Add the cognee integration page

Author: bilgeyucel

integrations/cognee.md

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+---
+layout: integration
+name: Cognee
+description: Add persistent, knowledge-graph-backed memory to your Haystack agents and pipelines with Cognee
+authors:
+    - name: deepset
+      socials:
+        github: deepset-ai
+        twitter: haystack_ai
+        linkedin: https://www.linkedin.com/company/deepset-ai/
+    - name: Cognee
+      socials:
+        github: topoteretes
+        twitter: cognee_ai
+        linkedin: https://www.linkedin.com/company/cognee-ai/
+pypi: https://pypi.org/project/cognee-haystack/
+repo: https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cognee
+type: Memory Store
+report_issue: https://github.com/deepset-ai/haystack-core-integrations/issues
+logo: /logos/cognee.png
+version: Haystack 2.0
+toc: true
+---
+
+### **Table of Contents**
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Available Classes](#available-classes)
+  - [Use in a Pipeline](#use-in-a-pipeline)
+- [License](#license)
+
+## Overview
+
+[Cognee](https://www.cognee.ai/) provides open-source, knowledge-graph-backed memory for AI agents and assistants. It helps Haystack applications store facts, conversation history, and contextual knowledge as a semantic graph, then retrieve the most relevant memories using graph-completion or other search strategies.
+
+The `cognee-haystack` package is part of [Haystack Core Integrations](https://github.com/deepset-ai/haystack-core-integrations) and provides:
+
+- `CogneeMemoryStore`: A persistent memory store backed by Cognee's knowledge graph API.
+- `CogneeRetriever`: A pipeline component for retrieving memories from Cognee as system `ChatMessage` objects.
+- `CogneeWriter`: A pipeline component for writing `ChatMessage` memories to Cognee.
+
+Cognee supports two memory tiers:
+
+- **Permanent knowledge graph** — rich semantic storage backed by an LLM extraction step; supports graph-completion queries and cross-session recall.
+- **Session cache** — fast, LLM-free writes scoped to a single session; can be promoted to the permanent graph later via `CogneeMemoryStore.improve()`.
+
+More information:
+
+- [Cognee website](https://www.cognee.ai)
+- [Cognee documentation](https://docs.cognee.ai)
+- [Cognee GitHub repository](https://github.com/topoteretes/cognee)
+
+## Installation
+
+Install the integration:
+
+```bash
+pip install cognee-haystack
+```
+
+Set your LLM API key (used by Cognee for knowledge 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"
+```
+
+Cognee reads LLM provider, database, and vector-store settings from environment variables. See the [Cognee documentation](https://docs.cognee.ai) for the full list of configuration options.
+
+## Usage
+
+### Available Classes
+
+- [`CogneeRetriever`](https://docs.haystack.deepset.ai/docs/cogneeretriever): Retrieves memories from Cognee as system `ChatMessage` objects.
+- [`CogneeWriter`](https://docs.haystack.deepset.ai/docs/cogneewriter): Writes `ChatMessage` objects to Cognee.
+- [`CogneeMemoryStore`](https://docs.haystack.deepset.ai/reference/integrations-cognee#cogneememorystore): The underlying store that wraps Cognee's V2 memory API (`remember`, `recall`, `improve`, `forget`).
+
+### Use in a Pipeline
+
+Use `CogneeRetriever` and `CogneeWriter` for explicit pipeline control over when memories are stored and retrieved.
+
+The example below seeds long-lived facts into the permanent knowledge graph, then runs an agent pipeline that retrieves relevant context before each response. `cognee.recall` auto-captures each turn as a session QA entry, so no `CogneeWriter` is needed inside the agent pipeline for session memory.
+
+```python
+from haystack import Pipeline
+from haystack.components.agents import Agent
+from haystack.components.converters import OutputAdapter
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.dataclasses import ChatMessage
+
+from haystack_integrations.components.retrievers.cognee import CogneeRetriever
+from haystack_integrations.components.writers.cognee import CogneeWriter
+from haystack_integrations.memory_stores.cognee import CogneeMemoryStore
+
+# Seed long-lived facts into the permanent knowledge graph (no session_id).
+seed_store = CogneeMemoryStore(dataset_name="my_agent_memory", self_improvement=False)
+persistent_writer = CogneeWriter(memory_store=seed_store)
+persistent_writer.run(
+    messages=[
+        ChatMessage.from_user("Alice is a senior data scientist specialising in NLP."),
+        ChatMessage.from_user("Alice's current project is a documentation search system built with Haystack."),
+        ChatMessage.from_user("Alice prefers concise answers with Python code examples."),
+    ]
+)
+
+# Build the agent pipeline with a session-scoped store.
+SESSION = "alice_session_1"
+chat_store = CogneeMemoryStore(dataset_name="my_agent_memory", session_id=SESSION)
+
+pipeline = Pipeline()
+pipeline.add_component("retriever", CogneeRetriever(memory_store=chat_store))
+pipeline.add_component(
+    "memory_context",
+    OutputAdapter(
+        template="{{ memories + user_messages }}",
+        output_type=list[ChatMessage],
+        unsafe=True,
+    ),
+)
+pipeline.add_component(
+    "agent",
+    Agent(
+        chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
+        system_prompt=(
+            "You are a helpful assistant with long-term memory. "
+            "System messages at the start of the conversation contain relevant memories. "
+            "Be concise; prefer short answers and Python code examples."
+        ),
+    ),
+)
+
+pipeline.connect("retriever.messages", "memory_context.memories")
+pipeline.connect("memory_context.output", "agent.messages")
+
+query = "What project is Alice currently working on?"
+result = pipeline.run(
+    {
+        "retriever": {"query": query},
+        "memory_context": {"user_messages": [ChatMessage.from_user(query)]},
+    }
+)
+print(result["agent"]["last_message"].text)
+
+# After the session, promote the session cache into the permanent graph.
+chat_store.improve()
+```
+
+For a full four-phase demo (persistent seeding → session seeding → agent chat → graph promotion), see the [example script](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/cognee/examples/demo_memory_agent.py).
+
+## License
+
+`cognee-haystack` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.