CogneeWriter

Writes ChatMessage objects to a CogneeMemoryStore as long-term memories.

Most common position in a pipeline	After an Agent or Chat Generator in memory-augmented pipelines
Mandatory init variables	memory_store: A CogneeMemoryStore instance
Optional init variables	session_id: When set, writes target the session-cache tier; when None, writes go to the permanent knowledge graph
Mandatory run variables	messages: A list of ChatMessage objects
Optional run variables	user_id: Cognee user ID to scope the write; pass None to use Cognee's default user
Output variables	messages_written: The list of ChatMessage objects that were written (passed through unchanged)
API reference	Cognee
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cognee
Package name	cognee-haystack

Overview

CogneeWriter persists a list of ChatMessage objects into a CogneeMemoryStore. Use it in a Haystack Pipeline to store conversation facts or user preferences after an Agent turn.

Messages are passed through unchanged to the pipeline output (messages_written), making this component easy to chain after an Agent or generator without breaking the pipeline flow.

The session_id init parameter controls which Cognee memory tier is targeted:

• Omit session_id (or set it to None) to write to the permanent knowledge graph — Cognee runs LLM extraction during ingestion, producing rich graph-completion-ready nodes.
• Set session_id to write to the session cache — fast writes with no LLM extraction, scoped to that session. Session content can later be promoted to the permanent graph via CogneeMemoryStore.improve().

The writer's session_id overrides the store's session_id per call, so a single store can back multiple writers targeting different memory tiers.

Installation

Install the Cognee integration:

bash

pip install cognee-haystack

Set your LLM API key (used by Cognee for graph extraction):

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.components.writers.cognee import CogneeWriter
from haystack_integrations.memory_stores.cognee import CogneeMemoryStore

store = CogneeMemoryStore()
writer = CogneeWriter(memory_store=store)

result = writer.run(
    messages=[ChatMessage.from_user("Alice prefers concise Python examples.")],
    user_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
)
print(result["messages_written"])

To write to the session cache instead of the permanent graph, pass a session_id:

python

session_writer = CogneeWriter(memory_store=store, session_id="alice_session_1")
session_writer.run(
    messages=[ChatMessage.from_user("Alice is currently debugging a vector store issue.")],
    user_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
)

In a Pipeline

This example connects an Agent's full messages output to CogneeWriter, so Cognee stores the conversation turn in the permanent graph.

python

from haystack import Pipeline
from haystack.components.agents import Agent
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

from haystack_integrations.components.writers.cognee import CogneeWriter
from haystack_integrations.memory_stores.cognee import CogneeMemoryStore

store = CogneeMemoryStore(dataset_name="my_agent_memory")

pipeline = Pipeline()
pipeline.add_component(
    "agent",
    Agent(
        chat_generator=OpenAIChatGenerator(model="gpt-4o-mini"),
        system_prompt=(
            "Answer the user and preserve durable user facts or preferences for future conversations."
        ),
    ),
)
pipeline.add_component("writer", CogneeWriter(memory_store=store))

pipeline.connect("agent.messages", "writer.messages")

result = pipeline.run(
    {
        "agent": {
            "messages": [
                ChatMessage.from_user(
                    "My name is Alice and I prefer concise Python examples.",
                ),
            ],
        },
        "writer": {
            "user_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
        },
    },
)

print(result["writer"]["messages_written"])