] = config.project_id
+
+    return MemoryClient(**client_kwargs)
+
+
+class Mem0Memory(MemoryBase):
+    """Memory store backed by Mem0's managed cloud service.
+
+    Mem0 handles embeddings, storage, and semantic search server-side.
+    No local persistence or embedding computation is needed.
+
+    Important API constraints:
+    - Agent memories use role="assistant" + agent_id
+    - user_id and agent_id are stored as separate records in Mem0;
+      if both are configured, an OR filter is used to search across both scopes.
+    - search() uses filters dict; add() uses top-level kwargs.
+    - SDK returns {"memories": [...]} from search.
+    """
+
+    def __init__(self, store: MemoryStoreConfig):
+        config = store.as_config(Mem0MemoryConfig)
+        if not config:
+            raise ValueError("Mem0Memory requires a Mem0 memory store configuration")
+        super().__init__(store)
+        self.config = config
+        self.client = _get_mem0_client(config)
+        self.user_id = config.user_id
+        self.agent_id = config.agent_id
+
+    # -------- Persistence (no-ops for cloud-managed store) --------
+
+    def load(self) -> None:
+        """No-op: Mem0 manages persistence server-side."""
+        pass
+
+    def save(self) -> None:
+        """No-op: Mem0 manages persistence server-side."""
+        pass
+
+    # -------- Retrieval --------
+
+    def _build_search_filters(self, agent_role: str) -> Dict[str, Any]:
+        """Build the filters dict for Mem0 search.
+
+        Mem0 search requires a filters dict for entity scoping.
+        user_id and agent_id are stored as separate records, so
+        when both are configured we use an OR filter to match either.
+        """
+        if self.user_id and self.agent_id:
+            return {
+                "OR": [
+                    {"user_id": self.user_id},
+                    {"agent_id": self.agent_id},
+                ]
+            }
+        elif self.user_id:
+            return {"user_id": self.user_id}
+        elif self.agent_id:
+            return {"agent_id": self.agent_id}
+        else:
+            # Fallback: use agent_role as agent_id
+            return {"agent_id": agent_role}
+
+    def retrieve(
+        self,
+        agent_role: str,
+        query: MemoryContentSnapshot,
+        top_k: int,
+        similarity_threshold: float,
+    ) -> List[MemoryItem]:
+        """Search Mem0 for relevant memories.
+
+        Uses the filters dict to scope by user_id, agent_id, or both
+        (via OR filter). The SDK returns {"memories": [...]}.
+        """
+        if not query.text.strip():
+            return []
+
+        try:
+            filters = self._build_search_filters(agent_role)
+            search_kwargs: Dict[str, Any] = {
+                "query": query.text,
+                "top_k": top_k,
+                "filters": filters,
+            }
+            if similarity_threshold >= 0:
+                search_kwargs["threshold"] = similarity_threshold
+
+            response = self.client.search(**search_kwargs)
+
+            # SDK returns {"memories": [...]} — extract the list
+            if isinstance(response, dict):
+                raw_results = response.get("memories", response.get("results", []))
+            else:
+                raw_results = response
+        except Exception as e:
+            logger.error("Mem0 search failed: %s", e)
+            return []
+
+        items: List[MemoryItem] = []
+        for entry in raw_results:
+            item = MemoryItem(
+                id=entry.get("id", f"mem0_{uuid.uuid4().hex}"),
+                content_summary=entry.get("memory", ""),
+                metadata={
+                    "agent_role": agent_role,
+                    "score": entry.get("score"),
+                    "categories": entry.get("categories", []),
+                    "source": "mem0",
+                },
+                timestamp=time.time(),
+            )
+            items.append(item)
+
+        return items
+
+    # -------- Update --------
+
+    def update(self, payload: MemoryWritePayload) -> None:
+        """Store a memory in Mem0.
+
+        Uses role="assistant" + agent_id for agent-generated memories,
+        and role="user" + user_id for user-scoped memories.
+        """
+        snapshot = payload.output_snapshot or payload.input_snapshot
+        if not snapshot or not snapshot.text.strip():
+            return
+
+        messages = self._build_messages(payload)
+        if not messages:
+            return
+
+        add_kwargs: Dict[str, Any] = {"messages": messages}
+
+        # Determine scoping: agent_id takes precedence for agent-generated content
+        if self.agent_id:
+            add_kwargs["agent_id"] = self.agent_id
+        elif self.user_id:
+            add_kwargs["user_id"] = self.user_id
+        else:
+            # Default: use agent_role as agent_id
+            add_kwargs["agent_id"] = payload.agent_role
+
+        try:
+            self.client.add(**add_kwargs)
+        except Exception as e:
+            logger.error("Mem0 add failed: %s", e)
+
+    def _build_messages(self, payload: MemoryWritePayload) -> List[Dict[str, str]]:
+        """Build Mem0-compatible message list from write payload.
+
+        Agent-generated content uses role="assistant".
+        User input uses role="user".
+        """
+        messages: List[Dict[str, str]] = []
+
+        if payload.inputs_text and payload.inputs_text.strip():
+            messages.append({
+                "role": "user",
+                "content": payload.inputs_text.strip(),
+            })
+
+        if payload.output_snapshot and payload.output_snapshot.text.strip():
+            messages.append({
+                "role": "assistant",
+                "content": payload.output_snapshot.text.strip(),
+            })
+
+        return messages