chore: import upstream snapshot with attribution

docs-website/docs/memory-stores/cogneememorystore.mdx

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+---
+title: "CogneeMemoryStore"
+id: cogneememorystore
+slug: "/cogneememorystore"
+description: "A persistent memory store backed by Cognee's knowledge graph API."
+---
+
+# CogneeMemoryStore
+
+`CogneeMemoryStore` is a persistent memory store backed by Cognee's knowledge graph API. It is the shared data layer used by [`CogneeRetriever`](../pipeline-components/retrievers/cogneeretriever.mdx) and [`CogneeWriter`](../pipeline-components/writers/cogneewriter.mdx).
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Used by** | [`CogneeRetriever`](../pipeline-components/retrievers/cogneeretriever.mdx), [`CogneeWriter`](../pipeline-components/writers/cogneewriter.mdx) |
+| **Optional init variables** | `search_type`, `top_k`, `dataset_name`, `session_id`, `self_improvement`, `timeout` |
+| **API reference** | [Cognee](/reference/integrations-cognee#cogneememorystore) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cognee |
+| **Package name** | `cognee-haystack` |
+
+</div>
+
+## Overview
+
+`CogneeMemoryStore` wraps Cognee's V2 memory API:
+
+- `add_memories` → `cognee.remember`
+- `search_memories` → `cognee.recall`
+- `improve` → `cognee.improve`
+- `delete_all_memories` → `cognee.forget`
+
+Cognee supports two memory tiers. Set `session_id` to use the **session cache** — fast writes with no LLM extraction, session-aware recall. Leave `session_id` as `None` to write to the **permanent knowledge graph**, which uses LLM extraction during ingestion and supports richer graph-completion queries.
+
+Cognee configuration (LLM provider, database, vector store) is read from environment variables. See the [Cognee documentation](https://docs.cognee.ai) for setup instructions.
+
+### Parameters
+
+- `search_type` is *optional* and defaults to `"GRAPH_COMPLETION"`. Controls which Cognee recall strategy is used. Other useful values include `"CHUNKS"` for raw retrieval and `"SUMMARIES"` for summarized graph nodes.
+- `top_k` is *optional* and defaults to `5`. Sets the default maximum number of memories returned per search.
+- `dataset_name` is *optional* and defaults to `"haystack_memory"`. Names the Cognee dataset backing this store.
+- `session_id` is *optional* and defaults to `None`. When set, reads and writes target the session-cache tier. When `None`, the permanent knowledge graph is used.
+- `self_improvement` is *optional* and defaults to `True`. When `True`, Cognee runs graph improvement inline after every write. Set to `False` when you want `improve()` to be the sole improvement trigger.
+- `timeout` is *optional* and defaults to `300`. Per-call timeout in seconds for any Cognee operation.
+
+### Installation
+
+Install the Cognee integration:
+
+```bash
+pip install cognee-haystack
+```
+
+Set your LLM API key (used by Cognee for graph extraction and queries):
+
+```bash
+export LLM_API_KEY="your-llm-api-key"
+```
+
+Optionally, set a separate embedding API key (defaults to `LLM_API_KEY` when unset):
+
+```bash
+export EMBEDDING_API_KEY="your-embedding-api-key"
+```
+
+## Usage
+
+### On its own
+
+```python
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.memory_stores.cognee import CogneeMemoryStore
+
+store = CogneeMemoryStore(search_type="GRAPH_COMPLETION", top_k=5)
+
+store.add_memories(
+    messages=[ChatMessage.from_user("Alice enjoys hiking and outdoor activities.")],
+    user_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
+)
+
+memories = store.search_memories(
+    query="What does Alice like?",
+    user_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
+)
+print([msg.text for msg in memories])
+```
+
+### Session tier vs permanent graph
+
+Use `session_id` to control which memory tier is targeted. A single store can serve both tiers — the writer's `session_id` overrides the store's `session_id` per call.
+
+```python
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.memory_stores.cognee import CogneeMemoryStore
+
+store = CogneeMemoryStore(dataset_name="my_agent_memory", self_improvement=False)
+
+# Write long-lived facts to the permanent graph (no session_id).
+store.add_memories(
+    messages=[ChatMessage.from_user("Alice is a senior data scientist at Acme Corp.")],
+)
+
+# Write transient session context to the session cache.
+store.add_memories(
+    messages=[ChatMessage.from_user("Alice is currently debugging a vector store issue.")],
+    session_id="alice_session_1",
+)
+
+# Promote the session cache into the permanent graph.
+store.improve(session_id="alice_session_1")
+```
+
+### Delete all memories
+
+```python
+# Delete only this store's dataset (session cache is unaffected).
+store.delete_all_memories()
+
+# To wipe everything including the session cache, call cognee directly:
+import asyncio
+import cognee
+
+asyncio.run(cognee.forget(everything=True))
+```

docs-website/docs/memory-stores/mem0memorystore.mdx

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+---
+title: "Mem0MemoryStore"
+id: mem0memorystore
+slug: "/mem0memorystore"
+description: "A memory store backed by the Mem0 cloud API."
+---
+
+# Mem0MemoryStore
+
+`Mem0MemoryStore` is a memory store backed by the Mem0 cloud API. It is the shared data layer used by [`Mem0MemoryRetriever`](../pipeline-components/retrievers/mem0memoryretriever.mdx), [`Mem0MemoryWriter`](../pipeline-components/writers/mem0memorywriter.mdx), and the [Mem0 Memory Tools](../tools/ready-made-tools/mem0memorytools.mdx).
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Used by** | [`Mem0MemoryRetriever`](../pipeline-components/retrievers/mem0memoryretriever.mdx), [`Mem0MemoryWriter`](../pipeline-components/writers/mem0memorywriter.mdx), [`Mem0MemoryRetrieverTool`, `Mem0MemoryWriterTool`](../tools/ready-made-tools/mem0memorytools.mdx) |
+| **Optional init variables** | `api_key`: Defaults to `MEM0_API_KEY` environment variable |
+| **API reference** | [Mem0](/reference/integrations-mem0#mem0memorystore) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/mem0 |
+| **Package name** | `mem0-haystack` |
+
+</div>
+
+## Overview
+
+`Mem0MemoryStore` wraps the Mem0 cloud API and provides two core methods:
+
+- `add_memories` — stores a list of `ChatMessage` objects as memories in Mem0.
+- `search_memories` — retrieves memories from Mem0 that are relevant to a query.
+
+Scope memories with at least one Mem0 entity ID: `user_id`, `run_id`, `agent_id`, or `app_id`. These are runtime parameters, so a single store instance can serve multiple users or sessions.
+
+The `infer` parameter on `add_memories` controls how Mem0 processes incoming messages:
+
+- `infer=True` lets Mem0 extract memories from the messages automatically. This is useful when storing a full Agent turn.
+- `infer=False` stores the supplied message text as-is. This is useful when the exact memory text has already been selected upstream.
+
+### Installation
+
+Install the Mem0 integration:
+
+```bash
+pip install mem0-haystack
+```
+
+Set your Mem0 API key:
+
+```bash
+export MEM0_API_KEY="your-mem0-api-key"
+```
+
+## Usage
+
+### On its own
+
+```python
+from haystack.dataclasses import ChatMessage
+from haystack_integrations.memory_stores.mem0 import Mem0MemoryStore
+
+store = Mem0MemoryStore()
+
+store.add_memories(
+    messages=[ChatMessage.from_user("Alice prefers concise Python examples.")],
+    user_id="alice",
+    infer=False,
+)
+
+memories = store.search_memories(
+    query="What does Alice prefer?",
+    user_id="alice",
+    top_k=3,
+)
+print([msg.text for msg in memories])
+```
+
+### Scoping with multiple entity IDs
+
+Mem0 supports narrowing the scope of reads and writes with `user_id`, `run_id`, `agent_id`, and `app_id`. Pass any combination at call time:
+
+```python
+store.add_memories(
+    messages=[ChatMessage.from_user("Alice is working on a documentation search system.")],
+    user_id="alice",
+    run_id="docs-assistant-session-1",
+    infer=True,
+)
+
+memories = store.search_memories(
+    query="What project is Alice working on?",
+    user_id="alice",
+    run_id="docs-assistant-session-1",
+)
+print([msg.text for msg in memories])
+```
+
+### Retrieving all memories in scope
+
+Pass `query=None` to return all memories matching the provided scope without a relevance search:
+
+```python
+all_memories = store.search_memories(query=None, user_id="alice")
+print([msg.text for msg in all_memories])
+```