Deep Dives
Explore advanced AI techniques and methodologies, including Retrieval-Augmented Generation (RAG), Graph-RAG, knowledge graphs, embeddings, and context management. Dive into the graph-based retrieval systems, large language models (LLMs) and innovative ways to enhance AI performance.
Latest
Latest
Memory as a Decorator
Adding memory to agentic workflows used to mean restructuring your stack. One decorator changes that. We ran 198 simulated sales conversations — and the results make a strong case for structured memory.
Cognee's CLI Replaces MCP OAuth in 100 Lines
MCP has real auth built in. CLI doesn't — or so the claim goes. The Claude Code plugin that wraps cognee-cli runs a full register-login-token handshake before the first command fires.
Agents Don't Need Another Protocol. They Need a Good CLI.
Your agent forgets everything between sessions. The fix isn't a bigger context window — it's persistent memory via a CLI. Four commands give your agent cross-session, graph-structured memory.
Expanding Custom Graph Models for Reliable Agent Memory & Retrieval
Learn how Custom Graph Models in cognee create a stable, domain-aware memory layer for agents — and how the Cascade feature progressively discovers missing schema from real data.
Memory as a Harness: Turning Execution Into Learning
Memory as a Harness: Turning Execution Into Learning
Grounding AI Memory: How Cognee Uses Ontologies to Build Structured Knowledge
Learn how ontology-based validation grounds AI memory in structured knowledge graphs. Reduce entity duplication and boost retrieval quality. Try Cognee now.
Building Self-Improving Skills for Agents
Learn how to build self-improving skills for AI agents with Cognee. Transform static SKILL.md files into evolving components that learn from failure. Try it now.
Context Graphs: Why Agent Memory Needs World Models and Behavioral Validation
Decision traces aren't enough for agent memory. Learn how world models and behavioral validation create AI that predicts outcomes. Build smarter agents.
Long-Term Knowledge for AI Agents: Why Memory Alone Isn't Enough
Memory stores what was said. Knowledge captures what it means. The difference is structure — typed entities, relationships, versioned facts — and it's the gap most agents still fall into. Three lines of code show what fixes it.
Memory as a Decorator
Adding memory to agentic workflows used to mean restructuring your stack. One decorator changes that. We ran 198 simulated sales conversations — and the results make a strong case for structured memory.
Cognee's CLI Replaces MCP OAuth in 100 Lines
MCP has real auth built in. CLI doesn't — or so the claim goes. The Claude Code plugin that wraps cognee-cli runs a full register-login-token handshake before the first command fires.
Agents Don't Need Another Protocol. They Need a Good CLI.
Your agent forgets everything between sessions. The fix isn't a bigger context window — it's persistent memory via a CLI. Four commands give your agent cross-session, graph-structured memory.
Expanding Custom Graph Models for Reliable Agent Memory & Retrieval
Learn how Custom Graph Models in cognee create a stable, domain-aware memory layer for agents — and how the Cascade feature progressively discovers missing schema from real data.
Memory as a Harness: Turning Execution Into Learning
Memory as a Harness: Turning Execution Into Learning
Grounding AI Memory: How Cognee Uses Ontologies to Build Structured Knowledge
Learn how ontology-based validation grounds AI memory in structured knowledge graphs. Reduce entity duplication and boost retrieval quality. Try Cognee now.
Building Self-Improving Skills for Agents
Learn how to build self-improving skills for AI agents with Cognee. Transform static SKILL.md files into evolving components that learn from failure. Try it now.
Context Graphs: Why Agent Memory Needs World Models and Behavioral Validation
Decision traces aren't enough for agent memory. Learn how world models and behavioral validation create AI that predicts outcomes. Build smarter agents.
Long-Term Knowledge for AI Agents: Why Memory Alone Isn't Enough
Memory stores what was said. Knowledge captures what it means. The difference is structure — typed entities, relationships, versioned facts — and it's the gap most agents still fall into. Three lines of code show what fixes it.
Cut Cognee's Vector Memory by 8x with Qdrant's TurboQuant
ScrapeGraphAI + Cognee: Turn Live Web Data Into a Knowledge Graph
