Uploaded August 2026 | Updated September 2026, 2 weeks ago
Most AI agents forget everything the moment a session ends — forcing teams to rebuild context from zero on every run. LLM Wikis take a different approach to agent memory: instead of just storing information, the agent reads, reconciles, and rewrites its own knowledge over time, much like a person maintaining a living wiki.
Join Izma Aziz for a live, hands-on build using LangGraph and Deep Agents — watching an LLM Wiki come together step by step, with the reasoning behind each design decision explained as it happens.
You'll learn:
→ What an LLM Wiki is and why persistent, growing memory matters for agents
→ How it's different from RAG, file search, and standard chat history
→ The full lifecycle: ingesting, organizing, querying, and updating knowledge
→ How to watch a working LLM Wiki get built live, not just described in slides
→ The design tradeoffs and limitations to expect in production
→ Why the cost of "forgetting" compounds as agents take on longer, more autonomous tasks
Most AI agents forget everything the moment a session ends — forcing teams to rebuild context from zero on every run. LLM Wikis take a different approach to agent memory: instead of just storing information, the agent reads, reconciles, and rewrites its own knowledge over time, much like a person maintaining a living wiki.
Join Izma Aziz for a live, hands-on build using LangGraph and Deep Agents — watching an LLM Wiki come together step by step, with the reasoning behind each design decision explained as it happens.
You'll learn:
→ What an LLM Wiki is and why persistent, growing memory matters for agents
→ How it's different from RAG, file search, and standard chat history
→ The full lifecycle: ingesting, organizing, querying, and updating knowledge
→ How to watch a working LLM Wiki get built live, not just described in slides
→ The design tradeoffs and limitations to expect in production
→ Why the cost of "forgetting" compounds as agents take on longer, more autonomous tasks
