Knowledge Graph

Here’s a problem I run into constantly: my AI coding assistant has zero memory of what I worked on last week. But every session starts fresh. And I keep dumping the same files into context, re-explain the same architecture, and burn tokens asking questions the agent answered yesterday. Graphify solves that in a way I hadn’t seen before. It’s a skill for Claude Code (and many other AI coding tools) that reads a directory — code, docs, PDFs, even screenshots — builds a persistent knowledge graph from it, and lets you query that graph weeks later without re-reading the raw files. It’s sitting at around 2.3k stars on GitHub right now. 71.5x fewer tokens per query on a mixed corpus of code + papers. That’s not a marketing number — that’s their benchmark against Karpathy’s repos with actual output files to verify. ...

You built an AI agent that handles complex workflows — then on the next conversation, it’s a clean slate. No memory of what you discussed, no context carryover. Every session starts at zero. That’s the problem Cognee solves. It’s the open-source AI memory platform (18.4k★, actively maintained) that gives agents persistent long-term memory using a self-hosted knowledge graph. Think structured recall, not just vector embeddings. I pip installed it, checked the API, and ran through the basics. Here’s what I found. ...

Look, LLMs are great at generating text but terrible at remembering what you told them five minutes ago. So every session starts from scratch. And you repeat your preferences, your project context, your API keys — yet the model still drifts off-topic by turn 15. So most “AI memory” tools handle this by keeping everything in RAM or shipping your data to a cloud API. But neither scales well when you’re running multi-session agent workflows. ...