Nowledge MemAdvanced Features
Transform your growing knowledge base into an intelligent, interconnected system
As your knowledge base grows, basic search starts to show its limits. You've captured hundreds or thousands of insights, but finding connections, identifying patterns, and getting a bird's-eye view becomes challenging. This is where Nowledge Mem's advanced features shine.
When You Need Advanced Features
You might be experiencing:
- "I know I saved something related, but search isn't finding it"
- "I have insights on this topic scattered across dozens of memories"
- "What are the main themes and patterns in my knowledge base?"
- "I want to see how my different learnings connect to each other"
- "I need to understand the structure of my domain expertise"
Advanced features solve these challenges by creating an intelligent, interconnected knowledge graph that understands relationships, identifies clusters, and reveals hidden patterns.
Memory Augmentation
Beyond basic keyword and semantic search, augmentation creates a rich web of connections that mirrors how you actually think.
1. Knowledge Graph Extraction
The problem: You have a memory about "microservices architecture in Node.js" but later search for "distributed systems" and don't find it. The concepts are related, but keyword search doesn't make the connection.
The solution: Knowledge graph extraction identifies entities (Node.js, microservices, distributed systems), concepts (architecture patterns, scalability), and their relationships—creating a semantic web that understands "microservices" is a type of "distributed system."
What you gain:
- Hidden connections: Find related memories even when they use different terminology
- Automatic tagging: Entities and concepts are extracted and linked automatically
- Contextual search: Search understands relationships, not just keywords
- Growing intelligence: Each new memory enriches the graph and improves future searches
How to enable:
During Memory Creation
When creating a memory from agent conversations, select the option to distill with knowledge graph extraction:
For Existing Memories
Click the Knowledge Graph button on any memory card:
The process:
The LLM analyzes your memory content
Extracts entities, concepts, and relationships
Creates connections to existing memories in your knowledge base
Updates the global knowledge graph with new insights
Behind the scenes:
The LLM analyzes each memory, extracts key information, and weaves it into your growing knowledge graph. Each extraction makes your entire knowledge base smarter and more interconnected.
2. Community Detection & Topic Clustering
The problem: You have 500+ memories about software development, but they're just a flat list. You want to understand the main themes, identify knowledge clusters, and see the big picture of what you've learned.
The solution: Graph algorithms analyze the structure of your knowledge graph to identify natural communities—groups of tightly connected memories that represent coherent topics or themes.
What you gain:
- Automatic organization: Your memories self-organize into natural topic clusters
- Topic discovery: See the main themes and areas of your expertise at a glance
- Better retrieval: Search leverages cluster information to surface more relevant results
- Knowledge audit: Identify gaps, overlaps, and opportunities to deepen specific areas
Real example: Your knowledge graph might reveal clusters for "React Patterns," "API Design," "Database Optimization," and "DevOps Practices"—giving you immediate visibility into your main areas of expertise and how they interconnect.
How to use:
Navigate to the Graph View in Nowledge Mem
Click "Compute" on the graph algorithm you want to apply
What you'll see:
- See bubble clusters over related nodes in the graph
- View community topic summaries (if "Summarize Communities" is enabled)
- Get improved search results that leverage community insights
- Understand the structure of your knowledge domain
3. Graph Neural Network Augmentation
Coming Soon...
Advanced graph analysis using Graph Neural Networks.
- Node Classification: Automatically categorize memories and entities
- Link Prediction: Suggest potential connections between concepts
- Graph Embedding: Create dense representations for similarity analysis
- Anomaly Detection: Identify unusual patterns or knowledge gaps
Visual Exploration: Navigate Your Knowledge
The scenario: You remember saving insights about "authentication" but you know there are related concepts—session management, JWT tokens, OAuth flows, security best practices. How do they all connect?
The solution: The interactive graph view lets you visually explore your knowledge base, seeing not just individual memories but the entire web of relationships, clusters, and connections.
What you can do:
- Visual navigation: Click on a memory to see everything connected to it
- Precise discovery: Cherry-pick specific insights from visual clusters
- Pattern recognition: See how different areas of your knowledge interconnect
- Serendipitous discovery: Find unexpected connections that spark new ideas
Perfect for:
- Understanding the landscape of a complex topic
- Preparing for deep work by gathering related context
- Discovering connections you didn't know existed
- Curating specific insights for a project or presentation
Next Steps
Learn more about connecting Nowledge Mem to your workflow:
- Integrations - Connect with AI tools via MCP and browser extensions
- Troubleshooting - Solutions to common issues