🚀 Meet the Memify Pipeline — The Future of Post-Processing for Knowledge Graphs

We’re excited to introduce a major evolution of the Cognee platform: the Memify Pipeline — a modular, extensible post-processing pipeline designed to make your memory smarter, faster, and continuously improving long after their initial creation.

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🎯 What is the Memify Pipeline?

Think of Memify as a “memory enhancement layer” for your knowledge base.

Once your Cognify memory layer is built, the Memify Pipeline takes over — running enrichment, optimization, and persistence steps without disrupting your core workflows. It operates as a structured, parameterized framework that enhances your graph database, vector collections, and metastore in a safe and incremental way.

In short: Memify doesn’t just build knowledge graphs. It keeps them evolving.

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🔧 How It Works

The pipeline runs in three clear stages:

Stage 1: Data Access

Extract the data from existing knowledge graph.

• Input: Knowledge graph, vector DB, and metastore
• Output: Data ready for processing
• Example: Reading all the data from a particular PDF on animals

Stage 2: Business Logic & Computation

Apply memory logic, ML models, and custom business logic.

• Input: Taken from Stage 1 in form of DataPoints
• Output: Enriched relationships, new embeddings, computed transformations
• Example: Create associations between mentions of penguins on different pages of PDF we process

Stage 3: Persistence

Commit the enhancements back to your system safely.

• Input: Processed results from Stage 2
• Output: Updated graph DB, vector collections, and metastore
• Example: Writing links between the term “penguin” on page 42 and description of penguin habitats on Antartical on page 85 to your graph database

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🚀 Why It Matters

Updating knowledge graphs no longer needs to be a disruptive or costly process. With this approach, you can improve memory dynamically keeping systems online while preserving the integrity of data relationships.

The architecture is extensible, built on a plugin-based and parameterized design that allows you to create custom memify packages tailored to your use cases.

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🏗️ Architecture at a Glance

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│ Stage 1:        │    │ Stage 2:         │    │ Stage 3:        │
│ Data Retrieval  │───▶│ Memory Logic &   │───▶│ Persistence     │
│                 │    │ Computation      │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘
         │                       │                       │
         ▼                       ▼                       ▼
   ┌─────────────┐       ┌─────────────┐       ┌─────────────┐
   │ Graph DB    │       │ ML Models   │       │ Updated     │
   │ Vector DB   │       │ Algorithms  │       │ Knowledge   │
   │ Metastore   │       │ Rules       │       │ Graph       │
   └─────────────┘       └─────────────┘       └─────────────┘

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🔍 Real-World Applications

1. Delete unused data → Remove data that is not frequently accessed
2. Optimize for relevancy→ Automatically infer which answers were relevant
3. Embedding Optimization → Tailored embeddings for specific workloads

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🛠️ Implementation Snapshot

A Memify pipeline is lightweight to set up and highly configurable:

from cognee import memify
from cognee.modules.pipelines.tasks.task import Task
from cognee.tasks.memify.extract_subgraph_chunks import extract_subgraph_chunks

async def extract_subgraph_chunks(subgraphs: list[CogneeGraph]):
    """
    Get all Document Chunks from subgraphs and forward to next task in pipeline
    """
    for subgraph in subgraphs:
        for node in subgraph.nodes.values():
            if node.attributes["type"] == "DocumentChunk":
                yield node.attributes["text"]
                
subgraph_extraction_tasks = [Task(extract_subgraph_chunks)]

async def your_custom_logic_1():
	"""
	Your custom logic 1
	"""
async def your_custom_logic_2():
	"""
	Your custom logic 2
	"""

your_custom_tasks = [Task(your_custom_logic_1), Task(your_custom_logic_2)]	

pipeline = memify(
extraction_tasks = [subgraph_extraction_tasks]
enrichment_tasks = [your_custom_tasks]
)

# Memify accepts these tasks and orchestrates forwarding of graph data through these tasks

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🎉 What’s Next

The Memify Pipeline redefines knowledge graph management by making post-processing a first-class capability. No more full rebuilds — just continuous improvement.

🔗 Next Steps:

1. Try the Beta (early access available)
2. Explore the docs & tutorials here
3. Share your feedback with the community on Discord